(About 5,000 words, target is probably 7500-10,000

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In this chapter I want to get across JUST embodiment, measurement, and correspondence with reality, and the consciousness vs prediction vs imagination issue.

 

(CD: Note, frame memory networks as assemblies also (per use in the field))

(CD; the visual process and the dominance of vision must be answered)

( CURT: Note” “use Everything evolved from movement for explaining how wayfinding and spatial relations evolved into higher concept associations”)

(CD: Process flow diagrams are ok, but avoid anatomical , we are trying to create an operational description of the mind, not an anatomical.)

 

 

 . . . Chapter . . .

PROBLEM STATEMENT
In this section ( … ) (mission)

–“Everything persistent in the universe survives by combining discrete somethings a vast hierarchy of cooperation discovered by trial and error, using the ternary logic of evolutionary computation.”–  (discreetness)

—

introduction

I. Embodiment 

How the body, brain, and mind create a system of measurement to it can use to calculate useful to necessary changes in state.

Embodiment refers to the process by which the body’s biological systems transform sensory inputs into measurements that are necessary for producing a world model, predictions from it, and the resulting actions consistent and correspondent with the world, within the body, the mind, and their current condition. Meaning,  that the body, it’s form, and its senses functions as a system of measurement by which the nervous system, brain, and mind, calculate actions that preserve the body within and over time. 

(WHAT: Promise:)

( … ) (introduction that explains basic concepts that they’re going to learn in the chapter, and especially the framing as Vitruvianism.) (IOW what are the take-away points). 

1. Constant relations ( ,,, )

2. Learn by competition (survival)

3. Division Of Labor (hemispheres, regions, senses)

4. All you can know is discovered(measured) by your nerves, and then organized incrementally in a hierarchy(valued) of other networks of neurons, and together using your body, brain, and mind as a system of measurement to model the state of the body, brain, mind internally and in relation to the world externally, then by comparison with memories, used to predict a field of risks and opportunities both in-time and across-time, the most valuable of which, whether danger or reward, succeed in gaining some portion of our attention, so that we may either reflexively respond immediately, or recursively consider routes to use responses to bring about those future states.

5. Sixth Sense, Space, World model – Triangles (ternary logic) by projecting three dimensions necessary for action upon our two dimensional images, sounds, vibrations, smells, tastes.

6. Auto association as imagination

7. (consciousness, experience, qualia problem – it’s not a hard problem, it’s a simple one, though overly discussed)

8. Adaptation (intelligence (rate))

9. Variation in structure is just a few variables in brain organization and all differences can be understood as little more than changing those variables to produce the experience of ‘more’.

10. Evolutionary Computation ( … )

 

(HOW: What we’re going to cover (answer) the above.) (TOC) (Developmental Sequence)

|Homeostasis|: Continuously Recursive Loop:

1. Homeostasis(Before) > 2. Synthesis(During) > 3. (Solution?) > 4. Enactment(After) > [ ? ]

|Measurements|: relations > constant relations > measurements > assemblies 

|Processes Sequence|: Vibration > Sensing > Disambiguating > Organizing > Indexing > Storing > Accessing (stimuli or association) > Responding (release of action to attention) > Reviewing > Recursing (wayfinding)

|Neurological Hierarchy|: Homeostasis(Equilibrium) > Measurements(Nerves, Stimuli) > Systems of Measurements(Senses) > Integration of Systems of Measurements(Perceptions) into States(Faculties) > Incentives(valuations) > Consolidation(Model) >  Records(Memory) > Possibilities(Predictions) > Plans(Routes) > [Release of Action(Movement) OR Loop: Recursion(Intelligence to Consciousness)] > Adaptation

|Procedural Memory|: (Basal Ganglia (learning) > Cerebellum (doing): Learning (Initial Learning > Sensory Input) > Practice (Repetition > Error Correction) > Automation (Skill Consolidation > Decreased Cortical Involvement) > Execution (Automatic Execution > Sensory-Motor Integration) > Feedback Loop (Continuous Refinement)

|Episodic Memory|: Hippocampal Formation: Encoding (Perception > Attention > Encoding in the Hippocampus) > Consolidation (Initial Consolidation > Sleep-Dependent Consolidation) > Storage (Distributed Storage > Hippocampal Indexing) > Retrieval (Cue-Driven Retrieval > Hippocampal Involvement > Reconstruction) > Feedback Loop (Updating Memories > Re-encoding)

|Reticular Activating System|: Brainstem: (Sensory Input(Nerves) > Degree of Activation(filtering and prioritization) > Modulation(prepare brain for level of awareness)) > Distribution: (Thalamus(route prioritized information) > Cortical Regions(Association)) > Degree of Conscious Awareness (from alarm to conscious consideration).

|Sentience (Awareness)|: Sensory Receptors > Peripheral Nerves > Thalamus > Limbic System > Cortex > Motor Cortex

• • Learning (Initial Learning > Sensory Input) > Practice (Repetition > Error Correction) > Automation (Skill Consolidation > Decreased Cortical Involvement) > Execution (Automatic Execution > Sensory-Motor Integration) > Feedback Loop (Continuous Refinement)

|Consciousness (Directed Though)|: ( … )  Regional Consciousness > Thalamus (priority, organization, attention) > Prefrontal Cortex (Recursion by cortico-thalamic oscillations) 

Big Question: Just what is transmitted by neurons and how are networks that produce experience, accessed and transmitted. what the heck is flowing along those lines, and why can’t we observe it with current tech.

 

 

AND;

Homeostasis (Equilibration)

|Embodiment|: Homeostasis > Sensation > Integration > Behavior > Enactment

How the body measures and regulates itself to persist itself.

The problem facing body, brain, and mind consists in keeping the body alive by maintaining homeostasis (stable relations, stable equilibria) of its internal environment, and as a consequence, where the results of that biological demand for Homeostasis produce our instincts to persist.

|Neurological Hierarchy|: Homeostasis(Equilibrium) > Sensation(Neurons) > Measurements(Nerves, Stimuli) > Systems of Measurements(Senses) > Integration of Systems of Measurements(Perceptions) into States(Faculties) > Incentives(valuations) > Consolidation(Model) >  Records(Memory) > Possibilities(Predictions) > Plans(Routes) > [Release of Action(Movement) OR Loop: Recursion(Intelligence to Consciousness)] > Adaptation.

Causality 
Therefore the origin of all behavioral causality is the demand for homeostasis.

• The Body: a hierarchy of vast numbers of stable relations between elements, molecules, biomolecules, cells, tissues (combinations of cells) organs, and organ systems, and the organism itself as a whole.
• Homeostasis: The body survives by maintaining various physiological parameters (stable relations) within tolerable limits to ensure the continuation of essential life-supporting processes, such as energy production, waste elimination, and temperature regulation: a condition of Homeostasis. 
  • Demand for Persistence of life is the first principle of Homeostasis.
  • (EC: the universe eliminates that which does not maintain the constant relations necessary for the maintenance of homeostasis)
• Sensation: Negative stimuli and feelings signal a stress to or failure of homeostasis, and positive stimuli and resulting responses (feelings) signal satisfaction (reward) for for the optimization of homeostasis. The body can regulate its internal processes (homeostatic imbalances – a demand), and it can stimulate its internal processes to respond to the external conditions (physiological responses – a supplied response), and it can motivate the brain and mind to satisfy a demand that requires deliberate and complex action(motivational incentives – a complex action) to restore homeostasis.
  • Demand for homeostasis is the first principle of Sensation.
  • (EC: The universe eliminates that which cannot sense failure to maintain homeostasis)
• Acquisition: Life demands homeostasis. Homeostasis generates demand for sensation. Sensation generates demand for acquisition. Acquisition consist of either obtaining resources(+) and or obtaining evasions of negative acquisition (loss) at all levels from the internals of a cell to the production and execution of plan. 
  
  • Demand for Homeostasis is the first principle of Acquisition.
  • (EC: The universe eliminates that which does not acquire a supply sufficient to satisfy the demand for homeostasis.)
• Behavior: All behavior is reducible to the satisfaction of demand for acquisition to satisfy the demand for homeostasis to satisfy the demand for life, to satisfy the demand for persistence, to satisfy the demand for stable equilibria, to satisfy the demand for the defeat of entropy. 
  • Demand for Acquisition is the first principle of Behavior.
  • (EC: the universe eliminates that which does not supply behaviors sufficient to satisfy the demand for acquisition.)
• Movement: Movement increases the possibility of acquisition. Movement(changing state) is the result of the satisfaction of demand for acquisition.
  
  • Cellular:  Cell Membrane > Molecular Motors > Intracellular Transport (organization) > Organelles (Specialization) > Cellular Locomotion (coordination) > Taxis (fully body movement, cells to organisms) 
  • Multi Cellular: Coordination (Sensation) > Tropism (Growth) (Sensation: Electrical Fields, Light, Gravity, Touch) >
  • Nervous: Homeostatic (temperature, blood pressure, respiration) > Reflex (Automatic) > Involuntary Muscular Coordination (Peristalsis, cardiac contraction, muscular contraction, gastrointestinal transport > Instinctual Behaviors > Conditioned Behaviors > Learned behaviors > Voluntary behavior.

All behavior originates in movement of an assembly (supply) to satisfy a demand for changes in state (demand) that in turn satisfies the demand for acquisition that satisfies the demand for homeostasis. (CD: Add ‘Continuation’ CSS div here for this paragraph.)

• • Behavior that satisfies demand for Acquisition is the first principle of Movement. (reaction, transport, etc)
  • (EC: The universe limits the complexity, adaptability, and scale of that which fails to supply movement necessary to satisfy the demand for increases in acquisition necessary for the conversion of energy into mass, and information.)
• Coordination of movement (response, sensitivity to direct stimuli):  All coordination of movement begins with Chemotaxis (cellular), then Tropism(Plants) consists of direct and immediate reactions to environmental stimuli, and simple, deterministic, reflexive movements.
  
  • The first principle of coordinated movement is response to external stimuli that improves acquisition.
  • (EC: the universe eliminates that which expends energy without producing returns on energy.)
  • Note: (predictable (deterministic (invariant within limits)) is different from predicted (variable, within limits (deterministic)), prediction requires the capacity for choice(success/failure).)
• Prediction Of Movement (action, sensitivity to predicted stimuli): All Prediction of movement reduces the cost of movement in relation to the acquisition necessary to preserve homeostasis. All predictive movement results from the specialization of cells for chemotaxic communication (neurons), faster, at lower cost, over longer distances, working cooperatively, in greater complexity. 
  • The first principle of predictive movement is response to potential for increasing acquisition (and avoiding loss or cost).
  • (EC: The universe eliminates that which does not predict well.)
• Adaptation: Neural systems learn by competing for success and failure  of producing a response to stimuli that satisfies an internal or external demand. Adaptation increases the possibility of Acquisition. Adaptation satisfies the demand for acquisition of increasingly scarce resources, in increasingly intolerable environments, increasing avoidance of harms, and increasing reproductive success, over longer periods of time.
  • The first principle of Adaptation is the reduction of the cost of behavioral modification necessary to increase acquisition.
  • (EC: The universe eliminates the maladaptive.)
• Cooperation (predictions of others’ movement): Cooperation evolves in a spectrum from the benefits of mere proximity (dilution effect) to territoriality, alarm calling, to sharing, grooming, hunting, defending, to parenting, teaching, tool use, and production to deliberate cooperation on abstract goals. Cooperation requires the gradual emergence of the spectrum of imitative behaviors: identity (i’m one of those) > imitation (actions) > empathy (feelings) > sympathy (thoughts).
  • The first principle of cooperation is the returns on the division of labor – from attention (alarm), to acquisition (hunting) to shared abstract goals (production).
  • (EC: The universe constrains the non-cooperative)
• “Division of Labor” Self Organization of Everyone’s Movement:  ( … ) Just as the specialization of cells benefits the organism, the specialization of higher life forms benefits not only the organism but the group of organisms.
  • The first principle of the division of labor is the increase in expertise and ability, increasing acquisition of a broader range of resources at lower behavioral cost.
  • (EC: The universe constraints those groups incapable of a division of labor.)
• Information(By Competition for Cooperation): The more specialization of individuals within the group, the more knowledge and information, the more opportunity for acquisition is available to the individuals and the group. The greater the division of labor the greater the divergence of knowledge the greater the dependency on one another for the diversity of good services and information. The greater the volume and precision of knowledge produced by specialization in the division of labor, the greater the potential of the group to discover novel opportunities.
  • The first principle of the expansion of acquisition, knowledge, and information is the expansion of the possibility for new acquisition, knowledge, and information.
  • (EC: The universe constraints those groups incapable of recursive specialization in the discovery of knowledge using the division of labor.)
• Variables: All life follows and must follow these laws, and the only variable is the body and nervous system at all scales: physics > life > physical body > social > economic > political > strategic > evolutionary.
• The Universal Algorithm: The physical world, the world of life, the social world, and evolution all function by this same simple set of principles: the evolutionary computation of persistence by the discovery and maintenance of stable relations that defeat entropy by the concentration of energy in and across time.

AND;

Measurement (Stimuli (change), Neurons, Nerves)

|Embodiment|: Homeostasis > Sensation > Integration > Behavior > Enactment

How the body collects information necessary to maintain homeostasis, as the nervous system identifies stable relations in and over time, recognizing patterns and then subjecting them to adversarial competition for coherence in vast hierarchy, and subsequently organizing resources, and ultimately producing persistence (survival).

|Neurological Hierarchy|: Homeostasis(Equilibrium) > Sensation(Neurons)  > Measurements(Nerves, Stimuli) > Systems of Measurements(Senses) > Integration of Systems of Measurements(Perceptions) into States(Faculties) > Incentives(valuations) > Consolidation(Model) >  Records(Memory) > Possibilities(Predictions) > Plans(Routes) > [Release of Action(Movement) OR Loop: Recursion(Intelligence to Consciousness)] > Adaptation

Sensation (Neuron, Measurement)

[ILLUSTRATION: a neuron]

1 – What is a Neuron? The neuron evolved as a specialized type of cell capable of transmitting information more quickly and over longer distances than is possible through purely chemical or electrical communication among non-neuronal cells.  The neuron measures charge across it’s membranes, and when it reaches its threshold (membrane potential) it fires (releases) it’s charge (action potential).

 2 – What Does a Neuron Respond to? Vibration: Neurons respond to vibration (change) in time. Vibrations are a universal form of energy that can manifest in various ways, such as sound waves, light waves, mechanical pressure, or chemical fluctuations. Each sensory modality is specialized to detect specific types of vibrations. For example, the auditory system detects sound waves, the visual system detects light waves, and the somatosensory system detects mechanical pressure and temperature changes.

• Mechanical energy: Physical pressure, touch, and vibration.
• Chemical energy: Changes in the concentration of neurotransmitters, hormones, or other chemical messengers.
• Electromagnetic energy: Light (for visual perception) and changes in electrical potential (such as those that occur during synaptic transmission).
• Thermal energy: Changes in temperature.

3 – How Do Neurons Vary: Types of Neurons: There are three macro types of neurons, though there are many variations upon those three themes.

• Input neurons (sensory) specialize in producing receptors that detect different types of stimulation. Each sensory neuron specializes in producing one type of receptor: either light(eyes), temperature(everywhere), chemical (everywhere), and pressure (skin). Input neurons are largely excitatory(+).
• Cognitive neurons(calculating) called interneurons or associative neurons, specialize in calculating inputs and transmitting outputs that connect neurons together. Cognitive neurons are both excitatory(+) and inhibitory(-).
• Output neurons (motor) specialize in causing muscles to contract or relax, and glands to output. Output neurons are largely excitatory.

4 – What Do Neurons Do? Signal Integration and Pattern Detection: Neurons receive, integrate, and respond to multiple input signals. They detect patterns in these inputs based on their spatial and temporal relationships. This pattern detection emerges from the collective behavior of neurons firing together in time and space. Individual neurons don’t just respond to absolute signal strength, but to the relationships between signals across the network. This allows neurons to extract meaningful information from complex input patterns, forming the basis for higher-level information processing in the brain.

• Signal Processing
  • Proximity:
    • Neurons are organized in the brain in a rough approximation of their spatial relationships on the body, into regions, and columns.
    • Neurons have a broadcast radius produced by secretions of molecular signals, and a reception radius produced by sensitivity to secretions from the extensions from other neurons.
    • The more signals in proximity to the neuron, the more connections to it, and the closer they are to the soma, and the closer those connections are to one another, the greater the amplitude of the received signal.
  • Signal (Pulse): Once the receptor potential (stimulation) exceeds  a threshold, it triggers an action potential(“fires”) producing a neural pulse. This action potential (pulse) represents the equivalent of a digital all-or-nothing electrical signal that travels along the neuron’s axon to its outputs. This pulse serves as a standardized unit of neural communication, conveying information about the intensity and nature of the stimulus. 
  • Frequency (Amplitude): The rate and pattern of these pulses encode data regarding  strength, duration, and frequency of the signal.
  • Firing and Frequency (Importance): A neuron fires or not, and if it fires, it fires with some frequency. The firing communicates that there is a signal, and the frequency communicates the importance of the signal to the downstream neurons.
  • Neurotransmitters: When neuron fires it can release neurotransmitters that are either excitatory(+), increasing the likelihood of others to fire, or inhibitory(-), decreasing the likelihood of others to fire. 
• Adaptation and Survival
  • Cooperation (Conformity): Neurons seek attention and cooperation from both upstream and downstream neurons by cooperating and competing in time and space because neurons that obtain attention get fed resources and grow.
  • Survival (Non-Ostracization): The frequency of a neuron’s firing, and the frequency it fires over time, determine the resources the network and environment supply to the neuron – the neuron can only survive by cooperating with the network.
    • |Firing|: Spatial Proximity(Signal) > Temporal (minimum) > Frequency (maximum)
• Emergent Information
  • Information (Voting for Coherence): Neurons participate in a collective process of establishing coherent representations. By firing together in specific patterns, they “vote” for particular interpretations of sensory inputs, memories, or planned actions. This process helps the brain converge on consistent and meaningful representations of the world and the organism’s state within it.

Summary: With this information, the rest of the nervous system translates diverse environmental stimuli into a uniform electro-chemical language that generates an internal model and its relation to an external model of the world with sufficient precision to enable predictable movement through the world with the body form containing the nervous system.

Disambiguation: Neurons disambiguate stimuli by organization into sensations using similarity in frequency, intensity, and proximity, by competition for coherence, which produces a measurement of the relation between the body and the world. Neurons disambiguate just as the universe disambiguates entropy into mass and mass into complexity: “informational negative entropy”.

5 – How Does a Neuron Function?

[ ILLUSTRATION: Three types of neurons (4) with labels on their features ]

Starting from input to output, and using a tree analogy, a neuron consists of dendritic spine heads (receptors), that respond to neurochemicals (resources), releasing a positive or negative electrical charge. These spine heads (blossoms) develop out of dendritic spines (fine roots, root hairs) when stimulated, and in turn dendritic spines feed electrical charges into dendrites (coarse roots), that in turn feed into the cell body of the neuron.  Then the cell body (Root collar or Root Crown) ‘sums’ the positive(excitatory) and negative(inhibitory) charges, and the axon hillock(valve) then determines whether the neuron should fire. If so, the entire charge exits the cell body, neutralizing the charge of the cell body again, beginning another cycle, and the signal travels through the axon hillock and into the axon (trunk) that carries the signal over some distance.

If the axon must transmit long distances, high volumes, with fast response times, and less degradation of signal, it may attract Schwann Cells that intermittently surround the axon, producing a fatty substance called myelin, that insulates the axon so that the signal can travel farther and faster without degradation allowing for larger life forms to develop.

Then the Axon(trunk) continues by feeding the charge out of the axon hillock, and into axon terminals(branches, twigs) terminating in boutons (transmission bulbs – imagine flowers(ovaries) on trees: reproduction), with boutons seeking in turn to once again mate with dendritic spine heads, dendritic spines, dendrites or even cell bodies or in rare cases axons, thus creating a synapse: point of transmission of neurochemistry and thus charge(+/-) from one neuron to another. 

In addition, axons can transport resources back and forth such as proteins, enzymes, and organelles in a primitive circulatory system necessary for maintenance of their long distance transportation of signal. 

The result is visible in the brain as grey matter (unmyelinated axons) dense neurons in microcolumns columns regions and lobes that calculate, and white matter (myelinated axons) that transport signals over distances.

Summary of The Responsibilities of the Parts of a Neuron (in process order)

• Dendritic Spine Heads (Bouton Receptors): Respond to neurochemicals (resources), releasing a positive or negative electrical charge.
• Dendritic Spines (Fine Roots, Root Hairs): Develop out of dendritic spines when stimulated, feeding electrical charges into dendrites (coarse roots).
• Dendrites (Coarse Roots): Feed into the cell body of the neuron.
• Cell Body (Root Collar or Root Crown): Sums the positive (excitatory) and negative (inhibitory) charges, determining whether the neuron should fire.
• Axon Hillock (Valve): If the cell body fires, the entire charge exits, neutralizing the charge of the cell body, and the signal travels through the axon hillock and into the axon (trunk).
• Axon (Trunk): Carries the signal over some distance, potentially attracting Schwann Cells that produce myelin for insulation.
• Axon Growth Cone: The tip of a growing axon that guides its extension and pathfinding during neural development and persists for subsequent adaptation.
• Axon Terminals (Branches, Twigs): Terminate in boutons (transmission bulbs) that mate with dendritic spine heads, spines, dendrites, or cell bodies, creating synapses.
• Axon Bouton: The end of an axon terminal that contains synaptic vesicles filled with neurotransmitters.

6 – How Neurons Develop

Neurons follow a complex developmental arc of gradual specialization:

|Neuronal Development|: Origination > Migration > Localization > Terminal Differentiation > Functional Maturation > Network Adaptation

Origination: Neurons evolve during early development of the nervous system, in a temporary region containing the developing spinal fluid. They originate from embryonic stem cells, into a specialization of a skin cell (ectoderm), that differentiate into the neural plate and tube, and then further differentiate into Neural Stem Cells (NSC). These Neural Stem Cells then further differentiate into Neural Progenitor Cells (NPC) by specializing as Oligodendrocytes (Myelin), Astrocytes (Support), or Neurons proper: Excitatory, Inhibitory, and the various neurons that produce and release neurotransmitters that influence motor control, sensory perception, emotional regulation, and homeostasis (Dopamine, Serotonin, Acetylcholine, GABA, Glutamate, Norepinephrine, Histamine, Glycine, Opioids, and ATP).

Migration: Neural Progenitor Cells then migrate during development to different regions of the brain, by a combination of: 

• Chemical Cues (Chemotaxis): Molecules (Cell Adhesion Molecules, Morphogens, Guidance Molecules, Neurotrophins, Growth Factors, and Chemokines) allow receptors on the Neural Progenitor Cell to detect a gradient of concentration of, and to migrate toward, higher concentrations of attractants and away from repellents. 
• Structural (Cellular) Pathway Guidance (Contact Guidance): Integrins and Cadherins provide physical (mechanical and positional) signals that guide neural cells along specific physical paths.
• Environment (Extracellular) Guidance (ECM): Laminin, Fibronectin, and Collagens provide signals (adhesion, directionality, and migration speed) to these neural progenitor cells in their movement along those specific paths.
• Structural (Environmental) Stiffness Guidance (Durotaxis): Migrating neural progenitor cells require sufficient traction to migrate but not so much traction that they cannot migrate. Cells respond to the structural and environmental guidance signals from the structural stiffness that facilitate their forward movement.
• Electrical Guidance (Electrotaxis, Galvanotaxis): Electrical fields generated by the movement of charged particles across membranes create differences in electric potential, producing currents that can influence neural progenitor cells to migrate toward regions with specific electric potentials.
• Intrinsic Guidance (Cellular Polarity): The organization of cellular components alters the Neural progenitor cell’s polarity, influencing its migration toward either positive or negative charges in the surrounding tissues.
• Intrinsic Bias (Gene Expression): Gene expression within the migrating neural progenitor cell, can determine its responsiveness to external signals (physical, chemical, electrical), influencing their migratory behavior (speed, direction, and destination).
• Note: the complexity of this process and the sensitivity of this process to genetic variation and environmental conditions (resources), result in greater sensitivity of the brain to developmental fragility than other organs and organ systems such as muscles. The only compensation for this fragility consists in the vast number of neurons and the adaptability of those neurons despite the fragility of the developmental process.

Localization:

• Cessation of Migration: The cell stops migrating, due to a decrease in responsiveness to chemotactic signals and an increase in cell adhesion to the surrounding environment: when the cell and its environment reach physical, chemical, electrical equilibrium.
• Fate Determination of NPCs: Once migration has ceased, neural progenitor cells develop into specific types of neurons or glial cells by a process that involves the integration of various intrinsic and extrinsic signals that influence gene expression patterns, leading to the differentiation of the progenitor cells into distinct neural lineages (Astrocytes, Oligodendrocytes, and Neurons and their variations).
  • Downregulating Adaptation: Upon cessation of migration, cells downregulate the expressions of genes that permit adaptation (‘stemness’). 
  • Positioning and Sorting: Variations in concentration of molecular signals in varied micro-environments influence adjustments to the positioning and sorting of cells.
  • Multiple Signals to Complete Maturation: The combination of molecules in the micro environment activate and suppress different transcription factors (genetic expressions) that drive differentiation into final cell lineage (type) – and completing the process of maturation.
  • Proximity Discrimination (Discreteness): Repulsive signals from the environment prevent commingling of cells – neurons express a ‘broadcast radius’ of their availability whether dendritic or axonal, and different types of cells require greater insulation (Excitatory Pyramidal) and others lesser (glial),
  • Continuing Environmental Regulation: This same process micro-environmental influences continues during development effectively generating demand for and resistance to cells moving to position in relation to one another, and specializing by those relations – and even causing cell death to prune unnecessary cells.
  • Neurogenesis: Although the full extent of neurogenesis (generating new neurons) remains debated, the hippocampus and the subventricular zone (what remains of the neurogenic zone of the embryo, containing a reserve of potential neural cells) continue to undergo neurogenesis, cellular specialization, maturation, and organization. Additionally, there is evidence to suggest that other regions, such as the striatum and certain cortical areas, may also continue these processes as well.
• In summary, cells are ‘baited’ into position by environmental composition, and then finally specialize once in position in an environment. By distributing the responsibility of neural cell migration, organization, and maturation, into a competition, the tolerance for variation in genetics and environment is distributed across multiple factors providing redundancy in genetic expression. This process reinforces the cooperative relationship between the environment, the cell, and the genetics of the cell, and not just the determinism of the cell’s genetic composition. 

Terminal Differentiation:

• Outgrowth: Continues the migratory behavior of the neurons from their final, static position by generating cellular extensions called Neurites, which then develop into dendrites (the dendritic arbor) and a single axon.
• Dendritic Outgrowth: As Neurites evolve, some are selected to form a dendritic trunk, and the arbor(branches) follow growth factors just as did neural progenitor cells during their migration, and then cease growth when either that gradient of molecules dissipates or they encounter inhibitory signals or other neuron’s dendrites, resulting in the expansion of the dendritic tree, the formation of dendritic spines, and the establishment of a neuron’s receptive field.
• Axon Specification (Specialization): A single neurite is selected for axonal growth by combination internal and external signals including the internal density of cytoskeletal structures (microtubules, micro and intermediate filaments), that provide the both the structural rigidity, the capacity to organize, and to rapidly transmit resources into and out of the cell body (soma) and its axon, along with the external influence of environmental growth factors that continue to ‘bait’ the growth of the axon.
• Axonal Discovery: The developing axon explores its environment using the same environmental cues used to regulate migration of neural progenitor cells, seeking to identify and establish connections with its target cells using the Growth Cone (containing receptors and signal molecules) at the end of the axon with a set of axon branches extending from it.
  • Broadcast Gradients of Guidance Molecules: The axon seeks a combination of chemical, electrical, and mechanical cues such as gradients of chemoattractants or chemorepellents, adding cell structure behind at the growth cone until it reaches a waypoint or a destination where the axon terminals form synapses with dendrites somas and even other axons. 
  • Wayfinding: The axon may discover a dendrite, soma, or axon to connect to, or it may discover a waypoint consisting of a gradient of molecules that are either sufficient for it to stop and wait for future potentials or less dense, causing it to desensitize and continue via other chemical stimuli.
  • Bi Directional Broadcast and Reception: Axon Terminals, Dendrites and the Soman can secrete these signal molecules. Even Non-Neural cells, such as muscle or skin cells can secrete molecules into the microenvironment that will cause axons to seek to supply stimuli to those regions.
  • Topographic Organization: In many neural systems, target cells are organized in a topographic map, where the position of a neuron in one area corresponds to the position of its target cell in another area, ensuring that the axons connect to the correct location.
• Synaptogenesis: Once dendrites and axons have outgrown from the soma matured, and discovered a possible connection, neurons can form synaptic connections with other neurons by the proximity between the presynaptic neuron’s axonal terminal (bouton) and the postsynaptic neuron’s dendritic spine, by the formation of clusters of synaptic vesicles into boutons and neurotransmitter receptors (bouton receptors) on dendritic spines, and then start absorption of synaptic proteins. Once these complete signals can transport across synapses, causing neurons to cooperate.
• Neuronal Activity: Once an axon reaches its general target area, the activity-dependent processes help refine the connections. Axons that form functional synapses with target cells may receive growth support, which strengthens and stabilizes the connection. In contrast, axons that fail to form stable or functional connections may undergo pruning or retraction.
• Synaptic Competition: Multiple axons may initially excite or inhibit the same target cell, but through synaptic competition, the most active and functional synapses are maintained, while less active ones are eliminated.
• Myelination: After maturation and synaptogenesis, Schwann Cells wrap around the axon, forming myelin sheaths (white matter) that insulate the axon limiting the decay of the electrical impulses as they travel from the soma through the axon to the axon terminals (boutons) and across the synapses where the synapse can connect on dendrite, soma, or even axon. These myelinations are spaced apart, leaving room for the cell wall to absorb molecules and producing charge. As the impulses cross these regions, regenerating the signal as it passes down the axon. 

Summary: Axon growth mirrors the migration of neural progenitor cells. Both processes are guided by a combination of environmental signals and cellular responses. Axons continue to grow until they discover their target – which could be a dendrite, soma, or even another axon. At this destination, axon terminals form synapses with the target cell, establishing a connection for information transfer – or receive and transmit information to skin, muscle, and other tissues where instead of axon terminals and synaptic boutons, they develop special structures that detect pressure, vibration, temperature, pain, and molecules for smell or taste.

Functional Maturation (Network Adaptation): Once the neuron has grown dendrites and axons such that it has discovered a network to participate in, it must transition to adapting to participation in that network, and begin to receive and transmit signals. Just as the neural progenitor cell emphasizes migration to a destination, and a terminally differentiating neuron emphasizes network discovery, a mature neuron emphasizes adaptation to the network it has discovered by synchronization with the network, and directs growth to axons and boutons as well as dendrites and bouton receptors. So the growth process follows these three stages of gradual increase in specialization. This final transformation consists of:

• Alter Gene Expression: Shift from developmental gene expression patterns to those supporting mature neuronal function, and upregulate genes involved in synaptic plasticity and neurotransmission:
  • Alter Neurotropic Factor Dependence: Shift from developmental gene expression patterns to those supporting mature neuronal function, and upregulate genes involved in synaptic plasticity and neurotransmission
  • Increase Energy Production: Increase protein synthesis necessary for maintaining synaptic components.
  • Electrochemical Signal Capacitance: Regulate the expression of surface proteins of the cell and its projections, regulating the electrochemical properties of the cell membrane, ensuring that the neuron can generate, propagate and regulate the timing and strength of signals, and fine tuning the threshold, duration, and refractory period of the action potential.
  • Dendritic Spine Stabilization: Transition from the ability to extend, retract, change structure, shape and move to stabilizing relations with the network.
  • Synaptic Refinement: Pruning excess synapses, strengthening frequently used synapses and regulating synaptic effectiveness.
  • Neurotransmitter Production: Increase synthesis and storage, and develop release mechanisms.
• Enhance Signal Transmission: For neurons with myelinated axons, complete the myelination process in cooperation with the schwann cells, enhancing the speed and efficiency of signals.

Network Adaptation (Refinement): Dedicate the neuron to the emergent behavior of the network by enabling long lasting changes in synaptic strength by adaptations that encourage long term potentiation (LTP) and long term depression (LTD)

• Adaptive Filtering of Inputs (inputs, before): Dynamically regulate sensitivity of signal detection in response to recent activity patterns, optimizing signal-to-noise ratio at the receptor level.
• Adaptive Balancing of Excitation and Inhibition (resources, during): Develop and refine cellular processes increasing or decreasing transmitters, receptors, and their resources to dynamically adjust the strength of inhibitory synapses, such as by increasing the surface area of synapses, by increasing the size and number of boutons and bouton receptors, thereby increasing the volume of resources, so that neither boutons or receptors exhaust their supplies of resources necessary for synaptic transmission.
• Adaptive Modulation of Outputs (outputs, after): Adaptively regulate the intensity and timing of the output to reflect the strength of the input from the network (cascades). In other words, dynamically reduce network noise in the output while preserving relevant signal features.

 

7 – How Neurons Survive

Neurons, like any other cells, require a stable environment and consistent energy supply to survive, and an information system to regulate supply and demand for excitement, inhibition, volume of activity, survival, growth, cooperation, and isolation. 

• Nutrient Supply and Metabolism (Maintain the Cell): Neurons require a consistent supply of glucose and oxygen to produce ATP through cellular respiration.
• Neurotrophic Support (Maintain the Network): In addition to Nutrients, neurons require a supply of Neurotrophic factors, such as nerve growth factor.
• Glial Cell Support (Maintain the Local Environment): Glial cells, including astrocytes, oligodendrocytes, and microglia work together to maintain the health of neurons by regulating the extracellular environment, removing excess neurotransmitters, supplying metabolic substrates, producing myelin which insulates axons and facilitates rapid signal transmission, and acting as immune cells in the central nervous system, providing defense against pathogens and cleaning up debris from dead cells.

8 – How Do Neurons “Compute”? With Dendrites, Somas and Axons

Neurons are not simple threshold devices, but rather complex, multi-faceted computational units that form the basis of the brain’s information processing capabilities.

Mechanisms of Neuronal Computation

Neurons compute information through diverse mechanisms across their structural components, enabling complex signal processing:

1. Dendritic Integration: Performs spatiotemporal and nonlinear computations, facilitating hierarchical processing.
2. Somatic Synapses: Provide direct, time-sensitive modulation of neuronal output.
3. Axo-axonic Synapses: Offer fine-tuned control over neurotransmitter release and population synchronization.
4. Axon Initial Segment Synapses: Crucial for action potential generation and network oscillations.
5. Volume Transmission: Enables broad neuromodulation through interstitial signaling (broadcast into the micro environment affecting, often affecting more than one neuron, especially in the cast of neurotransmitters such as Dopamine.

Dimensions of Neuronal Computation:

• Interstitial Macro, non specific, neural network regulation
  • Interstitial Modulation: of motivation (intensity), plasticity(adaptability), attention (duration) by resistance (downregulating) or capacitance (upregulating) using neuromodulators such as dopamine, serotonin, norepinephrine, histamine, acetylcholine 
• Dendritic input regulation
  • Spatially,
  • Temporally,
  • Hierarchically 
• Somatic, Axonal output regulation
  • Time sensitivity (override dendrites)
  • Synchronizing with other neurons (amplify)
  • Activation/Inhibition (go, no-go)

Methods of Neuronal Computation

Single Neuron Computation:

• Dendrites can perform AND-like operations on inputs.
• The soma then performs a final OR-like operation on the outputs of these branches.
• A neuron can perform
  • Logical operations: 
    • AND gates (requiring multiple inputs to fire)
    • OR gates (firing with any of several inputs)
    • NOT gates (through inhibitory inputs)
    • Thresholding: converting graded inputs into a binary output (fire or not fire) or 
  • Arithmetic operations
    • Summation (addition):
      • Spatial summation: Combining inputs from multiple synapses across the dendritic tree
      • Temporal summation: Integrating inputs that arrive close together in time
    • Subtraction:
      • Through inhibitory inputs that counteract excitatory inputs
      • Via shunting inhibition, which can effectively subtract from excitatory inputs
  •  Multiplication operations through:
    • Dendritic nonlinearities
    • Synaptic depression and facilitation
    • Interactions between excitation and shunting inhibition
  • Division:
    • Through mechanisms like gain control and normalization
    • Via shunting inhibition, which can effectively divide the impact of excitatory inputs
  • Exponentiation:
    • Through voltage-dependent ion channels, which can create exponential relationships between input and outpu
  • Bayesian Probability Computation by:
    • Synaptic weights represent prior probabilities
    • Neuronal firing represent posterior probabilities
  • Hybrid Computation by Analog-digital Conversion:
    
    • Analog in their dendritic integration and subthreshold membrane potential changes
    • Digital in their all-or-none action potentials
  • Convolution:
    • Through the interaction of inputs across time and space in the dendritic tree
  • Dimensionality reduction:
    • By combining multiple inputs into a single output
  • Parallel Processing: a single neuron, with its complex dendritic tree, can perform multiple parallel computations simultaneously.
  • Coincidence detection (requiring multiple inputs to arrive simultaneously).
  • Temporal integration (summing inputs over time).
  • Integration and Differentiation (responding to the rate of change of inputs)
  • Resonance production (responding preferentially to inputs at certain frequencies).
  • Nonlinear transformations (Reshaping the distribution of outputs to inputs)
    • Amplify or dampen signals selectively
    • Create sharp transitions in responses
    • Compress wide ranges of inputs into narrower ranges of outputs
    • Enhance sensitivity to certain input ranges while reducing sensitivity to others
  • Pattern recognition: By detecting temporal and spatial correlations in inputs (akin to coincidence detection), dendrites can identify specific patterns of activity, much like feature detectors in machine learning algorithms.
  • Amplification and Attenuation:
    • Through mechanisms like dendritic boosting or synaptic depression

In summary, a single neuron can be thought of as a sophisticated computational unit capable of performing a wide range of operations, from basic logical functions to complex temporal integrations and even probabilistic computations. This versatility allows neurons to form the basis of the brain’s incredible computational power.

Visualization of The Complexity of Neuronal Computation

Given that range of complexity of computational dimensions and potentials we can attempt to visualize the complexity of the output of a single neuron as a multi-dimensional set of inputs, summed and projected onto a on three dimensional map of a territory of possible outputs. In reality the map of possible neural inputs can consist of many more dimensions than two dimensions of X and Y and the height dimension of Z as the output. But visualizing such spaces is both difficult and unnecessary for the development of an intuition of the complexity of computation available to a neuron.

Analogy: Think of the output possibilities of a neuron as if it was the landscape depicting a complex mountain range. The terrain is non linear. There might be high cliffs or plateaus. And there might be low points below the minimum threshold of firing for the neuron. The X and Y location inputs are processed into a Z output as a height. This landscape is based on its inputs, with its output determined by the height it reaches. Some paths up the mountain (combinations of inputs) lead to high peaks (strong outputs), while others lead to valleys (weak or no output). The neuron’s job is to use the inputs to calculate the location on the map and respond by generating the height of that location accordingly, with lower probability and rate at lower heights and higher probability and rates at higher heights. 

This visualization helps to intuitively understand that:

1. The neuron’s response is not a simple sum of its inputs
2. Certain combinations of inputs can lead to dramatically different outputs
3. The neuron can perform complex pattern recognition by responding to specific “paths” through this input space

In essence, the ‘height’ metaphor represents the neuron’s state of excitation, which directly influences its output, but through a complex, often non-linear transfer function that includes thresholds, saturation points, and temporal dynamics.

Summary: This multi-dimensional, non-linear landscape visualization provides an intuitive way to grasp the complex computations a single neuron can perform, going beyond simple thresholding or linear summation.

Consequential Contribution to Computation With Other Neurons

The associative neuron’s job is to identify meaningful relations (firing patterns) with other neurons, that are sufficiently exciting in time and space, by summation of spatial and temporal inputs from those neurons, to cause the cell body of the neuron to fire, then to modify the dendritic tree to strengthen the dendritic relations to significant neurons, and to weaken those relations that are less significant. (Again, “Neurons that fire together wire together”.)

The dendritic arbor(tree) functions as an constantly adapting antenna that summarizes otherwise ambiguous and complex information into meaningful, simple, clear, information.

The dendrites do this ‘computing’ in infinite variety and precision by combining the effect of timing (temporal summation), and location (spatial summation) and recognition of signals from the cell body (back-propagation).

Dendrites can identify these temporal and spatial patterns (constant relations) in an infinite set of combinations, together producing complex computations, enabling one neuron to integrate information from various excitatory and inhibitory inputs (sources, synapses), at a level of complexity that is irreducible, and unanalyzable, and un-introspectable by us as observers. So unlike the serial computation we are familiar with, and very much like the neural networks in bayesian computing, neurons can ‘sum’ and respond to an nearly infinite number of combinations that are sufficient in amplitude to pass onward.

Threshold of Detection: Spatial Proximity(Signal) > Temporal (minimum) > Frequency (maximum)

–“It takes a village to fire, and the signal is frequency of firing.”–

The process of threshold detection involves spatially distributed synaptic inputs received by the dendrites, which are integrated and converted into combined signals (graded potentials). These graded potentials are influenced by the spatial proximity of inputs (signal integration), the temporal summation of inputs arriving within a short time window (minimum time integration), and the frequency of input signals (maximum frequency detection). If the integrated analog signals reach the threshold, they trigger a digital output (action potential) that is sent out to other neurons. This action potential also provides feedback by back-propagating into the dendrites, modulating synaptic strength and connections (Efferent and Afferent Neurons).

The process of spatial summation, temporal summation, and frequency detection in neurons allows them to integrate and respond to complex patterns of synaptic inputs. This integration ensures precise, efficient, and adaptable neural communication, supporting advanced cognitive functions and learning through synaptic plasticity. By understanding these mechanisms, we gain insights into the fundamental processes underlying neuronal firing and information processing in the brain.

Transformation: Digital > Analog > Digital

Transformation involves the process of detecting distributed digital inputs (synaptic signals) received by the dendrites, which are integrated and converted into analog signals (graded potentials). These analog signals are processed by the cell body (soma) and, if they reach the threshold, trigger a digital output (action potential) that is sent out to other neurons and internally to modulate dendritic connections (Efferent and Afferent Neurons).

The digital to analog process produces noise reduction, clarity, decidability on coherence and relevancy, energy efficiency, transmission across long distances, and with that clarity of decidability provides increasingly precise adaptability because of synaptic plasticity. 

Scaling Neuronal Cooperation by Analogy:

• The individual Person: Coffee Shop Model (Negativa (-)): In a coffee shop, with multiple groups (dendrites) engaged in multiple conversations (axon terminals of input neurons), each competing with more volume (number of synapses) for the attention of their members (common synapses and dendrites in time and space), one may focus attention on any of those conversations amidst the background noise (identifying patterns of excitation and inhibition). This requires identifying a coherent conversation (frequency and amplitude of those patterns) in one or more voices (excitation patterns) and filtering out (suppressing) those that aren’t coherent (discordant) with those voices. This process produces a coherent stream of related signals (constant relations in time and over time) and, subsequently, a coherent stream of meaning (frequency of feedback when firing, both within the neuron and from responses of other neurons).
  
  
• The Group of People: Orchestral Model (Positiva(+)): In an orchestra, with many people (dendrites), playing different instruments (collections of synapses in dendritic regions), following different sheet music (axon terminals from input neurons), and producing different sounds(patterns of inputs), the conductor controls the tempo (frequency), the participation of different sections (feedback when firing both within the neuron and responses from other neurons), and the volume (activation of more synapses) of groups of instruments (collections of synapses in dendritic regions), all in an effort to produce a coherent melody, which he recognizes by a response from the audience (feedback from downstream neurons).
  (Note: So brad says ‘more cowbell’)
  
  
• The Marketplace of People: Social model (Cooperation(=)): In a town’s market square, where numerous vendors (dendrites) set up their stalls (dendritic branches). Each vendor is competing to attract customers (synaptic inputs) to obtain profits (positive feedback, resources) from satisfied customers (from both the soma, and from synaptic inputs from other neurons). Vendors use various strategies (synaptic strength and plasticity) to stand out, akin to how dendritic branches enhance their influence by increasing synaptic strength and plasticity.
  
  Vendors often form alliances (synaptic clusters on dendritic branches), sharing resources or information(activating together), which enhances their individual and collective ability to compete (excite or inhibit the soma) and thrive (receive resources from the soma). 
  
  Sometimes, vendors collectively negotiate with suppliers(axon terminals of excitatory presynaptic neurons) or regulators (axon terminals of inhibitory presynaptic neurons) to achieve better terms (synaptic cluster cooperation), mirroring how dendritic branches collaborate to enhance synaptic efficacy for more effective signal integration (reducing prices, increasing profits).
  
  Some vendors focus on reducing excess noise (inhibitory signals) and disruptions (conflicting signals), preventing market saturation (overstimulation) in the market by modulating supply (synaptic activity), adjusting prices (synaptic strength), or requiring volume purchases (frequency of inputs). Some collections of vendors organize into a faction that can levy taxes on transactions by other vendors (strong inhibitory signals), thereby increasing their control over the market (neural network). (We could get into protection rackets but we won’t).
  
  (Note, the money supply problem in the neuron they can increase the money supply but they have to increase the productivity of the economy (cooperation with other neurons))

Summary

The sheer complexity and adaptability of computation in a single neuron surpass that of any conventional electronic (or computational) circuit. When multiple neurons interact, forming networks and layers, the potential for pattern recognition, learning, and processing is exponentially amplified such that for all intents and purposes a relatively small group of say 80 neurons can store an infinite amount of discrete pattern recognition information.

9 – What are Neurons Transmitting? While neurons transmit only signal and frequency, what information are they conveying with those signals?

• Information Content: The information transmitted by neurons represents:
  1. Presence and intensity of stimuli (through firing and frequency)
  2. Spatial relationships of stimuli (through which neurons are firing)
  3. Temporal patterns of stimuli (through timing of firing)
  4. Categories or qualities of stimuli (through which types of neurons are firing)
  5. Context and relevance of stimuli (through the broader network activation patterns)
• Emergent Meaning: While individual neurons transmit relatively simple signals (firing or not, and at what frequency), the collective activity of many neurons encodes complex information that generates coherent behavior attempting to adapt the body to the context: 
  1. Sensory features (e.g., edges, colors, sounds, textures) (by proximity)
  2. Models of objects, spaces, borders, places, and locations in a network of places.
  3. Abstract concepts and categories (by recursive hierarchy)
  4. Emotional states (by valuation of those abstract concepts and states)
  5. Motor commands (by organization of of the body to perform actions)
  6. Memory traces (by repetition that reinforces the network that produced the model and behavior in relation to the context)
• Network-Level Information: The overall market of patterns of activity across neural networks represents higher-order information:
  1. Current perceptual experiences (Experience)
  2. Internal states of the organism (Feelings)
  3. Ongoing cognitive processes (Thinking)
  4. Behavioral intentions and plans (Reasoning)

Summary: The nervous system is always asking the same question “What do I need to do to make it better (homeostasis)?” And the answer is always behavior that organizes the body and mind to produce behaviors that satisfy the demand for present and future homeostasis.

10 – How Are Neurons Organized (Division of Labor, Layers, Microcolumns)

The neocortex in the brain, is a thin sheet about the size and thickness of a high quality restaurant linen napkin. It’s is constructed largely of associative neurons, just as the peripheral nervous system is constructed largely of sensory and motor neurons. The associative neurons work together to process inputs from the sensory neurons (‘sense-story’ neurons, when the story is sufficiently interesting the associative neurons pay attention), and calculate what if anything the motor neurons might do in response. 

The neocortex is divided into six layers. Originally, there were only three layers that focused on motor coordination in response to external stimuli, and at some time in evolution they ‘folded over’ and doubled, and this doubling allowed the evolution of more abstract relationships producing ‘relatively’ higher capacity for associations in time and space. These layers vary in thickness and density across the regions of the brain depending upon the need for that region to bias it’s efforts to specialize in the category of information it’s processing.

These layers are organized into microcolumns of neurons, macro columns of microcolumns, and regions performing similar functions, organized into lobes of similar functionality, and then divided into two hemispheres (right and left) that are connected through the Corpus Callosum which is vast bridge consisting of networks that extend into both hemispheres, allowing coordination between the hemispheres, and the hemispheres specializing in one side of the body or the other (bilateralism).

|Hierarchical Organization of the Neocortex|: Associative Neurons > Layers > Microcolumns > Columns or Macro-columns > Regions > Lobes > Hemispheres > Neocortex

Organization into Layers

These six layers divide the labor of collecting associations in time and proximity in, on, and external to the body. From the bottom to the top layer their function consists of:

• Layer 6: Positional: Sends positional information of the part of the body that the region serves to the thalamus (attention) and other cortical regions.
• Layer 5: Output: The major output layer for the motor neuron system transmitting out other regions, across hemispheres, and into the body.
• Layer 4: Attention (inclusion): The attention layer, that receives information from the thalamus (attention) and distributes it to other layers.
• Layers 2 and 3: Integration, Association, Pattern Recognition, and Feedback (“Where the Work Is Done”): These layers work together to connect different sensory inputs and contribute to higher-order cognitive functions, such as pattern recognition and memory association both within regions in the same hemisphere and across the Corpus Callosum. And then processing feedback loops that help refine and modulate sensory inputs and motor outputs, ensuring precise control and coordination of cortical behavior.
  • Long Range Communication: Integrate information across the entire cortex, supporting complex cognitive processes such as language, abstract thinking, and problem-solving by connecting different cortical regions across both hemispheres.
  • Developmental Importance: During development, these layers produce the critical relations necessary for sensory and cognitive development by the formation of cortical circuits that establish functionally similar and functionally related networks both within their regions, but more importantly, across the whole brain. While these layers show sparse development at birth, within the first two especially but then the first four years in total, these layers form the dense connections that allow the brain, and body, to operate in the real world environment.
  • Learning: These layers in particular produce synaptic plasticity and cortical adaptation, allowing the cortex to learn from experience, adapt to new information, and store memories, thereby contributing to learning and cognitive flexibility.
• Layer 1: Regulation(Modulation, Synchronization): Regulates and modulates and synchronizes the other layers in the microcolumn, and then contributes to the generation and modulation of cortical rhythms across the brain, such as alpha(inhibition, calm) and gamma oscillation(synchronization causing binding of related information, and lower ‘noise’ competition promoting longer transmission of information, producing attention (amplitude) which instructs memory to ‘remember’), which are essential for various cognitive processes, including attention and memory. Ensure that neurons that are wired together continue to fire together so that cognition, attention, and memory operate on the same frequencies, because ‘firing together’ means ‘firing together in relatively synchronous time frames’.

The Result of The Organization of The Associations Between Neurons

But there is one only way for an associative neuron to ‘know’ anything: associative neurons must associate with other neurons, because an associative neuron has nothing to compare or compete with on it’s own.

So, just as you can’t have nothing, without something to differentiate from nothing, all of existence and all our perception of existence consists of bundles of detectable relations between things, even if our neurons organize those relations into signals into groups forming categories that we can recognize  as consistent in time, predict their future state, and respond to with some utility to ourselves. So, despite our conscious minds experiencing objects, spaces, and backgrounds, which consist of vast bundles of associations in memory, we perceive those bundles as a category(class) to an identity(single) that is persistent over time, therefore predictable, therefore actionable. And just as an ant sees the world in it’s scale and time frame and ability to act in relation to it’s body form, we see the world in our scale and time frame and ability to act.

This does not necessarily mean we see a different universe by different laws, but that we interpret that universe according to our ability to perceive and act within it given our body and mind’s ability to make use of it for our survival to flourishing. In our work we call this phenomenon, as well as this chapter’s purpose, embodiment, and we refer to human embodiment as ‘Vitruvianism’ (after Da Vinci’s Vitruvian Man illustration), to emphasize that man is the system of measurement of the universe for man, and that all measurement must be reducible to an analogy to experience by man, for the sets of relations we perceive in the world to be converted to something coherent, judge-able, predictable, and therefore actionable.

(FYI: Remember ‘invagination’ for dendritic spines)

11 – How Do Neurons Work Together (Cooperate)

• Competition for Coherence: Neurons compete for attention within a group of neurons, (using more spines, dendrites, axon terminals, boutons and synapses) and then groups of neurons compete together for attention with other groups of neurons, the success of the competition is determined by the coherence of the signal with other groups of neurons: agreement in time, 
• Growth of Connectivity: Growth of neurons is determined by discovery of neurons and groups of neurons that together respond to a pattern and fire together (cooperation), and then physically connect to one another (“Neurons that Fire together Wire together”) by expanding the physical connectivity of each neuron producing axons, axon terminals, boutons, and synapses connecting to dendritic spine heads, dendritic spines, and dendrites.
• Pattern Recognition and Adaptation: groups of neurons adapt to patterns. (‘communication of patterns of survival(agreement),memories’) They form specialized networks that respond to specific stimuli or concepts, reinforcing connections that lead to successful predictions or actions, and weakening those that don’t.
• Neuronal Logic: (Logic) neurons can use axons and dendrites to search for opportunities for connection (Search). They can form one or more connections (Create). They can activate together (Recall). They can adapt their connections (Update). They can break connections (Delete). In other words, they can perform the same changes as any database can.
• Neural Economy: Neural Economy. limited resources, proximity, density, layers(hierarchy)  (note: the body organizes information by competitive density,) (what produces neurochemistry)
• Neural Group Coherence: Signal coherence > temporal + spatial + spectral > larger groups of neurons are necessary to detect patterns of coherence in lower frequency signals over long periods of time (again, made possible by the hierarchical ‘memory’).

Conclusion: Neurons, Groups of Neurons, Microcolumns, Columns, Regions Transmit Suggestions for Inclusion of their fragments of memory in the  moment by moment stream of memories, that compete for coherence which accumulates in potential for your attention. This adaptive process allows the neural network to continually refine its ability to recognize and respond to relevant patterns in the environment, enhancing the organism’s survival and behavioral efficacy.

12 – What Is The Resulting Information Produced by Neurons?

Hierarchy of Memories (The long chain of Memories of Memories): (conscious experience)… experience consists of a continuous stream of a hierarchy of memories of memories down to the most basic pattern recognition of a single neuron.

At the lowest level, individual neurons respond to specific features or stimuli, such as edges, lines, or colors in the visual system. These neurons form synaptic connections with other neurons that respond to similar features, creating small assemblies that represent more complex patterns, such as shapes or textures.

As we move up the hierarchy of neurons, microcolums, columns, regions, these assemblies combine to form even more intricate representations, such as objects, faces, or scenes.

But the neural networks do not simply transmit information from one level to the next. Instead, they “wire together” through adaptation to experience creating a vast and interconnected web of memories. Each level of the hierarchy builds upon the information stored in the levels below it, creating increasingly integrated and coherent assemblies of memories that represent the world.

Our conscious experiences are the emergent result of the vast hierarchical network of memories stored within our brains. Each moment of awareness is shaped by the complex interplay of sensory input, prior knowledge, and the dynamic activation of neural circuits at multiple levels of abstraction.

However, just as we cannot directly perceive the individual layers of the artwork that give rise to the final projected image, we cannot introspect upon the myriad levels of neural processing that underlie our conscious experiences. The seamless nature of our subjective experience belies the immensely complex hierarchical structure of memories that supports it.

[ILLUSTRATION: projectors of layers creating images]

[CAPTION] These illustrations created by artists consist of a simple light source (projector) and separated layers of cardboard, metal, or colored class that results in a two dimensional ‘shadow’ or ‘projection’ on the wall that is a coherent picture – demonstrating how layers of fragments of memory produce our perception of the world around us.[/]

Consciousness is an emergent property of the sense of homeostasis in the brain stem wrapped in multiple competing hierarchies of memories of memories, and experience is a hierarchy of layers of memories we cannot introspect upon.

 

Commensurability of Measurements

Commensurability: Commensurability requires the production of a standard of measurement that allows comparison of different concepts (references) and by that comparison and confidence in that comparison: decidability.

In other words, in our context, of cognitive neuroscience, creating a standard of sense-able and sensible weights and measures from by the nervous system, from different parts of the body, producing different sets of measurements, transmitting different contextual information, the nervous system uses a competition for coherence between stimuli and memory of previously coherent stimuli, producing commensurability by tests of consistency and coherence between the body in relation to the world as its standard of measure. 

So just as counting creates commensurability between different things we want to count, and money creates commensurability between our wants, others wants, and the scarcity of those wants, neurons, work together to produce commensurability between the body, homeostasis, and the world. 

So, man is the system of measurement for all things between man in the world he exists in. We call this ‘vitruvianism’.

Perception (Detection, Groups of Neurons)
(Measurements: Cortical Microcolumn, Microcolumn, Cortical Region; Commensurability; Pattern Recognition)

Perception: Sensory Perception consists of those senses humans can employ to make measurements (in complex combination): (measurements of stable relations and stable equilibria)

|Processes Sequence|: Vibration > Sensing > Disambiguating > Organizing > Indexing > Storing > Accessing (stimuli or association) > Responding (release of action to attention) > Reviewing > Recursing (wayfinding)

|Neurological Hierarchy|: Homeostasis(Equilibrium) > Sensation(Neurons)  > Measurements(Nerves, Stimuli) > Systems of Measurements(Senses) > Integration of Systems of Measurements(Perceptions) into States(Faculties) > Incentives(valuations) > Consolidation(Model) >  Records(Memory) > Possibilities(Predictions) > Plans(Routes) > [Release of Action(Movement) OR Loop: Recursion(Intelligence to Consciousness)] > Adaptation

The Story? (introduction)

STORY – The Story of Disambiguation.  😉
Braile(touch) -> coffeeshop(sound) model I think gives us,  enough clarity because they ARE introspect-able, that we can explain vision that is not introspectable.   
Narrative: brail, to coffee cup, to coffee house sound to coffee house vision.

How Neurons Work Together to Create Measurements Relating the Body to the World: Now that we understand how neurons function, how do they work together to produce our senses, and how do those senses serve as a system of measurement?

What’s a Measurement? A measurement consists of a variation from a base by some degree, from as little as on off, to as sophisticated as an object or scene. When a group of neurons identifies a pattern to these signals, these patterns of signals form Measurements.

Measurements Consists of the detection of stable states and changes in the environment or within the body itself. These changes, regardless of the specific sensory modality, can be understood as signals whether mechanical, chemical, electrical, that disrupt the stable relations between the body and its surroundings or within the body’s internal systems.

Division of Labor: ( … ) (explain) The nerves and their downstream neurons are divided by specialization in the information they discover, largely by location in the body. (organized by the body)

1. Organization of Measurements 

(Not neurons or groups of neurons but neural systems)

• Organization (What): As action potentials (pulses) propagate through neural circuits(network), they are integrated and processed in the brain, which interprets these patterns of neural activity to identify changes, by adversarial competition for consistency or coherence (evolutionary computation) in detection of the the stable relations within the body itself, and between the body and its environment, creating a coherent representation of the sensory information in relation to the body and the body’s relation to the world. 

1. Competition in time for association   (Repeat for groups not just neurons – at least mention differences in frequency of groups.)

• • Spatial and Temporal Processing: (Missing The organization of stimuli within the central nervous system is a function of both spatial and temporal neural processing. As action potentials reach synaptic junctions, they contribute to (add to) the synaptic potential (sum of stimuli) of post-synaptic neurons, influencing (+/-) positively(excititory) or negatively (inhibitory) whether these neurons will also fire as a consequence of that stimulation. In other words, “Neurons that fire together in time and in proximity wire together.”
  • Temporal Summation: By summation of signals in time (temporal summation), where frequent signals over a short period increase (excite) or decrease (inhibit) the likelihood of a neuron’s action potential firing.

2. Competition for Compatibility(Coherence, Cooperation) of fragments and features.

• • Physical Structure: ( … ) pattern over time, sparseness, etc. ( … )
  • (walk thru the stack) fragment > feature > object > model > entity >
  • Objects, Spaces and Backgrounds: This integrated signal processing forms the basis for complex perceptual phenomena such as feature binding, where different sensory attributes of a single object (like color, shape, and motion) are combined into a coherent perceptual entity.

3. Competition for Compatibility(Coherence, Cooperation) of Objects

• • Regional Summation: By summation, neurons firing whether in micro-columns, layers, macro-columns, regions or across regions, where simultaneous signals from multiple sets of neurons in microcolumns and columns combine to influence the firing threshold of other neurons, microcolumns, columns, regions, the brain can integrate signals from diverse sources into a hierarchy of competition for attention between increasingly coherent collections of neurons firing.

4. Competition for Coherence(Cooperation) within a model(representation) of the body and world.

• • Consolidation: Advanced neural circuits, in a vast hierarchy, combining those in the Visual Cortex for processing visual stimuli, the Auditory Cortex for sound, the Entorhinal Cortex for location, space, direction, head direction, eye direction, turning, and speed in relation to environmental markers (landmarks), integrated by a competition for survival of coherence with one another. By consolidating these inputs, extracting patterns, and constructing a detailed, meaningful, and actionable representation of the external world is created.

Summary: ( … ) (this means information is stored as changes over time not states in time. In other words, the brain doesn’t take a photo, but a record of changes, and related changes that compose a fragment of a memory.)

• Response (Amplitude): This information is then used to generate appropriate physiological and behavioral responses to maintain homeostasis. ( … ) (this is not operationally stated)

 

What’s The Result (What Do Individuals and Groups of Neurons Achieve)?

11) Pattern Recognition vs Adaptation(response) vs Learning(prediction): At the level of individual neurons, groups, and even regions, what we observe is more accurately described as pattern recognition and adaptation. These collections of cells are constantly adjusting their responses based on the statistical regularities of the inputs they receive. Adaptation seeks immediate survival and stabilization, while learning incorporates adaptation into higher-level cognitive processes geared toward creating predictive models. 

1. Adaptation for Survival:

   • Basic Function: Adaptation in its most fundamental sense is the process by which organisms adjust to changing environments to maintain homeostasis or improve survivability. This often involves reflexive or instinctual responses, hardwired to maximize an organism’s chances of persisting in its current environment.
   • Short-Term Adjustments: These changes may happen quickly and without much cognitive involvement, as with physiological adjustments to temperature or pressure, or immediate behavioral changes in response to environmental stimuli.

2. Learning for Prediction:

   • Predictive Model Building: Learning, on the other hand, often implies that the organism is developing internal models or associations that allow for predicting future events. This involves creating, refining, and using mental representations to anticipate situations, behaviors, or outcomes based on past experiences.
   • Improving Decision Making: By seeking patterns that reveal underlying regularities, learning aims to improve future response, choice, decision, and action together increasing one’s ability to navigate the environment more effectively, in pursuit of what all action seeks: acquisition of something even if it’s acquisition of time and therefore life by evading a risk or harm.

Learning requires a more complex network of associations, often including cognitive processes where the behavior of an organism changes in a way that reflects acquired knowledge or skills. This is generally associated with systems-level changes across the brain, particularly when these adaptations lead to observable changes in behavior.

Learning then, is a subset of adaptation. Every process that results in learning is fundamentally an adaptive process, but not all adaptations constitute learning in the cognitive or behavioral sense. So “learning” is the outcome of complex interactions within larger networks that not only recognize patterns but also associate them with specific outcomes or contexts. 

What is a Nerve vs a Neuron? Nerves are bundles of the axons of neurons (nerve fibers) grouped together, often including both sensory and motor fibers. They carry the electrical signals produced by the neurons over distances.

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Senses (Systems of Measurement)

How the body organizes measurements and integrates neural activity from a range of related stimuli  (sense) to perceive the state of the body internally and externally in relation to the world.

|Neurological Hierarchy|: Homeostasis(Equilibrium) > Sensation(Neurons) > Measurements(Nerves, Stimuli) > Systems of Measurements(Senses) > Integration of Systems of Measurements(Perceptions) into States(Faculties) > Incentives(valuations) > Consolidation(Model) >  Records(Memory) > Possibilities(Predictions) > Plans(Routes) > [Release of Action(Movement) OR Loop: Recursion(Intelligence to Consciousness)] > Adaptation

Vibration > Sensing > Disambiguating > Organizing > Indexing > Storing > Accessing (stimuli or association) > Reviewing

Measurement, Dimensions, Relations ( … ), in-time,  in hierarchy, so reality is the BASE, and measurements are from competition just like in reality … senses are ,,, something…. (we already did adversarial competition and commesurability so make this point here.)

( … ) IMPORTANT

 

Division of Labor: ( … )

Each sense operates as a specialized measurement system that detects specific types of energy or chemical changes, which are then transduced into electrical signals. These signals provide real-time data about the environment, enabling the brain to create a coherent model of the world. This model is necessary for the organism to make predictions and decisions that facilitate its homeostasis and eventual survival.

External: Sight, Sound, Smell, Touch(Feel), Taste, vs Internal: (a) Pain, Temperature, Condition, (b) Position, Orientation, and Spatial Disambiguation (our unspoken sense that makes action in the world possible). 

While introspection upon most of our senses is either limited or impossible, our ability to discriminate sounds using our auditory senses lets us become aware of how our other senses achieve disambiguation, priority, and attention.

So for example, when we hear conversations in a noisy room we can discriminate (emphasize, de-emphasize) those different sound patterns by competition into the equivalent of visual and tactile objects, so the senses (regional networks specializing in disambiguation and organization of signals that respond to vibrations of different amplitudes and frequencies, in different mediums) organize what may be a nearly infinite  ‘objects’ (patterns) by competition for coherence (survival, and therefore evolutionary computation) into priorities (attention) by suppressing (via inhibitory neural networks) and concentrating (via excitatory networks), directed using attention (thalamus) whether voluntary (prefrontal cortex) or involuntary (reticular activating system).

The Role of Senses in the Neurological Hierarchy:

Senses serve a function in the neurological hierarchy, between external stimuli and the body’s internal regulatory mechanisms. They influence higher cognitive functions by providing the sensory inputs necessary for perception, which are then integrated, valued, and consolidated into memories and predictions, proposing potential actions.

Specialization: Neurons specialize into categories we call the senses.

The Senses: Sensory perception allows the body to detect and respond to changes in stable relations between the body and its environment, as well as within the body itself. By monitoring these changes and responding with appropriate responses, sensory perception maintains homeostasis ensuring the survival of the organism.

• Sensory faculties: The ability to detect and transduce vibrational changes from the environment or the body into electrical signals. This includes the various sensory modalities, such as vision, audition, touch, taste, smell, proprioception, and interoception.
• External vs Internal Sensory Faculties: 
  • Exteroceptive (External) senses: They provide critical data necessary for navigating and interacting with the external world.
    • Vision (sight): The ability to detect and process light and visual information.
    • Audition (hearing): The ability to detect and process sound waves and auditory information.
    • Somatosensation (touch): The ability to detect and process tactile sensations, including pressure, temperature, and pain.
    • Gustation (taste): The ability to detect and process chemical compounds in food and drink.
    • Olfaction (smell): The ability to detect and process chemical compounds in the air.
  • Interoceptive (Internal) senses: They monitor the internal state of the body, from muscle tension and balance to internal organ cues and temperature, enabling the organism to maintain physiological equilibrium.
    • Visceroception(Condition): The ability to sense internal organ states and processes, such as hunger, thirst, and digestive sensations.
    • Thermoception(Temperature): The ability to sense internal and external temperature changes.
    • Nociception(Pain): The ability to detect and process painful stimuli.
  • Spatial-Navigational Senses
    
    • Proprioception(Relative Position of Body Parts): The ability to sense the position, movement, and tension of the body and its parts in space in relation to the direction the torso is facing.
    • Vestibular sense(Balance and Orientation): The ability to sense balance, spatial orientation, and movement of the head.
    • Three-Dimensional Disambiguation (External Orientation): This involves the cognitive processing necessary to interpret and organize visual information into comprehensible structures like objects, spaces, backgrounds, and movements. It underpins our ability to navigate and interact effectively with our environment.

Summary: By synthesizing information from these diverse sensory, using competition for coherence to filter and organize information from these systems, the body produces the information (resources) to produce a coordinated response to both immediate and anticipated environmental changes, thereby optimizing its internal systems for the body’s survival. The integration of sensory data into perceptual models ensures that responses are not only reactive but also predictive, accounting for potential future states based on current sensory inputs.

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