Kinematics Of The Brain Activities Vol. V: Plasticity, Elasticity And Resonating Of The Neural Networks 341a56

Kinematics Of The Brain Activities

Plasticity, Elasticity and Resonating of the Neural Networks Vol. V

Mostafa M. Dini

Copyright © 2012 by Fluent Writing Ltd.

Library of Congress Control Number: 2012912671

ISBN: Hardcover 978-1-4771-4383-4

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CONTENTS

Preface

Introduction

Chapter I

Brain Activities From Different Aspects

Kinematics of the Brain Activities During Waking and Sleeping

How Thoughts and Dreams Are Created Differently

A Mentation Run

Principles of the Brain Activities

Chapter II

Networks and Elasticity

Neural Network Topology Variations Within the Elasticity Limits of the Brain Layers

Stress-Absorbing Range of Different Layers

Cellular to Layer Stresses

Stress Input and Attention

Electrochemical Reaction, Self-Induction Field and Stress Transfer

Stress Input and Stress Flow Through the Brain

Limitations to Strain-Stress Limits

Chapter III

Entry Stress Ranges; Strain-Stress Characteristics In Different Areas and Areas Time-Behavior Specialty

Different Regions’ Structures Capable to Provide Different Storing and Releasing Properties

Explicit/Implicit Memories, Consciousness and Visibility

Physical Meaning of Consciousness

Spectrum of Memories Saved In Different Magnification Levels

Working Memory and Carrying Substrates

Chapter IV

Brain Functionality

Hierarchical Arrangement of Modules

Hierarchical Model of the Modularity In A Brain Activity

Brain Functional Modeling By Substrate Structures

Neural Connectivity In the Brain

Connectivity and Pattern Communication

Chapter V

How Memory Components Find A Site to Sit On

How Object Perceptions are Segregated, Spread, and Saved In Individual Networks

A Crystallized Pattern of Networks, A Dynamic Pattern and A Continuity Pattern

Emotion Sites In the Amygdala and Functional Memory Over the Cortex

Matching Synapses’ Plasticity, Neural Network Complication, and Stress-Strain Marks

Type of Structure and the Related Memory Functions In the Middle Brain

An Emotion Is An Alarm and Defensive Mechanism to Prevent Tissue Damage

Strain Accumulation and Memory Build Ups

Resonance Between Far-Distance Neural Networks

Chapter VI

The Cerebral Cortex Hosts the Main Pathway, Having Attractors In the Other Parts

Elasticity Versus Plasticity In Brain Layers

Activity Interconnections Because of the Elasticity, Recalling of Characters and Behaviours Because of Plasticity and Resonation

Combination of Static and Dynamic Images In A Brain Activity

Pathway, Stress Forwarding Path Through Networks

Utilization of Pattern Separation and Pattern Completion In Ongoing Brain Activities and Memory Building

Counter Influence of Neuron Networks On Genes and Genes On Neuron Networks

Stress Flow Through A Substrate and Containing Network Changes

Structural Characteristics of the Brain Areas and Substrates and the Specialty

Growth of Neurons Under Strain and Formation of Functional Substrates

Pathway Parameters

Dynamics of the Neuron Networks

Measures In Mentations

Brain Development Along the Life

Chapter VII

Dreams

In Dreams

Types of Dreams

An Emotion Portrait In A Dream

Chapter VIII

More Detailed Principles of the Model

Future Works Based On the Model and Possible Gains

An Integrated Claimed Model

Conclusion

Endnotes

Preface

Continuum mechanics is the physics of continuum material. It branches into the solid mechanics and fluid mechanics. Elasticity (returning to the rest position after removing the stress source) and plasticity (deforming to a new configuration even after the removal of the stress source) are the two main subjects of the solid mechanics. In fluid dynamics, two categories of Newtonian fluids and non-Newtonian fluids are discussed. Rheology studies materials with both solid and fluid characteristics. Brain is a continuum material although divided into subdivisions of outer, middle, and inner brain with layers, regions, and areas in each section. Continuity in brain special is remarkable by neural networks connecting every location of it together. Kinematics of the activities in the brain considers a semisolid and semi-nonNewtonian fluid as a media for conducting free electrochemical energy through its neural networks with specific properties of elasticity and plasticity. In neuroscience, although neural networks have found a considerable highlighting in the recent decade, they have been considered generally within a solid brain. In the layout of the sections in this volume, it is tried to follow the same sequence of contents as the previous volumes, starting by giving a general view of the subject in chapter I; discussing the stress-strain relationship in chapter II, considering a neural network structure for the brain; interpretation of brain functions by the kinematic model in chapter III; brain function based on the functional modules of substrates in chapter IV; how the memories are build up in different areas in chapter V; Structures in different areas and the type of memories store in those areas in chapter VI; and finally, the type of mentations

occur during the regeneration period of sleep.

Introduction

Fundamentally, similar to time and length as basic dimensions responsible for dynamic and static parameters in the nature, temporal and spatial dimensions can be defined in longitudinal and spatial directions for the activities in the brain. Brain provides the same number of basic dimensions for patterns as nature provides for things. If structure, movement, and relationship between them (function) are the three basic concepts in the nature, then images (character) and motor outputs (behavior), and linking function between those are the overall brain principles; while neural plasticity, elasticity and the correlation between them are the fundamental elements of those activities. The functional aspect in the brain can be extracted from kinematics of the brain activities through the setting of the neural networks under strain. Psychologically, emotion conveys an equivalent meaning as the neural networks settings under strains. However, as one cannot concentrate on the functional aspect of the things in nature, it is difficult to focus on the functional aspect of patterns in the brain and easier to follow the brain activities by the appearing characters and behavioral images (static and dynamic aspects of the patterns). In kinematics of the brain activities, memory structures, memory links (as behavior) and governing interactions (emotions or reasoning) are the main subjects to be studied. All these activities appear in streams of dreaming, feeling and thinking and any other flow of thoughts and images. Memory building as the process of the input pattern separation and the involved emotion or reasoning as the higher plasticized connections with the other memories and new pattern integrations are the fundamentals for any brain activity. Memories are the well-plasticized neural networks, and brain outputs are the result of chains of neural networks elasticity behavior. To connect and relate plasticity and elasticity together, there is a spontaneous mechanism which provides vitality to the brain. Plasticity has a variable range; elasticity also has a variable range. When those variables in combination provide continuity between plasticity and elasticity, like a rubber, its structure can be changed slightly and absorb tensions as well, but within a limit of strains; and beyond that limit the tissues will yield to rupture. However, brain as a self-regulating system whenever external straining sources are quiet internally utilizes the strain-confined locations as the internal inputs to activate approaching a balance state. This spontaneous mechanism is applied all over the

brain, as a complex and a self-regulating system indicating with the strain waves. Straining within a linear limit and immediate releasing, accidental overstraining and following periodical releases or reconfiguration of synapses are the natural behavior of this complex system approaching the equilibrium bifurcation points to find structural stability. Approaching the equilibrium bifurcation points to find structural stability consists of a loop of inputs, processing, and outputs with many loops, checking internal and external resources. In all these phenomena, not only neurons and their connections are involved, but their detail configurations of the firing gates are also involved. Consequently, firing fractals and patterns undergo changes and create changing images. If the linear strains and releases are not highlighted, overstrains and following major reliefs are accompanied by pains and reliefs as the basic emotions. As a result, emotion links the static and dynamic images which are cited over neural networks through the firing patterns, and preserve a continuity of the strains and reliefs. Similar to a mathematical function which can be utilized for different media parameters, an emotion or reasoning logics as a functional operator several static and dynamic images together. For example, some general functions can be applied in the different fields of mass; heat or momentum transfers and even electricity currents. In brain also, similar operators either emotion or reasoning logics may run a variety of static and dynamic images. Those images governed by the same emotion are symbols for each other because activation of anyone evokes the same emotion and recalls the same emotionfamily images. Emotions and logics work as a pattern screener to build or to recall a memory by adopting the firing gates configuration in synapses and the resulting firing patterns. Static images, depending on the carrying neural network flexibility, have a range of variation; as well as dynamic images have similarly a range of variation. Overlapping of these two variations defines the emotion attached to those static and dynamic images. The overlapping variations indicate the dependency of the static and dynamic aspects of a firing pattern, as well as the independency of their combination from other images. If the shared variation goes beyond an elasticity limit, the related tissues overstrain, and the emotion will explode to release overstrain and save the tissue from rupturing; otherwise the overstrain will damage the tissue, and in its extremes, the adjacent vein may rapture and a stroke happens.

In short, there are three categories of parameters in the brain: memory structures, thought currents and emotions or logics. If the two first parameters are the static and dynamic characters of the brain, emotion and logic are the kinematics parameters of the brain. Brain is a complex and self-regulating system working spontaneously and therefore is vital and controls the vitality of the body. Emotions and logics develop, evolve, and increase in numbers during the life.

Chapter I

Brain Activities From Different Aspects

Physics, biology, neuroscience and psychology are the main domains for the brain activity studies. Synchronized firing clouds fractals and their changes; firing gates distributions and changes; synapses/axon network configurations and vibrations; neuron fibres network configurations and convolutions; synapses networks curvatures and waving in layers; brain layers folds and stress flow are the physical aspects of affecting the brain activities. Cellular Na+/K+ exchange and spiking; gene maps and functionality; underlying chemical and catalytic reactions, formation and decomposition are some of the biological processes in the brain. Motivation, perception, cognition, reasoning, memory building and ing; attention and the emotional awareness are some of the psychological processes which occur in the brain. The different processes in different above domains should somehow each other fundamentally. The kinematics model is the subject which explains how the brain activities in the physical domain are related.

Kinematics of the Brain Activities During Waking and Sleeping

The revelations of Devout and Learn’d Who rose before us, and as Prophet burn’d, Are all but Stories, which, awoke from Sleep They told their comrades and to sleep return’d.

Omar Khayyam

With one type of emotion, brain can make many narratives; but many stories may convey one type of emotion. Narratives employ characters, actions, and scenes when in opposite: characters, actions, and scenes make stories. If firing patterns initiated by sensory inputs evoke images which are seen by their consequence stresses and a stress-strain function regulates the forming of those with causing strains in the tissues elasticity limit, then strains release immediate output patterns and overstrains release regeneration images as dreams. The immediate outputs are based on a longitude stress-strain relationship, and the relaxation outputs are based on locally spatial stress-strain relationship. The earlier function employs component from different family of memories, but the later employs the components from the same location, and therefore, the same family of memories. Stories describe the scenarios for immediate outputs and are progressive, but narratives are self-telling expressions.

Characters and their behaviors in waking brain activities are widely spread with different characteristics; while characters and their behaviors in dreams share similar characteristics. Emotion in a waking activity has a wide distribution; but emotion in a dream is very unique and has a narrow distribution; Generally, the connecting grammar used in stories and narratives is physically a transformation of the physical stress-strain relationship and helical fluctuations of neuron fibers and synapses. When recalled characters, as objects, and their actions, in verbs, tie together, they describe a brain activity or at least a part of that. In stories and narratives, the connecting media employ different stress-strain relationships. In waking, story makes the stress flow in an average longitude direction through a pathway made of neural networks with different magnification levels; but in dreams, the stress-releasing streams are spread around one or few neighborhood locations.

An emotion describes a strain or releasing distribution along some tissues, and where there is an overstraining, it is significant in intensity. The overstrained location can be shared between different daily excited pathways that their working memories may still remain active in the sleep. Therefore, the dream narrative do not employ the same character took part in the stressful event, but can employ variety of characters saved in the overstrained neural networks as neighborhood memory families. In short, according to the pathway stress-strain relationship, flow of stresses either initiated from external inputs or strained locations continues along a related neural networks. Any neural network or a combination of those are either nests for definite detail memories or pixel memories resonating memories in the other locations. An stress flow along the pathway put the inputs and the recalled memories according the tissues stress-strain relationship and create the conscious thoughts of waking or subconscious images of the dreams in sleep. The combination of stress flows and the stress-strain relationship pathway describes the kinematics of the brain activity, whatever it is. The creativity of making subconscious narratives and conscious daily stories depend on the required activation energy of the substrates to accept or release strains. Emotion, attention and trained skills as attractors, define the pathway by picking up specific neural networks to include in the pathway from the initiation up to termination of a brain activity, intensifying the free energy flow by the stressstrain wormlike behavior along the pathway. Excited neural networks the chain of the memories or the memory pieces along the attracted neural networks that seem an extraordinary creativity.

How Thoughts and Dreams Are Created Differently

A sensory input enters as a package of many components (like pixels in a picture). They will be processed to perceive a unique input by recombining them. When they have a straining potential to be saved, they will saved separately over fine neural networks. To remind a memory, the memory pixels should re again by an attractor or attractors through following excitations of a chain of fine neural networks which they were sited on. The memories and memory pixels when saved in brain can in many other ways than the only way they had been saved. The other way of combinations of the memory pixels is known as the creativity; which such a capability exists in brain only. It means, many other options of virtual objects is possible while there is only one option practically possible as perceived from the environment. Different combination options are different ways of processing as brain activities which would create different virtual products, including feelings, free or focused thoughts, or dreams. Types of neural network combinations by stress flow streams in different routes with different media elasticity determines the type of functionality, or in other words, specialty of the area. There is a main pathway as a bus bar communicating with different areas in the brain to distribute and collect the retrieved branched activities in different brain areas. I would like here to borrow a term from instrumentation field and call it the bus bar pathway. The bus bar in distributed control systems randomly distributes and receives information to regulate information processing as desired. Although this way of description for pathways is still not very common; a neuron fibers bus bar helps so much for the modeling propose because creates more simplicity. The assumed bus bar in the brain is marked with the red color in the following illustration. The separation, distribution, and collection processes of all the neural networks in different parts of the brain which are involved in a brain activity happen by the networks amplitude and frequency resonations through

the bus bar.

A package of a memory or a created imagination as result of a brain activity like a thought, or feeling is made of the combination of inputs and previous memory pixels which are spread over different parts of the brain and together through the bus bar pulses; while there are different kinds of inputs in waking and sleep.

A Mentation Run

In a substrate configurations which are different in any area of the brain change from irregular to semi-regular and regular in firing gate configurations and accordingly shapes the emitting firing clouds. During the changes, if two firing clouds in two different areas become similar in the general pattern, they will resonate and the relevant substrate in the outer layer forms a part of the energy transfer pathway. Firing cloud’s similarity are reinforced by relaying centers in the inner brain (NCR); In the middle brain (amygdale) and in outer layer (prefrontal lobe), as well as in the input if it would receive similar inputs. Variations follow the transfer strengths and elasticity property of the areas. In short, when inputting working memory, past memories and the relying networks in the inner, middle and outer brain interactions, they make a cumulative dynamic firing cloud which is the ongoing brain activity. Any free energy flow which s a thought has a direction which is determined by site firings of inputs, needs, memories and relays. A brain activity becomes in a level of awareness, if an advanced relaying center like the amygdala or frontal lobe facilitates to combine the simultaneous physical activities in different brain parts and those activities follow a strainrelaxation process. Such a process repeats the need satisfaction status continuously throughout the pathway and remains aware of it. Therefore, the need as the initiator would remain as a goal up to termination of the activity. Most often, more than a goal is involved in a mentation and if that would not combine as a unique goal, the mentation and its pathway for the attached free energy transfer will be a non-determinative pathway. The termination point cannot be foreseen and planned. The pathway fluctuates and there is no specific need to be satisfied. Worry is an emotion attached to floating needs and pathways will fluctuate between different routes. In such a case, the concentration is spread. Conscious and subconscious thoughts differ in the degree of involved

concentration and determinacy. They are different in the continuation of the pathway into the frontal lobe too. A mentation is distinguished by its initiative input, boundary limits and termination determinativeness. Therefore, they may not be distinguished as a separate mentation, if it is not clear in those parameters. Different sources report a number of 12,000 to 50,000 thoughts per day and some up to 65,000. Even some researchers report definite number for this category of the mentation and the concerned sexes (e.g. 19 food, 18 sex and 11 sleep for men and 15 sex and 8.5 sleep for women). In addition, in volume I of this series of books, thoughts were categorized for subconscious, conscious and super-conscious types. The other types of categorization are being ive or introversion and active or extroversion. The ive type is also divided into mild ive during waking times and strong ive during sleeping as in dreams. These kinds of information may help to formulate brain functions in the kinematics modeling.

Principles of the Brain Activities

A general simulation of brain activities should consists of different steps: stimulation, pattern separation, pattern abstraction, patterns interaction, saving of the patterns and signal dispatching. Stimulation of an input substrate and the interaction of the initiated stress flow with the configured structure of synapses (which form the synchronized firing patterns) create a fluid intelligence which is called working memory. The variable part of the excited substrates (either by entry inputs or potential strained substrates in any location) can shape a substrate configuration to resonate with any accidentally self-similar network in different levels in the layer and region. The ing of these modules by resonation in a way that fits a template for a brain activity requires excitation of the saved memories of different types being synchronized with the working memory. In an organized interaction within the frame, inputs and memories would form an ordered brain activity. All the steps of: stimulation, pattern abstraction, interaction, memory saving, retrieving of the memories, ing of modules by transferring energy and resonating of modules with occurring similar configurations within their variable range should have a kinematics explanation which has and will be described in the kinematics model of a mentation.

Chapter II

Networks and Elasticity

In the kinematic model, it is assumed that the ties between neural networks are not plasticized fully and show elasticity as well in different layers and different levels in the layer. The elasticity factor is the connection strength between two connecting nodes in the neural networks in any layer. The factor shows the elastic variability range; which is very variable for simpler networks and lower variable in the more complicated networks like the lattic neural networks in the hippocampus. Variability is moderate in the complicated neural networks and thinner layers. Elastic neural networks in different areas can find a similar configuration in different parts of the brain and resonate together and take part in a same brain activity. Along these changes the networks in the amygdala can find an interface configuration between neural networks configurations in the hippocampus and the neural networks in the outer layer; or frontal lobe activating many neural networks in different brain parts simultaneously. Nodes connections have two values of either very high flexibility when the approaching to the required activation energy for the connecting networks are fulfilled or almost rigid when the network does not participate in the present brain activity. First, to have strong ties between nodes, the firings should be synchronized enough. Less synchrony means higher noises and higher turbulence in the current of free energy flow. Second, the imposed strains due to energy flow in each level of network complexity in the cortex should be enough to overcome fibers viscous part resistance. Third, because boosting of the energy current is required to provide an efficient brain activity, activation of participating networks in the pathway over the cortex by resonating should occur. Fourth, similar configurations of networks are required to make the resonation efficient; creation of similar networks configurations in the cortex and in the hippocampus and amygdala should occur too to make the resonating possible.

Resonating happens through long fibers connecting different areas of the brain. To have a mentation occur, all the steps above should be fulfilled. Different people have different memories, ways to spread memories in pixel neural networks, and use them in their own creative power to collect memory pixels to incorporate creating a unique brain activity different from anyone else. The creativity in the brain activity is determined by energy balance over the layers.

Global energy minimization [is the way] to find the energetically most favorable path between two predetermined pixels.

Brain is composed of a nested hierarchy of modules. The cerebral cortex (which is implicated in what we normally consider intelligence) is composed of six layers (numbered I through VI with I at the outer surface), which differ in thickness and constitution among the various cortical areas. ¹

Neural Network Topology Variations Within the Elasticity Limits of the Brain Layers

Many studies focused on changes of neuron fiber networks either spatially or temporally by age. However, this study focuses on the ongoing and daily variations of fiber networks’ plasticity. “Ongoing structural plasticity by forming or breaking of synapses, motile spines, and rerouting of axonal branches in the developing and adult brain”²and its influences on elasticity day by day is so minor and out of measurable capabilities which has never been initiated as an urge to research. However, it should be significant in decade periods of everybody’s life. In this book, it is suggested that time variations in brain elasticity property is as important as plasticity property. Although it is of lesser importance for inner brain, it is important for the middle and outer layers of the brain. In other words, its variations is very limited in the inner brain, limited in the cortex areas which house long-term memories, somewhat limited in middle brain areas which house short-term memories, and less limited for the frontal lobe which hosts working memories. However, if the number of plasticized network increases day by day, there is an increase in complexity; and this should be exponential with ageing. Few fine geometrical deformations are critical during a day but not statistically very different during a short time in life. However, it is tremendous during life, and memory increases are according to new-formed spatial configurations and elasticity changes. Depending on the type of neurons that mostly build different regions of the brain, different types of networks are concentrated in that region or area.

³

A network is made of a texture of fibers. A characteristic motif in neuron fibers consists of a network pattern that recurs repeatedly within a neuron fiber network. This is due to the fact that neurons grow in a fractal pattern.

Cortical motifs in different areas of the brain are responsible for storing same pattern family functions.⁴ Classification of memories of the inputs, processing, and outputs are the basic physical character of the brain and all other higher level processing somehow use this screening for general analyses. The motif repetition helps to categorize memories or functions for any specific location. However, a characteristic motif changes in different spatial scales, and by time which it is remarkable during brain development, it also continues for the rest of life. The changes are limited to physical changes of the neuron networks in the brain. To detail such a structure of networks, any network has nodes and higher resolution nodes themselves are made of subnetworks. Therefore, by increasing the resolutions, the scale can be changed from a micro—to macroscale.

The distribution . . . of characteristic motifs of single network nodes change[s] for human structural connectivity (DTI) during brain development . . . [in] temporal scales . . . and spatial scales ([different resolution of] nodes). ⁵

The same terminology will be used in the brain as it is used for image monitoring because it is believed that the synchronized firings as a physical media convey images which accompany brain activities. From different types of neuron networks, star and bus types are assumed would store longer-term memories. The synapses are configured in a definite plasticized neuron fibers networks in a more rigid way and have a spatial limit. According to these types of fibers networks, elasticity property of this type is expected to be less compared to the networks in the cortex which is majorly made from modular network type. Because networks in different spatial limits are covered or overlap smaller networks in hierarchy type, a family group of memories can be saved in the same location. Therefore, neuron fiber networks with specific spatial limit and specific configuration in a certain time convey a

certain memory.

Certain motifs only occurring at one spatial scale or for a certain [temporal scale] . . .

The type and localization of motifs differ for different spatial resolutions.

There is always a trade-off between temporal and spatial resolutions which follows the layer elasticity property at that location.

The number and diversity of motifs varies across the temporal . . . and spatial (network resolution) scale [, and they] are strongly correlated.

Any part of the brain has a special storing and releasing properties because of different utilized elastic material; therefore, its specialty differs. Accordingly in kinematics, any location has a memory and a functional specialty. However, different kinds of memory like working, short and long as well as declarative and nondeclarative types of memory are assigned to different locations, depending to type of majority of neuron networks which build the location. In general, different kinds of structural networks function differently. Networks in different areas have different types, as classified by small-world, scale-free, modular, and hierarchical.

Networks can be characterized at different levels. Aggregate measures that characterize a network as a whole can be used to distinguish different network types, e.g., small-world (Watts and Strogatz, 1998; Hilgetag et al., 2000; Sporns et al., 2000), scale-free (Eguiluz et al., 2005; Kaiser et al., 2007b), modular

(Hilgetag et al., 2000), or hierarchical (Kaiser et al., 2010) .

In a small world, the network expands proportional to number of nodes; while in scale-free type, each node has several connections to other nodes with different probability of connections distribution with synchronized firing possibility in neuron fiber connections.

Scale-free networks, brain areas with a large number of connections—so-called network hubs—might be crucial for integrating or distributing information.⁷

A modular neural network is made of independent neural networks in series and linked together; while each network has its own storing and releasing characteristics, and totally, they create an overall image. The total image is a gathering of sub-images which do not interact with each other. A hierarchy network of neuron fibers is similar to a physical tree topology with connection of higher level and lower level on nodes and more than three levels of branching. Family memories are saved on this type of memories so that all convey close theme of attached emotion when different sets of networks would be activated over the tree. Any region is not made purely of one kind of neural network structure, but a mix of them; when in any region one type is dominant. The growth of similar neural networks is initially due to growth of the same type of neurons in any area in a fractal way. It can be assumed that area structure and area speciality are strongly related and any new birth and growth up to isolation boundaries develop in the same way. Two processes of long-term potentiation and Hebb’s postulate regulate the similarity in growth within an area.

Long-term Potentiation:

An enduring (> 1 hour) increase in synaptic efficacy that result from highfrequency stimulation of an afferent (input) pathway

Hebb’s Postulate:

When an axon of cell A . . . excites[s] cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells so that A’s efficiency as one of the cells firing B is increased.

• Synapses become more or less important over time (plasticity) • LTP is based on experience • LTP is based only on local information (Hebb’s postulate)

The efficacy of a synapse can change as a result of experience, providing both memory and learning through long-term potentiation. One way this happens is through release of more neurotransmitter. Many other changes may also be involved.⁸

Stress-Absorbing Range of Different Layers

Different layers show different sensitivity to stress because of different stressstrain correlations: frontal lobe, highest; outer layer, moderate; middle brain, low; and inner brain, not any significant. Outside of the normal stress input, the frontal lobe would decline in efficiency. For example, in conflict situations, one may make huge mistakes; or in a public speech, mind can go blind and forget very obvious points. Anxiety or worry may vanish correct reasoning, and generally, it is said, “My brain does not work in those situations.” At the same time, one may related memories or show a very emotional behavior toward the stress source or relevant to that. It means the middle brain, including the hippocampus, amygdala, and the other related parts become very reactive in stressful situations. This is very obvious during the sleep; when a high volume of accumulated stress is relieved, it would excite emotion and create dream scenarios in the middle brain and apply reasoning considerations from frontal lobe as well. Why does different layers show different efficiency in operating related to input stress? Neuroscience believes it is due to molecular-scale neurochemical presence and describes the firing strength and neurons connectivity changes by chemical environment causing firings either to malfunction by chemicals like norephinephine, dopamine, cortisol, stress hormones, enzymes, and possible poison chemicals which may enter to body by food or pollutions. Kinematics looks to it in a different way and describe changes in firing synchrony and strength (short intervals) are due to macroscale strains followed by physical stresses caused by inputs and needs contradictions; which consequently, changes the distributions and sizes of firing channels. The chemicals either are transferred or produced as neurons environment are not the origin cause, but the associating parameter changing the media for the firings state. The middle brain is a site for the pieces of memories of inputs as entered through sensory gates in hippocampus and associated emotions in amygdala. The frontal

lobe is a site of abstract memories of the extracted patterns organizing the memories and associated connections as the reasoning. These layers are linked through the media of outer layer (Occipital, Parietal, Temporal lobes) which have different levels of low to high organized neural networks in the layer, where its lower level networks resonate with the middle brain structural networks and the higher level networks transmits the working memories into the frontal lobe with very high flexible neural networks. The parallel working memories in different chains of neural networks integrate together, and provides abstracted conclusive advanced cognition abilities, including concentration, planning, decision making, judgment, and retrieving memories by their patterns. The memories in the middle brain are retrieved by their individual time and place and emotional codes. As detailed above, reconfiguration of the synapses and firing gate orientations are generally formed by working memories; while the consolidation of the sensory entered memories, mostly declarative types, needs high release of energy to deform rigid structures, which accompanies an intensive emotion and this is possible when an accumulated strain would be released in sleep periods. In short, layer structures show different efficiency in operating related to input stress and behave differently toward the stress entries.

Cellular to Layer Stresses

The stresses over the brain layers are due to tiny electromagnetic forces imposed by self-induction fields made by synchronized firing currents through changing fiber routes. The subject was detailed in the volume one¹ . The synchronized firing currents in different routes establish electric fields which in turn generate the mentioned magnetic field; conversely any magnetic field interacts with other magnetic fields induced at the same time and in the same location generating new electric fields. The process continues up to the time that no more synchronized current would be created and consequently, the magnetic fields and resulting electric field(s) would be terminated. According to the above, the molecular level is the initiation source for a layer to be strained. Straining of any substrate in the layer makes the consisting fibers in the location tense and consequently, the firing gates distribution and sizes would be changed. This event causes the produced electric field firings current(s) changes and sends a different output signal. In any tiny electromagnetic field substrate, the self-induction forces initiate streams of stress flow which integrate those throughout the pathway and would have a product of output signal. Being absorbed in the streambed, it branches through the pathway and is relieved of excess strains during the regeneration period of the elasticity property. In the other side, neuron fibres and synapses will be stressed because of the other substrates inducing forces. The accumulated stresses change the chemical conditions, but do not harm molecular structures and genome. On the opposite, DNA configurations in the genome instruct how proteins and neuron fibres form. Indeed, the molecular structure acts as a catalytic site in any substrate to preserve its form and functional properties against the stress forces which it imposes from the layer level. With periodical stress releases the upper level forces and submolecular directions should balance to keep the neuron cells healthy. However, unbalancing and build up of abnormal shocks and long-standing strong stresses may harm the cells and are expected to prepare conditions which cause molecular disorders in their extremes. They also may have some influence on the

vein diameters in the overstrained locations. The healthy limits of veins and fibres contractions and extensions can be used to define the normal range of stress changes on any location in the brain.

Stress Input and Attention

Attention imposed from the environment and attention called from an ongoing brain activity have been discussed in the “input discrimination” section in the previous volumes through resonating process. Physically, it is essential that the input pattern and the ongoing brain activity in their connecting energy substrates resonate together by matching the same or multiple frequencies. At the resonating moment, the entering inlet and receiving substrates would be strengthened for the amplitude either strengthening ongoing activity or initiating an activity. These two processes are called internal-driven attention and external-driven attention. They are called top-down modulation and bottom-up processing too. Memory building is also very affected by attention as well. Working memory of a recent brain activity is a source for an internal-driven attention; while an external-driven attention initiates a brain activity by itself. Because any activity starts with the perception stage, the sensory region is the most involved region. It is said that “measures of neural activity are augmented in specialized sensory cortical regions,”¹¹ because the sensory memories have been saved in those area and ready to resonate when any of internal-driven or external-driven attention processes occur. All the processes named and briefed above are kinetically explained by stress flow and its interaction with neural networks along the pathway. This explanation simply shows how related these processes are physically.

Electrochemical Reaction, Self-Induction Field and Stress Transfer

To find that how a firing current which emerges from one substrate can induce a synchronized current by the self-induction in the neighbour substrate, consider the helical flagellum as a neuron loop:

Ricardo Ortiz

Postdoctoral Researcher

Stream arrows of flow field produced by a circular helical flagellum.¹²

A neuron firing network in the synaptic produces a firing orbital cloud, as a simplest energy level. The produced energy layer is deformed by imposing stress forces. An example illustration from Center for Computational Science in Tulane University would be very helpful to provide a clearer understanding of that:

Graham Cummins

Propagation of the electrical response to synaptic input through a compartmental model of an invertebrate interneuron. Greater changes in the membrane potential are represented with warmer colors.

Research conducted at the Center for Computational Biology, Montana State University.¹³

The temporary tendency to deform due to electromagnetic forces in a substrate is similar to the elastic ring experience exposed to a wave forces, simulated by Austin Griffith.

“The Wobbly Ring: Dynamics of a ring as predicted by a geometrically exact elastic rod model. Top images show the time evolution of a ring with an initial velocity intended to excite two traveling waves. Balls on the ring indicate orientation of rod cross sections”.¹⁴

Stress Input and Stress Flow Through the Brain

Stress in common refers to an excessive or a long term exposure to outside pressures and responses so that it is not limited to a continuous brain functions like creation of images, crystallized knowledge, problem solving, decisions and immediate expressions and commands. Stress in psychological is an overresponse to stimuli and the brain fails to shut off related responses immediately afterwards. However, stress in physics—as initially was taken from and introduced by Hans Selye—is defined as mutual actions of forces that take place across any section of the body, physical or psychological.¹⁵ Ranges of stress can be classified as normal, high and excessive. Excessive stress is the range for stress in psychological meaning and is not the subject of the study here, but stress with a definition of a continuous daily flow with continuous and immediate responses to timely entries through the sensory inputs is the concern in the kinematics. It is normally within the safe range of absorption in brain as an elastic material to be processed immediately and the respond to it would be shut off immediately afterwards. The normal period of input stress entry is very short; the period of its effects is as long as a brain activity continues; and the maximum response delayed time is as long as working memory life. Any overstrain because of a highly stressful input which is not considered as an excessive stress is the high extreme limit of stress as it will referred by overstrain. The two ranges of normal and high stress inputs with a maximum daily response period is the limit as refers in our study. Normally it can be handled in any moment by relieving responses and on its extreme the balance relieving will be completed during regeneration periods of sleeps. Stress, in general, (covering normal, high and excessive ranges) is defined as follows:

Stress is simply . . . forces from the inside or outside world affecting the individual. The individual responds to stress in ways that affect the individual as well as their environment . . . . from a biological point of view, stress can be a neutral, negative, or positive experience.

In general, stress is related to both external and internal factors. External factors include the physical environment, including your job, your relationships with others, your home, and all the situations, challenges, difficulties, and expectations you’re confronted with on a daily basis. Internal factors determine your body’s ability to respond to, and deal with, the external stress-inducing factors. Internal factors which influence your ability to handle stress include your nutritional status, overall health and fitness levels, emotional well-being, and the amount of sleep and rest you get.

Stress has driven evolutionary change (the development and natural selection of species over time). Thus, the species that adapted best to the causes of stress (stressors) have survived and evolved into the plant and animal kingdoms we now observe.”¹

Limitations to Strain-Stress Limits

Continuous strain-stress limits in any substrate (an integrated nerve network) are further limited by stroke signals, because the supplying vessels are attached to the neuron tissues. However, such a tension along tissues occurs within milliseconds to seconds and mostly does not approach the rupturing limit and therefore it is recoverable. In addition to that, any periodic continuous strain and release cycle does not last for more than tenth of a second, but both straining and releasing limits are boundaries for any free energy flow on the location.

“Stroke damage to synapses is apparent within minutes, but can be reversed with prompt reperfusion of blood.”

Rapid and reversible dendritic damage (images shown are from layer I cortex); J. Neurosci; 2008

Synapse elimination or addition in vivo. J. Neurosci; 2007

“The brain has plasticity, that is, it can be physically and chemically altered by experience. In a pioneering study of the influence of the environment on the brain, researchers found that rats that had been raised in a stimulating environment had more synaptic connections than rats that had been raised in cages that offered them no opportunities to explore or to manipulate objects.”¹⁷

“ . . . either chronic stress or chronic istration of glucocorticoids produces dendritic remodeling in prefrontal pyramidal neurons. Exposure to stress also causes an increase in the release of the excitatory amino acid glutamate, which binds to N-methyl-D-aspartate (NMDA) receptors, which are plentiful in mPFC. NMDA receptor activation is crucial for producing hippocampal dendritic remodeling due to stress and for dendritic reorganization in frontal cortex after cholinergic deafferentation. Thus, NMDA receptors could mediate stressinduced dendritic retraction in mPFC.”¹⁸

linking microscopic structure of vessels and synapses to function; PLoS Biol; 2007

During a strain-release cycle, when exposed to normal stresses, by releasing period, a substrate returns back to its origin configuration and shape; however, this is not the case for overstrain cases; when the substrate is exposed to high stresses.

A typical dendrite growth under stress situation

Chapter III

Entry Stress Ranges; Strain-Stress Characteristics In Different Areas and Areas Time-Behavior Specialty

The absorption capacity of the confined energy in substrates; immediate energy relieving and daily relieving behaviors are the main characteristics of a substrate. The general time-behaviors in each area, although within the normal range, depends on the substrate required activation energy either to receive or to release a packet of energy. Time variants for the brain’s visco-elastic material creates a separate sense of time, which interacts with the real time and regulates the main cycle of sleepwake. This system is located in the reticular formation region. It is believed that kinetically, the brain clock system is based on the activation potential of the integrated substrates and is well-known as a clock mechanism which would be described as follows. The stress-strain behaviours in different parts of the brain are relayed, regulated and undergo the specific functions by the reticular formation region.

“A key component of the arousal system is the suprachiasmatic nucleus (SCN), a tiny part of the hypothalamus located directly above the point at which the optic nerves from the two eyes cross.”

“An important component of the system is the reticular formation, a group of neuron-clusters scattered diffusely through the core of the lower brain. Reticular neurons send signals to the thalamus, which in turn sends activity-levelcontrolling signals to every part of the cortex. Damage to the reticular formation can produce a permanent state of coma.”¹

“The reticular formation is a part of the brain that is involved in actions such as awaking/sleeping cycle, and filtering incoming stimuli to discriminate irrelevant

background stimuli. It is essential for governing some of the basic functions of higher organisms, and is one of the phylogenetically oldest portions of the brain

The reticular formation consists of more than 100 small neural networks, with varied functions including the following:

1. Somatic motor control—Some motor neurons send their axons to the reticular formation nuclei, giving rise to the reticulospinal tracts of the spinal cord. These tracts function in maintaining tone, balance, and posture—especially during body movements. The reticular formation also relays eye and ear signals to the cerebellum so that the cerebellum can integrate visual, auditory, and vestibular stimuli in motor coordination. Other motor nuclei include gaze centers, which enable the eyes to track and fixate objects, and central pattern generators, which produce rhythmic signals to the muscles of breathing and swallowing.

2. Cardiovascular control—The reticular formation includes the cardiac and vasomotor centers of the medulla oblongata.

3. Pain modulation—The reticular formation is one means by which pain signals from the lower body reach the cerebral cortex. It is also the origin of the descending analgesic pathways. The nerve fibers in these pathways act in the spinal cord to block the transmission of some pain signals to the brain.

4. Sleep and consciousness—The reticular formation has projections to the thalamus and cerebral cortex that allow it to exert some control over which sensory signals reach the cerebrum and come to our conscious attention. It plays a central role in states of consciousness like alertness and sleep. Injury to the reticular formation can result in irreversible coma.

5. Habituation—This is a process in which the brain learns to ignore repetitive, meaningless stimuli while remaining sensitive to others. A good example of this is when a person can sleep through loud traffic in a large city, but is awakened promptly due to the sound of an alarm or crying baby. Reticular formation nuclei that modulate activity of the cerebral cortex are called the reticular activating system or extrathalamic control modulatory system.”

The pain is the limit to strain in its safe elasticity range and therefore determines the stress-absorbing capability of the brain tissues. All other functions , filter, discriminate and distinguish the periodical character of entry stress flows.

Different Regions’ Structures Capable to Provide Different Storing and Releasing Properties

The stains on different parts of the brain with different structure of neuron types and configurations produce different storing and releasing properties when a stress flow goes through them.

The detailed anatomy of each location can help to know the most common type of the tissues made up; to enable a definite type of memory or function would be allocated to that location.

²

Explicit/Implicit Memories, Consciousness and Visibility

Memories are generally are divided into explicit and implicit memories. An explicit memory is meaningful by itself; while an implicit memory is not understandable by itself and needs to be integrated by other memories to become explicit. An explicit memory is a reflection of an entry input; while an implicit memory is a separated pieces of it up to its pixels. The explicit memories are in the range of consciousness state; while implicit memories are in a range of subconscious state. The memories which integrate together as a dream are often implicit memories and integrate together to create a narrative meaningful for an emotion; and rarely a complete and exact memory of sensory entering. Wavelength in NREM are large (50-200 microvolts) and the frequency is low (28) because their associated neural networks are from high magnification neural networks level and are integrated of few pixel networks, therefore more energetic synapses to reconfigure. They are not in a conscious range of imagination. All stress flows are sensed as a non-conscious or subconscious currents. Wavelength in REM are smaller (less than 50 microvolts) and the frequency is higher (as waking 15-30) because many pixel memory and smaller pieces of memories rather than those received by sensory associate together around a strained location. Their frequency is in a conscious range of created images. Increase of network levels in a complicated neural network increases the activation energy to be easily motivated. The occupied volume of neural networks first increases with it complication and then condenses afterwards so that the it become more compact. This situation is expected to exist in the structure of the frontal lobe.

There is a spectrum of frequency for conscious level. The ranges lower or beyond that spectrum are called subconscious and superconscious ranges, which implicit memories would be processed in subconscious range. The explicit memories and their general classified patterns create the straight or abstract thoughts.

Physical Meaning of Consciousness

The images of firing fractals over the neural networks have a consciousness quality of different ranges according to the neural networks amplitude and frequency similar to the description given for the light spectrum. For a certain range, it is referred to as the conscious state of the brain activities; and for the other ranges as super—or subconscious states of activities. At a conscious state, the ranges of neural networks which participate in a brain activity have frequencies which match with the frequency of definite networks in amygdala or in the frontal lobe with an equal or multiple size to keep them resonating. In a brain activity, the free energy transfer occurs through different layers and levels in different areas and therefore a wide range of neural networks complexity, as the energy substrates, take part in the activity. The related brainwave shows a wide fluctuation. However, the dominant range of frequency and amplitude will be in the band ranges as given in the table below.

State

Frequency (Hz) Wavelength (microvolts) Neural network

Superconscious

60-135

Long-chained h

High conscious

30-60

Moderate- chain

Normal conscious (Waking)

15-50

< 50

Moderate chain

Presleep (low-conscious)

8-12

50

short chain of lo

NREM stage 1 (subconscious)

4-8

50-100

No chain of few

NREM stage 2 (subconscious)

4-15

50-150

No chain of few

NREM stage 3 (subconscious)

4-15

100-150

Few levels of n

NREM stage 4 (subconscious)

2-4

100-150

Straight synaps

< 50

Short chain of h

Dreaming (REM) (self-conscious) 15-30

The state of consciousness refers back to a frequency range of higher than 15 Hz and corresponds to a chain of neural networks linked by a stream of free energy transfer. Gamma and beta are the normal brain wavelengths during a state of either super—or normal conscious states. Gamma, in a frequency range of 60-135 Hz, represents frontal lobe activities as energy transfer and interaction with complicated and lengthy neural networks; and due to capability for immediate releasing, it is involved in a certain cognitive or motor function in superconscious states. The frequency range covers very complex and condensed networks of neurons. Gamma, in a frequency range of 30-60 Hz, represents the combination of energy flows in the frontal lobe and ending in a certain cognitive or motor function in high analytical-synthetic states. The frequency range corresponds to the complicated networks of neurons. Beta in a frequency range of 12 Hz to about 30 Hz is seen toward the front of the head and in fully conscious states. The frequency range corresponds to moderately complicated networks of neurons. Alpha, in a frequency range of 8-12 Hz, is seen in the posterior regions of the head, corresponding to the middle brain activities and neural networks with low magnifications. Delta, in a frequency range of up to 4 Hz, displays the highest amplitudes and the slowest waves; it is normally seen in sleeping adults, especially in NREM stages. It represents single neurons and simple synapses.

Spectrum of Memories Saved In Different Magnification Levels

Memories should be classified according to their arrangements between the related neural networks. One of their main identities is their frequency. Their frequency spectrum, like a spectrum of light, can be visible (within the consciousness range) or beyond that. The visible range of light for the human eye is from 400 nm (4*10-7 m) to 700 nm (7*10-7 m). Similarly, the conscious memories should have a definite range of neural network frequency as well. Then the frequencies with wavelengths within this range are considered as the known memories. The neural networks under and beyond the frequencies for the known memories build the functional and concluding memories. Pixel memories are saved by simple neural networks configurated when they are ed in strings of substrates synchronized firings as the energy eddies and in energy packets. Would be reassembled in activated pathways creating brain activities. The assembly can cause that sub-activities in pathway branches would be ed in a chain connecting ins-and-outs. In summary, it can be assumed that any neural network at any level of magnification all over the brain is a site of memory either physically, subconsciously, consciously or super-consciously; which can be classified as:

1. Amygdala is a site for memories which sort and store memories of strains and overstrains in its neural networks, and they relay the excitation of the neural networks that convey a similar strains and overstrains. It recognizes the memories within the conscious and subconscious ranges and does not recognize memories beyond that range. 2. Recognizability of the memories also can be categorized as declarable and non-declarable according to their wavelengths. Short—and long-term memories depend on the amplitudes and frequencies of their neural network oscillation when excited. The long-term memories are presented by the high amplitudes and low frequencies; while the short-term memories are sited on the neural networks with lower amplitudes and higher frequencies.

3. Cortex neural networks should be the site for storing of the decomposed sensory inputs with high frequencies and very short amplitudes. 4. Frontal lobe neural networks have very high frequencies and much shorter amplitudes reflecting very abstracted patterns of the similar memories in other parts of the brain.

Working Memory and Carrying Substrates

A working memory consists of recently activated pathways. The capacity to store the working memories depends on the size of the related area in the neocortex. There is a strong connection between working memory and fluid intelligence; speed of input processing and attentional control²¹. The short term and long term memories are slaves of the past experienced needs; when the working memories are formed around the present needs. Working memory interacts with other kind of memories attracted by basic and advanced needs and goals and all together complete a complicated pathway. In the individual substrate modules of the pathway, a part of flowing stresses would be absorbed and the excesses would be transmitted to following substrates in the pathway. The temporary absorbed portion will be consolidated periodically and develops the substrate modularity by crystallization of the synapses configuration changes within that substrate. Consequently, the substrate architecture will be changed by addition of finer grids of fibres and synapses. The growth of new synapses and change in underlying levels of the substrates reinforce their connections in specific ways. Thus, the receiving, processing and dispatching of the stress forces will be strengthen.

Chapter IV

Brain Functionality

Brain judges and concludes in the same way as previous similar input-events have been judged, with some new interpretations. The stress flows follow the same pathways as applied in similar circumstances. The theory of precedent emphasizes that each new processing of inputs generally follows the previous emotional or reasoning conclusion as saved in similar cases. The previous savings can be traced by overstrains and de-strains of relevant tissues, substrates and pathway.

1. an input event is saved as a case-memory and is configured in the outer brain; 2. the interfaces between different case-memories are saved as abstract memories like memories of names, numbers, colors, shapes. 3. a pathway of processing as a pattern is saved as a functional memory. the pathway will be squeezed by codification with related action operating memories 4. the interface of functional memory are saved as related emotional memories.

The place for these memories are less dynamic in the middle brain,

1. a memory of an overstrained state and de-straining memory of that state is saved as an emotional-memories; 2. the precedent memories which had been saved as the common-memories and had been squeezed by time, down size the relevant substrates and consequently increase the stress flow speed. They accelerate the processing functions;

The place for these highly dynamic memories is in relay centers, like amygdala or prefrontal lobe. An unsteady state analysis of stress flow receiving-absorption-desorption and discharging with dynamic parameter involved can formulate the processing phenomenon in each substrate. the overall integration provide the processing formula through a pathway. A computation power of thousands of thousands calculations per second can provide a realistic picture of the ongoing phenomena of the brain functionality.

Hierarchical Arrangement of Modules

A substrate is a module which is formed and reinforced by time and consists of its variant and invariant parts exposed to the transferring free energy during an involved mentation. It develops for more rigidity and invariant part by time, but still would play in variety of integration as a building block for larger modules based on neuron fibres and synapses configuration in the area. Modules play a role of hosting for a working memory to short and long term memories. If a recalled memory would be considered a reflection of environment on-going, either a previous or recent one, then a mentation predict a combination of those, which is not possible to be present in environment in one time. Therefore, the brain is much creative than the non-visco-elastic pathways if they go under any stress flow. A mentation is an arrangement of modules that are excited and ed by free energy transfers causing a synchronized firings for milliseconds to a second. If any module participation would be considered as an element for a brain activity, the whole brain activity is a combination of activities between an initial stimulation and a final response. Modules are linked locally along a pathway over a layer or connected together by resonating. A locally connected pathway is more lengthy over outer layer than middle or inner brains, because the elasticity of structures decreases from outer to middle and inner brains in sequence. Resonating occurs when two modules find a similar configuration of synchronized firing clouds during their excitation. Resonation is strong if they find very close configurations, but weak if configuration similarity is not prefect and the duration time for the same configuration is too short. Working memory modules with longer same-configuration period determine the attention direction. In any attention attempt self-recognitions controls inputs and working memories. In the hierarchic model of intelligence, perception, cognition and reasoning

processes has outcomes of senses, feelings and automatic thoughts as well as intentional thinking. Perception is a process consisting of organizing sensory modules and interpreting inputs through invariance parts of input zone modules. Cognition is a process consisting of configuration energy exchange storing and retrieving within modules. Reasoning is a process of evaluation of inputs or memories by very abstracted modules which reflect the general pattern of things and events. The mentation template is all pathways possibilities that the brain provides for an initiation to dispatch a stress flow with variety of flexible features of module combinations in any time. These possibilities depends on visco-elastic properties of the brain layers and resonating occasions which can occur between participating modules. The pathway with faster conduction of free energy has higher chance to be the one which forms the mentation. The higher portion of variability; the higher conduction rate. Such a pathway conduct fluid intelligence like problem solving including reasoning, inferring and abstraction. Most of such a pathway occurs over prefrontal cortex which is a carrier for working memories with all elements of attention and inhibitory controls. Interaction and activation speeds of short term memories are fastest in the prefrontal cortex. Modules weighted in invariable part are categorized for hosting crystallized intelligence like knowledge including facts, procedures and heuristics. These modules because of less flexible and variant networks of neuron fibres and synapses are located in rigid structure like hippocampus. This area is a site hosting the long term memories. Speciality of different areas of neo-cortex due to their structural difference can be followed considering Brodmann’s map.

Hierarchical Model of the Modularity In A Brain Activity

The brain as an elastic material has an invariance character which forms its plasticity and a variance character that makes its constraints. Any module of the brain in any region preserve the same characteristics, but in different strength. When a free energy input enters a module, the module would become a substrate for that energy amount and shows visco-elastic property toward energy flow due to temporary reconfiguration of neuron fibres and synapses. Modules in turn are made of sub-modules which are arranged in a definite configuration in the module. However, there is a limitation for sub-module in size and that is the limitation between molecular architectural and the network structures and the kinematic model applies to networks in layers. The invariant and variant portions of a module and the combination of them forms the static and dynamic or structural and behavioural characters of an activity. The invariant portion of a module works as a template for any variant changes in it. Similarly, the brain regions with less elastic structures form the free energy pathways along the more elastic structures. A brain activity will resonate with an emotion or reasoning; and an emotion or reasoning would run a brain activity by a resonating initiation. In other words, a neural network in amygdala or prefrontal cortex directs the releasing free energies from most strained substrates over outer brain; when a brain activity over outer layer would recall an emotion or reasoning to apply. Modules which are suitable to be a site for condensing and abstraction of patterns are an integrity of neural network; and degree of abstraction depends on how fine they are. Cognition modules are different from perception modules; can be an integration

of sensory and perception sub-modules. Lower-level modules build blocks of higher level modules²². Accordingly, the more static modules will build more fluent modules, by loading in the variant parts. A pathway of free energy transfers during a brain activity from initiation up to termination would be formed in a template of previous saved brain activities like long term memories, activated by an emotion pattern or intentional reasoning. In short, a brain activity is a combination of inputs processed with previous saved information as memories and genetic evaluations. The elements of inputs are screened with the modules of a similar pattern, although absorb a tolerance of deviations by their variance properties. Evolutionary, all these variations and development in modules search the survival and reproduction goals; however, specifically in human, they cover a wide range of needs consisting of physiological, safety, esteem, selfadvancement needs. Development of the needs and complexity of the modules improves selfprotection and self-advancement goals which in parallel strengthen the knowledge of self by advancing the crystallized intelligence. The development of needs, improvement of self-protection and self-advancement; together with advancement of the awareness about the self (as the subject to be protected and advanced) a complicated sense of the self.

Brain Functional Modeling By Substrate Structures

Divergent pathways of input memories as they are formed in the outer brain emerge with divergent thoughts; while convergent pathways formed in the frontal lobe, suppresses the changes between isolated pathways and dynamically ends in certain outputs. A mentation is a combination of different activities of an emotion; when excitement, feelings, free and focused thoughts are happening in a very small frame. Any thought is attached to a stream of free energy along a pathway. A mentation is an integration of all the streams in one overall pathway, within a limited frame of time. An emotion is connected to a localized substrate overstrained. It may cover several excited substrates having the most strained one in the core. A feeling is a short length of chained substrates in a pathway highly concentrated in substrates in that short path. A free thought is a long chain of substrates within a longer path; and a focused thought is a chain of substrates connected together in a balanced complex of branches. These types of emotions have the same number as substrates’ structural type and tissue property do.

Neural Connectivity In the Brain

To have a clear picture of neural networks, the brain for programming proposes can be thought as a jungle of trees, when their roots and branches connect and together. Leaves are exposed to winds, transferring stress loads to related branches, and the fine branches transmit the stress to thicker branches, each absorbing the strain up to a breaking point. Because a tree is not an elastic material as is the brain, it returns back generally to it’s original position when the source of the stress is removed. However, a medium like the brain behaves differently, and the release of strains is time-dependent and in stages. The remained strains should be balanced periodically and the portion which would not be consumed in reconfiguration of the synapses would release, creating a dream depending on the route they excite during releasing. Similar to trees that have branches connected to the larger branches, the flexible neural networks are backed with more plasticized neural networks.

Connectivity and Pattern Communication

Although, the pattern separation and pattern completion are the computerized numerical analysis which are utilized in brain memory analysis, but they seem that are the actual symbolic representation of processes which really happen in brain. In sleep pattern separation consolidate the memories (as case memory)and pattern completions create dreams and in waking, separated patterns are retrieved to enrich the pattern completions. Each communication (connection) bond width influences the total communication cloud in any time. Human with advance and so speedy brain internal communicating, because of developed areas, has advanced and dynamic outputs like decision making, planning. The most dynamic character of human mentation is the combination of pattern separation and completion processes during a mentations. The pattern separation process happens in regions with more rigid structures; when the pattern completion in more elastic structures. Therefore, the stress flow during pattern separation and pattern completion should be different. Connectivity is with a narrow band of frequency for resonating and can be in a wider range for the elasticity transfers. A combination of the different connectivity in the same time creates a more complex stress flow in brain during a mentation. Accordingly, accidental overstraining and their releasing would occur frequently. The cellular connectivity by firings develops to networks connectivity in substrates ing more complete memories, sorting them and making more abstract activities. Higher level of abstraction by using of memories in a hierarchic way provides higher consciousness in thoughts. This way patterns condense into denser lines of connectivity.

Chapter V

How Memory Components Find A Site to Sit On

Edge density is the most useful characteristic of a complex neural network to distinguish boundaries from neighborhood ones. Edge density differs by magnification level and section by section. The other parameter which significantly discriminates neural networks is the node configuration. By decrease in magnification degree of neural networks in any location, network complexity will decrease; so that in the finest neural networks with few neurons, the finest memory components will sit. Any node in a higher magnification level can consist of an internal network itself. If the nodes’ internal configuration is similar to the network itself, selfsimilarity is followed in the growth of the network up to that magnification level, and it forms a firing fractal that when associated presenting a static memory, like a face, an individual, or an object. If the nodes have dissimilar internal configurations, the general network configuration activates a dynamic memory like the memory of an event or a behavior or an action. In low magnification level, like a layer level, rarely does a single configuration node exist because of the related energy packet size limitation. Networks in such ranges preserve a wavelike energy package which represents a complete activity which can be expressed in a statement or a phrase. Memories in any sectional location of the layer forms during the growth ages and spatially with rapid neuron growth. Neural networks grow in any location for a family of memories which have a good similarity in the configuration and will be encoded by the same general emotion. The reason that in a dream, memories with the same emotion theme pop up is because releasing of the location strain initiates strain-releasing flows around the same location activating the same family neural networks. All those activated memories in dream have a similarity in the involved emotion; while in the waking brain activities, the stress flow crosses different networks’ families and experiences a smoother gradient of emotions. In sensory receiving, fine neural networks made of a few neurons, are involved. Similarly, the crystallized network configurations do not appear at a conscious level. But tissues containing many neural networks and in a macroscale level undergo a strain load, which physically encode an emotion memory in the amygdala. In this way, a boundary between subconsciousness and consciousness

will be defined. The activation of simultaneous sets of neural networks which represent a complete sensory input provide a conscious activity; when the activation of a set of neural networks representing an incomplete sensory input would be utilized in a brain creating activity. A neural network, at any magnification level, can become a nest for a recognizable memory if it has been sufficiently plasticized in ties and marked as a fingerprint. A complete memory has normally lower magnification levels of networks. In decreasing the magnification level, networks would be more robust and firm, and therefore have a better chance to fix a memory. While in high levels of magnification, networks have less inertia to resist against coming stress flows and show higher elasticity. However, they still can carry memories components with less details. On the other side, less complicated networks have a potential to link memories in different ways and play the role of functionality and continuum. The less complicated networks present different levels of creativity by different ways of combinations. The functional continuums which connect static and dynamic memories in a brain activity are either regulated by emotions or by a reasoning. In this way, an emotion or a logic combines different type of memories which had been encoded for the same range of frequencies and amplitudes in amygdala or in the frontal lobe. The fundamental rules are the same for waking and sleep brain activities, except the emotion variation which has a very narrow-bond of changes in frequency while dreaming, but has a wider variety in waking. In recent researches for mapping neural networks in the brain, a network with a node number of more than four hundred is recognizable and is considered in a range of the conscious state.²³ Any neural network as a kind of memory has a speciality (modularity) and a resonating capability when excited and accordingly it plays its role with related efficiency in the whole activity. However, by the time, its specialty and resonating capability will change, reinforcing during growth ages and degrading if not would be used frequently.

How Object Perceptions are Segregated, Spread, and Saved In Individual Networks

Any sensory input would be precept as a set of individual objects, their motions, and general circumstances of the individual objects combinations. During processing in the brain, after perception, different interactions with other substrates (which are connected by free energy transfers or resonates with inputs) occur in different areas which totally map an excited pathway with synchronized firings or developing synchronized firings during the course of a brain activity. The activity terminates either in a motor-output or will be trapped in a substrate by higher required activation potential. The latter will induce a depressed mood or escape to a new route with the least required activation energy, channelling into an output with different accompanying imaginations and reasoning which is uncontrolled and possibly opposite to the action expected before. On the other hand, parts of a pathway act as attracting regulators to give direction to the stress flow. Rejection and reward s are the famous mechanism known to induce free energy pathways in brain.

Examples:

— In a campground, a young man tries to set fire to wet woods; and when unsatisfied after several tries, suddenly jumps into the river beside it or starts to run without previous intention, just for joy. — Activists trying hard to make something better, fail several times at getting a result and see their difficulty along the way, act differently. — People under pressure, and after a while and especially with promises, act completely opposite to their ethical goals — Very distinguished examples happen in sleep; when free energy flows escape from overstrained locations in pathways made of substrates with the lowest

possible activation potential in the brain and create images in dreams which are not real.

The required activation potential in any substrate depends on the degree of neural networks plasticity in that substrate. The higher rigidity, the higher activation energy is required to enter or release from that. If the combination of neural networks in a substrate is considered as a structural pattern and synchronized firing energy fractal which is going to interact with that structure in the course of a brain activity is considered as a dynamic pattern, the reacting energy distributes in different structures according to the process called “pattern separation” and/or travel according to the process called “pattern completion.” This is a methodology used in neural connectivity computation, but physically conceptualized as described above. By this process, a stress flow finds a route which is a simultaneous flow in or out, either to interact or retrieve or dispatch.

Pattern separation is the process of reducing the average overlap between two representations, highly overlapping input is essential to episodic memory and the process of recollection . . .

Pattern completion is the process whereby a previously stored representation is retrieved, given a partial or otherwise degraded cue.

Pattern completion and pattern separation interact dynamically and that the hippocampus may require smaller changes in the input in order to perform pattern separation than cortical regions; hippocampus may be more prone to pattern separation, while the cortex may be more prone to pattern completion.²⁴

Pattern separation and pattern completion processes are required for accurate

storage and retrieval of episodic memories.

During encoding, regions in the hippocampus as well as in the adjacent MTL (medial temporal lobe) cortex predict overall encoding success.

During retrieval, regions in both the hippocampus and the adjacent MTL (medial temporal lobe) cortex exhibited activity correlated with overall successful retrieval of information.

During both encoding and retrieval, however, the pattern of activity in the hippocampus . . . [is] more complex . . . [comparing different areas together].

Signals in the parahippocampal gyrus were consistent with basic encoding and retrieval success . . . ²⁵

The established networks’ configurations were made due to formations developed either genetically or due to overstrains occurring in the past by exposure to high stressful events.

[Signals in the parahippocampal gyrus] did not fully differentiate all three trial types of interest (hits, false alarms to lures, and correct rejections of lures [pleasure attractors]).

Within the hippocampus, such differentiation was present. Thus, signals were present in the hippocampus that would allow participants to overcome the interference and accurately perform the task (e.g., correctly note that the present

stimulus was a true repetition vs. correctly note that it was only similar to a prior stimulus).

[There is] a distinction between hippocampus and cortex according to which the cortex slowly forms mnemonic representations.

The hippocampus, on the other hand, is thought to possess key circuitry in the dentate gyrus that is designed to quickly form distinct representations and perform pattern separation to a degree not found in other structures.

The medial temporal lobe (MTL) s the formation and retrieval of longterm declarative memories, or memories for facts and everyday events.

The process of “pattern separation” screens the entry inputs by distributing of the stress flow over the brain layer according to layer established network configurations and their flexibility to change. The stress flow between separated networks structures continues by “pattern completion” processes and this continues substrate to substrate up to brain activity termination. “Pattern completion” process links substrates in a pathway either by energy transfer for the next substrates or by resonating for far substrates. Different substrates in a pathway can act as attractor or repelling sources; both direct the pathway. However, the opposite sources could be spatially located in the same family structures of memories. Such a discrepancy not only causes an error or confusion when both are retrieving in a pathway, but degrades the memories in the location too.

Interference between information learned at different times is a major limitation on memory [interference theory].

[Medial temporal lobe] MTL, specifically the hippocampus, performs pattern separation, whereby overlapping representations are made less similar.

Here, it is intended to introduce a new naming classification for neural networks established in each area, only for proposes of use in the kinematic study of the brain activities, in line with pattern separation and pattern completion processes. In this way of naming, all the substrates anywhere in the brain establish a network configuration—because of stress flow forming—which can be called a kind of memory. The generalization of the term of memory is because it is to have a common terminology in kinematic study and the physics of elastic material, which use the same term with similar meaning as kinematics does.

• Input and output zones receive entry inputs in a set furnished by sensory channels. This is called case memory: a working memory of percept scene which includes different components. • Cross memory refers to a declarative type of memory. • Cumulative memory for nondeclarative memories over the cortex which integrates interactions of all playing networks in a pathway. • Core memory with its three types of logic attractors in the frontal lobe, and emotion attractors in areas store or refresh emotions like the amygdala and primary attractors in located in the inner brain.

As already mentioned, this categorization has been done based on the structure and function types of the areas, regions, or layers. In the following, some description of the sections and type of maintaining memories will be described:

Declarative memory, or memory for everyday facts and events, is critically

dependent on the medial temporal lobe (MTL) structures, including the hippocampus and the surrounding structures of the parahippocampal gyrus² .

The hippocampus, on the other hand, is thought to possess key circuitry in the dentate gyrus that is designed to quickly form distinct representations and perform pattern separation to a degree not found in other structures.

Hippocampus plays a key role over and above adjacent MTL structures when strong demands are placed on pattern separation.²⁷

The medial temporal lobe (MTL) and the prefrontal cortex (PFC) are . . . critical structures for human memory processes [and] they are part of a memory network.

Memory-modulated interaction between [profontal cortex] PFC and [medial temporal lobe] MTL has been observed at the hemodynamic level.²⁸

Any network configuration when excited shows a different oscillation frequency and amplitude:

Field oscillations in the theta band (4-8 Hz) facilitate PFC-MTL interaction²

And when they link by energy transfer or resonance, the connectivity between substrates will be strengthened:

A task-modulated increase in coherence values between PFC and MTL was seen during free recall . . . coherent PFC-MTL site pairs was significantly increased during recall.

The increased coherence is a consequence of higher bidirectional information flow between the two regions, with a generally greater driving from MTL to PFC, namely, (MTL —> PFC) > (PFC —> MTL).”³

A Crystallized Pattern of Networks, A Dynamic Pattern and A Continuity Pattern

The kinematics Model assumes that the working memories (in the shape of a stagnant confined strains) are ready to release or consolidation over the high magnified neural networks; the short term memories can be retrieved by energy transfers through the medium contrast of the neural networks; and the long term memories can be recalled by energy transfers through the low magnified neural networks. It seems a brain activity is like the casting of working memories in the frame of short memory patterns, when the short memories would be formed in a bed frame of the long memory patterns, maintained by the more rigidity of lower magnified neural networks. In other words, the short and long memory patterns would be mapped over the outer layer resonating with their permanent sites in the middle brain. Thus long and short individual identities are structured over the hippocampus and have their intermediate resonating media with a current brain activity through higher resonated neural networks. The long memory patterns and short memory patterns generally regulate the transfer of energy flow of the working memories. A transfer of energy flow through the layer can find a memory match with its related crystallized components in the hippocampus and refresh the related emotion through energy transfer connection with the amygdala. The general pattern in the outer layer, the crystallized structure in hippocampus, and the relaying sites in the amygdala and parts of the frontal lobe communicate through resonating of the consisting neural networks when excited by a stress flow over the brain outer layer. Their flexibility or rigidity toward stress forces and changes in their media of neural network configurations facilitate the resonating process among them. To excite any memory, the relaying structure in the amygdala or frontal lobe and varying configurations of the neural network in the outer layer because of the stress flow should find a sensitive resonating condition to recall them from the storing sites.

There are two different directions of the stress flow in the brain: (1) longitudinal by a sensory input or an internal stimuli and (2) spatial strain distribution over the magnification range to balance the layer. Longitudinal flow is reinforced by sensory inputs; while spatial flow, through different neural network magnification levels, are initiated by not balanced strain forces vertically in the layer. The continued balancing is compensated by forwarding of the stress flow and remained overstrains will be balanced during sleep by reconfiguration of synapses and therefore the neural (memory building) and energy distribution over the networks (dreams). In the later type, the overstrained point can be in any level of magnification of neural networks depending on the daily stress flow pathway; either indicating a long memory or a short memory. However, the general direction of the energy release is from the lower level of the networks magnification to the higher level because of the decrease of elasticity in the same direction. In any level, the general rule of distribution is governed by the lower magnified level because higher inertia against changes. In consideration of longer-term memories covering lower magnified levels and shorter-term memories in higher magnified levels, any overstrained point recalls the memories which are sited in related neural networks. In releasing an overstrained location during a REM period of sleep, characters, behaviors, and settings in long-term memories relayed by lower magnified networks of the locations judge the short-term memories and judge both the working memory of the current activities. For example, a dream picks up a mixture of components from different types of memories in a random order; but a general order of patterns from long-term to short-term memories would be followed. The routes of releasing from the strained points present the creativity of the dream. Applying the pattern separation role to separate the working memory components,³¹ during a brain activity; static, dynamic and procedural components from different parts of the brain would be ed by the free energy flow creating the brain activity. The free energy flows will confine overstrains in cases that all the strains would not balanced in specific locations. The overstrains will be recalled or consolidated in the local connections as the working, short and long memories and in the hippocampus will consolidate as facts and events memories.³² The separation and completion processes have been suggested to happen in different places in the middle brain.³³

Emotion Sites In the Amygdala and Functional Memory Over the Cortex

The plasticity of synaptic circuits, or in other words, neural networks, is widely accepted for memory consolidation. However, it is known as a function with continuous and gradual time variables. In this section, the process will be discussed as a function of distinct and a stepwise variable of strains. The reason for such a claim refers back to the present assumption of time-continuous changes in synapses structures; and according to the kinematic explanation, these changes happen intervallic during sleep.

1. Accordingly, there are definite strain-marks consolidated according to different eventual high stress forces; and these strain-marks save definite memories. 2. In any location of the brain, due to similarity in neural network configurations, a family of the memories are saved, and the networks’ strain marks are arranged in a very regular structure. 3. A local energy plan connecting strain-marks in the hippocampus by occurrence repetition induces the strain-marks over the amygdala according to the overstrain-marks which would be consolidated as an emotion memory.

³⁴

This model helps to relate the excitation of an emotion to the recollection of the associated memories in the hippocampus.

Auditory fear memory is consolidated in the lateral amygdala (LA) in a rapid (<

24 hours) and local manner.

Multiple traces representing different facets of fear memory interact to modulate fear memory strength.³⁵

4. Pathways connect strain-marks along the outer layer throughout different memory families, and therefore produce definite numbers of connected networks as a temporal energy plan. Each energy plan made of strain-marks connections would be consolidated in lower neural networks magnification levels in the outer layer relaying the declarative, nondeclarative, and emotion memories as well the functional build up in the brain outer layer (cortex) itself.

Matching Synapses’ Plasticity, Neural Network Complication, and StressStrain Marks

The physical stress-strain properties of the brain layers and their functions are widely different in different areas. First, because made-up neuron combinations and neural network structures are different. Second, because by housing different chains of networks during the day causes different rigidity in different locations. Third, because of new reconfigurations and changes in synapses. Therefore, the physical property of brain layers would change continuously. Even different strips and fractions in the same area could be different during the day. For example, any working memory up to the time that it is not vanished or erased by other coming stronger mentations, the corresponding tissues show different stress-strain behavior. However, all these influences are negligible in an overall layer stress-strain behavior. Except neuron cells, other components like glial cells and cerebrospinal fluid around those have a major role in different areas physical properties which indirectly affect the synapses and neural networks change and form memories.

Synaptic transmission can be either enhanced or depressed by activity, and these changes span temporal domains, ranging from milliseconds to hours, days, and presumably, even longer.³

Similarly, in each layer, different levels of magnification can be focused as in: low, medium, and high contrast levels. This fact is significant in thicker layers which contain higher levels of the networks complexity. Kinematics model assumes the major part of free energy travels through the higher neural networks resolutions, less in medium levels, and least through the low contrasting networks. The long-term potentiation occurs in low levels of magnification; and as much as going further toward the higher levels of magnification, the synapses’ plasticity will decreases because of lower inertia

toward energy flow and changes.

Different forms of synaptic plasticity: short-lasting forms of synaptic plasticity; termed long-term potentiation (LTP), and long-term depression (LTD).

Numerous forms of short-term synaptic plasticity, lasting on the order of milliseconds to several minutes, have been observed at virtually every synapse examined in organisms ranging from simple invertebrates to mammals (Zucker and Regehr, 2002). These are thought to play important roles in short-term adaptations to sensory inputs, transient changes in behavioral states, and shortlasting forms of memory. Most forms of short-term synaptic plasticity are triggered by short bursts of activity, causing a transient accumulation of calcium in presynaptic nerve terminals. In turn, this increase in presynaptic calcium causes changes in the probability of neurotransmitter release by directly modifying the biochemical processes that underlie the exocytosis of synaptic vesicles.³⁷

The tissues with higher frequent activity occurring during a day become more rigid than fresh ones.

When two stimuli are delivered within a short interval, the response to the second stimulus can be either enhanced or depressed, relative to the response to the first stimulus (Katz and Miledi, 1968; Zucker and Regehr, 2002). Pairedpulse depression is commonly observed at all synapses at short (less than 20 ms) interstimulus intervals, and most probably results from inactivation of voltagedependent sodium or calcium channels or from a transient depletion of the release-ready pool of vesicles docked at the presynaptic terminal. Many synapses exhibit paired-pulse facilitation at longer interstimulus intervals (20500 ms). A simple explanation for this phenomenon is that the residual calcium left over from the invasion of the first action potential contributes to additional release during the second stimulation.

The same synapse can display either facilitation or depression, depending on its recent history of activation and modulation.³⁸

Repetition of similar mentations through the higher levels shifts the momentum into the lower levels of the neural networks and makes stronger plasticity in those levels as a longer memory.

Longer-lasting forms of plasticity are observed following repetitive or tetanic stimulation of synapses with prolonged (approximately 200 ms to 5 s) trains of stimulation applied at high frequencies (10-200 Hz) (Zucker and Regehr, 2002).

At some synapses, repetitive activation leads to depression that can last for several seconds or even minutes (Betz, 1970; Zucker and Regehr, 2002).³

As discussed above, the glial cells and cerebrospinal fluid make easier momentum transfer vertically in the layer and provide conditions to convert easier working memories to short memories.

There is growing realization that glia may be involved in some forms of shortterm plasticity (Araque et al, 2001; Haydon, 2001). With their intimate association with synapses, astrocytes, and perisynaptic, Schwann cells are well positioned to regulate synapses. They have an established role in clearance of neurotransmitter and may participate in synaptic plasticity by controlling the speed and extent of such clearance (Bergles et al, 1999; Danbolt, 2001).⁴

Type of Structure and the Related Memory Functions In the Middle Brain

Hippocampus saves memories that can be explicitly verbalized called the declarative memories. The procedural memories for performing skills are probably saved in other parts of middle brain like basal ganglia and cerebellum⁴¹

conscious recollection, . . . depends on the hippocampus, and familiarity, . . . depends on portions of the medial temporal cortex.⁴²

A neural representation of the static layout of the environment would be mapped in hippocampus on its grid neuron cells networks made by them. A malfunction of the resonating function introduces a confusion the located place or how to get there, so the spatial memory remains not efficient. Feeling lost or do not know the way to destination should be investigated for a dream pathway which recollection of clear memories would not be possible. It means the resonating between the related areas are malfunctioning.

If virtual navigation task is a perception task of positions through grid cells, the pattern completion as cognitive task will be followed by the excitation of different other areas furnished by movement detectors, procedure or continuum detectors.

[The] field CA1 in the ventral hippocampus sends axons to the main olfactory bulb,[6] the anterior olfactory nucleus, and to the primary olfactory cortex. There continues to be some interest in hippocampal olfactory responses, particularly the role of the hippocampus in memory for odors, but few people believe today that olfaction is its primary function.[7][8]⁴³

The probable pathway during the waking is from basal ganglia and cerebellum toward the amygdala and during the sleep is in opposite direction. The pathway through all these areas would be found by neural networks with the lowest required activation energy which means a short cut route. Following is a preliminary try to search the static (position) pattern, the dynamic (motion) pattern and continuum pattern completion sites and their neural networks building blocks⁴⁴. The positional pattern is made by pixel of subject positions on hippocampus neurons which exhibit a high rate of firing whenever one is physically located in a specific location in an environment. In contrast, if that set of neurons are excited by an internal input the positional images would be created. Different neuronal cell types build the hippocampus layers and accordingly the hippocampus is divided in different segments of CA1, CA2, CA3 and CA4. In the CA1 and CA3 the positional saving pattern are made of pyramidal cells⁴⁵,

The dentate gyrus is another part of the midbrain and is an interface between entohinal cortex and hippocampus C1 and C3 segments. This part is probably is made of granule type of cells.

The dentate gyrus cells receive excitatory input from the entorhinal cortex, in, through the perforant path into the molecular layer (Xavier GF, et al., 2009). This activates pyramidal cells among the CA4 and CA3 pyramidal neurons. ⁴

The changes of distance from a target axis is accompanied by reduction in firing rates in these neural network sites.⁴⁷ Granule calls search a balance condition between a steady firings inertia and changing background landmarks distance; so the destination direction. This issue shows how important is the setting, space or field that the subject positions are mapped in it; or in other words the role of continuum which relates the static pattern and the motion pattern. Place code is one of suggestion that was given to the continuum. Entorhinal cortex as the sensory input and amygdada as internal input interfaces to middle brain probably complete a brain activity in conscious and subconscious or sleep times in opposite directions.

Hafting et al. (2005) suggested that a place code is computed in the entorhinal cortex and fed into the hippocampus, which may make the associations between place and events that are needed for the formation of memories.⁴⁸

Many species of mammals can keep track of spatial location even in the absence of visual, auditory, olfactory, or tactile cues, by integrating their movements— the ability to do this is referred to in the literature as path integration. A number of theoretical models have explored mechanisms by which path integration could be performed by neural networks. In most models, such as those of Samsonovich and McNaughton (1997)[16] or Burak and Fiete (2009),[17] the principal ingredients are (1) an internal representation of position, (2) internal representations of the speed and direction of movement, and (3) a mechanism for shifting the encoded position by the right amount when the animal moves. Because cells in the MEC encode information about position (grid cells[1]) and movement (head direction cells and conjunctive position-by-direction cells[11]), this area is currently viewed as the most promising candidate for the place in the brain where path integration occurs. However, the question remains unresolved, as in humans the entorhinal cortex does not appear to be required for path integration.[18] Burak and Fiete (2009) showed that a computational simulation of the grid cell system was capable of performing path integration to a high level of accuracy.[17]⁴

If misalignment of the positions toward goals frequently would be experienced very significantly, searching the balanced to complete a pathway would become inefficient. Feeling being lost or disappointing to be able to do a right navigation to find the destination would evoke attached to the related emotion. The combination of any static pattern and its movement can be similar to a phase precession process across neural networks to the middle brain areas. Phase precession process refers to a backwards shift in the time at which neural networks firing and layer oscillations due to straining and releasing causes any later phase of the successive synchronized firing overlaps the earlier synchronized firing of the following neural network.

Neural activity in nearly every part of the hippocampal system is modulated by the limbic theta rhythm, which has a frequency range of about 6-9 Hz in rats. The entorhinal cortex is no exception: like the hippocampus, it receives cholinergic and GABAergic input from the medial septal area, the central controller of theta. Grid cells, like hippocampal place cells, show strong theta modulation.[1] Grid cells from layer II of the MEC also resemble hippocampal place cells in that they show phase precession—that is, their spike activity advances from late to early phases of the theta cycle as an animal es through a grid vertex. Most grid cells from layer III do not precess, but their spike activity is largely confined to half of the theta cycle. The grid cell phase precession is not derived from the hippocampus, because it continues to appear in animals whose hippocampus has been inactivated by an agonist of GABA. [15]⁵

The different neurons types of place cells, grid cells, border cells, head direction cells, granule cells and spatial view cells make the hippocampus and related areas in the middle brain. Border cells as interface with grid or grid-like cells in entorhinal cortex, place cells and grid cells in hypocampus segments and spatial view cells as the interface with amygdala. Such an arrangement confirms the proposed pathway in the hippocampus from entorhinal cortex to amygdada for sensory inputs and reverse for internal (subconscious) inputs.

The entorhinal cortex-hippocampus system plays an important role in declarative and episodic memories and in particular spatial memories including memory formation, memory consolidation, and memory optimization in sleep.⁵¹

Following is a brief compare between the different cellular neurons which build this part of the brain, their functions and the place they are located(All the information are related to lab studies on animals and has been taken from wikipedia):

Neuron type

Configuration

place cell grid cell a set of small regions as a firing field; roughly equal in size and arranged i border cell The entorhinal cortex is the main interface between the hippocampus and n head direction cell granule cell spatial view cell fires when the animal views a specific part of an environment; associated e Pyramidal cell

An Emotion Is An Alarm and Defensive Mechanism to Prevent Tissue Damage

Stress in psychological refers to an excessive or a long term exposure to outside pressures and response to it is not limited to a continuous brain functions like a creation of images, crystallized knowledge, problem solving, decisions, and finally, immediate expressions and commands. Stress in psychological is an over-response to stimuli and the brain fails to shut off related responses immediately afterward. However, stress in physical is defined as mutual actions of forces that take place across any section of the body; here the brain layers by fine electromagnetic fields produced by the synchronized firings happen along neural network streamlines. Ranges of stress can be classified as normal, high, and excessive. Excessive stress is the range for stress in psychological meaning and is not the subject of the study here, but stress with a definition of a continuous daily flow with continuous and immediate responses to timely brain entries through the sensory inputs is the concern. It is normally within the safe range of absorption in the brain as an elastic material to be processed immediately and the response to it would be shut off immediately afterward. The normal period of input stress entry is very short; the period of its effects is as long as a brain activity continues; and the maximum response delayed time is as long as working memory life. Any overstrain because of a highly stressful input which is not considered as an excessive stress is the high extreme limit of stress as it will referred to by overstrain. The two ranges of normal and high stress inputs with a maximum daily response period is the limit as referred to in this study. It covers daily stress loads normally that can be handled in any moment by relieving responses and on its extreme the daily remaining reliefs will be completed during regeneration periods of sleep. A typical stress-strain curve has an elastic, plastic and fatigue ranges, as well as the yield and rupturing points.

Tissues in different resolution levels of neural networks each other against strain forces and far from overstrain damages, but the strain-stress curve is sensitive to the stress load increasing rate. For sharp stress load increases, it can be damaged. Emotion is an alarm to an overstraining event on a neural network made fiber in a definite resolution level. Due to such an overstrain, the network at a resolution level would go beyond the elastic range in a stress-strain curve and would be partially plasticized and then the network in the lower resolution level absorbs the excess stress load. Therefore, emotion—as a pattern of the plasticization in one level of network—distributes the load to the other resolution level and makes elastic behavior of the next resolution level of the local network to compensate the required elasticity and protect the whole tissue from fatigue and rupturing. In the kinematic model, emotions are assumed related to overstraining and relieving of strains. It is also assumed the origins of pain for strains and joy for reliefs are the main origins for a developed emotion tree.

Strain Accumulation and Memory Build Ups

A stress flow during a brain activity has different kinds of effects over different areas of the brain. Continuous stress flow is induced by inputs; and stress within its normal range comes and goes as normal motor outputs.

At a fine-grained temporal resolution of a few hundred milliseconds, cortical tissue can show distinct oscillations, e.g., in the α, γ, or δ frequency bands (Roopun et al., 2008).⁵²

The more lasting effects would be plasticized as short and long memories. Those effects which may end in lasting memories are referred to as working memories. It is assumed that neuron connectivity will be strengthened by higher strains and the high frequency of its occurrence. A long-lasting high-frequency stress flow is required to make a strong connection. Therefore, the stress-strain property in each area determines which kind of memory locates where. Plasticity by long-term potentiation in cortex forms neural networks with a speciality function; in the amygdala, it forms neural networks with specific emotion relaying function; in the frontal lobe, it forms neural networks with abstract patterns of the similar functions. In the hippocampus, it forms neural networks reflecting the facts and event patterns when fired synchronously. Further to the areas within each layer which contain different types of neural network arrangements, in each location column the neural networks have different complexities from a few neuronal connections to very complicated networks. Processes that increase the connections to create more complex networks of neurons include facilitation, depression, augmentation, and potentiation. All

these processes either occur by a repetitive activity or a very high stress shock. On a cellular scale, by strain, neurotransmitter releasing concentration will be affected. Consequently, the neurotransmission flux should define the relative strength in different networks. Let’s define the facilitation, depression, augmentation, and potentiation processes from a kinematics view in brief. Action Potential is the potential required for neural to send an impulse down its axon. The neural network’s required activation energy had defined in kinematics as the energy required for a stress flow to get into the network as a substrate, or a confined energy in the neural network to be boosted to that level to be released, and the next neural network to receive that.

1. Facilitation for the individual neuron is the connection strength which depends on the interval between successive neuron sparks to next neurons. When the number of action potential achieving increases more than two times per milliseconds, the connection is being built. Similarly, suppose that straining and destraining of synapses in a network happens simultaneously and it proceeds from neural network to neural network in a definite route, and it lasts for tens of milliseconds up to seconds. It is said that the neural network facilitation has occurred. Such a high facilitation process increases strains in lower magnified neural networks and decreases it in higher magnified neural networks. Dendrites in synapses within lower magnified neural networks had have changes in structures during reconfiguration stages to release high strains. Their new configurations, because of long-term potentiation, plasticizes the network to keep long-term memories from hours to years. If this process happens in lower magnified neural networks in the outer brain, it would be plasticized far more rigidly than in the middle brain hippocampus. The resonating process between networks in the outer layer and hippocampus is facilitated by such a structuring.

Stimulating different synapses in the hippocampal slice shows that the effect is specific to the one synapse being stimulated . . . LTP lasts for over a year in hippocampus . . . Unlike hippocampal LTD, LTD in the cerebellum is associative and occurs only when climbing and parallel fibers are active at the same time . . . After LTP (1 hour in the photo), synapses may increase in size or add new

dendritic spines (new synapses) . . . These occur over time scales from milliseconds to years . . . Synapses are strengthened or weakened as a result of their history of stimulation. “History of stimulation” of all synapses in our brains is experience and learning.⁵³

This process is not specific to the hippocampus and cortex only; it is a general process all over the brain but with different strengths. 2. Depression is the decreasing strength in the synaptic after long low-frequency stimulation. Receptors are removed from the postsynaptic side. Long-term depression may occur for days to years. During depression, the amount of neurotransmitter release reduces to seconds. 3. Augmentation is the short-term increase in synaptic strength (seconds). Neurotransmitter release increases. 4) Postpotentiation is a longer-term increase in synaptic strength which takes seconds to minutes after long high-frequency excitations. Neurotransmitter release increases during the process.

In kinematics of the brain activities, it is assumed that facilitation, depression, and postpotentiation processes occur not just in the hippocampus and cortex at most synapses and everywhere over the brain, but with a different strength based on brain region. In kinematics of the brain activities, it is also expected that all these happen periodically in sleep and NREM stages.

Resonance Between Far-Distance Neural Networks

Resonance in kinematics terminology is an energy add up between two energy substrates and in brain between two neural networks. It does not matter how complex each of networks is. In neural networks with a synchrony state which is never prefect and contains some noise, there is a tuning threshold that two and less probably a few of them that can resonate together.

A theoretical of how a large model network (up to 1000) of summed FitzHugh-Nagumo neurons could adjust the threshold of the system based on the noise level present in the environment was devised.[10] This can be equivalently conceived of as the system lowering its threshold, and this is accomplished such that the ability to detect suprathreshold signals is not degraded.

A similar meaning is applied between a sensory input or motor output and the related input or output channel.

Vision: The above demonstration using the Arc de Triomphe photo is a simplified version of an earlier experiment. A photo of a clock tower was made into a video by adding noise with a particular variance, the number of times to create successive frames. This was done for different levels of noise variance and a particularly optimal level was found for discerning the appearance of the clock tower.[3] Similar experiments also demonstrated an increased level of contrast sensitivity to sine wave gratings.[3]

Tactility: Human subjects who undergo mechanical stimulation of a fingertip are able to detect a subthreshold impulse signal in the presence of a noisy mechanical vibration. The percentage of correct detections of the presence of the

signal was maximized for a particular value of noise.[11]

Audition: The auditory intensity detection thresholds of a number of human subjects were tested in the presence of noise. The subjects include four people with normal hearing—two with cochlear implants and one with an auditorybrainstem implant.

The normal subjects were presented with two sound samples—one with a pure tone plus white noise and one with just white noise—and asked which one contained the pure tone. The level of noise which optimized the detection threshold in all four subjects was found to be between—15 and—20 dB, relative to the pure tone, showing evidence for stochastic resonance in normal human hearing.

Test in the subjects with cochlear implants only found improved detection thresholds for pure tones below 300 Hz, while improvements were found at frequencies greater than 60 Hz in the brainstem-implant subject.

In any resonance process, a high level of structural similarity is required; and there is a threshold of noise that, beyond that, the resonance diminishes. In neurobiology, the signal-to-noise ratio is the index that distinguishes the threshold which, before that noise level, the energy transfer is maximized. The three criteria of nonlinearity, the periodic nature of happening frequencies, and the randomness of the fluctuations around an average are the conditions to combine in such a way to release the maximum free energy transfer creating the resonance.⁵⁴

Chapter VI

The Cerebral Cortex Hosts the Main Pathway, Having Attractors In the Other Parts

The terminology used in this book on some occasions has been different from the terminology used in neuroscience. It is used to clear meanings in this section. However, the term kinematic of the brain activities refers back to neural connections dynamics, which study the stress flow or free energy transfer through their networks, and is accompanied with fibres strains and creation of new dense connections in distinguished small areas in different regions. This subject is a new idea being studied in this book to explain and predict the human behaviours. Accordingly, the term “connection” here is not a static concept and used to cover a free energy transfer through fibres which during the transferring has a configuration impact in building micro-architectural networks of axons, synapses and final firing gate geometries. The configuration impact is initially temporary; however, after a threshold would be fixed in the location which has absorbed such a strains. Some of the which has been used as connectivity, connections density and level of the connectivity in the neuroscience have an equivalent meaning in the kinematics. For example, the high connections density has the same meaning as substrate, if the distribution of the neurons connections follow a normal distribution curve. When the connections density in an area is high, but the distributions are screwed to left or right, it connects substrates in a pathway. Where there is a low connections distributions between two far high density connections areas, the smaller area is the substrate which acts as an attractor directing the wide area with the high connections density. The highly dense connections areas are located in outer later.

“ . . . the cerebral cortex (the outer layer of the brain where most rational thought takes place) has a core “central processor” region that helps organize the rest.

“ . . . the first graphical representations of the network structure of [the] brain [is shown as below. The larger the circle, the more neural connections—highly connected areas create big circles, and areas of few connections are dots . . . . the biggest circles are concentrated in the back of your brain, roughly in the middle between the two hemispheres. That central position for the core processor . . . . Though everyone’s brain is slightly different, most share this same fundamental neurological pattern

“ . . . the bigger the red dot, the more neural connections formed in one area. The thicker the blue line, the greater the nerve fiber densities . . . . areas that are highly connected, as well as connections that are used frequently.”⁵⁵

The description of the outer layer as functional continuum and frequently has been referred in this study as the following:

“The cerebral cortex is a sheet of neural tissue that is outermost to the cerebrum

The human cerebral cortex is 2-4 mm thick . . . . formed from neurons and their unmyelinated fibers,

The surface of the cerebral cortex is folded . . . such that more than two-thirds of it in the human brain is buried in the grooves, called “sulci” . . . . most recent part of the cerebral cortex, the neocortex is differentiated into six horizontal layers; the more ancient part of the cerebral cortex, the hippocampus . . . has at most three cellular layers, and is divided into subfields. Neurons in various layers connect vertically to form small microcircuits, called columns. Different neocortical architectonic fields are distinguished upon variations in the thickness of these layers, their predominant cell type and other factors such as neurochemical markers.

The term substrate which was defined as a small area with high dense and uniform connections conveys the same meaning as referred in the neuroscience:

“A cortical column, also called hypercolumn or sometimes cortical module, is a

group of neurons in the brain cortex which can be successively penetrated by a probe inserted perpendicularly to the cortical surface, and which have nearly identical receptive fields. Neurons within a minicolumn encode similar features, whereas a hypercolumn “denotes a unit containing a full set of values for any given set of receptive field parameters”. A cortical module is defined as either synonymous with a hypercolumn (Mountcastle) or as a tissue block of multiple overlapping hypercolumns . . . .”⁵

Therefore, a substrate has an equivalent meaning as a cortical column, minicolumn or cortical module section as is defined in neuroscience. To understand the local or resonating connectivity between substrates (taking part in a brain activity) and compare the complexity of the connectivity in substrates with the connectivity between them, it is required to know that the connectivity inside the substrates are made by their containing axons, synapses and firing gate distributions and the connectivity between substrates is through the different types of fibres in different parts of the brain, consisting of association, projections and corticopontine types. A brief definition of association, projections and corticopontine fibers is given from Wikipedia as follows:

“Functionally, bundles of axons within the brain, called fibers, can be categorized into association fibers, projection fibers, and commissural fibers.”⁵⁷

“The association fibers unite different parts of the same cerebral hemisphere, and are of two kinds: (1) those connecting adjacent gyri, short association fibers; (2) those ing between more distant parts, long association fibers.

The short association fibers (also often referred to as “U-fibers”) lie immediately beneath the gray substance of the cortex of the hemispheres, and connect together adjacent gyri.

The long association fibers include the following:

Name

From

To

uncinate fasciculus

frontal lobe

temporal lobe

cingulum

cingulate gyrus

entorhinal cortex

superior longitudinal fasciculus frontal lobe

occipital lobe

inferior longitudinal fasciculus occipital lobe

temporal lobe

perpendicular fasciculus

inferior parietal lobule

fusiform gyrus

occipitofrontal fasciculus

occipital lobe

frontal lobe

fornix

hippocampus

mammillary bodies

Arcuate fasciculus

frontal lobe

temporal lobe

“The projection fibers consist of efferent and afferent fibers uniting the cortex with the lower parts of the brain and with the spinal cord.

The principal efferent strands are:

(1) the motor tract, occupying the genu and anterior two-thirds of the occipital part of the internal capsule, and consisting of

(a) the geniculate fibers, which decussate and end in the motor nuclei of the cranial nerves of the opposite side; and

(b) the cerebrospinal fibers, which are prolonged through the pyramid of the medulla oblongata into the medulla spinalis:

(2) the corticopontine fibers, ending in the nuclei pontis.

. . . [in short] association fibers connect regions within the same hemisphere of the brain. Projection fibers connect the lobe to other parts of the brain and the spinal cord [and] commissural fibers⁵⁸ [make connections between hemispheres].

The synchronized firings within a substrate create an image, while proceeding of the firing synchronization by energy transfer between substrates complete a brain activity.

⁵

Elasticity Versus Plasticity In Brain Layers

In time, being exposed to stress flows initiated by inputs, neural networks will be plasticized in different resolution levels fitting into area structures, and this process continues through life. This happens by receiving inputs of repeated normal stresses, learning, psychological initiators, or stressful events.

Experiences, whether they be learning in a classroom, a stressful event, or ingestion of a psychoactive substance, impact the brain by modifying the activity and organization of specific neural circuitry. A major mechanism by which the neural activity generated by an experience modifies brain function is via modifications of synaptic transmission; that is, synaptic plasticity. ¹

Plasticity [is] the capacity of the neural activity generated by an experience to modify neural circuit function and thereby modify subsequent thoughts, feelings, and behavior. Synaptic plasticity specifically refers to the activity-dependent modification of the strength or efficacy of synaptic transmission at pre-existing synapses, and for over a century has been proposed to play a central role in the capacity of the brain to incorporate transient experiences into persistent memory traces.

Synaptic plasticity is assumed to be carried on the cellular level. With plasticity developing to the upper levels of neural network resolution, the functional behavior of the substrate changes to a macroscale level which has a tight relation with elastic properties of the layers and describe the dynamic behavior of the brain. Because of the covering architectural arrangement of the neural networks from higher levels to lower levels, dynamic behaviors of different levels are somehow related together. Therefore, any stress flow along a pathway has an energy

conduction in both longitudinal and spatial directions. The temporary absorbed energy in both directions also appears as strains and confined energy. Due to changes of curvatures in any level, finally, the synaptic level and the firing gates position and size will be affected. On the opposite side, any initiation of simultaneous firing in synaptic levels will cause other layers’ curvature be affected too. In summary, elasticity and plasticity in any substrate, spatially and by time, are connected and any unbalancing between those is compensated by energy exchanges in the shape of resonation. Energy transfers through the synaptic transmission can be either enhanced or depressed by a new activity, and these changes have a temporal domain ranging from milliseconds to hours, days, and presumably, even longer. ² According to the confined energy lasting duration as above, different types of synaptic plasticities can be categorized in short-lasting as the short term memory (milliseconds to several minutes) and long-lasting as the long term memory. The kinematic model assumes that the long-term memories are formed in lower levels of neural networks resolutions and short memory has been plasticized in higher levels of neural network resolution, when working memories are held in the highest possible resolution level of the network. To justify that, there should be a description of how a momentum transfer from higher levels to lower levels can happen to stabilize the neural networks with higher stability. Although, molecular explanations for higher probability of longer lasting connectivity could be sufficient, synapses in lower resolution levels of networks provide more stable distributions. The size of the firing gates; when in higher levels the firing gates distribution and sizes are more flexible are being exposed to free energy currents. In neuroscientific , long lasting is explained by long-term potentiation and long-term depression.

When two stimuli are delivered within a short interval, the response to the second stimulus can be either enhanced or depressed relative to the response to the first stimulus (Katz and Miledi, 1968; Zucker and Regehr, 2002). Pairedpulse depression is commonly observed at all synapses at short (less than 20 ms) interstimulus intervals, and most probably results from inactivation of voltage-

dependent sodium or calcium channels or from a transient depletion of the release-ready pool of vesicles docked at the presynaptic terminal. Many synapses exhibit paired-pulse facilitation at longer interstimulus intervals (20500 ms). A simple explanation for this phenomenon is that the residual calcium left over from the invasion of the first action potential contributes to additional release during the second stimulation . . . ³

The kinematic model believes that all molecular phenomena are reinforced by stability of the synapses in a network and the stability of the network in the layer are improved by being shifted into lower level of networks in the layer. Further to neurons, glial cells are the other component in the a layer. Although, this component is not involved in any firing, but they facilitate the connectivity by influencing the layer elasticity.

Glia may be involved in some forms of short-term plasticity (Araque et al, 2001; Haydon, 2001). With their intimate association with synapses, astrocytes, and perisynaptic Schwann cells are well positioned to regulate synapses. They have an established role in clearance of neurotransmitter and may participate in synaptic plasticity by controlling the speed and extent of such clearance (Bergles et al, 1999; Danbolt, 2001). ⁴

It is important to realize how the layer with lower complicated neural networks maintain the longer-term memories, consideration of glial cell function in the presence of a major amounts of presence of cerebrospinal fluid in the layer is important; when simple neural networks lack such conditions.

Activity Interconnections Because of the Elasticity, Recalling of Characters and Behaviours Because of Plasticity and Resonation

Specific memories are mapped over a complex of neural networks and not a single neuron.

“ . . . recent development of transgenic molecular devices will encourage a shift from mechanistic investigations of synaptic plasticity in single neurons towards an analysis of how networks of neurons encode and represent memory . . .” ⁵

“memories may create unique, reusable pathways in the brain along which signals flow .

In kinematics of the brain activities, it is suggested that a definite geometrical configuration of a chain of neuron networks represents a memory. Therefore, a set of network needs to find a definite state of configurations to store, refresh or recall a memory. In a more specific term, a neuron network should have the highest frequency of a definite geometrical configuration among other possible configurations, to present a specific memory.

“ . . . anatomical structures, neural circuits, and biochemical processes appear to play a role in memory.” ⁷

“noradrenergic modulation . . . could provide a mechanism for contextdependent modulation of associative plasticity and memory.” ⁸

“ . . . NE was shown to increase the high frequency firing” .

The long-term potentiation (LTP) measurements the idea of a memory that will be refreshed when a specific chain of the neural networks maintains a long lasting increase in excitability at synapses along a specific neural networks set. But the connection between memories in a brain activity occurs when a pathway including those memories goes under a stress flow.

“ . . . norepinephrine can decrease pain sensation under stress.”⁷

“long-term potentiation (LTP) is a long-lasting enhancement in signal transmission between two neurons that results from stimulating them synchronously. It is one of several phenomena underlying synaptic plasticity, the ability of chemical synapses to change their strength. As memories are thought to be encoded by modification of synaptic strength, LTP is widely considered one of the major cellular mechanisms that underlies learning and memory⁷¹.

An example of long-term potentiation recorded at Schaffer collateral/CA1 synapses in the hippocampus of rat brain. Graph: The rising slope of an extracellular EPSP field recorded in stratum radiatum of area CA1 in response to an extracellular stimulation of Schaffer collaterals. Test stimulus is a single shock of 100 us duration, approximately .01 mA. Tetanus is the test stimulus delivered at 100 Hz for 1s. Inset: a diagram of a hippocampal slice showing the electrode placement. Above the diagram are examples of the raw EPSP field potentials before and after tetanus.

. . . The graph illustrates the analysis of a single field potential recording from a rat hippocampal slice. The inset shows the placement of the stimulating and recording electrode within the slice, and above, two raw traces of EPSP field potentials before and after tetanic stimulation. Field potentials were evoked by stimulation of Schaffer collaterals and recorded in stratum radiatum of the hippocampal slice (i.e. the Schaffer collateral/CA1 synapses). The individual points on the graph each represent the measurement of the rising slope of one EPSP field potential. Black squares indicate the measurements taken before tetanic stimulation. The green squares are measurements taken immediately after tetanic stimulation (PTP or post-tetanic potentiation) while the blue squares are measurements taken between 3 and 60 minutes after tetanization (LTP or longterm potentiation). Test stimuli were istered once every 30 sec. Tetanic stimulation was the test stimulus given at 100 Hz for 1 sec. Note how the amplitude of the EPSP field settles into a new, more elevated level after tetanic stimulation.

NE concentration is an index for presence of such a stress flow, probably because it prepares a condition for all other required chemical bonds which the physical rigidity for a specific geometrical configuration.

“NE [norepinephrine] can contribute to the behaviorally-induced plasticity in the brain through its ability to modulate inhibitory inputs to projection neurons”⁷².

“ . . . while ne [norepinephrine] can produce a variety of the effects on synaptic and neuronal mechanisms in different regions of the brain, the expression patterns of different NE [norepinephrine] receptor subtypes in specific neural circuits could determine the resulting functional outcome”⁷³.

“ . . . norepinephrine would modulate synaptic plasticity in other neural circuits besides the hippocampus and amygdala”⁷⁴

“In the developing visual cortex in rats, LTP at inhibitory synapses could be induced by the high frequency stimulation when excitatory neurotransmission was blocked [90]. The induction of this form of LTP, mediated by presynaptic calcium entry, was facilitated by norepinephrine.”⁷⁵

“In the cerebellar circuitry, norepinephrine was affecting synaptic plasticity by decreasing the probability of release at the climbing fiber synapse . . .”⁷

A range of possible happenings of configurations and types of neuron circuits depends on the area of the brain; therefore, any type of memory can be stored in and recalled from a specific area. Configuration change is possible if an architectural variation happens on the location. The stress flow and kinematic changes of the layer can provide such an opportunity for the neuron circuit to the required state. In any location, although the structure of the neuron circuit is the same, but different extent limits for the location neuron circuit varies. Therefore, the location can maintain different contents of memories with different details, but some shared components as well. Furthermore, any memory is cited on a specific configuration of the established neuron circuit. Therefore, the number of memories cited by the location depends mainly on two parameters of “the extent

of the neuron circuit” (substrate size) and the layer fluctuation state due to stress flow at that time which creates a definite geometrical configuration of the circuit at the time. New memories are stored in new plasticized neuron circuit (substrate) and to balance the strains they facilitate dendrites growths and new cells forming.

“new brain cells are formed constantly in the dentate gyrus of the hippocampus”⁷⁷

“ . . . newly formed neurons are initially more excitable than mature neurons, so they may be more readily recruited into new neural circuits corresponding to memories”⁷⁸.

Memories depending on the structure form are either declarative which handles factual information including: words, definitions, names, dates, faces, events, concepts, and ideas; or declarative memories including actions, skills, conditional responses, and emotional memories. Memories can be relevant to self or non-self. They also can be tied to a time or free of any specific time. Any of these properties requires a specific structure and therefore, are sited in a specific location of the brain. For example, they are tightly connected to specific brain locations and to be retrieved they are expected only to participate in specific pathways.

“ . . . the recall of factual information generally depends on conscious, effortful processes, whereas memories for skills often require little effort and attention”⁷ .

Memories in connection to the mentation outcome are also divided into: prospective memory (which involves ing to perform actions in the future. The relevant site for these memories needs to participate in a pathway ending in the output zone); and, retrospective memory (which involves ing events from the past or previously learned information. Its role is to provide the attached images in the course of stress flow which the related energy will be absorbed totally within the layer, without any excess energy to dispatch into output zone).

Combination of Static and Dynamic Images In A Brain Activity

Connectivity is perfect in the plasticized neural networks; when connectivity depends on the degree of elasticity and free energy flux through those networks in the less plasticized neural networks. During an energy transfer over a layer, it is more fluent through the less complicated networks with higher elasticity, and less fluid (as a potential energy) through dense networks, which appears as the image fractals of memories. Blending the structural and transmitting images, during a free energy transfer, composes the perceptions, cognitions and generally the brain activities. Dissimilar distribution of energy, absorbed in the layer during a transfer, leaves dissimilar strain forces over the layer. Sleep is essential for the relaxation of remained strain forces in the layer to make uniform those strain forces over the entire layer. The denser connectivity of the numerous combined neural networks in the dense levels confirms the higher inertia in the more rigid sections of the layer. The network mapping will guide us where the denser connectivity is which produces the static images in denser networks and where the more elastic tissues are which carry the dynamic images. Thus the anatomy of a brain activity through the different routes can be more defined.

Pathway, Stress Forwarding Path Through Networks

A pathway has its continuity because of the layer elasticity and has the far connections by attracting through more rigid neural networks in the layer and the connecting fibers to the other areas, so that the least elastic modules in the pathway make the direction. The substrates in the different levels of the layer are overlapped; when those located in the most elastic levels carry the free energy flow and those located in the least elastic level resonate with the other areas. The Posterior parietal cortex consisting of Brodmann areas 5, and 7 is assumed as the layer for the main pathways in the brain with sensory inputs.

“most central part of the brain, is in the medial posterior portion of the cortex, . . . at rest, this area uses up a lot of metabolic energy . . .

⁸

A pathway output is the processed product of all substrates’ structure along the pathway.

. . . the structural connections of the brain . . . shape its dynamic activity

. . . [there is a] significant correlation between brain anatomy and brain dynamics.”⁸¹

the posterior parietal cortex comprises a mosaic of small areas, each specialized

for guiding particular movements of eyes, head, arms or hands.⁸²

The small areas which are shaped in mosaics are the building blocks as the substrates which make the main pathways. A substrate is a set of networks which can save a meaningful or known memory. Other small areas spread into other regions and layers can act as attractors strengthening and directing the pathways. By considering these attracting substrates, it can be concluded that a pathway is self-regulated when the attractors are located in areas other than the frontal lobe, where the intentional attractors are located. As an example,

Primary visual cortex contains multiple maps of features of the visual scene, including visual field position, orientation, direction, ocular dominance and spatial frequency. The complex relationships between these maps provide clues to the strategies the cortex uses for representing and processing information.⁸³

There are different models to map a pathway; however, the computational model of elastic nets best fits the kinematics study of the brain activity.

The elastic net uses parallel algorithms to analyze the visual field and allows for optimized trade-off between coverage and continuity.⁸⁴

. . . the “elastic net” model has been proposed after studying how the primary visual cortex overlaps multiple visual maps, such as visual field position, orientation, direction, ocular dominance, and spatial frequency.⁸⁵

The different features of the entry information are processed in the relevant spread substrates and sometimes other than initiating, play as attractors and influence the direction of the pathway, when they are synchronized in an activity.

Therefore, visual field position, orientation, direction, ocular dominance, and spatial frequency are the attractors’ modular substrates and make the pathway direction.

The preferred stimulus for each variable tends to change smoothly as one moves across the surface of the cortex, with occasional discontinuities⁸

Self-regulation of the pathway is performed by “cortical representations [which] attempt to optimize a trade-off between coverage and continuity”⁸⁷ and the tradeoff is a physical strain balance over the cortex. The integrated pathway is a geometrical combined pattern of the all the involved substrates.

The result is a set of coexisting maps of visual features with a rich set of geometrical relationships⁸⁸

The elastic net . . . can for the development of both topography and ocular dominance in the mapping from the lateral geniculate nucleus to primary visual cortex . . . the overall pattern of stripes produced is strongly influenced by the shape of the cortex⁸

A computational pattern of all processing for different property features can illustrate a pathway map. A self-regulated pathway when excites will continue processing to settle a balance under influence of involved attractors strength. Correspondingly, the involved substrates as scattered pattern will determine a complete pathway.

The posterior parietal cortex receives input from the three sensory systems that play roles in the localization of the body and external objects in space: the visual

system, the auditory system, and the somatosensory system .

Completing the pathway, the processed information will transfer into the dorsolateral prefrontal cortex which consists of Bromann areas of 9 to 12, 45, 46 and 47. ¹ Refer to the previous image.

“much of the output of the posterior parietal cortex goes to areas of frontal motor cortex: the dorsolateral prefrontal cortex, various areas of the secondary motor cortex, and the frontal eye field” ²

To illustrate how a trade-off between coverage and continuity will be optimized, physically the substrate plasticity, layer elasticity and balancing state of strain distribution over the layer should be considered.

Utilization of Pattern Separation and Pattern Completion In Ongoing Brain Activities and Memory Building

Sensory inputs as a collection of many components that enter convergence in time and space are treated together in one pathway and may make a coherent memory. A sensory input of an individual subject is decomposed over many synapses as sensory pixels and combined to be precept. During a cognition process, the related strain interactions of the precept input remain as a working memory. It is held by a confined energy load over the related neural networks which if it goes beyond an absorption limit, overruns into or resonates with the other neural networks. According to the neural networks characteristics, the overrun energy forms static, dynamic, and functional components. The undergoing input during the perception process interacts with neural networks along the way and resonates with far networks in other areas. Let’s call any entered input as a kind of memory after the entry channels. The neural networks over the cortex, which consists of input zones, make the main pathway (as an working memory during cognition); they resonate with different other static and dynamic neural networks in the middle brain and the inner brain (as spread or separated memories). The separated memories are the static memories formed during perception or cognition of the previous high stressful inputs. They have been plasticized in neural networks in the hippocampus for their excessive strains and in the amygdala for the excessive strainmarks within the neural networks in the hippocampus. The made strainmarks are the nodes for the previous neural networks and consists of a new created fine network as the memory of the new stressful event. A node as a network of a smaller size is backed to a functional neural network in the outer layer as the resonating interface. By the time, more networks in a layer are plasticized, the layer becomes less elastic. This is the history already gone for the inner brain and the middle brain during evolution history; where the cortex is still very elastic to work as an operating media. It itself has been developed for the frontal lobe with the highest efficiency in elasticity.

Although pattern separation and pattern completion have a specific meaning in neural computation studies, they conceptually can be applied to explain the brain activity mechanism. Let’s describe the pattern separation and distribution in neural networks over the brain regions and areas according to thekinematic model and check it against present accepted neuroscientific theories. The strain distribution over a pathway due to an input impact is carried by an activation energy plan which has sufficient flowability for a specific initiating stress flow. The strains will be spread by resonating into the hippocampus and the amygdala. During the regeneration period, all the spread-confined energy would be consolidated according to the specialty of each area: spatial strains plan migrates into the amygdala and longitudinal strains will modify the hippocampus, as well as the remaining strains over the outer layer transfers from networks in the higher elastic levels into the networks in the less elastic level, reinforcing the location functionality. The spatial strain plan is made of connecting spatial strain marks, and the longitudinal strain plan is made of longitudinal strain marks. The related strainmarks plan will be consolidated as the related emotion memory in the amygdala and declarative or non-declarative memories in the hippocampus; and the remaining confined energy over the outer layer reinforce or modifies an existing functional memory over it as a node. Any excitation of the same node in the outer layer recalls the related emotion and random memories in the hippocampus which had been saved with the same emotion. The recollection would initiate a flow of stress as a pattern completion. For example, creation of a narrative in a dream is a pattern-completion process. Automatic thoughts in waking are products of a pattern-completion process. Path integration in the brain has been argued by different models, in which the most recent one explains, that the three elements of static memory (like the subject’s positional representation), dynamic memory (like the subject’s speed and direction representatives), and functional memory (which determines the way a shift from one position to the other position occurs) are spread by the neural network structures over the different parts of the brain—in the middle brain, outer layer, and by attractors located everywhere.

Many species of mammals can keep track of spatial location even in the absence of visual, auditory, olfactory, or tactile cues, by integrating their movements— the ability to do this is referred to in the literature as path integration. A number of theoretical models have explored mechanisms by which path integration could be performed by neural networks. In most models, such as those of Samsonovich and McNaughton (1997)[16] or Burak and Fiete (2009),[17] the principal ingredients are (1) an internal representation of position, (2) internal representations of the speed and direction of movement, and (3) a mechanism for shifting the encoded position by the right amount when the animal moves. Because cells in the MEC encode information about position (grid cells[1]) and movement (head direction cells and conjunctive position-by-direction cells[11]), this area is currently viewed as the most promising candidate for the place in the brain where path integration occurs. However, the question remains unresolved; as in humans, the entorhinal cortex does not appear to be required for path integration.[18] Burak and Fiete (2009) showed that a computational simulation of the grid-cell system was capable of performing path integration to a high level of accuracy.[17]

Hafting et al. (2005) suggested that a place code is computed in the entorhinal cortex and fed into the hippocampus, which may make the associations between place and events that are needed for the formation of memories. ³

The path integration is not limited to spatial location and map navigation in the environment, but similar mechanism have been developed by the developing of neuron grids in the outer layer and especially in the frontal lobe for activity pathways inside the brain itself. Pattern completion is the general pathway integration process for mentation pathways in the brain.

⁴

Counter Influence of Neuron Networks On Genes and Genes On Neuron Networks

According to the kinematics model, all inter-exchanges of absorbing, reconfiguration, and desorbing of transferring energy during a brain activity is accelerated and directed catalytically in molecular and cellular network sites.

Under stress, certain regions in the genome could migrate and turn genes on and off in their new location . . . More resent research on mobile elements in the brain had focused on retrotranspons, which employ a copy-and-paste approach to insinuate themselves into new areas of genome . . . Retrotransposons make up as much as half of the nucleotides, or DNA building blocks, in the human genome . . . [retrotransposition and fluoresce green are] observed in certain brain areas, including the hippocampus . . . ⁵

Catalytic role of genes in neural network configurations relates the heritage patterns into daily routine behaviors.

Gage and his team demonstrated that L1 retrotransposons are on the move in the human brain. They add hundreds of extra copies to the genome of neurons, randomly rearranging our mental structure, [by] alterations in the functionality of the neuronal circuitry . . .

If we are correct and L1 jumping does increase as the nervous system learns and adapts to outside world, the finding would indicate that individual brains and the neuronal networks that make them up are constantly changing and alter with each new experience . . . ⁷

Kinetically, one of the areas which would find most reconfiguration in structure during the life is the hippocampus because it does not show a wide range of linear stress-strain correlation. Following the stress-strain relationship for different tissues of the occipital lobe and middle brain shows a very high sensitivity to the strain in the middle brain.

⁸

This assumption has been confirmed by new studies reflected by the Salk Institute for biological studies.

Stress Flow Through A Substrate and Containing Network Changes

Although, many theories regarding the saving of memories in molecular components are still not rejected, however, the memory saving in higher scales has gotten more stronger in recent years.

“the recent development of transgenic molecular devices will encourage a shift from mechanistic investigations of synaptic plasticity in single neurons towards an analysis of how networks of neurons encode and represent memory . . . synaptic plasticity is necessary and sufficient for information storage in the brain.”

The network plasticity is a daily process in adult brains although different from fast growth rate in developing brains. Furthermore, any specific network would be formed in definite configuration. However, this plasticity remains as semiinvariant and semi-variant. The variant part enables the network for further changes, though not always very significant. The variant part enables a substrate to absorb more stresses and dispatching strains as free energies. In any case, the traveling stress flow should overcome the network threshold to enter, to be absorbed or to be transmitted for excess into the next substrate.

“Plasticity in the brain reaches far beyond a mere changing of synaptic strengths. Recent time-lapse imaging in the living brain reveals ongoing structural plasticity by forming or breaking of synapses, motile spines, and re-routing of axonal branches in the developing and adult brain. Some forms of structural plasticity do not follow Hebbian—or anti-Hebbian paradigms of plasticity but rather appear to contribute to the homeostasis of network activity”¹

Such a temporary deforming is limited to the configuration of the network and the balancing condition of the layer which the network is located in. The morphological remodeling limit directs the substrate functionality and is responsible for encoding and retrieving of the pattern it represents. The represented pattern would be interpreted as the memory it retains. The network pattern is the foundation for the synapse’s geometry and related firing gates distributions. In other words, the synchronized firings in that substrate will form a definite shape and because it happens in a small area and the growth of the neurons happens in a fractal shape, the firings are expected to happen in the shape of fractals as well. This topic will be developed in another section.

“A defining characteristic of the brain is its remarkable capacity to undergo activity-dependent functional and morphological remodelling via mechanisms of plasticity that form the basis of our capacity to encode and retain memories. Today, it is generally accepted that the neurobiological substrate of memories resides in activity-driven modifications of synaptic strength and structural remodelling of neural networks activated during learning.”¹ ¹

Plasticity will be enforced by the birth and growth of new neurons to an extent that it can retain a memory like a long term memory.

“Since the discovery of long-term potentiation, the role of synaptic strengthening in learning and memory has been the subject of considerable investigation, and numerous studies have provided new insights into how this form of plasticity can subserve memory function. At the same time, other studies have explored the contribution of synaptic elimination or weakening; synaptogenesis, the growth of new synaptic connections and synapse remodeling; and more recently, neurogenesis, the birth and growth of new neurons in the adult brain . . . . While they have long been considered as alternative mechanisms of plasticity underlying the storage of long-term memories, recent evidence suggests that they are functionally linked, suggesting the mechanisms underlying plasticity in the brain required for the formation and retention of memories are multifaceted.”¹ ²

Plasticity of the memory is common for the hippocampus, amygdala, as well as the cortex. However, the types of plasticity are different.

“Changing the strength of connections between neurons is widely assumed to be the mechanism by which memory traces are encoded and stored in the central nervous system. In its most general form, the synaptic plasticity and memory hypothesis states that “activity-dependent synaptic plasticity is induced at appropriate synapses during memory formation and is both necessary and sufficient for the information storage underlying the type of memory mediated by the brain area in which that plasticity is observed.”¹ ³

Creation of new memories is by forming of new configurations of neuron networks during the periodical stress relief of the layers where those substrates are located in and happen everywhere including the hippocampus and neo-cortex and are experience-dependent plastics. The inherit information memories are made by the molecular changes such as in genes and DNA during generations. Both the molecular scale memories and neuron network memories are common in the way they are operating during retrieving; they make a firing cloud or catalytically cause a neuron fire in a definite shape through synapses.

“Synaptic plasticity has often been argued to play an important role in learning and memory . . . By using the modulation of synaptic plasticity and hippocampal-dependent learning and memory by acute stress . . . [there are] convincing evidence indicating that alterations in synaptic plasticity underlie the changes in learning and memory produced by acute stress”¹ ⁴;where, Acute stress is followed an immediate perceived threat, either physical, emotional or psychological which it can be real or imagined.”¹ ⁵

“Information processing, memory formation, or functional recovery . . . depend

on . . . neurons’ . . . connections. At the cellular/molecular level, structural modifications of neural circuits are finely regulated by intrinsic neuronal properties and growth-regulatory cues in the extracellular milieu . . . . that stimuli coming from the external world, which comprise sensory inflow, motor activity, cognitive elaboration, or social interaction, not only provide the involved neurons with instructive information needed to shape connection patterns to sustain adaptive function, but also exert a powerful influence on intrinsic and extrinsic growth-related mechanisms, so to create permissive conditions for neuritic remodeling.”¹

Structural Characteristics of the Brain Areas and Substrates and the Specialty

The brain functions and their locations are summarized in the following table¹ ⁷. The related micro-architectural and the strain-stress behaviors of the area have a strong correlation which are encouraged to highlight the kinematics aspect of the brain activities.

Area Ventral horn Oculomotor nuclei Cerebellum Basal ganglia Motor cortex Premotor cortex Supplementary motor area Prefrontal cortex

Location Spinal cord Midbrain Midbrain Forebrain Frontal lobe Frontal lobe Frontal lobe Frontal lobe

Function Contains motor neurons that directly activate muscles Contains motor neurons that directly activate the eye Calibrates precision and timing of movements Action selection on the basis of motivation Direct cortical activation of spinal motor circuits Groups elementary movements into coordinated patte Sequences movements into temporal patterns Planning and other executive functions

Growth of Neurons Under Strain and Formation of Functional Substrates

The objects in the environment have the same role as images in the brain. However, changes as interactions, growth, decay, development and evolution are very limited for the objects than for the images. Thermodynamic, physical and biological principles govern all the changes in the environment; when the governing rules in the brain, except electrochemical, include the visco-elastic and chaos physics rules. In the environment, objects interact together creating a new creation, but the brain converts any phenomenon of the environment into patterns: entered patterns interact with the previous saved patterns and the produced pattern has a potential to create more complicated and advanced created images because of the brain functioning as a self-regulating system. Recent discoveries show that the brain growth is in parallel to the load of sensory inputs into it, especially during the early months and years of life. The growth of the brain is exponential, and with the growth of media, interactions and virtual creations increase exponentially too, when exposed to new inputs at a parallel rate. Just perceiving faces (appearing patterns) and changes in behaviours are enough for the brain to estimate and search for approval of the contents.

“ . . . specific areas of the temporal cortex are . . . highly sensitive to invariant forms of information conveyed by human faces (the exoskeleton-including bilateral fusiform gyrus) and subareas within and adjacent to this region-change in response to repeated exposure to the same face (the soft internal structures— including the right fusiform gyrus)”¹ ⁸

“ . . . the left and right fusi form gyri, as well as other areas of the temporal cortex are selectively activated when people process conspecific faces.

. . . only portions of the right fusiform gyrus were activated when processing highly familiar faces.

. . . the right fusiform gyrus was activated during the processing of faces and that the right temporal pole was activated during the discrimination of familiar and unfamiliar faces.

. . . the processing of perceptual stimuli such as “oo” could activate the same neural systems that are activated by faces, but only if these stimuli were repeatedly presented in the context of a face. in other words, the perceptual, cognitive, and neural systems that process facial stimuli may be activated by stimuli that the individual interprets as facelike.”¹

However, by forming the knowledge memories and intelligence of more inputs, cognition processes accelerate in the perception of the objects. If the early perceptions are focused on ing and recognizing faces, the attentions are attracted by the moves and behaviours. If genetic traits are considered as invariant parts of the brain substrates in initial days, the variant parts will form gradually by experience. The next steps in cognition development is the growing of the patterns by experience and creation of the abstracted patterns. Every day more abstracted patterns will be added to the cellular genetic patterns and crystallized intelligence.

“ . . . category formation . . . show a high degree of functional plasticity . . . . an evolved plasticity in the ability to vary the nature of group-level relationships . . .”

“just as one recognizes the face and voices of a familiar person, one also comes to understand how each individual thinks . . . . functional plasticity analogous to the rules of grammar.”¹¹

Here below are the ing researches taken from The Origin of Mind, David C. Geary, 2004 ing the idea:

“ . . . repetitive stimulation or repetitive activity results in cortical and subcortical modifications within the limits of pre-existing systems. e.g., repeated sensory stimulation can result in a several fold increase in the size of the corresponding somatosensory cortex, and simultaneous stimulation of adjacent body regions can result to stimulation of both body regions.

. . . change in cortical weight was due to increased dendritic branching . . .

. . . experience can also maintain newly generated neurons in at least one brain region, the hippocampus”

three forms of plasticity [in brain include]: exoskeleton; role-based category formation; and resource distribution

. . . brain organization and most functions of mind are dependent on a mix of gene-driven constraints and patterns of early experiences.”

. . . plasticity refers to the balance of inherent constraint and developmental experience that results in the ability to adopt brain and cognitive systems and processes to socially and ecologically salient information, such as the basic shape of the human body and differences in the body shape (e.g. height and weight) of one person to the next.”¹¹¹

Pathway Parameters

mentation Instinct/ reflexive signals Emotion/ dreaming feeling Free thought Focused thought

Chain of substrates length Too short localized short long Very long

Intensity of the branching Too high Very high high low Low and linearized

time frame Too short Very short short lengthy Very lengthy

A substrate structure in a definite strained condition configures a specific firing cloud shape (which can be assumed as an orbit) and any configuration is formed as the memory of its initiator either a sensory input or a processed information. Kinematiclly, the brain is an elastic material and the memory in kinematic has a definition different from the meaning used in psychology or neuroscience; while the real concept means the same. The origin position that a tensed elastic will return to, after the removing of the tensing source, is defined as its memory. Therefore, the different types of memories which depend on the location they save should follow the mechanical definition, considering that for a visco-elastic material such a return to the origin shape is time-dependent. These type of definition may help to relate the locations and the type of functions they perform. Cumulative memories recall case memories to integrate, stabilize and codify them as a deduction process through integrating patterns which form the frontal lobe structure. The resulting output will evaluate and process the sensory inputs as a new case memory in the outer brain. The interchanging may be repeated several times to find a configuration balance as an energy wake. Case memories judge the new sensory inputs building the functional property of the outer brain. Cross memories search for the shared elements of memories formed as nodes in the neural networks. Any case memory will break down in fine elements of names, events and actions and will be structured in a location like hippocampus. they have been formed when repeated as a shared part of case memories during life. The structure in amygdala receives the deforming forces from the other parts of the middle brain like hippocampus and store an overall picture of the case memories neural networks. This type of memory can be called the core memory and it is the basic type of cumulative memories. Core memories would be configured as emotions in a part of the middle brain (amygdale) and reasoning memories as the cumulative memory in a part of outer brain (like Broca’s area). The natural memories are the basic needs and instincts to survive the individual and would be mapped in the inner brain neuron fibres networks.

According to the above specifications given for variety of memories, the outer brain save sensory memories which are lengthy and are made of chains of main and sub-branches; the middle brain save crossing of the saved sensory as the nodes of the pathways over the outer brain as static memories like names; and the inner brain had been formed for natural behaviours to protect the self. Evolutionary, a center in the inner brain during creation of the middle brain had formed to relay the communications between those layers. Similarly, a center has been developed for all relying of the communications between different brain regions in outer layer as well. By developing of frontal lobe Broca’s area in outer brain was developed for language production and to facilitate communications between energy pathways in outer and frontal parts. Having the new definition of memories as the main function of the brain, mentation can be defined as an integration of different types of memories which are connected by clouds of firings shapes in a very short time frame of a working memory. It is assumed that the integration is in shape of a fractal. The transferring and connected clouds of firing induce stresses on their pathways and the stress flow determines the direction of the free energy as well as input and output terminals. An integration which would be approximately stabilized present a wake of energy ready to dispatch as an output. Relying between pathways in different parts of brain is through the substrates that have found similar structure during straining. Stress flow has the lowest speed in outer brain; medium in the middle brain and fast in the inner brain. The combination creates focused, free, thoughts with feeling and ends expression or action commands as the products. Substrates in outer layer show more flexibility to temporary deforming under stress. Therefore, there is a main stream of free energy flow during a mentation. Size of the involved substrates depends on structural networks of neuron fibres on each region and part of the brain. Differentiation between energy transfers in different layers and regions is distinguished by the functional strength between the two different relay centers of attention center in the inner brain, amygdala in middle brain and Broca’s area in outer brain. Therefore, awareness and degree of it depends on which center is determinant in a brain activity.

Dynamics of the Neuron Networks

The brain substrates, which behave as springlike neuron networks with two elements of elastic and viscous parts. They undergo spatial and temporal changes to an extent that permits networks activation potential to be activated. The spatial aspect can change from a cellular level up to a pathway in layers in which they are a part of. The temporal measure of a substrate involved in a brain activity is in a range of milliseconds up to higher durations of minutes, hours, or days depending on the releasing period. A substrate under a stress flow has an oscillation of absorbing and releasing periods of milliseconds, and for the remains of the strain, a delay time of hours to days. The MRI monitoring of few seconds for a period of a few minutes has already been recorded, although this is far less than required to the kinematic model This monitoring is not able of presenting continuous straining/releasing periods. In addition, the experiments have been carried out for minutes and are not capable of presenting changes during a day. It is especially required to record differences during waking and sleeping times to provide evidence of straining during waking and releasing during sleep. Furthermore, it is required to monitor during different periods of sleep covering REM and NREM periods to distinguish sudden and smoother changes in these periods. The mechanical behavior as an elastic accompanies chemical and electrical changes accordingly which is the subject studies in neuroscience. On a macroscale level, neural network behaviour in a layer and in a specific pathway of networks is very essential.

Human brain networks can be characterized at different temporal or spatial scales . . . ¹¹²

Sets of connections at different levels of the structural organization—from neuronal networks of connections between neurons to fiber-tract networks between brain regions—provide a good idea regarding how the activated substrates in different regions communicate together during a course of a brain activity. Far activated substrates resonate, developing a pathway to complete an energy balance between incoming free energy and firing gates spatial distributions. Long-term data regarding “topological and spatial network features change across age and network resolution for structural connectivity, based on diffusion tensor imaging (DTI)”¹¹³; however, data for the continuous spatial changes for individuals in a short frame of time are a future necessity for a kinematic study. In the following, the elasticity and connectivity relations will be discused: • A neuron receives input from many other neurons through spikes. When the approximate sum of input charges exceeds the activation potential, the neuron discharges a spike. • The extent to which the signal from one neuron is ed on to the next depends mainly on the amount of neurotransmitter available, the number and arrangement of receptors, and the amount of neurotransmitter which is produced or absorbed. This portion defines the electrochemical part of the connection. • Neurons with sending and receiving signals make connections through synapses and receptors. • Neurons with a high density of connectivity make links in a small network. • Small networks act as a node in a higher level network. The arrangement can be in different types which depends on the type of neurons and fibers which build the location. • Groups of small networks, as nodes, make a cluster, which furnishes an area with different strengths of ties and so on. • If any network in a scale is assumed as a substrate of definite amounts of spike charges, the free energy which hosts it depends on (1) internal varying connectivity and (2) external varying connectivity. Therefore, a network as a substrate links a population of varying subnetworks which can have a variation

within a physical limitation of local plasticity and on the other side elasticity. • The trade-off is between the (1) plasticity of the network which determines the scope of its speciality and the (2) elasticity of the network which is bound to changes (either in shape of oscillation or periodic) in layers.

The cellular structure of neurons preserves genetic functions of the brain and works as a location catalytic property to facilitate experimentally gained or activity-dependent synaptic plasticity in different resolution scales of neural networks. On other hand, neural networks build the memories and all other advanced functions of the brain which depend on saved experiences in life.

Two facts about the hippocampus have been common currency among neuroscientists for several decades. First, lesions of the hippocampus in humans prevent the acquisition of new episodic memories; second, activity-dependent synaptic plasticity is a prominent feature of hippocampal synapses. Given this background, the hypothesis that hippocampus-dependent memory is mediated, at least in part, by hippocampal synaptic plasticity has seemed as cogent in theory as it has been difficult to prove in practice. Here we argue that the recent development of transgenic molecular devices will encourage a shift from mechanistic investigations of synaptic plasticity in single neurons toward an analysis of how networks of neurons encode and represent memory, and we suggest ways in which this might be achieved. In the process, the hypothesis that synaptic plasticity is necessary and sufficient for information storage in the brain may finally be validated.¹¹⁴

The activity-gained structures of the brain develop and increase in complications according to two methods of s from processing outputs and new entries from environment, in parallel to networks development in different areas of the brain.

Plasticity in the brain reaches far beyond a mere changing of synaptic strengths.

Recent time-lapse imaging in the living brain reveals ongoing structural plasticity by forming or breaking of synapses, motile spines, and re-routing of axonal branches in the developing and adult brain. Some forms of structural plasticity do not follow Hebbian or anti-Hebbian paradigms of plasticity but rather appear to contribute to the homeostasis of network activity.¹¹⁵

Not only the sites of memories, but all other sites which are responsible for processing and motor-outputs develop by activity-dependent neural networks changes. In general , it can be said that sensory inputs with accompanied stresses are distributed in different layers’ streams according to the layers’ structural properties and develop neural networks with the same types in related areas. By such a variety of stress flows, inputs are screened, processed, counteracted and communicated for outputs to be integrated as a complex unit and advance output signal.

A defining characteristic of the brain is its remarkable capacity to undergo activity-dependent functional and morphological remodeling via mechanisms of plasticity that form the basis of our capacity to encode and retain memories. Today, it is generally accepted that the neurobiological substrate of memories resides in activity-driven modifications of synaptic strength and structural remodeling of neural networks activated during learning. Since the discovery of long-term potentiation, the role of synaptic strengthening in learning and memory has been the subject of considerable investigation, and numerous studies have provided new insights into how this form of plasticity can subserve memory function. At the same time, other studies have explored the contribution of synaptic elimination or weakening; synaptogenesis, the growth of new synaptic connections and synapse remodeling; and more recently, neurogenesis, the birth and growth of new neurons in the adult brain. In this review, based on work in the hippocampus, the authors briefly outline recent advances in their understanding of the mechanisms and functional role of these four types of brain plasticity in the context of learning and memory. While they have long been considered as alternative mechanisms of plasticity underlying the storage of long-term memories, recent evidence suggests that they are functionally linked, suggesting the mechanisms underlying plasticity in the brain required for the formation and retention of memories are multifaceted.¹¹

The following is a description of the plasticity of continuous neural networks in the hippocampus, amygdala, and cortex and neocortex which have been ed by new neuroscientists:

Changing the strength of connections between neurons is widely assumed to be the mechanism by which memory traces are encoded and stored in the central nervous system. In its most general form, the synaptic plasticity and memory hypothesis states that “activity-dependent synaptic plasticity is induced at appropriate synapses during memory formation and is both necessary and sufficient for the information storage underlying the type of memory mediated by the brain area in which that plasticity is observed.” We outline a set of criteria by which this hypothesis can be judged and describe a range of experimental strategies used to investigate it. We review both classical and newly discovered properties of synaptic plasticity and stress the importance of the neural architecture and synaptic learning rules of the network in which it is embedded. The greater part of the article focuses on types of memory mediated by the hippocampus, amygdala, and cortex. We conclude that a wealth of data s the notion that synaptic plasticity is necessary for learning and memory, but that little data currently s the notion of sufficiency.¹¹⁷

Experience-dependent plasticity in the neocortex . . . [as well as] hippocampus¹¹⁸ is a fact.

Activity-dependent synaptic plasticity is induced at appropriate synapses during memory formation and is both necessary and sufficient for the information storage underlying the type of memory mediated by the brain area in which that plasticity is observed.¹¹

In all studies the growth and development of the new neural networks by stresses accompanied with the sensory inputs or imaginary and internal stress releases

are focused. Although the term stress term has been used to convey a physical, emotional, or psychological meaning as acute stress, and in response to an immediate perceived threat, either a real or imagined threat, in general , it can be expanded to repeated normal stresses as well, which occurs in normal life.

Stimuli coming from the external world, which comprise sensory inflow, motor activity, cognitive elaboration, or social interaction, not only provide the involved neurons with instructive information needed to shape connection patterns to sustain adaptive function, but also exert a powerful influence on intrinsic and extrinsic growth-related mechanisms, so to create permissive conditions for neurotic remodeling.¹²

Synaptic plasticity has often been argued to play an important role in learning and memory. The discovery of long-term potentiation (LTP) and long-term depression (LTD), the two most widely cited cellular models of synaptic plasticity, significantly spurred research in this field. Although correlative evidence suggesting a role for synaptic changes such as those seen in LTP and LTD in learning and memory has been gained in a number of studies, definitive demonstrations of a specific role for either LTP or LTD in learning and memory are lacking. In this review, we discuss a number of recent advancements in the understanding of the mechanisms that mediate LTP and LTD in the rodent hippocampus and focus on the use of subunit-specific N-methyl-d-aspartate receptor antagonists and interference peptides as potential tools to study the role of synaptic plasticity in learning and memory. By using the modulation of synaptic plasticity and hippocampal-dependent learning and memory by acute stress as an example, we review a large body of convincing evidence indicating that alterations in synaptic plasticity underlie the changes in learning and memory produced by acute stress.¹²¹

Information processing, memory formation, or functional recovery after nervous system damage depend on the ability of neurons to modify their functional properties or their connections. At the cellular/molecular level, structural

modifications of neural circuits are finely regulated by intrinsic neuronal properties and growth-regulatory cues in the extracellular milieu. Recently, it has become clear that stimuli coming from the external world, which comprise sensory inflow, motor activity, cognitive elaboration, or social interaction, not only provide the involved neurons with instructive information needed to shape connection patterns to sustain adaptive function, but also exert a powerful influence on intrinsic and extrinsic growth-related mechanisms, so to create permissive conditions for neuritic remodeling.¹²²

There are evidences that plasticity in molecular scales due to established plastic neural network structures in layer scales would happen in long . Even one of descriptions of evolution in species takes the same concept as its mechanism basis.

Memorizing new facts and events means that entering information produces specific physical changes within the brain. According to the commonly accepted view, traces of memory are stored through the structural modifications of synaptic connections, which result in changes of synaptic efficiency and, therefore, in formations of new patterns of neural activity (the hypothesis of synaptic plasticity). Most of the current knowledge on learning and initial stages of memory consolidation (“synaptic consolidation”) is based on this hypothesis. However, the hypothesis of synaptic plasticity faces a number of conceptual and experimental difficulties when it deals with potentially permanent consolidation of declarative memory (“system consolidation”). These difficulties are rooted in the major intrinsic self-contradiction of the hypothesis: stable declarative memory is unlikely to be based on such a non-stable foundation as synaptic plasticity. Memory that can last throughout an entire lifespan should be “etched in stone.” The only “stone-like” molecules within living cells are DNA molecules. Therefore, I advocate an alternative, genomic hypothesis of memory, which suggests that acquired information is persistently stored within individual neurons through modifications of DNA, and that these modifications serve as the carriers of elementary memory traces.¹²³

A similar type of influences in spatial dimension happens between neural networks with increasing resolution from a group of a few neurons group to a complicated group in a layer level which plasticity increase in one level and will increase the plasticity in the next levels. This phenomena can be traced by the mutual interdependence of neuronal activity, neurotransmitter release, and neuronal morphogenesis at the cellular level to network formations and curvature changes in upper levels and finally increases of folds in layer levels which happen by spontaneous and experience-dependent structural plasticity of neurons and synapse configurations on its ending.

Measures In Mentations

The quantity of the free energies transferring through substrates and modules during a mentation is too small to be traced. However, the blood distribution changes through the veins, which accompany the nerve conduction velocity to provide an approximate measure. The latter can be inferred by use of electrophysiological recordings during reaction-time tasks. Similar experiences have been carried for the general intelligence measures and I.Q. scores:

• Recalling speed from long term memories has been estimated by distinguishing between AA an Aa within 80 milliseconds as it found for the college student¹²⁴, • Considering standard tests to measure primary abilities like verbal comprehension, word fluency, numbering facility, spatial abilities, perceptual speed, memory and reasoning, relevant scores show a kind of correlation¹²⁵. Verbal comprehension, word fluency, numbering facility are all different aspects of the general intelligence of everybody. General intelligence also has two components of crystallized intelligence like skills and knowledge and fluid intelligence like reasoning. In addition to general intelligence, visualization, perception speed and language fluency skills are other components of the general intelligence. I.Q. tests, by different measures: perception and response, working memory, judgment and reasoning also indicate that intelligence measures are related to low I.Q. scores, but more irrelevant for higher scores, • the higher the score in the above ability tests, the faster retrieving speed from long term memory, • The higher the scores in spatial abilities, the higher the ability to rotate geometrical images, • The higher the score in ability tests, the faster the cognition processes,

• The higher the score in ability tests, the shorter the identification, decision making or response time for relevant tasks, • Speed of processing within a substrate (which physically is the rate of synapses configuration change because of energy entering, the combination of existing network and deforming forces configurating and releasing) is related to the variability margin of the substrate module and depends on the general intelligence measures. Identification, decisions or responses are products of a combination of several substrates in different areas. Combination is the resonation of the synchronized substrates, considering the conduction rate of the stress flow. Experimental measures has shown a straight relation between higher scores of general intelligence tests and speed of processing along a pathway has a range of correlation coefficient from strong to weak¹² , • The resonating of substrates in different brain layers increases the strength of the working memory and an explicit production of its function, • The time needed to sense an input and a perception process was measured between 50 to 100 milliseconds¹²⁷; when it was not related to intelligent measures. • Higher fluid intelligence is associated with a larger size of prefrontal cortex and its more white material and denser synapses causes higher activations, • The larger neo-cortex volume, faster cognition processing measures¹²⁸, • Positron emission tomography (PET) provides a measuring of metabolic activities which are associated with cognition processes¹² , • High fluid intelligence is associated with lower glucose metabolism during problem solving activities¹³ , • Continuity of energy transfers through a pathway¹³¹ • Any location in the brain has a property of receiving, storing and releasing as a visco-elastic material to perform any process of pop up, interaction, match up and conversion. In the other words, it cannot discriminate between different areas for memory or non-memory regions. These properties depend on types of structures developed in the area. The areas are considered as the sites for the other functions than memory and are strong in functional property than storing

property. But it does not mean those locations fully lack the storing property. Energy transfer through substrates located anywhere is accompanied by substrates undergo a kind of processes of receiving, functional, storing and releasing steps. • If the duration time would be considered as the measuring parameter for storing of a saved memory, then input-output zones, outer layer, frontal lobe, the amygdala and the hippocampus maintain a memory of inputs, implicit or primary memories, working memory, tertiary or secondary memories in sequence. Furthermore, those areas specialized in memory saving, demonstrate property for any types of: working memory, implicit memory, explicit memory, memory of the inputs, primary memory, secondary memory and tertiary memory. In a scale of time these memories can be categorized for tens of milliseconds, hundreds of milliseconds, thirty seconds, days, months and even a life time. By going from the frontal lobe to the middle brain and inner brain, the memory life time would be increased. Within each layer and region, the communicating time is more or less in the same range compared to the other layers and regions. For example, prefrontal areas are associated with the working memory. • Other than the saving property, there are other substrates’ functional properties like releasing, interaction and conversion. The functional property is reversed to save the property and increases with sequentially evolved layers and regions in the brain. This is very basic for the inner brain and most complicated for the frontal lobe. • In a typical pathway, a received a visual, auditory, or somatosensory stimulus would take an estimated time of about six hundred milliseconds after any one second event-related processing including error detections for inputs¹³².

Brain Development Along the Life

Brain growth is extraordinary one’s during growth years, but it continues during the whole of life as well. The growth develops for the structure as well as the functions like the short-term memory and long-term forming modules. Increasing in the fine synapses configurations within a substrate is according to the stress flow associated to inputs and therefore the changes are relevant to the inputs information. Considering the elastic property of the substrate, the substrate gradually will find a speciality relevant to the family of the inputs it is exposed to. This exposure accompanies the growth of more synapses; when the substrate modularity will develop in parallel. Changes in micro-architecture results shows sensitivity and a faster responding to a certain category of information. In parallel, and in general, the configuration it finds, will become able to resonate with short and long memories saved in the other locations, but has a similar configuration. In the earlier stages of finding a special modularity, there is a more variable capability, communicating with a wider range of inputs and memories. By time, the building neural networks finds higher complexity; it becomes more rigid and the communication will be more tuned and exact. Different substrates in different parts of the brain would develop as a suitable site for instincts, feelings, emotions, short memories, long memories, crystallized intelligence and fluid intelligence. Input information is screened and categorized to enter and encode. The entered information in a pattern would be processed in certain pathways, interacting with all other substrates by energy transferring or resonating. The product of all these interactions is accompanied with more changes in the elasticity and plasticity properties of the substrates. The elasticity variations range depends on the different layers and regions architectural structure; therefore, more rigid in lower brains than in the upper layers. Accordingly, more rigid modular substrates are developed in lower levels and modules with functional properties are built in the upper brain layers. In the other words, going from the inner brain to the outer layer and frontal lobe, crystallized intelligence decreases and fluid intelligence will be increased. The region modularization will continue according to the suitability of neuron

types in the region and the physical property of the layer in addition to fluidity of free energy flow through the layer. Brain growth for different individuals continues according to genes, shared environment and their individual world of activities and exposures¹³³. In the following there are some experimental facts from The origin of Mind by David C. Geary, which are mostly based on successive researchers:

• shared genetic influences nearly explain the relation between information processing measures and measures of general intelligence, • between 30 and 60 percentage of the individual differences of measures of short-term memory are heritable, • about 50 percentage of the individual differences in working memory are related to genetic influences and the remaining 50 percentage to unique environmental influences, • the common genetic influences explain 64 percent of the individual differences in fluid intelligence, but only 26 percent of the individual differences in crystallized intelligence, • the relation between working memory and fluid intelligence is mediated, in part, by a set of genes that is related to performance on both types of measures, • individual differences in fluid and crystallized intelligences, working memory, speed of processing, brain size, and so forth are influenced by heritable and environmental factors, • human genomes composed of about 30000 functional genes and many more functional proteins,

Chapter VII

Dreams

Dreams area stereotyping of the memories around a shared emotion. The rules of linking is so creative and mostly different from scenarios experienced in reality. Therefore, the memory combination is different for any individual, even with the same emotion. The static and behavioural memories find an order in a chaotic condition of strain relieving of the overstrained locations. However, the pattern remains the same if the attached emotion would be the same. A dream is very sensitive to initial condition of the strained location when forming; and follow a similar pattern as a past memory was saved in the brain. In opposite, a focused thought in waking is very sensitive to sensory inputs when it forms; and follows past cumulative memories and working memories patterns.

In Dreams

A dream is a brain activity attracted by the daily overstrains releasing emerging narratives and scenarios which carry a similar emotion known by the self. Dream creation accompanies the relaxation of strained location (s)during elasticity regeneration period (sleep). It means the strained path would be normalized for uniformity of the configuration energy over that part of the layer. The changes cover a family of memory substrates which had been coded by the same emotion. The relaxation creates a new scenario which governs relations between characters and their actions by the outer brain elasticity rules; and a related emotion in the amygdala emerges from the elastic rules which recalls the memory of the characters and behaviours from the hippocampus by resonating. Blending of the characters and behaviours from different memory areas with the same emotion produce the dream scenario. The limbic system as at the seat of emotion and some parts of the frontal lobe as at the seat of reasoning will maintain the integrity of the dream. Accordingly, the feeling and logic distributed through a dream is based on the overstraining distribution in a location and regulates the dream narrative:

— characters are categorized as dominant, slave and null; — attractors are emotions and normally the same emotion experienced strongly the day before; — noncontingency in a dream is much higher than waking thoughts; — characters are selected randomly through same-emotion-memories; — abstraction can be in different degrees, depending on any number of common characters in different memories; — dream time length depends on the strained location structure and the fluidity

of stress releases into surrounding substrates as well as the strain intensity; — emotion depth depends on strain intensity;

Types of Dreams

Instinctual, emotional, logic, informative, and inspiring are the different types of dreams depending on the relevant de-straining location. Dreams are believed to be accompanied by memory consolidations. They are a part of processes which provide a new balanced state to a strained brain after a day of functioning. The accumulated strain forces during life and ing generations get settled as new folds in their extremes. A pathway starts from an input zone or from a strained location in the brain during waking ending in an output zone. It normally has different subbranches checked by the intermediate input. If two local neural networks in different areas and under strain would find the same configuration either during the waking or sleep, they resonate together and excite each other. Resonating means synchronized firing in one location initiates synchronized firing in other locations and together reinforce the energy level in a substrate. This happens if natural frequencies of two resonating sites are equal or next to each other and in other words find a similar configuration pattern. Energy packets resonate and exchange along a route in an outer layer and determine a pathway. Energy packets connect neural networks through a layer and in a pathway and behave like a branched chain melt polymer in respect to stress-strain correlations. Memories are made corresponding to either copying the stress source (learning) or a very stressful input (emotional events). The plasticization of a neural network in a reconfigured family networks would configure a memory of the stress source causing strain. Different resolution levels of neural networks with different spatial boundaries preserve different memories of different volumes of contents. However, generally, those memory sites are all in one family branch and may excite each other during a stress flow happening through that location. This could happen during waking time brain activities or dreams.

Fibers of neural networks which have been plasticized more severely and in a higher number of magnified levels contain more depth and intensive emotion.

An Emotion Portrait In A Dream

In a dream, sometimes a few changes in a scenario happens, which can happen because of: (1) the interruptions of synapses reconfigurations by stabilization of the reconfigurations by energy uniformation over the layers; and in other words by interruptions of the REM stages by the NREM stages; (2) they are related to energy balancing of the next two locations in a combined REM period.

- Stress sources are of a different category of challenges between needs and the circumstances, all to protect or advance the self. - Dreams are like a theater show and are seen in different stages rather than the fluent stages of a movie. - Dreams are full of the feelings which are most emotional; when thoughts are less, and thinking least emotional. - Smooth patterns are the most rigid, but patterns with higher variety have higher dynamics. - Emotion is one of the main ways of draining the excess energy to an energy layer it can absorb. It is a drive to dream and possible motivator to act, but the main cause to dream. Lower emotions are the reason for the low level of the awareness in waking, but higher levels of them provide higher levels of awareness. Emotions are the key to ing memories of any type. The ing is not due to the fact that all memories have been encoded by a type of emotion. Emotion has a significant core role in the dream to recall daily, short and long memories as well as a fragment of those to create the scenario of the dream. - The sequence and frequency of NREM and REM stages shows that in the early hours of sleep the brain activities are very in-depth and intensive, probably there are more reconfigurations; but towards the morning, it gets smoother and smoother and more REM activities are in the shape of dreams. The evidences of

deep breaths, higher blood circulation, and movements which need higher boosting energy accompanied in NREM stages and lower boosting energy consumptions in REM stages confirms the distribution of these stages during sleep. - The daily worry as an emotion is mostly the drive for a dream. - “Anybody dreams what they were thinking about the day before”. This is a popular cliché and it is very true if the subject of the thinking is stressful. Daily stressful events are mapped, perceived, cognized, and would have an output. But mostly they leave partial strains which will not disappear immediately.

- A dream can be made of more than one scenario. In a dream for example, three scenarios of (1) the relationship between the family and relatives, (2) the relationship with self like feeling of being left alone and (3) the relationship with people out of home in society can appear. The releasing stress flow, in such a case, would distributes in a wider area between more family neural networks. - Dreams have continuity and discontinuities in the narrative depending on the influencing attractors or the number of stressful events integrating the dream. If more than one attractor or the causing stress, narrative jumps from one scenario to another one. - Emotion is defined kinematically as an overstrain alarm to protect the brain tissues from injury. The related feeling is followed by an energy release of overstrained tissues to the adjacent tissues protecting them from injury. - The energy-releasing transfers through some structures like the amygdala to the motor-outputs, commanding relevant body adjustments and behaviors. - Emotion is involved in any interaction which overstrains tissues in the brain. It is one of the dimensions for any brain activity, when it goes beyond a linear range of stress-strain correlation. However, its intensity depends on local nerve structure and is different for pathways in different regions, and therefore it is different for the different brain activities. Accordingly, some of the brain activities are considered emotional and some unemotional. - Any deviation of the resting synapses configurations in a place due to the

interacting with a traveling free energy, if goes beyond the linear limit of stressstrain correlation, and would be considered as overstraining. An emotion is an integration of a few, several or many declarative or procedural memories which can be repeated in several combinations of those memories. - Declarative and nondeclarative memories saved on overstrained networks are integrated and form a virtual circumstance with a degree of affordability for needs satisfaction. The produced images reflect the associated emotion. The amygdala conducts a set of the saved memories of facts and events to a selfexpression output. - Long-term memories would attract and judge the more recent ones. It means a working memory is attracted by a short memory and a short memory will be judged by long memory for the memories which have been saved in the same location. Physically, the longer-term memory has a more stable synapses configuration in structure and naturally has a higher inertial energy tending to reshape less stable interacting configurations. No pathway is smart by itself, and any pathway is initiated by an input and developed between more rigid networks as the boundary limits to the pathway. The speed of the energy transfer along the pathway would be influenced by the attractors over other regions. - A circumstance is a combination of static and dynamic elements which challenge the needs and induces overstrains relevant to a satisfactory degree. - A pathway would be activated by a sensory or psychological input if it approaches the required activation energy. Similarly in dreams, overstrain locations need to be boosted by resonating with a similar pattern of previous memories to overcome the required activation energy as the threshold to release the confined energy. If the reinforcing condition is not available, the confined energy remains trapped in the location to be released in later periods or would form disordered behaviours. - The functional and forming characteristics of brain locations are according to plasticity and elasticity properties biased based on viscous and elasticity properties of the brain material. - In dreams, a brain activity comes in awareness surface and is meaningful, if it is emotional; while in waking a brain activity is meaningful if it interoversionally and extroversionally satisfies a need (physiological, safety,

social, esteem or self-advancement) in any way. - In dreams, any declarative memory can be a symbol of another one if they convey the same emotion. Images shift between those which are symbols of each other following the same rules as metaphoric and simile statements. They should have similar hosting networks in their general configurations. - The international and general symbols common between different nationalities would be few and mostly depend on regional, cultural, and personal emotional experiences. - However, a dream is a mirror of daily feelings and how we believe in our heart and how far it is from our desire. - An example of symbols is a competitor colleague who often tries to attract the boss is dreamed as a tea boy who serves everybody. - In dreams, scenes are like episodes in a theatre; they change sharply and are less dialectic and more virtual. The selected pieces of different memories from the past depends on the neural networks on the strained location boundaries and the neural networks’ required potential energies to activate. - The dream’s general pattern reflects the feeling experienced during a daily stressful event resonated by a similar-feeling past memory. The core for all these similar patterns is the same emotion theme. - A dream is an indication of the challenges between needs and the available or thought circumstance and the patterns of the scenarios in waking. In dream the ing memories are the patterns of the related emotions as saved structure.

Regeneration is the process of the relaxation of strained location(s) to recover the elasticity property of the brain layers. The strained neural network resonates with the same configuration neural networks as in other layers. Influencing networks of the outer layers on the main pathway are considered as attractors resonating with specific substrates and so determine the pathway direction.

The narrative telling processes in dreams are automatic brain activities; made from the emotion retrieval and the memory recalls by resonating, whenever a strain-driven energy flow starts.

The limbic system of the brain is active and evokes an emotion because of strains due to daily needs. These needs should had not satisfied, when there is a feeling like: being rejected, worthless, miserable, sympathy, disrespect, misunderstanding, left behind, risk of privacy attacked, or else which create images to distribute the strains and balance the layer. Thus the interface between physics and psychology appears. Based on the kinematic model, emotions motivate the automatic thoughts and automatic thoughts develop awareness and remedies to satisfy needs. For example, the feeling of being lost or left somewhere, and not ing where there is an inside hint of a problem. Thoughts would initiate to troubleshoot the problem; either by: (1) concluding in a command for an action to resolve (asking for help and direction or else) or (2) feeling anxiety and worry it cannot make a conclusion.

Chapter VIII

More Detailed Principles of the Model

The model is made according to the following basis:

• The stressful inputs (during the day) initiate physical stress on the brain which is a specific elastic material. • Imposed stresses are either within linear range of stress-strain range of neuron tissues or beyond that. • Very stressful events beyond the linear range overstrain some locations in daily pathways. • Increase of the overstrained locations temporarily decay elastic properties and make the outer layer more stiff and rigid. • Sleep is a necessity to recover the elastic properties of the brain. • The confined energy of strained points will be absorbed by memory consolidations and emotional reliefs in NREM and REM stages in sequence. • Dreams are ongoing scenarios that accompany strain relieving as a selfexpression cored by specific emotion. Therefore, dreams are considered as an indicator for psychological health as well. • The pattern of the strained neural networks is mapped by daily input stressful events. If these patterns are resonated patterns of long-term memories, the ed pattern can overcome the required activation energy for releasing the strained network and it will start to release. • The relieving process consumes the confined energy in consolidation and discharging effects. This means the daily input pattern, overstrained pattern, and ing memory pattern will be similar in firing fractal shapes.

• If emotion is defined as an overstraining index, then the working and the past memories involved in the dream have the same type of emotion in common. • The neural networks with the same general pattern of configurations, when releasing the confined energies, freshen the same emotion, recalling the memories saved on those locations.

Future Works Based On the Model and Possible Gains

In parallel to works which try to prepare a detailed map of neuron connections, a region can be searched for by the same family of networks with the same configuration; therefore to determine the networks which host the same family memories. The same family memories are the memories which evoke the same emotion.

• Depending on the type of neural network on a location, families of emotions can be sorted out to follow the reasoning and the intentional thoughts. The same family emotions are the emotions which run the same reasoning or logic. • Dream analysis helps to study the brain activities during regeneration periods and can determine the site of individual memories as well as helping to map the dynamic connectivity for those networks which are highly plasticized. • The relationship between firing fractals which happen in networks and images building over them may determine the level of awareness for different states of dreaming, feeling, emotional thoughts (automatic thoughts) and reasoning (intentional thoughts). • The model describes that: strains make firing gates change in size and distribution, therefore in firing synchrony and fractal form of the firings. The neural network excitation can happen either in course of straining during the day or de-straining during sleep. • Synchronous firings in a pathway cause the induction of shear forces on the route and shear forces on a route of networks enable us to follow the sequence of firing fractals happening in the course of one brain activity. If a brain scanning for an individual would be conducted with short intervals of tenth of seconds during waking and during sleep on the same night, the working memories of the same day and the memory building of the potential ones can possibly be mapped.

• Relation between the firing fractals in neural networks and free energy transfer through them should be of the same relation as attached images and the brain activity consisting of those neural networks, when the imposed strains goes beyond the linear limit of stress-strain correlation. • Populated synchronized firings induce strains. Induced strains work on elastic material of the brain causing its stiffness; when during regeneration, the same deflected elastic tissues will recover by releasing of the confined energy and create clouds of new synchronized firings and attached images.

One of the stress sources can be the difference between promises, expectations and outcomes for the need satisfactions. In reality, the expectation can be far from achievable and the needs are continuously are monitored, argued and modified to be achievable as goals. An active initiator of a brain activity can be a sensory input or an internal simulation. Both of them create firing fractals which proceed network by network, while any firing fractal has a molecular or cellular activator (like genes and DNA molecules) to accelerate a definite type of energy transfer as a function. On other hand, a medium level of substrates and an overall level of a layer are the streambeds relaying the short and longer term memories under the same function. One of the frontal lobe functions is the problem-solving mechanism of problemsolving. Problem-solving physically is a process of de-straining the confined overstrains caused by the conflict of the needs and an unsatisfactory environment. It excites all possible pathways, either random or planned, boosting the most energetic pathway. If no pathway ends in a solution, an overall ending is the feeling of worry. Similarly, the mechanism for future planning is boosting of a pathway of a possible blending of past and current working memories providing the possible outcomes in the future, where without the brain it would be a natural selection event.

An Integrated Claimed Model

Growth of firing fractals occurs over neural networks as an evidence of the reaction in a substrate. It develops over a neural network and is facilitated by strain imposed on network synapses strings. Crystallization of the memory by a slight reconfiguration in a network occurs as an overstrain mark appearing as a node in the network. The idea of having an energy packet made of firings energy layers can be exampled by an orbital layer of electrons around the nucleus of an atom. For an energy packet, synchronized firings over neural networks make it possible to see energy layers as firing fractals. Except the ideal shapes in mathematics, the real objects in nature are governed by the fractal mathematics. The synchronized firings over neural networks seem to follow the same rules. During a brain activity, several of those fractals interact or resonate together and the resulting energy layers would be formed over ed streams of synapses patterns in different areas of the brain. Fractal knowledge is still not developed to cover all existing patterns in reality. Similar to the patterns in the environment, it is not known how many fractals are applicable to synchronized firings in the brain. Discovering more fractals would expand the knowledge regarding many brain functions including memories. Inputs in their static and dynamic forms are variable. They enter into the brain and their components spread in pixel neuron cells for the different types of vision, hearing, smell, taste, and touch in definite areas. When an input enters a pathway for perception and or cognition it would be aroused. Needs of the different categories will appear as the goals for the brain activity termination. The more complicated the need, the more complicated the goal. Needs employing the real inputs and memories challenge the circumstances for satisfaction. Consequently, the connected emotions are excited. Following raised emotion, sequence/s of fractals would build up through a pathway as a wake of energy with an awareness of its happening in shape of a mentation.

When the related input and following activities contain a big package of information, only some pieces of it impose a strain beyond the linear stressstrain correlation limit. Only these pieces of information appear in the surface of awareness. They will be considered as overstrain and possibly will be consolidated for its memory. The working memories of the pieces will temporarily remain over the related pathway on the outer layer. The working memories with sufficient potential to resonate with the same—configuration neural networks in other parts of the brain induce an added confined energy into those networks which are subject to be consolidated as a memory. Consolidations of the component memories in same family memory locations (neural networks within a close range of frequencies) would occur by resonating and plasticisation, if the straining strength is sufficient by overstraining or repetition. Overstrains can be traced by an optimum level of dopamine and norepinephrine¹³⁴; while crystallization of overstrains over the hippocampus with the large-amplitude-patterns can be traced more significantly. During any configuration change by crystallization, recall or excitation in the pathway, the excited neural network brings the memory into an awareness level by evoking the related emotion. Emotions recall and integrate the reconnected components in the hippocampus by tuning them to resonate stochastically by a signal-to-noise ratio process. The combination of memory components can happen with different possibilities which can be very limited by the combination of objects in environment. Except for the orchestration of memory components by an emotion memory, it can be carried by parts of the frontal lobe in more advanced manner. Forming of a pathway occurs by transmission of the extra strains through which it had not been absorbed by a neural network elasticity and continues network by network through a pathway. Memories of the overstrains in the amygdala and memories of balanced strains in the frontal lobe would orchestrate recalling and ing memory components is by the resonating process between far networks. The addition of memory pixels developing in different areas will be integrated over the main “bus bar” from initiation to termination of a brain activity. The amygdala orchestrates and combines the memories over the hippocampus,

when some parts of the frontal lobe orchestrate the sensory and functional memories over the cortex. The effectiveness of the orchestration either by the amygdala or frontal lobe is distinguished by their combined amplitude and combined frequency or the level of stress chemicals around the areas. Stress chemicals (dopamine and norepinephrine) work as neurotransmitters and indicate how fluent energy is being transmitted through a route network by network. Each memory network (substrate) is distinguished by a strain mark or a node either in the amygdala or frontal lobe. A strainmark is limited to a certain elasticity limit which defines the boundaries of the related memory. It is the threshold indicated by the required potential energy to enter or dispatch it and is related to the energy layers in the network which submit energy to the next network. The energy state of each memory is the maximum strain that each network can absorb without a major deformation or by the releasing of the excess energy to the next network. It is a definite amount of energy accompanied by a certain configuration of the synchronized firing fractal which the synapses laid out in the network produce.

Conclusion

In this volume, it is claimed that elasticity and resonating of the similar neural network structures, in addition to plasticity, are required to complete a model which is able to describe the different brain functions in a contingent way. The kinematic model covers all these dimensions, although still there is a long way to develop the idea, providing the subject in a well-proven level.

Endnotes

1 Human intelligence and brain networks; http://www.researchblogging.org/;2009 2 Activity-dependent structural plasticity; Butz M, Wörgötter F, van Ooyen A.; 2009 3 Turing’s Neural Networks; Jack Copeland and Diane Proudfoot; 2000 4 Turing’s Neural Networks; Jack Copeland and Diane Proudfoot; 2000 5 Activity-dependent structural plasticity; Butz M, Wörgötter F, van Ooyen A.; 2009 6 Integrating temporal and spatial scales: human structural network motifs across age and region of interest size; Christoph Echtermeyer, Cheol E. Han, Anna Rotarska-Jagiela, Harald Mohr, Peter J. Uhlhaas4 and Marcus Kaiser; 7 Simulation of robustness against lesions of cortical networks; Kaiser, M., Martin, R., Andras, P., and Young, M. P.; 2007 8 Simulation of robustness against lesions of cortical networks; Kaiser, M., Martin, R., Andras, P., and Young, M. P.; 2007 9 This is your Brain in Meltdown; Amy Arnsten, Carolyn M. Mazure, and Rajita Sinha; Scientific American; April 2012 10 Kinematics of the Brain Activities; Volume I; Outline of a Metation; Mostafa M. Dini 2010 11 Neural Mechanisms of Attention and Memory; Adam Gazzaley; http://gazzaleylab.ucsf.edu/topdown-background.html 12 Simulations of Dinoflagellate Organisms in collaboration with Hoa Nguyen, Ricardo Cortez and Lisa Fauci.

13 Montana State University; the Center for Computational Biology 14 Tulane University; Center for Computational Science Art Show; 2009 15 Melissa Conrad Stöppler; MedicineNet.com; Jan.2012 16 Melissa Conrad Stöppler; MedicineNet.com; Jan.2012 17 The Biological Basis of Behavior; cwx.prenhall.com/bookbind/pubbooks/morris5/../deluxe-content.ht.. 18 NMDA Receptor Blockade Alters Stress-Induced Dendritic Remodeling in Medial Prefrontal Cortex; Kathryn P. Martin and Cara L. Wellman; 2011 19 Dragoi, V. “Ocular motor system”; Neuroscience Online. http://neuroscience.uth.tmc.edu/s3/chapter07.html; 2011 20 Psychology: Theme and Variations; Wayane Weiten; 2008 21 The Origin of Mind, David C. Geary, 2004 22 The Origin of Mind, David C. Geary, 2004 23 The evolution of the brain, the human nature of cortical circuits, and intellectual creativity; Javier DeFelipe; 24 Overcoming interference: An fMRI investigation of pattern separation in the medial temporal lobe; C. Brock Kirwan1 and Craig E.L. Stark 25 Overcoming interference: An fMRI investigation of pattern separation in the medial temporal lobe; C. Brock Kirwan1 and Craig E.L. Stark 26 The medial temporal lobe; Squire, L.R., Stark, C.E.L., Clark, R.E.; 2004 27 Overcoming interference: An fMRI investigation of pattern separation in the medial temporal lobe; C. Brock Kirwan1 and Craig E.L. Stark 28 Overcoming interference: An fMRI investigation of pattern separation in the medial temporal lobe; C. Brock Kirwan1 and Craig E.L. Stark 29 Overcoming interference: An fMRI investigation of pattern separation in the

medial temporal lobe; C. Brock Kirwan1 and Craig E.L. Stark 30 Theta oscillations mediate interaction between prefrontal cortex and medial temporal lobe in human memory; Anderson KL, Rajagovindan R, Ghacibeh GA, Meador KJ, Ding M. 31 Overcoming interference: An fMRI investigation of pattern separation in the medial temporal lobe; C. Brock Kirwan and Craig E.L. Stark; 32 Synaptic plasticity, memory and the hippocampus: a neural network approach to causality; Guilherme Neves, et al; 33 Pattern Separation in the Human Hippocampal CA3 and Dentate Gyrus; Arnold Bakker, et al; 34 Synaptic plasticity, memory, and the hippocampus: a neural network approach to causality; Guilherme Neves, Sam F. Cooke,* and Tim V. P.; Bliss; 2008 35 Reversible Plasticity of Fear Memory-Encoding Amygdala Synaptic Circuits Even after Fear Memory Consolidation; Ingie Hong, et al;2011 36 Synaptic Plasticity: Multiple Forms, Functions, and Mechanism; Ami Citri and Robert C Malenka; 2007 37 Synaptic Plasticity: Multiple Forms, Functions, and Mechanism; Ami Citri and Robert C Malenka; 2007 38 Synaptic Plasticity: Multiple Forms, Functions, and Mechanism; Ami Citri and Robert C Malenka; 2007 39 Synaptic Plasticity: Multiple Forms, Functions, and Mechanism; Ami Citri and Robert C Malenka; 2007 40 Synaptic Plasticity: Multiple Forms, Functions, and Mechanism; Ami Citri and Robert C Malenka; 2007 41 Wikipedia 42 Ibid

43 Memory, Amnesia, and the Hippocampal System; Eichenbaum, H; Cohen NJ; 1993 44 References are taken from the Wikipedia in this section 45 Place cells, grid cells, and the brain’s spatial representation system; Moser, E.; Kropff, E.; Moser, M; 2008 46 Wikipedia 47 Ibid 48 Ibid 49 Ibid 50 Ibid 51 Ibid 52 Integrating temporal and spatial scales: human structural network motifs across age and region of interest size; Christoph Echtermeyer, Cheol E. Han, Anna Rotarska-Jagiela, Harald Mohr, Peter J. Uhlhaas and Marcus Kaiser; 53 http://132.236.112.18/wytt/3/slides3w.pdf; Feb. 2012 54 wiki, stochastic resonance(sensory neurobiology) 55 56 Wikipedia 57 Wikipedia 58 Wikipedia 59 Wikipedia 60 Activity-dependent structural plasticity; Butz M, Wörgötter F, van Ooyen A; 2007

61 Synaptic Plasticity: Multiple Forms, Functions, and Mechanisms; Ami Citri, Robert C Malenka; 2007 62 Synaptic Plasticity: Multiple Forms, Functions, and Mechanisms; Ami Citri, Robert C Malenka; 2007 63 Synaptic Plasticity: Multiple Forms, Functions, and Mechanisms; Ami Citri and Robert C Malenka; 2007 64 Synaptic Plasticity: Multiple Forms, Functions, and Mechanisms; Ami Citri and Robert C Malenka; 2007 65 Synaptic plasticity, memory and the hippocampus: a neural network approach to causality; Guilherme Neves, Sam F. Cooke & Tim V. P. Bliss; 2008 66 Memory: The Key to Consciousness; Richard F. Thompson, 2005 67 Norepinephrine enables the induction of associative long-term potentiation at thalamo-amygdala synapses; Keith Tully, Yan Li, Evgeny Tsvetkov, and Vadim Y. Bolshakov; 2007 68 Norepinephrine enables the induction of associative long-term potentiation at thalamo-amygdala synapses; Keith Tully, Yan Li, Evgeny Tsvetkov, and Vadim Y. Bolshakov; 2007 69 Norepinephrine enables the induction of associative long-term potentiation at thalamo-amygdala synapses; Keith Tully, Yan Li, Evgeny Tsvetkov, and Vadim Y. Bolshakov; 2007 70 Norepinephrine enables the induction of associative long-term potentiation at thalamo-amygdala synapses; Keith Tully, Yan Li, Evgeny Tsvetkov, and Vadim Y. Bolshakov; 2007 71 wikipedia 72 Norepinephrine enables the induction of associative long-term potentiation at thalamo-amygdala synapses; Keith Tully, Yan Li, Evgeny Tsvetkov, and Vadim Y. Bolshakov; 2007 73 Norepinephrine enables the induction of associative long-term potentiation at

thalamo-amygdala synapses; Keith Tully, Yan Li, Evgeny Tsvetkov, and Vadim Y. Bolshakov; 2007 74 Norepinephrine enables the induction of associative long-term potentiation at thalamo-amygdala synapses; Keith Tully, Yan Li, Evgeny Tsvetkov, and Vadim Y. Bolshakov; 2007 75 Norepinephrine enables the induction of associative long-term potentiation at thalamo-amygdala synapses; Keith Tully, Yan Li, Evgeny Tsvetkov, and Vadim Y. Bolshakov; 2007 76 Norepinephrine enables the induction of associative long-term potentiation at thalamo-amygdala synapses; Keith Tully, Yan Li, Evgeny Tsvetkov, and Vadim Y. Bolshakov; 2007 77 (Gould, 2004) Norepinephrine enables the induction of associative long-term potentiation at thalamo-amygdala synapses; Keith Tully, Yan Li, Evgeny Tsvetkov, and Vadim Y. Bolshakov; 2007 78 (Beckerand Wojtowicz; 2007) Norepinephrine enables the induction of associative long-term potentiation at thalamo-amygdala synapses; Keith Tully, Yan Li, Evgeny Tsvetkov, and Vadim Y. Bolshakov; 2007 79 (Johnson, 2003) Norepinephrine enables the induction of associative longterm potentiation at thalamo-amygdala synapses; Keith Tully, Yan Li, Evgeny Tsvetkov, and Vadim Y. Bolshakov; 2007 80 Wikipedia 81 New map IDs the core of the human brain; (http://www.cienciaysociedad.info/science/new-map-ids-the-core-of-the-humanbrain/); 2012 82 Wikipedia 83 A Computational Model for the Development of Multiple Maps in Primary Visual Cortex; Miguel Á. Carreira-Perpiñán, Richard J. Lister and Geoffrey J. Goodhill; 2004 84 A Computational Model for the Development of Multiple Maps in Primary

Visual Cortex; Miguel Á. Carreira-Perpiñán, Richard J. Lister and Geoffrey J. Goodhill; 2004 85 Knudsen, E. I., S. Lac, and S. D. Esterly.; Computational Maps in the Brain, Annual Review of Neuroscience; 1987 86 A Computational Model for the Development of Multiple Maps in Primary Visual Cortex; Miguel Á. Carreira-Perpiñán, Richard J. Lister and Geoffrey J. Goodhill; 2004 87 A Computational Model for the Development of Multiple Maps in Primary Visual Cortex; Miguel Á. Carreira-Perpiñán, Richard J. Lister and Geoffrey J. Goodhill; 2004 88 A Computational Model for the Development of Multiple Maps in Primary Visual Cortex; Miguel Á. Carreira-Perpiñán, Richard J. Lister and Geoffrey J. Goodhill; 2004 89 Elastic Model of Ocular Dominance; Goodhill, Gj; Willshaw, Dj; 1994 90 Wikipedia 91 Wikipedia 92 Wikipedia 93 Wikipedia 94 adopted from Science Discovery through Advanced Computing; http://www.scidacreview.org; Dec.2011 95 Your brain is special; Scientific American; March 2012 96 http://www.salk.edu/insidesalk/; Salk Institute for biological studies; Feb. 2011 97 Your brain is special; Scientific American; March 2012 98 The mechanics of human brain tissues; Giulia schini; 2006 99 Synaptic plasticity, memory and the hippocampus: a neural network approach

to causality; Neves G, Cooke SF, Bliss TV. 100 Activity-dependent structural plasticity; Butz M, Wörgötter F, van Ooyen A. 101 Brain plasticity mechanisms and memory: a party of four; Bruel-Jungerman E, Davis S, Laroche S. 102 Brain plasticity mechanisms and memory: a party of four; Bruel-Jungerman E, Davis S, Laroche S. 103 Synaptic plasticity and memory: an evaluation of the hypothesis; Martin SJ, Grimwood PD, Morris RG. 104 Synaptic plasticity in learning and memory: stress effects in the hippocampus; Howland JG, Wang YT. 105 “Activity-dependent plasticity and gene expression modifications in the adult CNS; Carulli D, Foscarin S, Rossi F 106 “Activity-dependent plasticity and gene expression modifications in the adult CNS; Carulli D, Foscarin S, Rossi F 107 Adopted from the Wikipedia 108 The Origin of Mind, David C. Geary, 2004 109 Same reference as above 110 Same reference as above 111 Same reference as above 112 Integrating temporal and spatial scales: human structural network motifs across age and region of interest size; Christoph Echtermeyer, Cheol E. Han, Anna Rotarska-Jagiela, Harald Mohr, Peter J. Uhlhaas4 and Marcus Kaiser; 113 Integrating temporal and spatial scales: human structural network motifs across age and region of interest size; Christoph Echtermeyer, Cheol E. Han, Anna Rotarska-Jagiela, Harald Mohr, Peter J. Uhlhaas4 and Marcus Kaiser; 114 Synaptic plasticity, memory and the hippocampus: a neural network

approach to causality; Neves G, Cooke SF, Bliss TV. 115 Activity-dependent structural plasticity; Butz M, Wörgötter F, van Ooyen A. 116 Brain plasticity mechanisms and memory: a party of four; Bruel-Jungerman E, Davis S, Laroche S. 117 Synaptic plasticity and memory: an evaluation of the hypothesis; Martin SJ, Grimwood PD, Morris RG. 118 New life in an old idea: the synaptic plasticity and memory hypothesis revisited; Martin SJ, Morris RG. 119 Synaptic plasticity and memory: an evaluation of the hypothesis; Martin SJ, Grimwood PD, Morris RG. 120 Activity-dependent plasticity and gene expression modifications in the adult CNS.; Carulli D, Foscarin S, Rossi F.; 2011 121 Synaptic plasticity in learning and memory: stress effects in the hippocampus; Howland JG, Wang YT. 122 “Activity-dependent plasticity and gene expression modifications in the adult CNS; Carulli D, Foscarin S, Rossi F 123 can the hypothesis of synaptic plasticity explain long-term memory consolidation?; Arshavsky YI.; 2006 124 The Origin of Mind, David C. Geary, 2005 125 ibid 126 The Origin of Mind, David C. Geary, 2005 127 Ian J. Deary, Looking Down on Human Intelligence: From Psychometrics to the Brain, 2000 128 The Origin of Mind, David C. Geary, 2005 129 The Origin of Mind, David C. Geary, 2005

130 The Origin of Mind, David C. Geary, 2005 131 Cognitive event related potentials during a learning task (thesis); Mohamed Fath EL-Bab; April 2001 132 Event-Related Brain Potentials Following Incorrect in a TimeEstimation Task: Evidence for a “Generic” Neural System for Error Detection; Wolfgang H. R. Miltner, Christoph H. Braun, Michael G. H. Coles; 1997 133 The Origin of Mind, David C. Geary, 2004 134 This is your brain in meltdown: Amy Arnstem, Carolyn M. Mazure and Rajila Sinha; Scientific American; April 2012