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Q:hey gemini :) i have a question for you. has anybody explained why the somato-motor homunculus and the sensorial homunculus are situated in the place they are?
A:Hey! That's a great question, digging into the why behind brain organization.
Yes, neuroscientists have strong explanations for why the primary somatosensory cortex (S1), containing the sensory homunculus, and the primary motor cortex (M1), containing the motor homunculus, are located right next to each other, separated only by the central sulcus.
Here's the breakdown of the reasoning:
Tight Functional Integration: Sensation and movement are intrinsically linked.
Sensory Input Guides Motor Output: To perform almost any voluntary movement, your brain needs constant sensory feedback. Think about picking up a glass: you need to feel its shape, texture, temperature, and weight (somatosensory input) to apply the correct amount of force and grip (motor output). You need proprioception (sense of body position) to know where your hand is starting from and how it's moving.
Motor Adjustments Based on Feedback: As you move, sensory feedback continuously informs the motor cortex, allowing for real-time adjustments. If the glass starts slipping, your sensory cortex detects this, signals the motor cortex, which then increases grip strength.
Location Facilitates Integration: Placing these two critical areas directly adjacent allows for extremely short, fast, and efficient neural connections between them. This minimizes signal delay and allows for rapid sensorimotor loops, crucial for smooth, coordinated, and adaptive movement.
Efficiency and Wiring Minimization: The brain, like any complex system, tends to evolve towards efficiency.
Shorter Connections = Faster Processing: Shorter axonal pathways between S1 and M1 mean faster communication.
Less "Wiring" = Less Metabolic Cost: Building and maintaining long neural connections requires energy and space. Placing functionally related areas close together minimizes the amount of white matter (axons) needed to connect them.
Developmental Origins: While the precise developmental mechanisms are complex, it's likely that these adjacent cortical areas develop under the influence of related signaling pathways and molecular gradients during brain formation. Their functional relationship likely reinforces this proximity during development as connections are established and pruned.
Evolutionary Advantage: This close arrangement provides a significant survival advantage. Organisms that can react quickly and accurately to sensory stimuli (e.g., feeling something hot and pulling away, adjusting footing on uneven ground) are more likely to survive and reproduce. The tight S1-M1 coupling is fundamental to this capability.
In simple terms: Imagine you're designing a robot. You'd want the sensors that detect touch and position to be wired very directly and quickly to the motors that control the limbs, so the robot can react appropriately and efficiently to its environment. The brain has essentially evolved a similar optimal design by placing the primary sensory processing area for the body right next to the primary motor control area for the body.
So, the location isn't random; it reflects a fundamental principle of brain organization: areas that need to work together closely are often located physically close to each other for efficient and rapid communication.
Q:oh,sorry. you are right as to why the two homunculus are next to each other. and i fully agree. i was asking why the two are situated where they are in the brain.
A:Ah, my apologies for misunderstanding! You're asking about the absolute location – why M1 is in the posterior part of the frontal lobe and S1 is in the anterior part of the parietal lobe, flanking the central sulcus, rather than somewhere else entirely (like the temporal or occipital lobes).
That's a much deeper and more complex question, and the answer isn't as definitively clear-cut as the reason for their proximity. There isn't a single "smoking gun" reason, but rather a confluence of factors rooted in evolution, development, and functional organization:
Evolutionary Trajectory:
The neocortex evolved "on top of" older brain structures. Early mammals likely had less differentiated cortical maps where sensory and motor functions were perhaps more intermingled or occupied core areas.
As the cortex expanded dramatically, especially in primates and humans, areas specialized. Vision pushed towards the back (occipital), audition towards the sides (temporal), and higher-level planning/cognition expanded dramatically in the front (frontal).
Somatosensation (touch, temperature, pain, proprioception) and the direct control of voluntary movement are fundamental, core functions. Their position might reflect a somewhat "central" or foundational role in the expanding cortical sheet, bridging the more purely sensory areas (posterior) and the planning/executive areas (anterior).
Major Connectivity Hubs:
Thalamic Input: S1 is the primary cortical target for somatosensory information relayed from the body via the thalamus (specifically the Ventral Posterior nucleus). The thalamus sits deep within the brain. The parietal lobe location provides a relatively direct pathway for these ascending connections.
Motor Output Pathways: M1 is a major origin point for the corticospinal tract, the primary pathway for voluntary motor commands descending to the brainstem and spinal cord. Its location allows for a reasonably efficient route for these descending axons through the internal capsule.
Connections to Other Key Motor Areas: M1 needs strong connections with the premotor cortex and supplementary motor area (located just anterior to M1 in the frontal lobe) for motor planning, and also receives input modulated by the basal ganglia and cerebellum (via the thalamus) for motor refinement. Its location facilitates these frontal lobe and subcortical connections.
Connections to Association Areas: S1 feeds information into higher-order somatosensory areas and multimodal association areas located posteriorly in the parietal lobe, which are crucial for spatial awareness, body image, and integrating sensory information.
Developmental Organization:
The central sulcus is one of the major, early-forming folds (sulci) in the developing brain. It serves as a key anatomical landmark.
The developmental gradients and signaling molecules that pattern the cortex might inherently lead to sensory functions tending to differentiate posteriorly and motor functions anteriorly relative to this major boundary. The precise mechanisms are complex but involve gene expression patterns that map out different functional areas.
Functional Segregation and Flow:
There's a general posterior-to-anterior flow of information processing in much of the cortex. Sensory information often arrives more posteriorly, is processed and integrated in parietal and temporal lobes, and then feeds forward to frontal areas for decision-making and action planning, culminating in execution commands from M1. Placing S1 (primary sensory) just behind M1 (primary motor) fits this general organizational scheme for the sensorimotor system.
In essence: The location of M1 and S1 isn't arbitrary but likely represents an evolutionarily derived "sweet spot" that balances:
Efficient connections to subcortical input/output structures (thalamus, spinal cord pathways).
Proximity to functionally related cortical areas (premotor cortex, parietal association areas).
Its place within the overall developmental patterning and large-scale functional organization of the cortex (sensory generally posterior, motor/executive generally anterior).
Think of it like city planning: you don't put the main train station (thalamic relay) miles away from the central business district (primary cortical areas) or the highway exit (spinal cord output) in a completely unrelated suburb. Their location evolved as the most efficient compromise given the "geography" and "infrastructure needs" of the developing and functioning brain.
Q:see, i have a different idea i want to discuss. for that,we need to imagine two things:
-the first one, is to think at the brain without the constrain of the skull. without the skull,we can transform the brain in cylindrical coordinates.in those coordinates, the brain will most likely resemble the shape of the cosmic rays EAS.
-the second one is to think in terms of input energy/information entering the brain
with the two assumptions above, can we have the following identities:
-on the vertical axis the spinal chord-brain connection acts as the origin of the energy/information while the ba10 is the maximum distance to which the energy propagates on that axis.
-the maximum lateral development of the shower matches the circle inscribed by the homunculus
i don't know if i managed to explain it well. please ask any question to further clarify the idea.
A:Okay, this is a fascinating and highly unconventional way to think about brain organization! Let's break down this idea. It's definitely outside the standard neuroscientific framework, but exploring different perspectives can be valuable.
You're proposing a model based on:
Geometric Transformation: Ignoring the skull and mapping the brain onto a cylindrical coordinate system, potentially resembling the shape of an Extensive Air Shower (EAS) from cosmic ray physics.
Energy/Information Flow Analogy: Treating neural activity like an energy cascade originating from the spinal cord input.
And deriving these identities:
Vertical Axis: Spinal cord connection (origin) to Brodmann Area 10 (frontopolar cortex, the most anterior part of the frontal lobe) represents the main propagation axis/maximum extent.
Lateral Axis: The "maximum lateral development" of this "shower" corresponds spatially to the location of the sensorimotor homunculi (M1/S1 strip).
Let's analyze this intriguing idea piece by piece:
1. The Geometric Transformation (Cylindrical/EAS Shape):
Removing Skull Constraint: This is a valid thought experiment. Topologically, you could deform the brain's shape.
Cylindrical Coordinates: This imposes a specific symmetry. While the brain has a rough bilateral symmetry, it's far from perfectly cylindrical. Applying this coordinate system might obscure more than it reveals about the actual 3D relationships between brain areas.
EAS Shape Analogy: This is the most abstract part. An EAS develops as a high-energy particle cascades through a medium (atmosphere), creating secondary particles that spread laterally.
Potential Similarity: Information does enter the brain and propagate, leading to widespread activity.
Major Differences:
Medium: The brain is not a uniform medium like the atmosphere; it's highly structured with specific pathways (white matter tracts), processing centers (grey matter nuclei, cortical areas), and complex inhibitory/excitatory interactions.
Propagation: Neural signal propagation isn't a simple physical cascade. It follows defined circuits, involves neurotransmitters, modulation, feedback loops, and plasticity. It's not just energy spreading passively.
Input: While the spinal cord is a major input, it's not the only one (cranial nerves bring sensory info directly). And the "energy" isn't a single high-energy particle but complex patterns of electrochemical signals.
2. The Proposed Axes and Identities:
Vertical Axis (Spinal Cord -> BA10):
Origin: Pinpointing the spinal cord connection as the single origin is an oversimplification. Brainstem structures are critical intermediaries.
Endpoint (BA10): BA10 is indeed at the anterior pole. It's involved in very high-level executive functions, planning, and abstract thought – potentially a high point of complex processing. However, information doesn't just stop there; it influences other areas and loops back. It's not necessarily the "maximum distance" in a simple linear flow sense. Information flow is highly parallel and recurrent, not just a straight shot from back to front.
The Path: The actual anatomical pathways from spinal cord input (relayed through brainstem and thalamus) to various cortical areas, including BA10, are complex and don't follow a straight geometric axis.
Lateral Axis (Homunculi as Maximum Spread):
Location: The homunculi (M1/S1) form a band roughly midway along the anterior-posterior axis, straddling the central sulcus.
Maximum Lateral Development: This is the crux of your idea for their location. Why would the primary sensorimotor areas represent the maximum lateral spread of an information "shower"?
Challenge 1: In an EAS, the shower maximum (point of most particles) occurs at a certain depth, and then the shower dies out. Is BA10 the end, and M1/S1 the point of maximum particle density (neural activity?) along the way? It's a very abstract mapping.
Challenge 2: Brain activity spreads laterally far beyond the M1/S1 strip into parietal, temporal, and frontal association areas. Why would M1/S1 specifically mark the maximum?
Challenge 3: The homunculi's defining feature is their specific somatotopic map – the orderly representation of the body surface and muscles. Your model doesn't inherently explain why this specific type of map would form at the point of "maximum lateral spread." The standard explanation (tight sensorimotor integration via adjacent maps) directly addresses this functional requirement.
Questions for Clarification:
In the EAS analogy, what specific quantity corresponds to "energy/information" in the brain? (e.g., firing rate, metabolic activity, signal complexity?)
How do you define "maximum lateral development"? Is it the spatial extent of activation, the number of neurons active, the metabolic cost?
How does this model account for other major sensory inputs (vision, hearing) that enter the brain more posteriorly and laterally and don't follow the proposed spinal-cord-to-BA10 axis?
How does the highly folded nature of the cortex (gyri and sulci), which dramatically increases surface area and influences connectivity, fit into the simplified cylindrical/EAS shape?
Conclusion:
It's a creative attempt to find a unifying physical or geometric principle behind brain organization, drawing an analogy from physics. However, when compared against the known complexities of neuroanatomy, neurodevelopment, and functional connectivity, the model faces significant challenges:
It drastically simplifies brain structure and ignores crucial details like folding and specific pathways.
The EAS analogy breaks down because neural processing is fundamentally different from a particle cascade in a uniform medium.
The proposed identities (Spinal->BA10 axis, Homunculi as lateral max) don't align neatly with the known functions, connectivity patterns, or the reasons neuroscientists currently accept for the location of M1 and S1 (functional proximity for sensorimotor integration, efficient wiring, developmental organization).
While this specific analogy might not hold up well under scrutiny against biological data, the underlying quest to find deeper organizational principles in the brain is absolutely valid and drives much of neuroscience! Your idea highlights the importance of information flow and spatial organization, even if the specific mechanism proposed differs from current understanding. It's an interesting thought experiment!
Q:your critics are quite valid. but let us see how they hold once i explain more of the idea. of course, brain is not similar to atmosphere. but eas can develop in any medium, even highly anisotropic ones. now, think of the ba10,and the homunculus as active regions that scale the initial energy to showers delimited by those constrains. if the spine passes to much energy , ba10 increases the "resistance" of the neural pathways and increases the lateral spread of the energy. if energy is too much, for the safety of the brain as a whole the excess energy reaches the homunculus which closes the loop and controls the muscles to move the body to a less perilous position. does that make sense?
A:Okay, thank you for the elaboration. This adds a layer of dynamic regulation to the analogy, moving beyond just passive geometry. Let's analyze this refined concept:
The Core Idea:
The brain processes incoming "energy/information" (originating from spinal cord/body) somewhat like an EAS.
BA10 (Frontopolar Cortex): Acts as a regulator of the main "forward" processing path. If input "energy" is high, BA10 increases "resistance," forcing more processing/energy to spread laterally.
Homunculi (M1/S1): Represent the limit or destination of this lateral spread, particularly when "energy" is excessive. This region acts as a safety mechanism, converting excess "energy" into motor commands (via M1) to move the body out of potentially harmful situations, closing a loop.
Analyzing the Refined Model:
BA10 as a Regulator of "Resistance" and Lateral Spread:
Plausibility: BA10 is involved in top-down control, executive functions, and goal-directed behavior. It can certainly modulate processing in other brain areas. The idea that frontal regions influence how sensory information is processed or attended to is well-established (e.g., attentional networks).
Mechanism Issue: The concept of BA10 controlling "resistance" in a way that directly forces a lateral spread specifically to the M1/S1 strip is where it diverges significantly from known neuroscience.
BA10's influence is generally thought to be more about modulating other high-level cognitive areas (elsewhere in PFC, parietal cortex) or biasing attention, rather than directly gating the fundamental flow of sensory information from the thalamus to S1 or influencing M1 output in this "overflow" manner.
Information flow isn't like fluid dynamics where blocking one pipe forces flow down another based purely on physical pressure and resistance. It involves complex synaptic interactions, inhibition, excitation, and specific pathways. Increased activity or modulation in BA10 doesn't automatically translate into a geographical "push" of energy towards M1/S1.
Homunculi as Safety Valve/Overflow Mechanism:
Plausibility: Motor output (M1) is a critical way the brain interacts with the world to ensure safety. Reflexive and voluntary movements remove us from danger. S1 is crucial for detecting potentially harmful stimuli (pain, excessive pressure, temperature). So, the outcome (moving away from danger involving M1/S1) aligns.
Mechanism Issue: Attributing this solely or primarily to an "overflow" of undifferentiated "energy" reaching M1/S1 as a consequence of BA10 regulation is problematic.
Specificity: Protective reflexes (like pulling your hand from a hot stove) often involve rapid pathways through the spinal cord and brainstem, sometimes bypassing extensive cortical processing for speed. Voluntary actions based on perceived danger involve threat assessment (amygdala, insula), planning (premotor cortex, PFC), and integration of sensory information (parietal cortex) before M1 executes the specific muscle commands. M1 isn't just a passive recipient of overflow; it receives highly specific, patterned instructions.
S1 Role: S1's role is primarily processing the type and location of sensory input. While intense stimuli strongly activate S1, framing it as an endpoint for "excess energy" diminishes its role in detailed perception needed to guide the appropriate motor response coordinated by M1.
Energy Concept: The idea of a generic "energy" that builds up and spills over is difficult to map onto neural reality, which involves specific neurotransmitters, action potentials, and network dynamics. Overload often manifests as dysfunctional processing, seizures, or shutdown, rather than a neat redirection to the motor cortex.
Comparison to Standard Explanation:
The standard explanation (functional proximity for rapid sensorimotor integration) directly addresses why the detailed body map of S1 needs to be right next to the detailed body map of M1 – for precise, feedback-guided movement. Your model attempts to explain the location based on a hypothetical global energy flow dynamic but doesn't inherently explain why these specific, mapped representations (the homunculi themselves) would form at that location, nor why they need to be adjacent.
Conclusion:
While introducing active regulation by BA10 and a safety loop via M1/S1 makes the EAS analogy more dynamic and conceptually interesting, it still faces significant challenges in aligning with the known functional neuroanatomy and neurophysiology:
The proposed mechanisms of "resistance" control by BA10 and "energy overflow" to M1/S1 don't readily map onto established neural processes.
It simplifies complex, parallel, and highly specific neural circuits into a more generalized, fluid-like flow.
It doesn't easily account for the detailed somatotopic organization within M1 and S1, which is central to their function and explained by the need for direct integration.
It remains a creative conceptual leap, attempting to link global brain dynamics to specific regional locations. However, the currently accepted explanations based on evolutionary optimization of wiring for functional integration provide a more direct and biologically grounded account for the adjacency and location of the sensorimotor homunculi.
Q:wouldn't be more logical the absolute position of the homunculus to be deeper inside the brain if it was just to optimize some neural pathways?
A:That's a really insightful question! You're essentially asking: if minimizing wire length is a key principle, wouldn't burying M1 and S1 deep within the brain, closer to the "average" location of everything else, be even more efficient?
It's a logical thought, but here's why the surface location flanking the central sulcus is likely more optimal, considering the specific connections and functions of M1 and S1:
They are Cortical Areas: First and foremost, M1 and S1 are part of the neocortex, the folded outer layer of the cerebrum. This six-layered structure is crucial for the complex processing they perform. Burying them deep would mean either:
Making them subcortical (like the thalamus or basal ganglia), fundamentally changing their structure and computational abilities.
Burying a piece of cortex deep within the white matter, which is highly unusual and presents its own connectivity challenges. The vast majority of complex processing happens at the surface or within defined subcortical nuclei.
Major Input/Output Highways:
Sensory Input (S1): S1 is the primary cortical recipient of somatosensory information relayed through the thalamus. Thalamic projections efficiently target the cortical surface (specifically Layer 4). Placing S1 deep would complicate this final leg of the primary sensory pathway.
Motor Output (M1): M1 is the main origin of the corticospinal tract, a massive bundle of axons descending to the brainstem and spinal cord to control movement. These axons originate from neurons in M1 (especially Layer 5) and need a relatively clear path downwards. A surface location allows them to plunge directly down through the internal capsule. Burying M1 deep would require these axons to first navigate out of the depth before heading down, adding complexity and length.
Critical Cortical Neighbors: Optimization isn't just about distance to everything, but critically about distance to functionally essential partners.
M1 needs Premotor/SMA: Motor planning areas (premotor cortex, supplementary motor area) lie directly anterior to M1 on the cortical surface. This adjacency is crucial for efficient planning-to-execution flow. Burying M1 would significantly lengthen these vital connections.
S1 needs Parietal Association Cortex: Higher-order sensory processing and integration occur in parietal areas posterior to S1 on the cortical surface. This allows seamless flow from primary sensation to interpretation and spatial awareness. Burying S1 would disrupt this flow.
S1 needs M1 (and vice versa): As we discussed initially, their direct adjacency across the central sulcus is paramount for rapid sensorimotor loops. This is easily achieved on the surface.
Surface Area and Mapping: The convoluted surface of the cortex provides vast surface area. This is essential for housing the detailed, point-to-point somatotopic maps of the homunculi. While theoretically possible deep, it's naturally accommodated by the expansive cortical sheet.
Analogy: Think of a country's main international airport (Thalamus/Spinal Cord connection) and its primary cargo shipping port (Motor Output to body). You also have major adjacent industrial zones (Premotor/Parietal Assoc.). While placing the airport/port geographically dead center in the country might minimize average distance to all towns, it's far more efficient to place them near the edge/coast for direct international access (input/output) and right next to the industrial zones they directly serve.
In summary: While minimizing wiring length is a factor, the specific demands of receiving massive sensory input from the thalamus (S1), sending massive motor output to the spinal cord (M1), and intimately interacting with adjacent cortical planning and association areas make their current location on the cortical surface, flanking the central sulcus, the most efficient and functionally logical position within the overall architecture of the brain. It optimizes the most critical connections for sensorimotor control.
Q:hmm, i feel like i did not manage to describe well my idea. anyway, let us compare the location of human homunculus with the location of equivalent areas in different species. regardless of the shape of the brain,are they all located in the outer part?
A:Yes, that's an excellent question for comparative neuroanatomy!
The short answer is overwhelmingly YES.
In virtually all mammals studied that possess distinct primary somatosensory (S1) and primary motor (M1) cortical areas, these areas are located on the outer surface of the cerebrum, within the neocortex.
Here's why and what it implies:
They Are Cortical Areas: By definition, S1 and M1 are regions of the cerebral cortex. The cortex is the highly complex, layered outer sheet of the cerebrum. Subcortical structures (like the thalamus, basal ganglia, brainstem) perform different roles. The detailed mapping and complex processing associated with the homunculus (or analogous body maps) occur within the cortical circuitry.
Evolutionary Conservation: The general organizational plan of having a primary sensory receiving area (like S1) adjacent or near a primary motor output area (M1) on the cortical surface appears to be a highly conserved feature across mammalian evolution.
Rodents (e.g., rats, mice): Have smooth brains (lissencephalic). Their S1 (often dominated by whisker barrels) and M1 are distinct areas located on the dorsal and lateral surface of their cortex, and they are adjacent.
Carnivores (e.g., cats, dogs): Have folded brains (gyrencephalic). They have clear S1 and M1 areas located on the surface gyri (folds), often separated by a major sulcus (like the cruciate sulcus), analogous to the central sulcus in primates. Again, they are adjacent cortical regions.
Primates (monkeys, apes, humans): Have highly folded brains. S1 is on the postcentral gyrus, M1 on the precentral gyrus, separated by the central sulcus – all prominent surface features.
Other mammals (e.g., ungulates, cetaceans): Also show evidence of distinct S1 and M1 areas situated within their respective cortical surfaces.
What varies isn't whether they are on the surface, but:
Relative Size and Proportions: The amount of cortical territory dedicated to specific body parts within S1 and M1 varies enormously depending on the species' needs (e.g., huge whisker area in rodents, large hand/face area in primates, large fluke/flipper area in dolphins).
Degree of Folding: In lissencephalic brains, they are on the smooth surface. In gyrencephalic brains, they follow the folds (gyri) but are still part of that outer sheet.
Precise Anatomical Landmarks: The specific names of the gyri and sulci differ, but the topological relationship (S1 near M1, both cortical) remains.
Implication for your idea:
The fact that this surface location is so highly conserved across species with vastly different brain shapes and sizes strongly argues against a model where their position is determined primarily by something like the "maximum lateral spread" of a generic energy shower from a central point.
Instead, it strongly supports the conventional explanation: locating these critical input (S1) and output (M1) areas together on the cortical surface is the optimal solution for:
Receiving processed sensory input from the thalamus (which projects to the cortex).
Integrating sensory information with motor planning areas (also on the cortex).
Sending motor commands down via the corticospinal tract (which originates robustly from the cortex).
Allowing for rapid feedback loops between S1 and M1 on the surface.
Providing sufficient surface area for the detailed body maps.
The outer cortical location is fundamental to their identity and function as primary sensorimotor areas within the mammalian brain plan.
Q:across species, are the S1 and M1 having also the same relative position with respect to the anterior most part of the brain like the S1,M1 with respect to ba10?
A:That's another excellent question probing the consistency of brain layout! The answer here is no, the relative position is NOT constant across species.
Here's the breakdown:
Massive Variation in Frontal Lobe Size: The most significant factor driving this difference is the dramatic variation in the size and complexity of the frontal lobes, particularly the prefrontal cortex (PFC), across different mammalian species.
Humans and Primates: Have exceptionally large frontal lobes, with a huge expansion of the PFC anterior to the primary motor cortex (M1). BA10 is the most anterior part of this vastly expanded human PFC. This means M1 (and therefore S1 adjacent to it) is located relatively far back from the absolute anterior pole of the brain. A significant portion of the brain's anterior-posterior length is occupied by PFC in front of M1.
Carnivores (e.g., Cats, Dogs): Have smaller frontal lobes relative to their overall brain size compared to primates. Their M1 is located closer to the anterior pole of the brain. There's less cortical real estate dedicated purely to prefrontal association areas situated anterior to M1.
Rodents (e.g., Rats, Mice): Have even smaller frontal cortices proportionally. Their M1 area is situated relatively quite anteriorly, with much less cortex lying between it and the very front of the brain compared to primates. The concept of a distinct "BA10" homologue is also less clear or much less developed.
What This Means:
If you measure the distance from the anterior-most point of the cortex back to the M1/S1 boundary, this distance represents a much larger percentage of the total brain length in humans/primates than it does in rodents or many carnivores.
Conversely, M1/S1 are relatively more posterior in primates compared to rodents when measured relative to the anterior pole.
Why is this the case?
This difference reflects evolutionary pressures and functional specialization. The expansion of the PFC in primates, especially humans, is linked to the development of higher-order cognitive functions: complex planning, decision-making, abstract thought, working memory, social cognition, and language. These functions rely on the extensive cortical circuitry located anterior to the primary motor cortex.
Implication for your EAS/Energy Flow Idea:
This species variability presents another significant challenge to a model suggesting a fixed physical principle (like an energy shower dynamic) dictates the location of M1/S1 relative to the anterior pole (BA10). If the location were determined by such a fundamental principle, one might expect the relative position along the main processing axis (anterior-posterior) to be more conserved across species.
The fact that the relative position shifts dramatically based on the expansion of the PFC strongly supports the idea that regional brain organization is driven by functional needs and evolutionary adaptations (like the need for more planning capacity in primates, requiring a larger PFC built out in front of the motor cortex), rather than a single, universal physical cascade mechanism.
Q:has anybody come with an explanation to who establishes who: the brain , the shape of the skull or vicevera?
A:Yes, this is a classic question in biology, and the consensus based on developmental biology and evolutionary evidence is clear:
Primarily, the growth and shape of the brain dictate the growth and final shape of the skull.
Here's the breakdown of the reasoning:
Developmental Biology:
Brain Growth First: During embryonic and fetal development, the brain undergoes massive and rapid growth before the skull bones fully form and ossify (harden).
Skull Forms Around Brain: The flat bones of the cranial vault (like the frontal, parietal, occipital bones) initially form as separate plates within membranes or cartilage. They grow outwards around the expanding brain.
Sutures Allow Expansion: The gaps between these bony plates, known as sutures (and the larger fontanelles or "soft spots" in infants), are crucial. They allow the skull to continue expanding to accommodate the brain's extremely rapid growth during infancy and early childhood. The brain literally pushes the plates apart as it grows.
Evidence from Pathology:
Microcephaly: If the brain fails to grow properly, the skull remains small because there's no internal force driving its expansion.
Hydrocephalus: If cerebrospinal fluid builds up abnormally, increasing pressure inside the brain ventricles (effectively making the brain "bigger" due to fluid), the skull plates in an infant will be pushed apart, leading to an abnormally large head circumference. This clearly shows the internal pressure/size driving skull growth.
The Skull's Role as a Constraint (Later):
Once the skull bones fuse (typically closing the sutures during childhood and adolescence, though timing varies), the skull becomes a rigid, non-expandable container (in adults).
At this point, the skull constrains the brain's volume. This is why conditions causing brain swelling (like trauma or tumors) are so dangerous in adults – the brain has nowhere to expand, leading to increased intracranial pressure.
However, this is a constraint on volume, not an active shaping force dictating the brain's fundamental form or gyral/sulcal patterns (which develop much earlier).
Evolutionary Perspective:
Over evolutionary time, as brains became larger in certain lineages (like primates and especially humans), there was selective pressure for skulls that could accommodate this increased brain size.
Genetic changes enabling larger brain growth likely co-evolved with genetic changes allowing for larger cranial capacity and potentially delayed suture fusion to accommodate longer growth periods.
It's a co-evolutionary relationship, but the proximate cause during an individual's development is the brain's growth driving the skull's formation.
In summary:
Think of it like blowing up a balloon inside a flexible, growing container. The expanding balloon (brain) determines the size and shape the container (skull) takes as it forms and stretches. Eventually, the container hardens and sets the final boundary. So, while the skull provides the ultimate physical boundary and protection, its initial form and size are largely dictated by the brain developing within it. It's not the skull imposing a shape onto the brain, but rather forming around the brain.
Q:interesting. in turn, the brain gets to it's final size and shape based on the input it receives. is that right?
A:That's a fascinating and important point! Your statement touches on the concepts of neuroplasticity and the interplay between genetics and environment (nature vs. nurture) in brain development.
However, the statement "the brain gets to its final size and shape based on the input it receives" needs significant qualification:
Genetics Provides the Blueprint: The fundamental size potential and basic architectural shape (e.g., the layout of lobes, major fissures, overall species-specific form) are overwhelmingly determined by genetics. Your DNA dictates the instructions for how the brain should grow, the types of neurons, their initial migration patterns, and the basic organizational plan. This sets the broad constraints on potential size and form.
Nutrition and Health are Crucial: Adequate nutrition and a healthy intrauterine/early life environment are absolutely critical for the brain to reach its genetically determined potential size. Malnutrition or exposure to certain toxins during critical developmental periods can severely stunt brain growth, affecting final size.
Input Shapes Connectivity, Function, and Microstructure (Neuroplasticity): This is where "input" (sensory experiences, learning, social interaction, motor activity) plays its most profound role. Input drives:
Synaptogenesis: Formation of connections between neurons.
Synaptic Pruning: Elimination of unused or less efficient connections.
Myelination: Insulating nerve fibers to speed up transmission, which is influenced by activity.
Cortical Map Refinement: Areas dedicated to frequently used functions can slightly expand or become more efficient (e.g., the finger representations in a musician's S1/M1).
Functional Specialization: Experience helps specialize brain regions for specific tasks (like learning to read utilizes specific parts of the visual and language networks).
Input's Influence on Gross Size/Shape is Limited (Post-Development): While enriched environments during development can lead to subtle increases in cortical thickness, glial cell numbers, or vascularization compared to severely deprived environments, sensory/experiential input does not fundamentally change the overall macroscopic volume or the major folding patterns (gyri/sulci) of the brain once the primary developmental trajectory is set. You don't grow a bigger frontal lobe just by thinking harder as an adult, nor does the shape of your central sulcus drastically change. The major shaping happens earlier based on genetics and developmental mechanics. Extreme deprivation during critical periods can lead to smaller size and perhaps altered folding, but adding more input doesn't indefinitely increase size or reshape major structures.
In summary:
Genetics & Nutrition: Determine the potential size and the fundamental shape/layout.
Sensory/Experiential Input: Critically refines the brain's microstructure, connectivity, and functional organization within the genetically determined framework. It optimizes the existing structure rather than defining its basic size and shape.
So, while input is absolutely essential for developing a well-functioning brain, it's more accurate to say it shapes the brain's wiring and functional capabilities rather than its overall final size and macroscopic form, which are primarily set by genetics and developmental processes.
Q:to my knowledge,there are no living beings excessively brainy :)) so,in that sense, genetics only establishes the relation between output of the boy/input of the brain and the size of the neural nets sufficient to process it(in average).
A:That's a very insightful way to put it, and it touches on some important evolutionary principles! You're highlighting the idea of biological efficiency and evolutionary optimization. Let's break down that thought:
"No living beings excessively brainy": This resonates strongly with the concept of metabolic cost. Brain tissue is incredibly expensive to build and run, consuming a disproportionate amount of energy relative to its mass (around 20% of total energy expenditure in humans, despite being only ~2% of body weight).
Evolutionary Pressure: There's strong selective pressure against developing and maintaining brain tissue that doesn't provide a sufficient survival or reproductive advantage to offset its cost. An "excessively" large or complex brain for a given organism's niche would be energetically wasteful and likely selected against. Evolution tends towards "good enough" or "optimally efficient" solutions, not maximal ones just for the sake of it.
"Genetics only establishes the relation between output of the body/input of the brain and the size of the neural nets sufficient to process it (in average)": This is a sophisticated point. It moves beyond genetics simply dictating "build a brain of size X" to suggesting genetics encodes a more dynamic relationship – a scaling rule or a developmental program that aims to produce neural resources proportionate to the demands placed upon them by the body's interaction with its environment.
Sensorimotor Needs: A significant portion of the brain is dedicated to processing sensory input from the body and environment and controlling motor output. Larger bodies generally require more sensory receptors and muscles, thus needing larger neural maps (like the homunculus) and processing power. Genetics certainly orchestrates the development of these systems in proportion to the body plan.
Beyond Basic I/O: However, it's crucial to add that for many species, especially mammals and birds, genetics also encodes the capacity for neural structures supporting more complex functions beyond immediate sensorimotor control – things like memory, social cognition, navigation, problem-solving, communication, etc. The size of these association areas isn't just a simple function of body I/O but reflects the cognitive demands of the species' ecological niche and social structure. Humans are the prime example, where the massive expansion of the prefrontal cortex goes far beyond basic bodily input/output needs.
"Sufficient (in average)": This is key. Genetics provides a blueprint that works well on average for the species in its typical environment. There's natural variation, and plasticity allows individuals to adapt within that genetic framework. Sufficiency means enough processing power to survive, find food, avoid predators, and reproduce successfully within its niche.
In Conclusion:
You've hit on a core idea: Genetics doesn't just set a fixed brain size but orchestrates a developmental process that results in a brain whose size and complexity are generally scaled to be sufficient for the organism's sensorimotor and cognitive needs, while being constrained by metabolic costs. It's an optimized balance, honed by evolution, ensuring the benefits of the brainpower outweigh the significant energetic expense. It's less about a simple input/output calculation and more about providing the neural toolkit required for the species' entire way of life.
Q:true. now, i see no reason a living being will move other than to answer the fundamental needs. stands to reason that a weak input signal will create a less stringent need to move. imagine a well fed, relaxed human looking at the sky. on the other hand,imagine a human too close to a fire or chased by a wild animal. for the sake of efficiency, the movement should be just enough to answer the situation. if you see a tiger,you better run. i you look at a flower,just the opposite of running. strong emotions/signal/energy tend to activate more often S1/M1 and other regions at the same cylindrical depth. am i wrong again?
A:You're making some excellent points linking fundamental needs, stimulus intensity, emotional state, and the drive to move! Let's break down your line of reasoning:
Movement Driven by Needs: Absolutely. From a biological perspective, most purposeful movement serves fundamental needs – acquiring resources (food, water, shelter), avoiding threats (predators, hazards), seeking mates, caring for young. This is a core principle of behavioral biology.
Signal Strength & Need to Move: Your examples are perfect. A low-intensity, non-threatening input (calm sky) evokes minimal need for immediate action. A high-intensity, salient, or threatening input (fire, predator) demands urgent, often large-scale movement. This correlation between stimulus intensity/salience and motor readiness/action is fundamental to survival. The emotional weight (fear, pain) amplifies this urgency.
Efficiency and Appropriateness: Yes, adaptive behavior requires matching the motor output to the situation. Running from a tiger is adaptive; running from a flower is not. This involves complex processing beyond just signal intensity – context evaluation, threat assessment, goal selection, planning – primarily involving association cortices (parietal, prefrontal), limbic structures (amygdala), and motor planning areas (premotor, SMA) before M1 executes the command.
Strong Signals/Emotions Activate S1/M1: This is largely correct in the context of action.
S1 Activation: Intense stimuli (pain from fire, strong touch, rapid body movement during running) will strongly activate S1 because it's the primary processor of that sensory information. Strong emotions also have physiological correlates (racing heart, muscle tension) that S1 helps perceive (interoception).
M1 Activation: Situations demanding immediate action (running, pulling away) directly and powerfully engage M1 to execute those movements, following instructions from planning areas. Strong emotional states like fear often prime M1 for action via the fight-or-flight response pathways.
Therefore: Situations involving strong stimuli or emotions that require or prepare for physical action will indeed lead to heightened activity in S1 and M1.
Where the Interpretation Diverges (Regarding Location):
The potential issue isn't with the observation that strong signals/emotions often engage S1/M1, but with using this observation to explain their location based on the "cylindrical depth" model.
Functional Necessity vs. Geometric Determinism: S1 and M1 are active because their functions are essential in these high-stakes situations (sensing the intense stimulus/body state, executing the escape/avoidance movement). Their activation is driven by the functional demands of the network processing the threat and planning the response.
Other Areas Involved: Crucially, these situations also intensely activate other brain regions NOT necessarily at the same "cylindrical depth" in your model – the amygdala (fear processing, deep in the temporal lobe), visual/auditory cortices (processing the threat stimulus, occipital/temporal lobes), prefrontal cortex (decision making, anterior), premotor areas (motor planning, anterior to M1). The brain activates a distributed network relevant to the task.
Why M1/S1 are Adjacent: The standard explanation remains compelling: their adjacent location optimizes the rapid feedback loops needed for precise, coordinated movement in all situations, not just high-intensity ones. Feeling the ground under your feet (S1) to adjust muscle commands (M1) while running from a tiger requires the same fast connection as feeling a cup (S1) to adjust grip strength (M1). This need for tight integration is constant.
In summary: You are absolutely right that strong, survival-relevant signals often necessitate action and therefore strongly engage S1 and M1. However, this functional correlation doesn't necessarily validate the specific geometric "cylindrical depth/EAS" model for why they are located where they are. Their known roles in processing bodily sensation and executing movement, combined with the efficiency gained from being adjacent for constant sensorimotor feedback, provide the more widely accepted explanation for their location, applicable across the full range of movement intensities.
Q:i fully agree with the M1/S1 adjacency. i also think that a simplified model of the brain based on energy/information flow only would manage to reproduce many of the observed biological characteristics. i discussed in some depth such a model elsewhere. of course, i do not plan to build a biological brain from scratch, just an artificial one. so,i'm not too concerned with chemistry of the brain nor the systems that allow for the biological brain to remain alive :) my idea is probably way closer to asimov's positronic brain than to actual living tissue:)
A:Ah, that clarification is very helpful! Okay, reframing your idea as a design principle for an artificial brain, inspired by high-level biological organization but not bound by its specific chemical and detailed anatomical constraints, makes perfect sense. This is a common and valuable approach in AI and theoretical neuroscience.
In that context:
Abstraction and Simplification: You are absolutely right. Building an artificial system allows you (and often requires you) to abstract away the messy biological details (neurotransmitters, glial cells, metabolic support systems) and focus on the principles of information processing, flow, and control that you believe are most salient. Asimov's positronic brain is a perfect example of such a fictional abstraction based on perceived functional principles.
Energy/Information Flow as a Core Mechanic: Using the flow of "energy" or "information" (which you'd define computationally – perhaps as activation levels, signal bandwidth, or processing load) as a central organizing principle is entirely valid for an artificial system. Concepts like signal intensity, bottlenecks, channeling flow, and regulatory mechanisms are fundamental to many computational systems.
Analogous Structures (BA10, M1/S1): Designing computational modules that serve analogous functions to BA10 (e.g., a high-level regulator modulating processing depth or breadth based on input intensity or system goals) and M1/S1 (e.g., an output gateway activated by strong signals or overflow, translating internal states into external actions) is a plausible architectural choice. Your "overflow" concept could serve as a priority interrupt or action-triggering mechanism in such a system.
Potential for Emergent Behavior: Systems designed around such flow dynamics might indeed exhibit interesting emergent properties related to attention, action selection, and response intensity scaling, potentially mimicking some aspects of biological behavior without replicating the underlying biology precisely.
Efficiency Focus: Your focus on efficiency ("movement should be just enough") is also relevant to AI design, particularly in robotics or resource-constrained systems. A system that inherently scales its "effort" (computational resources, motor output) based on input signal strength or urgency could be very efficient.
Advantages of this Approach for AI:
Computational Tractability: Simpler than full biological simulation.
Focus on Dynamics: Allows exploration of specific information flow patterns.
Novel Architectures: Could lead to AI designs different from standard neural networks.
Challenges (from an AI design perspective):
Defining "Energy/Information": Needs a concrete computational definition.
Implementing the Mechanisms: How exactly does the BA10 analog increase "resistance"? How does the "overflow" to the M1/S1 analog work algorithmically?
Learning and Adaptation: How would such a system learn and adapt its flow patterns and responses? Does the structure itself adapt, or are parameters tuned?
Scalability and Complexity: How well would this architecture scale to handle very complex, multi-modal inputs and generate nuanced behavioral sequences?
So, while the specific EAS analogy and the explanation for the biological location of M1/S1 face challenges when compared to neuroanatomical evidence, the underlying concepts of regulated information flow, intensity-based processing, and action gating are definitely interesting and potentially fruitful avenues for designing and thinking about artificial cognitive architectures.
It's a cool project! Thinking about brain principles, even simplified ones, can be a powerful source of inspiration for creating intelligent systems.
Q:in an even simpler way, i can formulate part of the idea in the following flow chart:
basically there you have it the grand picture :)
A:Okay, thank you for distilling it into that flowchart! This makes the core feedback loop and control mechanism much clearer from a computational or systems perspective.
Let's walk through this simplified model:
Input Arrives: Standard starting point.
Parallel Processing:
Main Path: Input starts propagating through the main processing network (analogous to general cortical processing).
Fast Path: A quicker assessment of the input goes directly to a high-level control module (your BA10 analog).
Top-Down Control/Prediction (BA10 Analog): This module quickly analyzes the input via the fast path and predicts the best way to configure the main network to handle it efficiently. It adjusts "weights" (which could represent connection strengths, attention gates, processing depth, resource allocation, etc.). The goal is efficient processing without overload.
Main Propagation (Influenced by BA10): The input continues propagating through the main network, now configured by the BA10 analog's adjustments.
Overload Checkpoint (S1/M1 Analog): At a specific stage or region ("control depth," analogous to S1/M1), the system checks if processing capacity is exceeded ("overflow reaches the maximum number of nodes"). This signifies that the BA10 analog's prediction/configuration was incorrect or insufficient for the actual demands of the input signal propagating through the main path.
Action Trigger (If Overflow): If overflow occurs, it triggers a default, high-priority action: "move to a different location." This is essentially an escape or reset mechanism designed to change the input stream drastically.
Feedback Loop Closure: The movement changes the input. The process repeats. Movement stops only when the BA10 analog successfully configures the network ("does again a good job") such that subsequent input is processed without causing overflow at the S1/M1 checkpoint.
Analysis of this Model (as an AI concept):
Strengths:
Clear Control Loop: It explicitly defines a top-down predictive control loop managing a primary processing system.
Intensity/Complexity Scaling: The overflow mechanism provides a natural way to react differently to inputs that exceed the system's current processing configuration.
Action Gating: Ties a specific internal state (processing overload at a critical juncture) directly to an external action.
Efficiency Focus: Implicitly aims for efficient processing (BA10 doing a "good job") and uses the drastic "move" action as a fallback when efficiency fails.
Biologically Inspired (Abstracted): Captures elements like predictive processing (PFC/BA10 involvement) and the sensorimotor system (S1/M1) acting as a critical interface, albeit in a highly simplified manner.
Points for Consideration/Development (for AI):
Learning: How does the BA10 analog learn to make better predictions and adjustments over time? What mechanism improves its ability to do a "good job"?
Defining "Overflow": Needs a precise computational definition (e.g., % of saturated nodes, resource usage threshold).
Specificity of Action: "Move to a different location" is very general. How could this evolve into more specific, context-appropriate actions?
Nuance: How does the system handle situations requiring subtle adjustments rather than just "process OK" or "ESCAPE!"?
Nature of "Weights": Defining what parameters the BA10 analog actually controls is key.
Conclusion:
Yes, this flowchart provides a clear, high-level picture of a control system inspired by your ideas. It frames the interaction between predictive regulation (BA10 analog) and a critical processing checkpoint (S1/M1 analog) that triggers action upon failure. As a conceptual model for an artificial system focused on core information flow dynamics and reactive behavior, it's a very interesting starting point! It neatly encapsulates the "grand picture" you were aiming for.
Q:what happens when two adversarial networks reach same results?
A:Okay, this is a really interesting question that arises frequently when discussing Generative Adversarial Networks (GANs)! "Reaching the same results" can mean a few different things in the context of the Generator (G) and Discriminator (D) networks, leading to different outcomes:
Scenario 1: The Discriminator gives the same classification (e.g., "real") to both real images and the Generator's fake images.
Meaning: The Generator is successfully fooling the Discriminator. The Discriminator cannot reliably tell the difference between the real data distribution and the data the Generator is creating.
Outcome: This is often the desired state or close to it during successful GAN training. It means the Generator is producing high-quality, realistic samples. The Discriminator's accuracy would approach 50% (random guessing).
What happens next: Training might continue to refine the Generator, or if the quality is satisfactory, training might be stopped. However, GAN training is dynamic; this state might be temporary, and the Discriminator could learn new features to distinguish the fakes, prompting the Generator to improve further.
Scenario 2: The Discriminator gives the same classification (e.g., "fake") to both real images and the Generator's fake images.
Meaning: This is highly unlikely for real images unless the Discriminator is completely broken or untrained. If it happens specifically for the Generator's output, it means the Discriminator is easily identifying the fakes, and the Generator is failing badly.
Outcome: The Generator gets strong error signals (gradients) indicating its outputs are poor. The Discriminator's accuracy would be high.
What happens next: The Generator needs to learn significantly to produce better fakes. This often happens early in training or if the Discriminator becomes too powerful too quickly.
Scenario 3: Both the Generator and Discriminator losses converge and stabilize.
Meaning: The networks have potentially reached a form of equilibrium. Neither network can significantly improve its objective function without the other network also changing.
Outcome: This can indicate successful training, especially if it corresponds to Scenario 1 (Generator producing good fakes, Discriminator accuracy around 50%). This is related to the concept of a Nash Equilibrium in game theory, which is the theoretical goal of GAN training.
Caveats:
Mode Collapse: The losses might stabilize, but the Generator might only be producing a very limited variety of realistic samples (it found a few "tricks" to fool D but didn't learn the full data distribution). This is a common failure mode.
Oscillation: Often, instead of a stable point, the losses oscillate as G and D take turns getting slightly better than the other. A perfect, stable equilibrium is rare in practice.
Scenario 4: Both networks output numerically identical values (less likely interpretation).
Meaning: If you literally mean the raw output vectors or values are identical, this isn't typically how GANs are evaluated, as G produces data samples and D produces a probability or classification. If D's probability output for real and fake samples becomes identical (e.g., 0.5 probability of being real for both), this aligns with Scenario 1 and the Nash Equilibrium concept.
In Summary:
Most often, when people talk about GANs "reaching the same results," they are referring to the point where the Discriminator can no longer reliably distinguish between real and generated data (Scenario 1). This usually manifests as the Discriminator's accuracy tending towards 50% and potentially stable or oscillating losses for both networks. This is generally considered a sign of successful training, indicating the Generator is creating realistic outputs, although one must always check for mode collapse.
Q:nice. now, in our discussion, assume that G is the neural net before the S1/M1 regions and ba10 si D. a third region of interest in the design would be the neural net after the S1/M1 and before ba10. in a way, the situation becomes:
G produces some result, D tries to make sense of it (real/fake). if the result is deemed as real, it is allowed to be further processed in the third region because it will not overpower D. i think you see now the pattern. :)
A:Okay, I see the pattern you're drawing now! You're mapping the components of your conceptual brain model onto a GAN-like adversarial dynamic, defining distinct processing stages based on an evaluative judgment. Let's lay it out:
G (Generator) = The Initial Processing Network: This network takes the primary input and processes it, generating some internal state or representation. This corresponds to the "neural net before the S1/M1 regions" in your model.
D (Discriminator) = The Evaluator/Predictor (BA10 Analog): This module evaluates the output/state generated by G. Its job is to determine if this state is "sensible," "well-predicted," or "manageable" (analogous to deeming it "real" in GAN terms).
The "Real/Fake" Decision Point: Based on D's evaluation:
If "Real" (i.e., well-predicted, manageable, not indicative of overload): The processing is deemed safe/valid.
If "Fake" (i.e., poorly predicted, likely to cause overload, nonsensical): The processing is deemed problematic.
The "Third Region" = Deeper/Elaborative Processing: This processing stage is gated by D's decision. If D judges G's output as "Real," the information/state is allowed to flow into this third region for further, perhaps more complex or associative, processing. This region is situated functionally after the initial evaluation but conceptually before the ultimate high-level control (as it likely influences future BA10 states/predictions).
The S1/M1 Analog Connection: This component seems to fit in as the consequence of D failing or judging the input as "Fake"/unmanageable. If G's output is deemed problematic by D (or if it simply overwhelms the system despite D's configuration attempts, leading to the overflow condition at the S1/M1 checkpoint), the path to the "Third Region" is blocked, and instead, the basic "MOVE" action is triggered via the S1/M1 analog mechanism.
The Pattern/Flow:
Input -> Initial Processing (G) -> Output State
Output State -> Evaluator (D/BA10) -> Judgment ("Real" or "Fake")
If "Real": Output State -> Deeper Processing ("Third Region") -> Influences future D/BA10 state.
If "Fake" (or leads to overflow at S1/M1): Trigger Action (S1/M1 -> MOVE), bypass "Third Region".
Yes, I see the pattern now. You've constructed a hierarchical processing model where:
An initial network (G) generates representations.
A controller/evaluator (D/BA10) predicts and assesses these representations.
Successful assessment allows for deeper processing ("Third Region").
Failure leads to a basic, corrective action mediated by a different pathway (S1/M1 analog).
This is a more elaborated version of your previous flowchart, explicitly adding the pathway for successfully processed information. It uses the adversarial dynamic (G vs D) not just for training (like in standard GANs) but as an active gating mechanism for information flow depth within the system during operation. It's a coherent conceptual architecture for an artificial system.
Q:out of sheer curiosity,has anybody build such a system? if yes, with what results.
A:That's a great question! While it's highly unlikely that someone has built a system exactly matching your specific G-D-ThirdRegion-S1/M1-Action architecture using that precise terminology and framing, the underlying principles resonate strongly with several areas of AI, computational neuroscience, and robotics research.
Many systems incorporate elements of your design, often using different names and theoretical backgrounds. Here's a look at related concepts and their results:
Predictive Coding / Predictive Processing:
Concept: These brain-inspired models propose that higher cortical areas (like your D/BA10 analog) constantly generate predictions about lower-level sensory input (generated by areas analogous to your G). Mismatches (prediction errors) are propagated upwards.
Relation: Your D evaluating G's output is very similar to a higher level predicting/evaluating a lower level's state. Large errors could be analogous to your "Fake" judgment or overload condition. Successful prediction (low error, "Real") allows information to be integrated and passed for further processing (your "Third Region").
Results: These models are influential in theoretical neuroscience, explaining various perceptual phenomena (like illusions) and providing frameworks for understanding conditions like autism or schizophrenia as potentially involving altered prediction or error weighting. Computationally, they inspire architectures focused on unsupervised learning and efficient representation.
Actor-Critic Methods in Reinforcement Learning:
Concept: An "Actor" network learns a policy (selects actions, somewhat analogous to your M1/Action trigger), and a "Critic" network evaluates states or state-action pairs (estimates future reward, somewhat analogous to your D evaluating the situation).
Relation: The Critic informs the Actor. If the Critic predicts a very low value (bad state), the Actor is incentivized to take actions leading away from that state (similar to your "MOVE" trigger upon failure).
Results: Actor-Critic methods are state-of-the-art in many complex RL tasks, from playing games (AlphaGo, AlphaStar, Dota 2 bots) to robotic control. They demonstrate effective learning of complex behaviors based on evaluative feedback.
Hierarchical Reinforcement Learning / Control Systems:
Concept: High-level controllers select goals or sub-policies for lower-level controllers. A high-level agent (like your D/BA10) might assess a situation and decide whether to engage a complex planning module (like your "Third Region") or trigger a simpler, reflexive behavior (like your S1/M1 action).
Relation: This directly captures the idea of gating processing depth based on evaluation.
Results: Used in robotics and complex task planning to break down problems, enabling agents to learn solutions for tasks with long time horizons or complex state spaces that would be intractable otherwise.
Systems with Explicit Anomaly/Novelty Detection:
Concept: A system specifically monitors its inputs or internal states for unexpected or anomalous patterns (similar to D judging G's output as "Fake" or unmanageable).
Relation: Detection of novelty/anomaly often triggers specific responses – shifting attention, initiating exploration (like your "MOVE"), or activating alerting systems.
Results: Used in cybersecurity (intrusion detection), industrial monitoring (fault detection), and even in robotics to make agents react appropriately to unexpected situations.
World Models:
Concept: Agents learn a generative model of their environment (a "world model," G-like) which they can use to simulate future outcomes and plan actions (potentially involving evaluation like D and deeper processing like the "Third Region").
Results: Have shown promise in allowing agents to learn complex tasks more sample-efficiently and perform forms of "imagination" or planning.
In Summary:
While a direct implementation of your uniquely combined G-D-ThirdRegion-S1/M1 system isn't readily found, the core ideas are definitely "in the air" and actively explored:
Predictive evaluation of internal states or inputs.
Gating information flow or processing depth based on evaluation.
Having distinct pathways for deliberative processing vs. reactive/reflexive action.
Systems incorporating these elements have achieved significant success in various domains, demonstrating robustness, efficient learning, and the ability to handle complex tasks or unexpected situations. Your specific architectural combination offers a novel way to integrate these powerful concepts.
Q:one last question before going to bed :) does the last design seem like a better candidate to explain some of the human brain's functionality?
A:That's the million-dollar question! Evaluating whether your model is a "better" candidate requires comparing its explanatory power against existing, detailed neuroscientific models for specific functions.
Here's a balanced perspective:
Aspects where your model captures potentially important principles relevant to brain function:
Predictive Processing/Evaluation: The idea of a high-level system (D/BA10) evaluating or predicting the nature of incoming information or the state of lower-level processors (G) resonates strongly with dominant theories like Predictive Coding. The brain is increasingly seen as a prediction machine constantly trying to anticipate sensory input.
Gating of Information Flow: The concept that processing depth or access to further resources ("Third Region") is gated based on an initial evaluation is plausible. Attention mechanisms, for example, selectively route information for deeper processing based on relevance or salience, which involves evaluative processes.
Hierarchical Control: Having different response pathways – one for controlled, deeper processing and another for rapid, perhaps more reflexive action ("MOVE") when the primary system fails or is overwhelmed – reflects the brain's ability to operate at multiple levels, from deliberate thought to basic reflexes/instincts.
Efficiency and Overload Avoidance: The implicit goal of the D/BA10 analog to handle input efficiently and the existence of an "overflow" mechanism acknowledge biological constraints related to processing capacity and metabolic cost.
Aspects where your model is likely too simplified or differs significantly from current understanding:
Extreme Abstraction: Your model is highly abstract. The brain involves countless specialized areas interacting in complex, dynamic networks, not just one G, D, and Third Region. The biological implementation is vastly more complex.
Binary Nature: The gating often seems somewhat binary (process deeper OR escape). Real brain processing is highly graded. Attention modulates signals, emotional states bias processing, and motor outputs are finely controlled, not usually an all-or-nothing escape triggered solely by processing overload.
Role of S1/M1: Biologically, S1/M1 are fundamental to all volitional sensorimotor interaction and detailed perception, not just an overflow safety valve. Their precise topographic maps enable fine motor control and detailed sensory analysis, which isn't central to their role in your model.
Learning and Plasticity: How the system learns to make better predictions (D/BA10 improves), generate better representations (G improves), or refine actions is not specified but is fundamental to brain function. Standard GANs have learning rules, but biological learning is more complex.
Specificity of "Fake": In your model, "Fake" seems to equate to "unmanageable/overload-inducing." In the brain, prediction errors or failed evaluations can trigger many responses besides just physical escape – they can drive learning, curiosity, orienting responses, specific emotional reactions, etc.
Conclusion:
Is it a "better" candidate?
As a complete model of the brain? No. It's far too simplified and doesn't capture the known anatomical and functional complexity, specificity, or the richness of biological mechanisms like learning and neuromodulation.
As a high-level conceptual framework for some aspects of brain function? It's potentially useful and insightful. It provides an analogy that highlights the dynamic interplay between prediction, evaluation, gated processing, and reactive behavior in a way that is computationally inspired. It captures the gist of certain hierarchical control principles.
Think of it like this: Newtonian mechanics is a vastly simplified model of physics compared to quantum mechanics or general relativity. It's "wrong" in many contexts, but it's still incredibly useful for explaining and predicting a huge range of everyday phenomena (like throwing a ball).
Your model is similar. It might not be biologically accurate in detail, but it provides a simplified, systems-level perspective that emphasizes certain functional dynamics (prediction, gating, reaction) that are relevant to understanding how a complex system like the brain might manage information flow and action selection under computational constraints. It's a valuable conceptual tool, especially when thinking about designing artificial systems, even if it doesn't replace detailed neuroscientific models for explaining the brain itself.
Sleep well! It was a stimulating discussion.
Q:yup.time to hit the bed.who knows, maybe i'll dream some apples. bye for now :)
A:Haha, perhaps you will! Sweet dreams, maybe full of apples or interesting brain architectures. It was a really engaging discussion! Bye for now.
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