# Stuff and Things > HISTORY, veterans & science >  Is consciousness continuous or discrete? Maybe it's both, argue researchers

## Oceander

Is consciousness continuous or discrete? Maybe it's both, argue researchers

September 3, 2020
Cell Press

Two major theories have fueled a now 1,500 year-long debate started by Saint Augustine: Is consciousness continuous, where we are conscious at each single point in time, or is it discrete, where we are conscious only at certain moments of time? In an Opinion published September 3 in the journal Trends in Cognitive Sciences, psychophysicists answer this centuries-old question with a new model, one that combines both continuous moments and discrete points of time.

"Consciousness is basically like a movie. We think we see the world as it is, there are no gaps, there is nothing in between, but that cannot really be true," says first author Michael Herzog, a professor at the Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. "Change cannot be perceived immediately. It can only be perceived after it has happened."

Because of its abstract nature, scientists have struggled to define conscious and unconscious perception. What we do know is that a person moves from unconsciousness to consciousness when they wake up in the morning or awake from anesthesia. Herzog says that most philosophers subscribe to the idea of continuous conscious perception -- because it follows basic human intuition -- "we have the feeling that we're conscious at each moment of time."

On the other hand, the less-popular idea of discrete perception, which pushes the concept that humans are only conscious at certain moments in time, falls short in that there is no universal duration for how long these points in time last.

Herzog and co-authors Leila Drissi-Daoudi and Adrien Doerig take the benefits of both theories to create a new, two-stage model in which a discrete conscious percept is preceded by a long-lasting, unconscious processing period. "You need to process information continuously, but you cannot perceive it continuously."

Imagine riding a bike. If you fell and waited every half-second to respond, there would be no way to catch yourself before hitting the ground. However, if you pair short conscious moments with longer periods of unconscious processing where the information is integrated, your mind tells you what you have perceived, and you catch yourself.

"It's the zombie within us that drives your bike -- an unconscious zombie that has excellent spatial/temporal resolution," Herzog says. At each moment, you will not be saying to yourself, "move the bike another 5 feet." The thoughts and surroundings are unconsciously updated, and your conscious self uses the updates to see if they make sense. If not, then you change your route.

"Conscious processing is overestimated," he says. "You should give more weight to the dark, unconscious processing period. You just believe that you are conscious at each moment of time."

The authors write that their two-stage model not only solves the 1,500-year-old philosophical problem but gives new freedom to scientists in different disciplines. "I think it helps people to completely fuel information processing for different prospects because they don't need to translate it from when an object is presented directly to consciousness," Herzog says. "Because we get this extra dimension of time to solve problems, if people take it seriously and if it is true, that could change models in neuroscience, psychology, and potentially also in computer vision."

Though this two-stage model could add to the consciousness debate, it does leave unanswered questions such as: How are conscious moments integrated? What starts unconscious processing? And how do these periods depend on personality, stress, or disease, such as schizophrenia? "The question for what consciousness is needed and what can be done without conscious? We have no idea," says Herzog.

*  *  *

Source: Is consciousness continuous or discrete? Maybe it's both, argue researchers -- ScienceDaily

----------

RMNIXON (09-09-2020)

----------


## Dan40

Is unconsciousness contagious?

----------


## nonsqtr

Ta-da!

"Both".

The magic word is "embedding".

Fractal surfaces - sets embedded into other sets.

M-theory - spacetime embedded into higher dimensions

Timelines in the brain - points "unfolded" onto embedded intervals.

I use that word "slightly" differently than its current formal definition in topology, but the meaning is about the same, it's intuitive 

I keep trying to point out, the topological dimension of a Cantor Dust is ZERO. At some stage, you've made the transition from a vanishingly small interval, to an actual point. If you go the other way, that's an unfolding.

----------

Oceander (09-09-2020)

----------


## Oceander

> Ta-da!
> 
> "Both".
> 
> The magic word is "embedding".
> 
> Fractal surfaces - sets embedded into other sets.
> 
> M-theory - spacetime embedded into higher dimensions
> ...


I was debating whether to use an "@" or not; guess I was correct not to!

----------


## nonsqtr

BTW - psychophysics was the very first branch of science ever, to attempt to link physical reality directly to subjective experience.

It failed pretty miserably in its early days, except for one or two noticeable pieces like the Weber-Fechner law. But they were starting from scratch - how they started out was trying to define the JND ("just noticeable difference") by asking the subjects to report their experiences.

----------


## SharetheHedge

We're characters in a video game, being played for who knows what's amusement. Our consciousnesses, our true selves, are trapped, suspended in another dimension, having been stripped of all memory of what we were, where we originated, and what our true location is. If our character dies, it is replaced by another, with no knowledge on our part that we have been in the game before. All we know is illusion, and "our reality" itself is only a sub-set of a higher reality - which maybe a sub-set of a still HIGHER, or MORE "real", reality, making reality itself,  RELATIVE. 

It is beyond our ken to figure out what is really going on here, and how this all came about. If someone appears to be beginning to realize a small part of it (myself, for example  :Sofa: ) he will NEVER be believed by the rest, which is the final stroke of genius installed by the GAMERS. The "software" that provides the illusion of our supposed physical existence, and in which, and by which, our true selves are held prisoner, is infallible, and doesn't depend on PG&E or CON-ED for "juice" so a perceptible glitch is almost impossible.   

Cheer up. It's just life in the matrix  :Cool20:

----------

Kris P Bacon (09-10-2020),Northern Rivers (09-09-2020)

----------


## nonsqtr

> I was debating whether to use an "@" or not; guess I was correct not to!


Well... I could argue with their definition of "consciousness" (all day), but that would be missing the point.

The point is, there is no "duality". There is NO difference between physical reality and subjective experience, they are part and parcel of the same thing.

You can have the subject report their subjective experiences, and you find they're actually consistent and reproducible! They even obey a "law of physics"!

If you ask yourself what is the salient feature of our experience that makes it different from anything in the physical world, the thing that ends up leaping out at you is the passage of time. You are aware "of" the passage of time, and all "events" are experientially embedded into that passage. Ergo, you must have a 5-dimensional process (at least) going on in your brain, because otherwise you could not be aware "of" the flow of time.

You can look at time as a "flux" of information that passes through a reference frame in your brain, which is stationary and centered on "now". The reference frame is egocentric, as distinct from time itself which is allocentric. The flux induces a "flow" of information along the timeline, but the neural network is set up in such a way that there's a whole spectrum of velocities - so your environmental flow becomes embedded into a "dimension" of these velocities, which in turn allows the brain to map the flow into a higher dimensional space.

In real life the situation is far more complex than the OP and far more complicated than I've described. Information is "compressed", re-encoded, as it travels along the timeline. There is specific anatomy in the brain to track and match events along the timeline. Consciousness, in my opinion, is a cognitive function, and all cognitive functions involve "executive systems" that control the timeline in different ways. For example there is a brain wave called a P300 that resets the tracking activity along the timeline. "Attention" is defined in terms of the patterns that are being tracked along the timeline. Attentive disorders often involve switching too fast or not switching fast enough.

On the other side, all drugs that cause hallucinations act on the forward side of the timeline just ahead of "now". For instance the dopamine systems end in the N. Accumbens which is right there. They end in the striatum which is right there, and the frontal cortex which is right there. What they're doing is altering the relationship between what is being put into the point at "now" and what's coming out of it. Without the drug your brain is saying "I'm issuing command X so I expect response Y", and then it has to figure out which parts of the sensory input belong to Y, before it can determine whether the action succeeded or not. To do that, it loads an "expectation template" into the timeline, which is a filter that looks for certain features. The features have to be extracted from the sensory input at the same time that ordinary sensory processing is going on. So the brain makes use of the phase of the population dynamic to encode the local relationship between the template and the sensory flux, and if that changes, so do your perceptions.

There is a well known but not very well studied condition under LSD called "synesthesia", where you can actually smell sounds and hear colors. That's what we're after, THAT math. We're very close, very close indeed. If you explain why red is red you have the keys to the kingdom.

----------


## RMNIXON

When was the last time Joe Biden was conscious of anything?  :Thinking:

----------


## nonsqtr

In my vocabulary consciousness is different from simple awareness. Consciousness is specifically awareness "of self".

In a working brain, you have environment-related fluxes along the timeline, and you also have fluxes that aren't related to environmental events. We do have some limited knowledge of what happens in the brain when the subject imagines a "what if" scenario - something that must be compatible with the usual environmental rules but has no actual instantiation in the environment "now". (Call it imagination, or a fantasy). It's pretty interesting stuff.

----------


## Jen

> Well... I could argue with their definition of "consciousness" (all day), but that would be missing the point.
> 
> The point is, there is no "duality". There is NO difference between physical reality and subjective experience, they are part and parcel of the same thing.
> 
> You can have the subject report their subjective experiences, and you find they're actually consistent and reproducible! They even obey a "law of physics"!
> 
> If you ask yourself what is the salient feature of our experience that makes it different from anything in the physical world, the thing that ends up leaping out at you is the passage of time. You are aware "of" the passage of time, and all "events" are experientially embedded into that passage. Ergo, you must have a 5-dimensional process (at least) going on in your brain, because otherwise you could not be aware "of" the flow of time.
> 
> You can look at time as a "flux" of information that passes through a reference frame in your brain, which is stationary and centered on "now". The reference frame is egocentric, as distinct from time itself which is allocentric. The flux induces a "flow" of information along the timeline, but the neural network is set up in such a way that there's a whole spectrum of velocities - so your environmental flow becomes embedded into a "dimension" of these velocities, which in turn allows the brain to map the flow into a higher dimensional space.
> ...


All of this is way beyond my comprehension,  but I have two observations/ comments to make.  Forgive me if they are totally irrelevant. That's not the intent.

1. So my consciousness is streaming. Though I believe I am conscious throughout, I may flip in and out of consciousness.  There is a person next to me doing the same thing.  Does the observer see my unconscious moments in time?  Do I see his?  Or does the streaming interface such that it looks the same to both of us?

2. Does this apply at all:   When I am playing a piece of music that I know well, have played many times.......  I can "unconsciously" play it through with all the mood and nuance necessary to make it nice to listen to.  If, at some point during the playing of it, my brain actually connects and realizes what I am doing, I may lose my place and have no idea how to continue the song. As long as I disconnect and let my hands play without conscious thought it's okay.  As soon as there is conscious thought........it's not okay.

----------


## nonsqtr

> When was the last time Joe Biden was conscious of anything?


Walter J Freeman just died a few years ago, he was the first neuroscientist to apply control systems theory to the brain 

The hardest thing about consciousness is measuring it, so answering your question would be difficult.

Freeman worked on the rabbit olfactory cortex.

His father invented the lobotomy. (That would be Walter J Freeman II). His great grandfather was William Williams Keen, the first brain surgeon in the United States.

William Williams Keen - Wikipedia

----------


## nonsqtr

> All of this is way beyond my comprehension,  but I have two observations/ comments to make.  Forgive me if they are totally irrelevant. That's not the intent.
> 
> 1. So my consciousness is streaming. Though I believe I am conscious throughout, I may flip in and out of consciousness.  There is a person next to me doing the same thing.  Does the observer see my unconscious moments in time?  Do I see his?  Or does the streaming interface such that it looks the same to both of us?
> 
> 2. Does this apply at all:   When I am playing a piece of music that I know well, have played many times.......  I can "unconsciously" play it through with all the mood and nuance necessary to make it nice to listen to.  If, at some point during the playing of it, my brain actually connects and realizes what I am doing, I may lose my place and have no idea how to continue the song. As long as I disconnect and let my hands play without conscious thought it's okay.  As soon as there is conscious thought........it's not okay.


Wow, great questions.

Okay, 

1. "Qualia". The network structure is the same, but the information stored in the synapses is different. So we may both see red, but the "quality" of your red may be somewhat different from mine. Damasio coined the term "qualia", it's supposed to represent an indivisible unit of experience, like a quantum of experience.

2. "The magic number 7, plus or minus 2".

The Magical Number Seven, Plus or Minus Two - Wikipedia

----------


## UKSmartypants

The mechanism by which consciousness is created, and the reason for consciousness has become clearer over the last few years, and there has been several  interesting  papers on it.

Understand what you are - you are a self aware entity encased in a black world. your only connection to the outside world are you eyes, ears, touch, smell. Your consciousness does not make the decisions, your conscious  is a feedback checking mechanism, a Real Time Simulation world model.

Your brain, cut off from direct contact with the outside world, has to rely on the electrical signals from your senses, and it has to learn how to interpret them into patterns of firing neurons. . It constructs a real time model of what it thinks is going on outside. As time passes, it updates its real time model with the results of the senses. You do this 24/7 from the second you are born.

So if someone throws a ball at you, your unconsciousness detects the ball using the eyes. It then feeds all the relevant information to the Amygdala, in the center of the brain. That forms a model of the  world. it then calculates the path and speed of the ball, sends that back to the unconsciousness, which then instructs the correct muscles to work to catch it. It then feeds the expected result to the consciousness.  200 ms later, another set of external data is collected and the model updated to see if what was expected to happen is actually happening., and round and round we go, constantly sampling and updating to form a correct real world simulation called 'consciousness' Thus your world model, incorporating expected and actual results, is updates about 5-10 times a seconds. So from that point of view, your consciousness, your world model,  is in discreet steps and runs at about 10 frames a second.

----------


## nonsqtr

Time perception - Wikipedia

https://plato.stanford.edu/entries/time-experience/

The Man Who Mistook His Wife for a Hat - Wikipedia

----------


## nonsqtr

> The mechanism by which consciousness is created, and the reason for consciousness has become clearer over the last few years, and there has been several  interesting  papers on it.
> 
> Understand what you are - you are a self aware entity encased in a black world. your only connection to the outside world are you eyes, ears, touch, smell. Your consciousness does not make the decisions, your conscious  is a feedback checking mechanism, a Real Time Simulation world model.
> 
> Your brain, cut off from direct contact with the outside world, has to rely on the electrical signals from your senses, and it has to learn how to interpret them into patterns of firing neurons. . It constructs a real time model of what it thinks is going on outside. As time passes, it updates its real time model with the results of the senses. You do this 24/7 from the second you are born.
> 
> So if someone throws a ball at you, your unconsciousness detects the ball using the eyes. It then feeds all the relevant information to the Amygdala, in the center of the brain. That forms a model of the  world. it then calculates the path and speed of the ball, sends that back to the unconsciousness, which then instructs the correct muscles to work to catch it. It then feeds the expected result to the consciousness.  200 ms later, another set of external data is collected and the model updated to see if what was expected to happen is actually happening., and round and round we go, constantly sampling and updating to form a correct real world simulation called 'consciousness' Thus your world model, incorporating expected and actual results, is updates about 5-10 times a seconds. So from that point of view, your consciousness, your world model,  is in discreet steps and runs at about 10 frames a second.


An excellent macroscopic description!

Now imagine 10,000 copies of what you just said, running asynchronously. So, the updating you're talking about in the third paragraph, is both continuous and cyclic.

The part about constructing the real time model of what it thinks is going on, is what I've been talking about. The model is only necessary "in" the singularity, in the immediate neighborhood of "now". Everywhere else, the brain knows for sure what's going on, because it has direct neural input. It's only in the tiny little vicinity around "now", that a model becomes necessary. Because there's no direct information there.

Ask yourself - why is a model necessary "at all"? Why can't the brain just ignore what's in the gap?

The answer is: continuity. The threads are dynamic, if you lose them you can't pick them up again. "Oh, I lost my train of thought". You have to start all over again - from a well known reference point.  :Smile:

----------


## UKSmartypants

it also explains the phenomena of Blindsight, the ability of people who are cortically blind due to lesions in their striate cortex, also known as the primary visual cortex or V1, to respond to visual stimuli that they do not consciously see. Clearly the signals are passing into the Amygdala, and being incorporated into the unconscious part of the system, but the data is not being fed to the real time model (the conciousness).

----------


## nonsqtr

> it also explains the phenomena of Blindsight, the ability of people who are cortically blind due to lesions in their striate cortex, also known as the primary visual cortex or V1, to respond to visual stimuli that they do not consciously see. Clearly the signals are passing into the Amygdala, and being incorporated into the unconscious part of the system, but the data is not being fed to the real time model (the conciousness).


And phantom limbs...

So let's talk about the real time part some more.

A neuron, is a very sophisticated device. It's not at all the passive integrator of McCulloch & Pitts.

Neurons in the cerebral cortex have long dendrites, and on those dendrites are little "bumps" called dendritic spines.

Dendritic spine - Wikipedia

They generate their own action potentials, they're called dendritic spikes.

Dendritic spike - Wikipedia

They work on calcium (not sodium or potassium).

They do lots of things. They are laden with neurotransmitter receptors. They are the locus of synaptic plasticity. They interact with snippets of mRNA that cluster inside the shaft of the spine, which in turn affects local gene expression.

But it's worse than that. Each branch of a dendrite, including the shaft of a spine, is a decision node. It may or may not be independent from it's neighbors, depending on the conditions at the time. 

So in effect, each branch and spine is a stochastic generator - so the first thing we could look at is a bunch of stochastic generators arranged in a sequence, like spines along a shaft. The distance between spines is typically a couple of microns, so we end up with something resembling a .arkov chain, which "automatically" does Bayesian inference.

Markov chain Monte Carlo - Wikipedia

Accelerated Bayesian inference using deep learning - NASA/ADS

So this would occur on scales of 10 to 100 microns. And there are 100 billion neurons, each of which have 10,000 synapses.

The other thing is, spines sometimes have specialized synapses that perform exclusive or logic. Marvin Minsky was an idiot

----------


## nonsqtr

So yes, the amygdala.

This is related to the "qualia" that Jen asked about.

As with any analog computer, in the brain it's a lot about the time constants. 

In the amygdala, the incoming sensory information is evaluated for emotional significance. The sensory information changes pretty quickly, but the emotional relevance is in the context of homeostasis (things like fear, fight or flight) which is generally more stable and changes more slowly. This evaluation against a stable base was important in Damasio's original theory of qualia - it was in fact what we call an "anchor", which is the intersection between a timeline and the environment.

Consider the concept of time locking in the simplest monosynaptic reflex "loop". The origin is arbitrary, in the sense that we can rotate the loop anywhere relative to our electrode - but the anchor is the spot exactly between the muscle and the spindle receptor. (It is, in effect, "environmental zero").

In most neurons in the cerebral cortex, when they "fire", the action potential travels up the dendritic tree as well as down the axon. At 1 m/sec it'll take about a microsecond for the action potential to enter the dendrite. Once there, it will affect the dendrite according to the configuration of ion channels at the base of the dendritic stalk.

In the dendritic spines we were talking about, the upwards action potential will interact with the downward calcium-based dendritic spikes that "just happened about a microsecond ago". Then, if the action potential is allowed into the spine it will have effects on transmitter release and plasticity.

The transmission speed along the axon and dendrite is approximately the same, so the axon and dendrite can both connect to the same neuron that is perhaps 10 microns (10 microseconds) away. So the "loop" doesn't have to go from the spinal cord to the big toe anymore, it just goes to its neighbors. The range of distances in the cerebral cortex would be about 10 microns to 10 cm.

----------


## Northern Rivers

> _We're characters in a video game,_ being played for who knows what's amusement. Our consciousnesses, our true selves, are trapped, suspended in another dimension, having been stripped of all memory of what we were, where we originated, and what our true location is. If our character dies, it is replaced by another, with no knowledge on our part that we have been in the game before. All we know is illusion, and "our reality" itself is only a sub-set of a higher reality - which maybe a sub-set of a still HIGHER, or MORE "real", reality, making reality itself,  RELATIVE. 
> 
> It is beyond our ken to figure out what is really going on here, and how this all came about. If someone appears to be beginning to realize a small part of it (myself, for example ) he will NEVER be believed by the rest, which is the final stroke of genius installed by the GAMERS. The "software" that provides the illusion of our supposed physical existence, and in which, and by which, our true selves are held prisoner, is infallible, and doesn't depend on PG&E or CON-ED for "juice" so a perceptible glitch is almost impossible.   
> 
> Cheer up. It's just life in the matrix


Close. We are all holograms subject to the vagaries of quantum realities. We don't really exist as we think we do. Anyone remember my 'red house brick' post? That says it all.

----------


## Physics Hunter

> Ta-da!
> 
> "Both".
> 
> The magic word is "embedding".
> 
> Fractal surfaces - sets embedded into other sets.
> 
> M-theory - spacetime embedded into higher dimensions
> ...


UGGGGHHHHH.  SOSDD.

----------


## nonsqtr

> UGGGGHHHHH.  SOSDD.


That's what a lot of mathematicians say when they look at this stuff. They're more into the fancy schmantzy 12 dimensional whirling dervishes.  :Wink:

----------


## Physics Hunter

I could argue as Newton.  At the limit, discrete and continuous are virtually indistinguishable.

Heisenberg might argue that since our neural network wetware has a natural firing interval time, that time and matter/energy is quantized, and our cognition is way slower than that in a brain, and moreover sensing way.

However, that is all micro, and I think the point here is macro.

A mind-body dualist, like me, would argue that I am always here.  My cognition is more than this cage that my brain/body provides, and it is a separate thing.  If we are discussion my attentiveness, then that changes the discussion.

I know from experience that I can slow down my perception of time, act faster than expected and change things.  My kids freak out when I catch things they drop, usually in the kitchen.  This resulted from being a big kid, that grew too fast, became uncoordinated, and made a conscious decision to recover well from klutzy actions.

The ability to focus my mind and slow down (my perception of) time, is really useful.  Riding Motorcycles through challenging terrain, drawing, aiming and loosing an arrow at an already alerted deer, ...  
I consider this a conscious decision to sharpen focus.  I notice this in particularly skilled NFL linebackers.  Once the offensive line sets, and the QB barks out the call, they force their eyes wider and not to blink.  
Clearly, I am not the only person to discover this ability.

Therefore, if I can willfully set the speed of my consciousness, perhaps we have part of the answer.

----------


## nonsqtr

> Close. We are all holograms subject to the vagaries of quantum realities. We don't really exist as we think we do. Anyone remember my 'red house brick' post? That says it all.


Yikes.

https://arxiv.org/abs/1705.06283

The matrix is real!

A holographic duality from lifted tensor networks | npj Quantum Information

Brains have the ability to amplify an information subspace and make it the whole space. What is the ultimate destination of all experience? It is memory, is it not? And, on the other side, "voluntary" motor behavior is something you want combined with a way to get it.

----------


## nonsqtr

> I could argue as Newton.  At the limit, discrete and continuous are virtually indistinguishable.
> 
> Heisenberg might argue that since our neural network wetware has a natural firing interval time, that time and matter/energy is quantized, and our cognition is way slower than that in a brain, and moreover sensing way.
> 
> However, that is all micro, and I think the point here is macro.
> 
> A mind-body dualist, like me, would argue that I am always here.  My cognition is more than this cage that my brain/body provides, and it is a separate thing.  If we are discussion my attentiveness, then that changes the discussion.
> 
> I know from experience that I can slow down my perception of time, act faster than expected and change things.  My kids freak out when I catch things they drop, usually in the kitchen.  This resulted from being a big kid, that grew too fast, became uncoordinated, and made a conscious decision to recover well from klutzy actions.
> ...


No. In the limit, discrete and continuous time processes are DISTINCTLY DIFFERENT.

I'll show you. Let's talk a little about stochastic processes. (Don't let that word "stochastic" scare you, it just means a process that generates a random variable).

Stochastic math is merely a superset of regular math. It deals with the case of a regular process "plus noise". Regular math is just stochastic math "without the noise".

For example, there is a noisy equivalent of the Schrodinger equation, it's called the Fokker-Planck equation. Same as in physics, you're looking for the time evolution of the system.

A stochastic process is defined by a stochastic differential equation (SDE). It could be as simple as:

dx/dt = F(x) + A ° gauss(t)

Where A is a matrix of coupling constants to white noise. The independent variable doesn't have to be time, if it's space the result is a stochastic image, if it's spacetime it's a stochastic field.

The SDE is a uniquely defined mathematical object when it is viewed as a continuous time flow of diffeomorphisms. The Fokker-Planck equation can be transformed into the Schrodinger equation (and vice versa) by rescaling a few variables.

At some level all interesting SDE's (stochastic processes) are topologically supersymmetric. Stochastic dynamics describes the conservation of the phase space under continuous time flow. Breakdown of supersymmetry results in chaos (catastrophes in the phase space).

Supersymmetric theory of stochastic dynamics - Wikipedia

A "continuous" stochastic process can be considered as a discrete process in the limit as the step size becomes infinitesimal - in other words we're talking about the difference between a stochastic "differential" equation and a stochastic "difference" equation - however, there are two different ways of looking at this and they don't always agree. They are called Ito and Stratonovich in the literature. The Goldstone theorem describes the difference in dynamics for the two cases. In a discrete embedding you don't always get self-organized criticality, whereas in a continuous embedding you do. Turns out the Stratonovich approach makes path integrals friendly, which is a big deal in topology.

----------


## nonsqtr

> I could argue as Newton.  At the limit, discrete and continuous are virtually indistinguishable.
> 
> Heisenberg might argue that since our neural network wetware has a natural firing interval time, that time and matter/energy is quantized, and our cognition is way slower than that in a brain, and moreover sensing way.
> 
> However, that is all micro, and I think the point here is macro.
> 
> A mind-body dualist, like me, would argue that I am always here.  My cognition is more than this cage that my brain/body provides, and it is a separate thing.  If we are discussion my attentiveness, then that changes the discussion.
> 
> I know from experience that I can slow down my perception of time, act faster than expected and change things.  My kids freak out when I catch things they drop, usually in the kitchen.  This resulted from being a big kid, that grew too fast, became uncoordinated, and made a conscious decision to recover well from klutzy actions.
> ...


The rest of your logic is sound, IMO.

The "velocity" of the flow of time through your awareness, is variable, as discussed very nicely by William James as early as 1910.

In my timeline model I represented this as dT/dt and tried to show how the variability is due to the continuum of "loop sizes" defined on the flux manifold.

In terms of the neural network it makes no difference at all what the velocity is, however the actual velocity induces a "flow" along the timeline and it is fair to say that perceptual shifts involving this flow are generally slow rather than instantaneous. (There are exceptions, but "generally" this is true).

My suggestion is specifically that there is an optimization process that aligns the speed of information along the timeline with the flow of actual events in the environment. This process is "very fast", its threads connect in the femtosecond range.

Neurally, it works like this: clusters of neurons process an interval of time. There are 1 billion such clusters, they overlap. Together they "cover" a macroscopic interval on either side of "now". Physical events flow "through" this covering, they essentially "travel along the timeline" at a rate dT/dt, and this is how the brain can easily partition environmental and non-environmental activity and accurately target points in time relative to environmental events.

If you have an event E and it's flowing along the timeline, the relationship between its representation at any two points is described in terms of conditional expectation (since all points along the timeline "exist" at the same physical time).

----------


## UKSmartypants

> Therefore, if I can willfully set the speed of my consciousness, perhaps we have part of the answer.



You cant, really, it fixed by the speed of chemical reactions.  Its fixed by the speed at which sensory information can pass up your nerves round your brain and back again. This is why you cant swat a fly.

It takes about 50ms for a nerve impulse to travel up from your toes to your brain, 50ms to process it,  and then another 50ms to send a miscle signal back down. So it takes you 150ms  to swing at a  fly.

A fly, on the other hand, has three neurons between its primitive brain and its wing actuators. It can see your hand coming, calculate the trajectory, and plot an escape route in about 1/1000th of a second. To a fly, you operate in super super slow speed.



Flies excel in the sorts of sensors that they carry to this problem. They have antennae that sense odors and detect wind detection. They have a sophisticated eye which is the fastest visual system on the planet. They have another set of eyes on the top of their head. We have no idea what they do. They have sensors on their wing. Their wing is covered with sensors, including sensors that sense deformation of the wing. They can even taste with their wings. One of the most sophisticated sensors a fly has is a structure called the halteres. The halteres are actually gyroscopes. These devices beat back and forth about 200 hertz during flight, and the animal can use them to sense its body rotation and initiate very, very fast corrective maneuvers. But all of this sensory information has to be processed by a brain, and yes, indeed, flies have a brain, a brain of about 100,000 neurons.

----------


## nonsqtr

So consider - when babies begin, they "babble".

Why?

Because their brains are learning the downstream ("expected") results of their random emissions.

Eventually they learn, that "this" vocalization results in "that" sound.

So, after learning, the brain will place "that" sound into the sensory portion of the timeline, before the sound actually occurs. It's an "expectation" based on experience.

So, expectation "can" be considered as information that flows along the timeline faster than the representation of environmental events.

The particular vocalization can be "interrupted" at any time, either during the movement of the speech muscles, or the resulting audio could even be occluded by some other loud noise. (So the concept of expectation is "continuous" along the timeline). If at any point the expectation doesn't match the reality, the brain detects the anomaly and generates a P300. The actual detection doesn't occur at a "point" (because that would mostly be treated as noise), it's a whole cascade of deltas from the expected values, and the point at which it begins is associated with "some other event" which must be elucidated to find the "cause".

We know it works this way because the P300 is a whole-brain response, it's an "executive function". If I say, "*I take my coffee with cream and dog*" your brain is going to generate a P300 and most likely you'll then go and re-read the word "dog" to make sure you got it right. And after that, you identify the "meaning" of the word dog, and maybe you laugh and maybe you don't - if you do, you've got another vocalization along the timeline.

On the sensory side, some events are expected, and some are not. Those that are, are the ones that are being actively tracked along the timeline, via the "attention" process. If an unknown stimulus arrives, it takes a while before you focus attention in it (first you "notice" it, then you "direct your attention" to it). The moment the attention is engaged, it means it's being actively tracked. Which means in turn, that not all the information entering the timeline at T>0 is "motor" in nature (even though it's all intentive). This is where the "control of information subspaces" comes in, because there is such a thing as attention without activity.

But that's it, it's a very simple model. It gets more complex at the 401 level, but the essentials are very accessible, especially if you have a physics background.

----------


## nonsqtr

> You cant, really, it fixed by the speed of chemical reactions.  Its fixed by the speed at which sensory information can pass up your nerves round your brain and back again. This is why you cant swat a fly.
> 
> It takes about 50ms for a nerve impulse to travel up from your toes to your brain, 50ms to process it,  and then another 50ms to send a miscle signal back down. So it takes you 150ms  to swing at a  fly.
> 
> A fly, on the other hand, has three neurons between its primitive brain and its wing actuators. It can see your hand coming, calculate the trajectory, and plot an escape route in about 1/1000th of a second. To a fly, you operate in super super slow speed.
> 
> 
> 
> Flies excel in the sorts of sensors that they carry to this problem. They have antennae that sense odors and detect wind detection. They have a sophisticated eye which is the fastest visual system on the planet. They have another set of eyes on the top of their head. We have no idea what they do. They have sensors on their wing. Their wing is covered with sensors, including sensors that sense deformation of the wing. They can even taste with their wings. One of the most sophisticated sensors a fly has is a structure called the halteres. The halteres are actually gyroscopes. These devices beat back and forth about 200 hertz during flight, and the animal can use them to sense its body rotation and initiate very, very fast corrective maneuvers. But all of this sensory information has to be processed by a brain, and yes, indeed, flies have a brain, a brain of about 100,000 neurons.


Yes. There are basically 3 types of brains:

1. Primitive brains ("barely cephalized") organisms
2. Insect brains
3. Vertebrate brains

"Most" of the structure of a human brain is present in a goldfish.

All insect brains are basically the same.

All vertebrate brains are basically the same.

----------


## nonsqtr

So then - the thing that distinguishes human brains is the proliferation of the cerebral cortex. The cortex has two main architectures: one is a matrix, where the matrix elements are vertical columns consisting of perhaps 100 neurons each. The other is a diffusely interconnected horizontal organization like a Boltzmann machine.

Generally speaking there are two functional layers in each column and in the diffuse network. The bottom layer is topographic and modality specific, and it embeds the timeline into the environment. The top layer is associative, its connections are typically widespread and its axons cross from one side of the brain to the other.

Both architectures are present and active at the same time. A processing "column" has many functions. For example in the primary visual cortex it processes a local orientation, which makes feature representation and pattern recognition a lot easier and more efficient to encode. But each column can also be "allocated" to an interval along the timeline, which does not necessarily correspond to the location of its anchor. Thus each column can process the conditional expectation between disjoint intervals. The collection of such expectations forms a computational manifold. The connections of the upper layer are such that forms on this manifold can be "optimized" in certain ways.

Vertebrate brain architecture always contains a voluminous pathway that connects the back end of the timeline to the front end. This pathway by definition handles "non-environmental" information, as the direction of its information flow is backwards relative to the timeline.

There are actually multiple such pathways in a human brain. They all feed "forward", into the basal forebrain, which is what the frontal cortex hovers over. The frontal cortex is the part that is arranged more like a Boltzmann machine, the newer portions of it especially are less columnar and more diffuse. This suggests optimization "over" the timeline, which is exactly what I've been proposing. The whole thing is driven from the front, and the anatomy, physiology, and chemistry agree with that concept.

If you take the cerebral cortex and unfold it like a sheet, you'll see that the temporal lobe structures are at the very back of the timeline (feeding into the hippocampus), whereas the frontal lobe structures are up front, and the parietal and sensorimotor areas are kinda in the middle. The "hippocampal-septal axis" is an example of a backwards flow, as is the Circuit of Papez. The cingulate cortex "spans" the timeline, it arches across the inside of the brain from front to back and receives most of the callosal (side to side) connections. Obviously, information returning in this manner can update the distributions of the generators of downstream expectation

----------


## nonsqtr

There is one key sentence in the STS link, it goes like this:

"The critical points have different indexes of stability so that the states <a| and |b> are topologically inequivalent as they represent unstable manifolds *of different dimensionalities*".

So now you're at the limit of my knowledge.

I'm a fountain of detail, if you're interested, but generally "you take it from here".

I tried to show how the "unfolding" works, which is the foundation of awareness and the "mechanism" for qualia. The unfolding makes use of the *information* in the distribution, which is instantiated in the form of a "wobbling" over time, which in turn serves to distinguish points in time relative to a network that is fundamentally agnostic to them.

There are 1000 trillion qubits in a human brain, and I have no answers for many-particle solutions with that many interacting wave functions.

And, one can not "replicate a consciousness" based on neural activity, even if one had a complete 3d map in real time - because, the whole thing is relative to information stored in the synapses, which represents years of experience and supervised and unsupervised learning. You'd have to know the weights of each individual synapse AND their configurations, which is technologically infeasible.

In theory according to this model it would be possible to create a self organizing aware intelligence in machine form, but the machine would require homeostasis and it would also require an effector (a "capability").

The model also suggests that all vertebrates are aware, differing only in degree.

----------


## Physics Hunter

> You cant, really, it fixed by the speed of chemical reactions.  Its fixed by the speed at which sensory information can pass up your nerves round your brain and back again. This is why you cant swat a fly.
> 
> It takes about 50ms for a nerve impulse to travel up from your toes to your brain, 50ms to process it,  and then another 50ms to send a miscle signal back down. So it takes you 150ms  to swing at a  fly.
> 
> A fly, on the other hand, has three neurons between its primitive brain and its wing actuators. It can see your hand coming, calculate the trajectory, and plot an escape route in about 1/1000th of a second. To a fly, you operate in super super slow speed.
> 
> 
> 
> Flies excel in the sorts of sensors that they carry to this problem. They have antennae that sense odors and detect wind detection. They have a sophisticated eye which is the fastest visual system on the planet. They have another set of eyes on the top of their head. We have no idea what they do. They have sensors on their wing. Their wing is covered with sensors, including sensors that sense deformation of the wing. They can even taste with their wings. One of the most sophisticated sensors a fly has is a structure called the halteres. The halteres are actually gyroscopes. These devices beat back and forth about 200 hertz during flight, and the animal can use them to sense its body rotation and initiate very, very fast corrective maneuvers. But all of this sensory information has to be processed by a brain, and yes, indeed, flies have a brain, a brain of about 100,000 neurons.


Ah, I love to freak out my friends when I catch flies in my closed fist and kill them by throwing them against a wall.

No joke, really.

----------


## Physics Hunter

> No. In the limit, discrete and continuous time processes are DISTINCTLY DIFFERENT.
> 
> I'll show you. Let's talk a little about stochastic processes. (Don't let that word "stochastic" scare you, it just means a process that generates a random variable).
> 
> Stochastic math is merely a superset of regular math. It deals with the case of a regular process "plus noise". Regular math is just stochastic math "without the noise".
> 
> For example, there is a noisy equivalent of the Schrodinger equation, it's called the Fokker-Planck equation. Same as in physics, you're looking for the time evolution of the system.
> 
> A stochastic process is defined by a stochastic differential equation (SDE). It could be as simple as:
> ...


Knock it off with the stochastic scare me crap.  I really have a Physics degree and a EE degree.
I worked in military applied and basic research for 30+ years.

When I went thru Physics we had to derive all the way out to the field strength tensor.  I graduated with honours.

----------


## Authentic

> Ah, I love to freak out my friends when I catch flies in my closed fist and kill them by throwing them against a wall.
> 
> No joke, really.


I have carried flies outside on the fly swatter. They have figured out that landing on that will keep them alive. It's their version of getting close to the enemy so your artillery can't fire without risking friendly casualties.

----------


## Physics Hunter

> There is one key sentence in the STS link, it goes like this:
> 
> "The critical points have different indexes of stability so that the states <a| and |b> are topologically inequivalent as they represent unstable manifolds *of different dimensionalities*".
> 
> So now you're at the limit of my knowledge.
> 
> I'm a fountain of detail, if you're interested, but generally "you take it from here".
> 
> I tried to show how the "unfolding" works, which is the foundation of awareness and the "mechanism" for qualia. The unfolding makes use of the *information* in the distribution, which is instantiated in the form of a "wobbling" over time, which in turn serves to distinguish points in time relative to a network that is fundamentally agnostic to them.
> ...


You are going way too deep into theoretical conjecture, and into controversial unproven science.  

We can, and have been modeling neural systems for 30 years, and they are all the rage right now for big data, images, video, self driving cars...  I don't care for them much since few approaches to incremental learning have been successful.  Once trained they are pretty good at an intended task, but they are then frozen and then age poorly as the world changes around them.  Futurists (think Kurzweil...) have been calculating the Moore's law timeframe where modern computers would have the ability to model a skull full of mush in real time.  It comes real soon.  This is at synaptic electro-chem interaction levels, not your quantum folding...

I have always chased AI through introspection.  I have a rather rarefied view of what kind of AI we can, should, and will create, and how to use it.

----------


## nonsqtr

> You are going way too deep into theoretical conjecture, and into controversial unproven science.  
> 
> We can, and have been modeling neural systems for 30 years, and they are all the rage right now for big data, images, video, self driving cars...  I don't care for them much since few approaches to incremental learning have been successful.  Once trained they are pretty good at an intended task, but they are then frozen and then age poorly as the world changes around them.  Futurists (think Kurzweil...) have been calculating the Moore's law timeframe where modern computers would have the ability to model a skull full of mush in real time.  It comes real soon.  This is at synaptic electro-chem interaction levels, not your quantum folding...
> 
> I have always chased AI through introspection.  I have a rather rarefied view of what kind of AI we can, should, and will create, and how to use it.


Bosh. I was doing neural network modeling before anyone else was. I got hired by Fischer Black specifically for my neural network knowledge, and that was back in 1984. I'll put my knowledge of neural networks both real and artificial, against anyone in the world bar none. 

Like I said, Marvin Minsky was an idiot. The guy who "proved" that neural networks couldn't do anything interesting. "Modeling" is highly overrated. You gotta know what you're looking at before you try to model it, and trust me, VERY few of the so-called "experts" know what they're looking at. Neural networks are not perceptrons, they're something entirely different.

----------


## nonsqtr

The simple observation is, ALL neurons have spontaneous activity. In the absence of any input.

Why?

Go look at the neural network models. How many of them are compatible with this simple observation? Very few. Almost none.

Well, I've just shown you why it matters.

No wobbling, no consciousness. It's that simple

----------


## nonsqtr

> Knock it off with the stochastic scare me crap.  I really have a Physics degree and a EE degree.
> I worked in military applied and basic research for 30+ years.
> 
> When I went thru Physics we had to derive all the way out to the field strength tensor.  I graduated with honours.


So, "noise" should be right up your alley.

Do you know about the Volterra kernels?

Look, this is the ONLY way to make the subjective world accessible to physicists. We need to describe accurately and precisely how the brain actually works.

If you're "scared" by noise or something, that's your own problem. 

One of the easiest and best ways to test a black box is to perturb it with noise. You can learn more about it this way, more quickly, than any other kind of behavioral analysis.

Norbert Wiener was the MAN, we wouldn't have won the war without him.

----------


## nonsqtr

Generally, biologists are not mathematicians. (There are exceptions, but that's the rule).

Up until about 1990, this was the biggest deal in neural networks:

Adaptive resonance theory - Wikipedia

Then, because of this and the success of the Boltzmann machine, people started paying closer attention to how synapses actually work.

Because, the time constant issue was adequately covered by Gerald Edelman in the 80's but the biologists like Ken-Ichi Naka kept pointing out that these models are highly non-biological. So you can get a layer to learn another layer's subspaces, so what? An ordinary linear matrix multiplication will do that for you, it's nothing special.

Naka and David Lange and I spent endless hours recording the NON-linear responses of fish neurons, at the Scripps Institute in La Jolla.

For example - in neural network modeling there is a learning rule called "Hebbian". It's basically a coincidence detector, it says the synaptic weights get updated whenever the neuron fires. All the years of people trying to use one-way neurons was great for explaining brain waves but couldn't address the simple question of coincidence in time. As a matter of fact the whole concept of "back propagation" came about for that very reason, it was because no one could find a biologically plausible coincidence detector.

But now we know a LOT about how it works, which is exactly like I said - dendrites have "segments" which are entire miniature neurons into themselves. A lot of the nonlinear behavior we were seeing was due to this segmentation. (We didn't know it at the time, but we certainly talked about it).

We're talking about processing elements that are only a cubic micron or less!

The most important thing is what happens at branch points along the dendritic tree. You have bidirectional fluxes on the ion channels, and there have been plenty of models describing the range of interactions. Each segment can be either connected (allowed to talk) to its neighbors, or isolated, or something in between. The branch point has an "axon hillock" almost like its main counterpart, and it can generate spontaneous activity all on its own, independently of any other segments. Under the right conditions, a 1-micrin segment of a dendrite can generate standing waves along its length, and when it's talking to its neighbors the relative phase of the signals matters.

What's happening in neural network modeling TODAY is that people are trying to understand the cerebral cortex. Which is very complex. We already understand the cerebellum and the hippocampus pretty well, whose neurons look similar to cortical pyramidal cells in many ways. You can look up potentiation and adaptation in the hippocampus, the synapses are very sophisticated.

The allocation issue, is what people are trying to understand. And to do that, it has become abundantly clear that we need topology. And physicists are the experts at topology, pretty much.

So the only thing we biologists can do, is to try to translate the intuition into something resembling correct vocabulary, and then pose the problem.

I figure, once the physicists understand WTF we're talking about, they'll go "oh that's easy", bang zoom, and then we can start having real fun.

Physicists, unfortunately, don't seem to know much about noise. (For that we have to go to the radar engineers, or audio people like me). As you say, some are intimidated by it.

The thing we biologists understand very clearly is that "noise" is not the nasty stuff you want to get rid of - in fact it's not even NOISE, it's spontaneous random activity that equates with a stochastic generator, without which the brain wouldn't work AT ALL. (That is to say, it would be nothing more than a perceptron, a machine for extracting features and classifying patterns).

----------


## nonsqtr

An interesting thing about the hippocampus - it is the first area in the brain where all the sensory modalities come together, in a topographic way, and the topography is maintained throughout the limbic system AND in the cingulate cortex.

Like for example, the visual system that Smarty was talking about. It goes

Retina => LGN => V1 => V2 => V3/4/5 => temporal lobe => hippocampus

Similarly in the auditory and somatic sensory systems, same type of chain, ending in the hippocampus. The olfactory system has a slightly different input concept but it too feeds into the entorhinal cortex and from there to the hippocampus.

Single neurons in the hippocampus correlate with approach/retreat in egocentric coordinates. (This is an example of the change of basis I was talking about, the hippocampus is also fed from the parietal cortex which translates and computes body orientation in space and time).

The hippocampus is also the location of one of the major kinds of short term memory (a timeline function at the far left end of the sensory timeline - it's like a buffer, and its outputs feed forward into the basal forebrain structures on the opposite side of the timeline).

The hippocampus also handles "attention", it determines which sensory entities are being tracked. What it's doing is "aligning" the input from all the different sensory modalities, and determining for example which auditory signals may be related to which visual signals for any given object. Since the inputs may not occur at the same time, a buffer is needed.

One of the major outputs of the hippocampus feeds into the septal region at the base of the forebrain. The septal areas like N. accumbens are involved among other things with "reward", and so, there must be a correlation of which sensory input is rewarding ("approach") and which is an obstacle ("navigate").

And then, once a set has been selected for tracking, with emotional value attached, it is fed into the Boltzmann machine in the orbital frontal areas for optimization and the striatum for execution.

All of this is perfectly logical if you're looking at a timeline, and it's confusing as hell and makes no sense at all if you're not.

The hippocampus lives at the far left end of the timeline at T << 0. This statement is supported by both anatomy and electrophysiology, as well as hundreds of ablation and lesion conditions in human beings and animals.

The subjective condition of a person without a hippocampus, is somewhere between alcoholic Korsakoff's syndrome and nitrous oxide. Such people can't transfer anything into long term memory. Because the buffer is gone, it's non-functional.

----------


## nonsqtr

So then, what the N. Accumbens does, is select behavior based on the meaning and type of reward.

The N. Accumbens is fed by dopamine neurons at the top of the midbrain, in a way that is consistent with a teaching signal. Dopamine neurons reflect the probability of reward. The N. Accumbens evaluates that probability in the context of other possible rewards, and selects behavior accordingly. (Obviously, reward is an egocentric concept and it's being attached to outcomes in an egocentric reference frame).

For example - a lesioned rat is "impulsive". A normal rat will forego a small reward if it knows a bigger reward is coming. A lesioned rat has severe problems with "delayed reinforcement".

The point being, the N. Accumbens handles the "when" associated with expected reward. It suppresses (constrains) the initiation of goal directed behavior. This is an example of the processing I was talking about along T > 0, where motor possibilities are gradually reduced as we move inwards towards the singularity.

Subjectively, the N. Accumbens is handling *choice*. Do I go after this reward or that one? What are the chances I can get either one, and how much are they worth? Nah, I don't think I'll go for that one, I'll wait for the big enchilada.

N. Accumbens is controlled in part by vmPFC, the ventromedial prefrontal cortex which handles social behavior and social meaning. 

There are extensive executive functions in the frontal lobe that operate in "distributed time" on the timeline as a whole. For example the P300 is a whole-brain event that has target-related generators in the parietal and cingulate cortex and novelty-related activators in the prefrontal areas.

https://en.wikipedia.org/wiki/P300_(neuroscience)

----------


## nonsqtr

The ventral tegmental area has the dopamine neurons.

Locus coeruleus is the noradrenaline neurons in cell group A6, which feed up into the hippocampus and help regulate "attention".

The cingulate cortex is the blue-shaded part of the cerebral cortex.

----------


## nonsqtr

The cingulate cortex



feeds into a tiny little area near the hippocampus called the subiculum.

There are basically two halves distinguishable anatomically, a front half and a back half. The front half is involved with error and conflict processing. The back half handles emotional significance regardless of valence (that is, identifying objects of emotional value regardless of whether that value is positive or negative, and then tracking these objects/values along the timeline).

Both halves are also implicated in schizophrenia.

Cingulate cortex - Wikipedia

Posterior cingulate cortex - Wikipedia

----------


## patrickt

If consciousness interest you, I recommend The Origin of Consciousness in the Breakdown of the Bicameral Mind by Julian Jaynes.

The Origin of Consciousness in the Breakdown of the Bicameral Mind - Kindle edition by Jaynes, Julian. Politics  Social Sciences Kindle eBooks @ Amazon.com.

I was sent out of town on business and looking forward to boring evenings in a hotel so I bought the book and read it on the trip. I was surprised at how interesting it was. Now, I might read it again.

----------

nonsqtr (09-11-2020)

----------


## nonsqtr

> If consciousness interest you, I recommend The Origin of Consciousness in the Breakdown of the Bicameral Mind by Julian Jaynes.
> 
> The Origin of Consciousness in the Breakdown of the Bicameral Mind - Kindle edition by Jaynes, Julian. Politics  Social Sciences Kindle eBooks @ Amazon.com.
> 
> I was sent out of town on business and looking forward to boring evenings in a hotel so I bought the book and read it on the trip. I was surprised at how interesting it was. Now, I might read it again.


I have a world class Julian Jaynes story.

Unfortunately I can't (or won't) repeat it here (out of respect, cause I liked the guy).

My office used to be three doors down from his, he used to come in at 4 in the morning when I was working with the rats.

I like Jaynes' definition. It's a tad nit-picky, but IMO he's on the right track.

I used his definition earlier in this thread.

----------


## nonsqtr

UK mathematician wins richest prize in academia | Science | The Guardian

----------

Oceander (09-11-2020)

----------


## Oceander

> UK mathematician wins richest prize in academia | Science | The Guardian


 @nonsqtr

You, by any chance?

----------


## nonsqtr

> @nonsqtr
> 
> You, by any chance?


Nah, I'm a musician.  :Smile: 

Reservoir computing - Wikipedia

This is the subspace amplification I was talking about.

The "task space" is a subspace of all available task-related scenarios.

It's not "all of memory", it's just the parts of memory that are relevant to the task.

A neural network model for the orbitofrontal cortex and task space acquisition during reinforcement learning

----------


## Oceander

> Nah, I'm a musician. 
> 
> Reservoir computing - Wikipedia
> 
> This is the subspace amplification I was talking about.
> 
> The "task space" is a subspace of all available task-related scenarios.
> 
> It's not "all of memory", it's just the parts of memory that are relevant to the task.
> ...


Musicians can be math whizzes, too!

----------


## nonsqtr

Here's how you get a timeline from the cerebral cortex:



These are "functional" locations, deciphered from the wiring pattern.

----------


## nonsqtr

Hm. It's a hot dog with a handle.

https://europepmc.org/articles/pmc12...00107-0055.pdf

----------


## patrickt

> Musicians can be math whizzes, too!


My sons a geek and his interest in music was mathematical.

----------

Oceander (09-11-2020)

----------


## Oceander

> My sons a geek and his interest in music was mathematical.



Music is very - or at least can be - very interrelated with mathematics.

----------


## Physics Hunter

> Bosh. I was doing neural network modeling before anyone else was. I got hired by Fischer Black specifically for my neural network knowledge, and that was back in 1984. I'll put my knowledge of neural networks both real and artificial, against anyone in the world bar none. 
> 
> Like I said, Marvin Minsky was an idiot. The guy who "proved" that neural networks couldn't do anything interesting. "Modeling" is highly overrated. You gotta know what you're looking at before you try to model it, and trust me, VERY few of the so-called "experts" know what they're looking at. Neural networks are not perceptrons, they're something entirely different.


Yeah, I have found few neural models that I like in the sense that they model biology in any meaningful way.
Then again, being a Physics guy I do not like many models, people cut too many corners.
That got me my first DARPA entre.  Something about missiles...

The Perceptron debacle was just stupid.  "Hey, I created a shitty model of synapses that can't do a Nand gate.  Let's give up on the entire idea..."

All that said, connectionist approaches, that I generally dislike because of the lack of explanation..., are useful for parallel stuff like analyzing pictures (and thus video).

----------


## Physics Hunter

> Music is very - or at least can be - very interrelated with mathematics.


It is, it's just that most musicians don't know it.

----------

nonsqtr (09-12-2020)

----------


## nonsqtr

> Yeah, I have found few neural models that I like in the sense that they model biology in any meaningful way.
> Then again, being a Physics guy I do not like many models, people cut too many corners.
> That got me my first DARPA entre.  Something about missiles...
> 
> The Perceptron debacle was just stupid.  "Hey, I created a shitty model of synapses that can't do a Nand gate.  Let's give up on the entire idea..."
> 
> All that said, connectionist approaches, that I generally dislike because of the lack of explanation..., are useful for parallel stuff like analyzing pictures (and thus video).


Yes. Your name implies you like physics - this whole set of discussions started with me trying to point out to    @UKSmartypants that information is more fundamental than geometry.

So if you like physics, you'll appreciate this viewpoint.

Consider the second order equations, like, the wave equation, the heat equation, fluid dynamics...there's a whole ton of em.

There are basically three kinds of 2nd order pde's: elliptic, parabolic, and hyperbolic.

Just so I don't have to use math symbols, I'll write the second order derivatives as D - so we're looking at equations of the form

aDxx + bDxy + cDyy + (r dx + s dy + z) = 0

or the simple wave equation 

Dtt = c Dxx

The first version is hyperbolic if b^2 - ac > 0, parabolic if = 0, and elliptic if < 0.

The hyperbolic waves "propagate" - that is to say, if you perturb the equation at a single point in spacetime the effects are not felt everywhere at the same time - the information "propagates" along the characteristics of the equation. Characteristics are the only curves along which solutions (with smooth parameters) can have discontinuous derivatives.

Elliptic equations have no real characteristic curves, so there is no "propagation".

The parabolic versions "diffuse", and if you're really a math wiz you can look at the generalization of all this to arbitrary shapes

Ricci flow - Wikipedia

The point being, that there is another meaningful way of understanding this:

CxDx = 0 where Cx is a connection.

In an elliptic setting the information from a perturbation is felt "everywhere all at once".

In cosmology this scenario defines the deSitter, Minkowski, and anti-deSitter versions of the universe.

In the brain, elliptic manifolds are a piece o cake, and instead of describing steady state scenarios like they do in most of classical physics, they describe the connections where the time intervals cover the same point.

In other words - let's say you have a time sequence

A B C D

and if you map it onto a linear coordinate system you can permute the mapping order, so for example

A B C D => 1 2 3 4

could become

A B C D => 2 4 1 3

Well, if the sequence on the right represents "time" you're in trouble, unless you're in an elliptic setting.

It turns out, many kinds of stochastic equations naturally give you exactly the elliptic shape you need.

So like, if I have a neural network whose connectionist planes look like A => B => C => D and they're mapped to time like the visual pathway we were talking about (or the auditory or any other), I can put a layer "across" the top of it that spans the entire sequence, and in effect, this layer no longer cares what time it is, because now the mapping looks like (A, B, C, D) => N where N is the new layer. It's parallel instead of sequential, so all points in time end up being equivalent.

This does lots of things for us (for one, it gives us time-invariant representations), but what should register is that time becomes "fuzzy" this way. Which means that it becomes somewhat difficult to recover "points" in time -

So what you see in the brain, is that there are two whole separate and distinct systems, one processing the "precision of time" and the other processing "no time at all". The first one involves the cerebellum, like when you're trying to time your fingers really fast when you're playing the piano. The second one involves a mapping in the cerebral cortex which "may" be controlled in part by the inferior parietal lobe, that ends up being "timeless", and where these two mappings come together is where you find the brain areas having to do with "delay", like delayed reinforcement.

----------


## East of the Beast

I bounce between streams of consciousness all the time.What is happening now in this present world and the countless multitudes assembling on the other side for the grand finale ......Victory is waiting!..it will be Glorious!

----------


## East of the Beast

> My sons a geek and his interest in music was mathematical.


The entire universe is mathematically calculated to flawless perfection

----------


## Physics Hunter

> Yes. Your name implies you like physics - this whole set of discussions started with me trying to point out to    @UKSmartypants that information is more fundamental than geometry.
> 
> So if you like physics, you'll appreciate this viewpoint.
> 
> Consider the second order equations, like, the wave equation, the heat equation, fluid dynamics...there's a whole ton of em.
> 
> There are basically three kinds of 2nd order pde's: elliptic, parabolic, and hyperbolic.
> 
> Just so I don't have to use math symbols, I'll write the second order derivatives as D - so we're looking at equations of the form
> ...



Like?

Your name implies that you are illogical.  See how that works?

If we cannot agree on simple facts then more complex discussions are not possible.

----------


## nonsqtr

> Like?
> 
> Your name implies that you are illogical.  See how that works?
> 
> If we cannot agree on simple facts then more complex discussions are not possible.


No, my name implies that I don't listen to CNN.

Here, look - this is "simulation" today.

Neuromorphic Computing - Next Generation of AI

I said "allocation" - here's one kind of allocation and what we know about it:

Neuronal memory allocation - Wikipedia

But the allocation I was referring to, is more like the real time allocation of computational resources, like in networked (cloud, or plain old parallel) computing.

Remember, everything I've said pertains to an infinitesimal neighborhood around t=0. You have to do some mental gymnastics to get from the connectionist network to the topology of the active process it supports, but it's no more difficult than a quantum field.

You can look through the literature, the idea I'm proposing is already "implicitly" instantiated in many places, like for example scale aggregation networks where they do up- and down-convolutions across the timeline. (They just don't use the same vocabulary, is all).

It's not about the connections anymore (hasn't been for a long time), that's why I showed you the adaptive resonance bit. It's about dynamics in a topology, the manifold in question being an active process "supported by" the connections in question.

For example - in a Boltzmann machine you have a "thermodynamic" process where the network essentially "cools down" to get to it's favored state. Well, that doesn't work at all unless the population dynamics are such that your network is allowed to occasionally (or periodically) cool down this way.

In real life the "cooling down" process is embedded in a brain wave, and it has to be somewhat synchronous across the population (that is to say, most of the network has to cool down at approximately the same time and at approximately the same rate).

When you discover how the "cooling down" actually works, it boils down to neurons changing states at random times. If the times aren't random, it doesn't work anymore, because of the information.

In other words, the update has to be "diffuse" relative to the timeline. I call it "distributed" to distinguish it from a diffusion process - it means the information is spread out ("diffuse", distributed) in space and time.

Which makes some connections more accessible, and others less so. The machine learning types define learning as conditional expectation given some function, and that is precisely what is being instantiated in my timeline model, which makes learning as simple as taking a snapshot, which has already been studied to death as early as the Perceptron. And, there is already a whole literature about "expectation propagation" in topological neural networks, which simply has to be translated to a microscopic level).

The answer to your question is, "like" the N Accumbens discussed in the earlier post, which is involved in delayed "reinforcement", or delayed "reward".

One has to abstract a bit when thinking about the brain. "Reward" is no different from any other sensory modality, it happens to come from homeostasis instead of the retina, but in every other regard it's just another signal from another peripheral system. You can anticipate reward just as easily as you can anticipate speech or the position of a bird flying across your visual field.

The brain has "separate systems" by modality, which is the first clue, and they all come together at the same place, which is the second clue, and the topography is preserved in downstream mappings "until" we get into the areas handling executive functions (which operate on all ongoing operations simultaneously).

Another clue is that in addition to per-object "attention, there is attention by modality. You can pay attention to a visual target while totally disregarding auditory input. Turns out, this question relates to why there are two halves of the brain, simultaneously learning each others' subspaces. And it also relates to the diffuse embedding of neuronal updates in time.

----------


## nonsqtr

Oh I see - "connection"ist neural network, as distinct from the mapping "connections" on affine surfaces.

Hm... well... it's a collision, what can I say.

Lemme think about it for a minute...

Hopefully the meaning is clear in context so far.

----------


## nonsqtr

The reason we need Monte Carlo methods is because we can not update all the neurons at the same time. It's more than just a digital computing artifact, the 100% synchronous system actually does not work. It lets you do a Perceptron, but it's totally incapable of thermodynamics or annealing. (If you see them in your synchronous computer models, they're artifacts).

Ask yourself - how far apart do the updates actually have to be, for the system to still work?

The answer is: dT.

The updates can be "infinitesimally" close, and the system still works.

But put them on top of each other, and it doesn't work anymore.

And then you can continue from here, like what fraction of the population can be synchronous and still have the whole system update properly - there's a whole huge literature around this stuff which you can peruse as your time permits.

----------


## nonsqtr

IEEE: Neuromorphic Systems Hit 8 Million Neurons, 100 Million By 2020

Not to be outdone...

A Rare Peek into IBMâs True North Neuromorphic Chip

----------


## nonsqtr

Expectationâmaximization algorithm - Wikipedia

----------


## nonsqtr

So, to be perfectly honest, I'm not really interested in computations, except insofar as they're relevant to the subjective world. What we call "experience", but including concepts like "free will".

Free will is kinda like "I can do whatever I want".

And, my opinion is that neuroscience has/is adequately addressing the "want" part - it's the "I" part they know nothing about. (The "whatever" part is covered by information geometry, that's the subspace amplification I was talking about).

What can you say about "I" right off the bat?

Well, it's a reference frame. When my head tilts, "I" go with it.

It's an egocentric reference frame. In my perception I measure things as distance "from me" and angle relative to me.

"Me" looks forward, kinda normal to the plane of the eyes.

In my experience, the future is ahead of me and the past is behind.

These simple observations are a first step, they're probably sufficient for some purposes. But they're not enough to describe "free will". For that we need an actual mechanism, because part of the deal is it gets "instantiated".

Well, we probably know enough by now to describe in general terms how this mechanism works. The timeline model I've presented dovetails perfectly with brain anatomy and physiology at every level. And, it provides a conceptual foundation for the mathematicians to do their thing. (Which I think, they've already done - it's mainly a matter of porting their work over to the world of biology instead of cosmology).

Quantum tunneling in the brain is real, it's already been demonstrated, even in small segments of dendrites. If you look at IBM's latest solid state quantum computer, it's made of phosphorus, which is paramagnetic and it's one of the most common elements in the body. It's the energy currency, it's in every cell. It's also the second messenger in many synapses. Some form of it is bound to practically every protein.

We're not exactly talking "solid" state computing, it's more like liquid state computing, the brain is wet. You can read about "spin liquids", there's some interesting concepts in that mix.

To actualize the mapping of time, all that is needed is a manifold of velocities built around dT/dt, and that is easy peasy in the brain. This allows one to construct the appropriate metric from which the volume form can be defined.

I can tell you right up front it's a stochastic metric. The brain can only define points in time "approximately". (There is no clock in the brain). The brain is naturally much better with "intervals" because that's the way neurons work. So it uses the intervals to define (approximately, sometimes to a very darn impressive precision) where the points are. The brain has a whole huge system devoted to nothing but that - it contains hardwired hardware delay lines in the range of 10 microseconds to 100 milliseconds, so it can expand the neighborhood of the origin and make the approximation more precise.

"Free will" is a survival advantage, it will always evolve in any ecosystem. But you know what, stupid earthworms with only 302 neurons already exhibit behavior that looks like "free will". So we want to be very precise with the vocabulary. Any psychologist will tell you that "free will" is a totally inadequate description of the human capacity. First of all it's not "free", there are some stringent limitations. Secondly the term "will" is a subjective description so we need to break it down into parts and translate the parts to physiology. "Will" is at LEAST the intersection of memory and homeostasis (in the form of "desire"). The timeline model makes sense of all this. No other model does, at least none that I've found. It explains 100% of the executive functioning, which is a huge deal in and of itself. And it tells us DIRECTLY how the brain really works, it practically leaps off the page at you.

----------


## nonsqtr

The quantum view -

The Posner molecule is (Ca9PO4)6.

The enzyme that cleaves the pyrophosphate into two phosphates generates entangled pairs of qubits.

https://www.kitp.ucsb.edu/sites/defa.../mpaf/p174.pdf

Posner qubits: spin dynamics of entangled Ca9(PO4)6 molecules and their role in neural processing

The cleavage of Posner molecules is under direct neural control.

----------


## UKSmartypants

> If consciousness interest you, I recommend The Origin of Consciousness in the Breakdown of the Bicameral Mind by Julian Jaynes.
> 
> The Origin of Consciousness in the Breakdown of the Bicameral Mind - Kindle edition by Jaynes, Julian. Politics  Social Sciences Kindle eBooks @ Amazon.com.
> 
> I was sent out of town on business and looking forward to boring evenings in a hotel so I bought the book and read it on the trip. I was surprised at how interesting it was. Now, I might read it again.


If I may interject for the uninitiated.....

In 1976, psychologist and Princeton lecturer Julian Jaynes published The Origins of Consciousness in the Breakdown of the Bicameral Mind, a radical detailing of mankind's ascension to a state of true consciousness. According to Jaynes, humans only developed the ability to think for themselves, consider their actions, and stand aware of their own awareness about 3,000 years ago. Before then, they took orders from voices inside their heads, which they believed to be deities. The left hemisphere of the brain would shout "jump" and the right hemisphere would say "how high?" Jaynes's major evidence for this separation is the stark contrast between Homer's Iliad, written by "non-conscious minds" and lacking in introspection of any kind, and the Odyssey, where characters reflect on their surroundings and act on their own volition. Jaynes believes that between the tellings of these two stories, Earth's sure-footed denizens realized the "words from the Gods" echoing through their minds were the product of their own instincts.  As you may expect, reviews for The Origins of Consciousness in the Breakdown of the Bicameral Mind were glowing and vicious -- "the bicameral mind" theory had holes, but Jaynes's narrative thrust left a community thirsty for answers drunk on possibility.

Jaynes's theory delves deep into the academic, but the gist is that human consciousness erupted from the bicameral dynamic through language. In the time of Iliad, man could use words, but not reckon. Only when humans could picture their own lives as narrative, use metaphors to process "me" and the vast unknown of "why we do," then they could become fully self-aware. The X-factor was stress; there was war and famine and natural disasters that couldn't be explained. These outside forces brought down the wall. It wasn't the brain that changed, but the stimuli around it.

----------


## Oceander

> If I may interject for the uninitiated.....
> 
> In 1976, psychologist and Princeton lecturer Julian Jaynes published The Origins of Consciousness in the Breakdown of the Bicameral Mind, a radical detailing of mankind's ascension to a state of true consciousness. According to Jaynes, humans only developed the ability to think for themselves, consider their actions, and stand aware of their own awareness about 3,000 years ago. Before then, they took orders from voices inside their heads, which they believed to be deities. The left hemisphere of the brain would shout "jump" and the right hemisphere would say "how high?" Jaynes's major evidence for this separation is the stark contrast between Homer's Iliad, written by "non-conscious minds" and lacking in introspection of any kind, and the Odyssey, where characters reflect on their surroundings and act on their own volition. Jaynes believes that between the tellings of these two stories, Earth's sure-footed denizens realized the "words from the Gods" echoing through their minds were the product of their own instincts.  As you may expect, reviews for The Origins of Consciousness in the Breakdown of the Bicameral Mind were glowing and vicious -- "the bicameral mind" theory had holes, but Jaynes's narrative thrust left a community thirsty for answers drunk on possibility.
> 
> Jaynes's theory delves deep into the academic, but the gist is that human consciousness erupted from the bicameral dynamic through language. In the time of Iliad, man could use words, but not reckon. Only when humans could picture their own lives as narrative, use metaphors to process "me" and the vast unknown of "why we do," then they could become fully self-aware. The X-factor was stress; there was war and famine and natural disasters that couldn't be explained. These outside forces brought down the wall. It wasn't the brain that changed, but the stimuli around it.


Other than literary stylistics, what was his evidence that humans were not self-aware in the time of the Iliad?

----------


## nonsqtr

> Other than literary stylistics, what was his evidence that humans were not self-aware in the time of the Iliad?


You have to read the book. (It's an enjoyable read, it's not that long).

His evidence is sparse because we didn't know a whole lot about "consciousness" (or even brain wiring) when he wrote the book - BUT - the concept of it is brilliant and dovetails with what we know about the brain (except for the left-brain/right-brain part, he overdoes that a bit), the concept being that the brain can attain a new equilibrium through social and cultural interaction.

----------


## UKSmartypants

> Other than literary stylistics, what was his evidence that humans were not self-aware in the time of the Iliad?


as it says,  "_Jaynes's major evidence for this separation is the stark contrast between Homer's Iliad, written by "non-conscious minds" and lacking in introspection of any kind, and the Odyssey, where characters reflect on their surroundings and act on their own volition."

_Other than ther isnt a lot. I dont subscribe to it, because  I dont think conciousness and self awareness are anything to do with it. Chimps are conscious and self aware, do they believe the voice in their heads (and they must have one,..) is a god ?

----------

Oceander (09-14-2020)

----------


## nonsqtr

K - let's talk about the visual system a bit, since people seem to know about it.

First, let's get a clear picture of the anatomy, it's important. Briefly, visual information gets into the cerebral cortex (primary visual area V1 which corresponds with occipital lobe area 17 in the anatomical charts, it's at the very back of the brain), from the retina via a relay station in the thalamus called LGN ("lateral geniculate nucleus"). This initial pathway doesn't do much of significance, a little contrast enhancement - its major feature is that it separates visual information into static and motion channels, and color and black and white channels. (These mappings overlap, and they maintain topography relative to the visual field). But it's a straight through linear pathway, retina => LGN => V1. (There are backwards connections and other interesting stuff, but by and large the initial system feeds visual information from the retina into V1 through several channels).

From V1, things split up into what are essentially two pathways: one going to the temporal lobe that has to do with object and feature recognition, the other going up into the parietal lobe that has to do with positioning of objects in space and time.

Here's the way it actually looks in a human brain:



Visual cortex - Wikipedia

Both of the visual subsystems feed into the hippocampus, via a small area of cortex immediately next to it called the parahippocampal gyrus. It looks like this:



Parahippocampal gyrus - Wikipedia

From there it enters the well know CA1 => CA3 circuitry in the hippocampus.

Frontiers | Architecture of the Entorhinal Cortex A Review of Entorhinal Anatomy in Rodents with Some Comparative Notes | Frontiers in Systems Neuroscience

Some Temporal and Parietal Cortical Connections Converge in CA1 of the Primate Hippocampus | Cerebral Cortex | Oxford Academic

Stimulation of the parahippocampal visual area in a live human brain creates intense hallucinations of entire "scenes", including people places and objects.

The most salient feature of this architecture is the splitting up of visual information into separate channels and their eventual realignment and recombination in a whole different reference frame (which is the egocentric orientation of things relative to the organism, instead of their binocular mapping in the visual field).

Note that each "stage" in the visual pathway processes information at a slightly different time - V5 is looking at what V1 saw 30 msec ago. Yet the maps from the various visual stages and channels are recombined "all together" in the parahippocampal area. From the standpoint of population dynamics this tells us there must be tracking of which features in V1 correspond with which features in V5 (say), and it has to be top down relative to the recognition of an object and its features and its position in 3d space and its changing position relative to time.

"All" sensory modalities ultimately feed into the hippocampus this way, not just vision. Part of the job of the hippocampal circuitry is to "align the maps" so objects can be tracked. Naturally, since information about the object is coming in at different times, the hippocampal circuitry must be able to link events that are happening "now" with events that happen in a neighborhood immediately around "now". Some of this linkage can be described in terms of simple delays, some of it can't.

----------


## nonsqtr

Visual processing can therefore be understood as follows:

1. Transmission of raw visual information into the cerebral cortex.
2. Feature analysis to extract "objects" from background noise.
3. Object identification, using memory.
4. Deciding if the object is of significance, and if it is,
5. Tracking it through space and time, and possibly then
6. Manipulating it to achieve some goal.

The hippocampus is involved in recognition, and tracking.

The amygdala assigns emotional significance to objects and events, so step 4.

The motor systems involved in step 6 connect with the parietal subsystem as expected, rather than the object recognition system in the temporal lobe.

It is illuminating to then understand the oculomotor side of this equation, which begins in the "frontal eye fields" in Brodmann's area 8 of the frontal lobe.

The frontal eye fields look like this:



Frontal eye fields - Wikipedia

----------


## nonsqtr

To bring an object into visual focus in the center of the visual field is to "foveate" it.

There is a separate visual subsystem running through the optic tectum ("superior colliculus") that has to do with the cat-like tracking of rapidly moving targets, but this is not the same subsystem that foveates an object after recognition.

Foveation can be a reflex (particularly if the object is considered dangerous in some way), or it can be voluntary.

There are two types of eye movements that must be coordinated between the two eyes: conjugate and vergence. Conjugate is when the two eyes move side to side together in a saccadic manner, and vergence is when they move in opposite directions for depth perception.

The eye movement pathways do the same thing the visual pathways do: they break the movement into components. The conjugate saccades go one way, the depth related vergence movements go another.

When the eye movement is actually executed, the saccade begins first, then it is interrupted in midstream and stops while the vergence movement is executed, then it resumes and completes.

Types of Eye Movements and Their Functions - Neuroscience - NCBI Bookshelf

Vergence - Wikipedia

Eye movement - Wikipedia

----------


## nonsqtr

Here's a long but thorough review, about how "context" is defined in the brain.

For example, a kitchen has a refrigerator, and a refrigerator has a certain visual shape and makes humming noises and occasionally leaks.

The role of the parahippocampal cortex in cognition

----------


## nonsqtr

So, all these "connections by area", are NOT what I'm talking about with the concept of dT/dt.

The timeline mainly exists in the LOWER layers of the cerebral cortex, specifically layer V where all the projective pyramidal cells are.

Superimposed on that, above that, living in layer II/III, are diffusely connected "associative" cells that don't adhere to the underlying topography. Vertically they are organized in a similar way, but horizontally they're not.

The two mappings cover each other but they're very different. We'd like to know about their alignment if any, or how they're embedded into each other.

This is where the topology comes in, and the quantum stuff, and the concept of the limit as dt => 0.

We're still talking about discrete v continuous.

"Discrete" essentially means quantized, which is the opposite of the smooth 4d spacetime we usually consider.

----------


## nonsqtr

So, to complete the picture about the visual system - the hippocampus feeds into an area in the base of the forebrain call the septum, and in particular with one big cluster of cells called N. Accumbens (which means "nucleus right next to the septum"). The N. Accumbens is the red dot here:



The N. Accumbens is part of a system related to reward and reinforcement. It is heavily innervated with dopamine fibers from the ventral tegmental area.

Nucleus accumbens - Wikipedia

What we have learned about the N Accumbens is there are two parts of reward, the part that says "I like" and the part that says "I want more". These two parts are separable, anatomically and physiologically.

Turns out there is a "hedonistic hot spot" in the N Accumbens which is about 1 cu mm and stimulation of which produces a sensation of "liking".

Destruction of the N Accumbens also eliminates the hippocampal theta rhythm, which is seen mostly in times of cortical desynchrony (like, during attention).

Destruction of theta makes reinforcement learning impossible, and the understanding of how and why is still a hot topic in active research.

But attached to each "object", visual or otherwise, is an emotional value (amygdala) and a reward value (N. Accumbens) - and the areas around the N Accumbens (the striatum, including the caudate nucleus) encode the motor strategies for how to acquire the reward, either now or in the future.

The most interesting aspect of all this is the "expectation" of reward. The brain encodes feel-good stimuli in such a way that whenever the object is seen again, there is an expectation of reward. And, during the learning phase, the object precedes the reward but later on there is an "expectation of imminent reward" which can be very pronounced even in animals.

----------

Oceander (09-14-2020)

----------


## nonsqtr

The "encoding" that happens as we proceed through any sensory pathway, appears to be progressively more comprehensive. First there is just local orientation and contrast enhancement, then there is edge detection along with co-movement analysis to identify "objects", then the objects are located in space and time, and then they're associated with feelings, the simplest of which are pleasure and pain - which are the beginnings of "meaning" in the organism's internal reference frame.

The motor systems are organized the same way, just the direction of the information flow is reversed. A whole-body trajectory gets decomposed into individual muscle movements. I could show you all those central and peripheral motor systems, but you can easily find them yourself if you're interested.

Quickly in passing though, I'd like to point out this one area which is at the very opposite side of the brain from the visual system, it's right up front just over the eye sockets, it's called the orbitofrontal cortex. (It includes the vmPFC that's appeared in previous unrelated discussions of psychopathy and etc). This is what it looks like:



Focal epilepsy can occur anywhere in the brain, and sometimes the only way to treat severe cases us to lesion the focus. Well, first they have to find it - so they poke around in the patients' brain with an electrode (usually they know where to look) until the patient reports the "aura" (the patient is awake and alert while they're doing this).

Many epileptics, before a seizure, will experience an "aura", it's basically a memory or a combination of sensory stimuli but it can be very specific - and it's usually identical and consistent from one seizure to the next.

So, in temporal lobe epilepsy which is most common, they're poking around in the general vicinity of the hippocampus and the amygdala, and as expected, the auras dovetail with the processing previously discussed, they're brief snippets of memory or sensation in context, like the patient might report that they briefly smell grandma's cooking just before the seizure.

These auras do not depend on current experience or what is currently being attended to. They're not dependent on activity in the hippocampus or cingulate cortex.

However - when the epilepsy occurs in the orbitofrontal area, things are very different.

First of all, the orbitofrontal area is involved in every significant high level brain pathology, including depression, bipolar disorder, adhd and autism spectrum disorder, ocd and borderline personality, schizophrenia, psychopathy and conduct disorder... Direct brain stimulation of the orbitofrontal cortex "immediately" alleviates depression.

Deep Brain Stimulation of Orbitofrontal Cortex Relieves Depression | The Scientist MagazineÂ®

So the scientists got curious and started poking around elsewhere in the orbital areas, and what they found is an encoding that includes not only reward and punishment (in the form of desire and aversion), but also ANTICIPATION of reward, AND the handling of errors and mismatches between expected reward and actual reward.

Changes in subjective experience elicited by direct stimulation of the human orbitofrontal cortex | Neurology

People with orbitofrontal damage exhibit "impulsive behavior", and they have a difficult time unlearning stuff when the conditions change.

So here's really the reason I had to show you all this and build so much context:

You can see that the brain is very logically organized, it splits information into separate processing pathways and progressively encodes information in more comprehensive ways as we move centrally in the nervous system.

BUT - experience is still experience, as you stimulate the cerebral cortex with an electrode. If you poke around in the occipital lobe you "see" things, if you poke around in the medial temporal lobe you "hear" things, if you stimulate in M2 you have an uncontrollable desire to move a digit - if you stimulate near the hippocampus you smell grandma's cooking, and if you stimulate in the orbitofrontal area your mood changes and you're not depressed anymore. In all cases you get 'experience", it's just the "type" of experience that changes.

This pretty much tells us that experience is something different - and it probably has very little to do with brain wiring and neural encoding. The intensity of an occipital experience is the same as the intensity of a frontal experience, it's just the quality that differs.

And, here's the kicker - you don't have to stimulate in the brain to get an "experience". You can simply prick your finger with a pin, that's an experience. You'll feel it, you'll say ouch.

It's like, it makes no difference "where" in the neural network you perturb, you still get an experience. So the heirarchical models (anatomical, psychological) aren't going to help us much.

But there is a way to understand this. It begins with simple stuff like SETS. In the skin surface there are a million glabrous receptors, that's "discrete" - but if you ask the subject what the "just noticeable difference" is, you get a different answer, it's almost like it's continuous. Same in the retina, there are a million photoreceptors but the "experience" is continuous. Why? Well, the easy thing is there's overlap, right? The receptive fields of neighboring receptors overlap each other. One receptive field isn't a "point", it's a distribution in space. In the eye the receptive fields aren't "points", they're Gaussian - and in the first processing layer they become "Mexican hats", so we have Gaussian neurons sampling spacetime and hardware-filtering the information before it's even encoded.

In the orbitofrontal cortex, the expectation of reward is derived anatomically by connections, and from information in memory. But the "experience" of that expectation is something entirely different, it's a whole-brain topological process that depends on the receptor in your finger as much as it does in the neuron in your forebrain.

Once this is understood and accepted, the real discussion can begin. People have already stumbled on some of this stuff when they play with deep learning and quantum computers and such, but the brain has many secrets we've yet to unlock.

What's clear though, is that the embedding between the discrete and the continuous is fundamental. Processing compartments in the brain are only 1 cubic micron! And they're very sophisticated, they're whole quantum computers unto themselves.

----------

