Connectionists: Early history of symbolic and neural network approaches to AI

[Grossberg, Stephen]

Dear Danny,

Thanks for your interesting comments below!

I will reply to your last question, namely:

“My question, to those who believe that symbols and the concepts to which they refer are represented in a complex distributed manner, is the following: Are such representations likely to be static in nature (e.g. a single activation within a small region of an embedding space),  or are they likely to be dynamic in nature (e.g. a series of activations within a more complex temporal-spatial manifold of an embedding space).” [boldface mine].

I will focus on one aspect of this “big” question, notably how Adaptive Resonance Theory, or ART, explains how we learn categories that may represent symbols and concepts of variable abstractness, ranging from concrete and specific to general and abstract.

All learning in ART is regulated by interactions between an Attentional System, in which categories are learned and remembered, and an Orienting System, which enables ART to respond to novel situations.

These systems obey computationally complementary laws, in keeping with the fact that the Attentional System represents information that becomes expected as it is learned, whereas the Orienting Systems enables novel information to be categorized by the Attentional System.

The Attentional System is associated with processes like attention, learning, recognition, and consciousness, whereas the Orienting System is associated with processes like orienting, hypothesis testing, and memory search.

INTERACTIONS between these systems make learning in a changing world possible.

See the attached figure for a schematic of an ART Hypothesis Testing and Learning Cycle.

See the second attached figure for how this memory search cycle explains data about Event Related Potentials, or ERPs.

Back to your question:

As learning of a category proceeds, it converges upon a stable critical feature pattern that embodies the information that the category represents, in several senses: Critical features are the ones that are incorporated through learning by the adaptive weights in its bottom-up filter and top-down expectation. They are the features to which its top-down expectation pays attention, and the ones that drive the predictions that the category controls. All other features are suppressed as irrelevant outliers.

In terms of your question, this means that there is  controlled refinement of category learning, one that enables the category to be stably remembered, and to thereby avoid the problem of catastrophic forgetting.

The degree to which a category’s representation can vary depends upon a vigilance parameter that is computed in the Orienting System. Vigilance determines how big a mismatch of new information with available categories will be tolerated before a memory search for a new category is triggered.

If vigilance is chosen low, then general and abstract categories are learned. Here, a high degree of variability is tolerated.

If vigilance is chosen high, then concrete and specific categories are learned, such as a frontal view of your mother’s face. Here, very little variability is tolerated.
I and my collaborators have discovered a lot about how vigilance works, including its anatomical, neurophysiological, biophysical, and biochemical realization in our brains. Here are a few articles about it.

The concept was introduced in the 1987 in an oft-cited article with Gail Carpenter that proves mathematical theorems about how it works during category learning:

Carpenter, G.A., and Grossberg, S. (1987). A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer Vision, Graphics, and Image Processing, 37, 54-115.
https://sites.bu.edu/steveg/files/2016/06/CarGro1987CVGIP.pdf

Neurobiological details followed in a series of later articles. I cite two of them here. See my web page sites.bu.edu/steveg for others:

Grossberg, S. and Versace, M. (2008). Spikes, synchrony, and attentive learning by laminar thalamocortical circuits. Brain Research, 1218, 278-312.
https://sites.bu.edu/steveg/files/2016/06/GroVer2008BR.pdf

Palma, J., Versace, M., and Grossberg, S. (2012). After-hyperpolarization currents and acetylcholine control sigmoid transfer functions in a spiking cortical model. Journal of Computational Neuroscience, 32, 253-280.
https://sites.bu.edu/steveg/files/2016/06/PalmaGrossbergVersaceTR2012.pdf

A good place for one-stop shopping that offers a self-contained and non-technical overview and synthesis of my work is my Magnum Opus

Conscious Mind, Resonant Brain: How Each Brain Makes a Mind
https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/dp/0190070552

Best,

Steve

From: Danny Silver <danny.silver at acadiau.ca>
Date: Saturday, February 24, 2024 at 9:13 PM
To: Jeffrey Bowers <J.Bowers at bristol.ac.uk>, Grossberg, Stephen <steve at bu.edu>, KENTRIDGE, ROBERT W. <robert.kentridge at durham.ac.uk>, Gary Marcus <gary.marcus at nyu.edu>, Laurent Mertens <laurent.mertens at kuleuven.be>
Cc: connectionists at mailman.srv.cs.cmu.edu <connectionists at mailman.srv.cs.cmu.edu>
Subject: Re: Connectionists: Early history of symbolic and neural network approaches to AI
Dear Jeff, Stephen and others … The encoding of a concept or a symbol associated with a concept using a single neuron (grandmother cell) would be a poor choice both from a representational perspective as well as from a functional perspective for a lifelong learning and reasoning agent.

First and foremost, representational redundancy make sense for an agent that can suffer physical damage. Steve’s position in the email below seems to support this. It also makes sense to encode representation in a distributed fashion for the purposes of new concept consolidation and fine tuning of existing concepts and its variants. This would seem fundamental for a lifelong agent that must learn, unlearn  and relearn many concepts over time using a finite amount of representation (memory).


From a functional perspective an intelligent agent “knows” concepts through the integration of several sensory and motor modalities that provide primary inputs as well as secondary contextual information.  When an intelligent agent thinks of a “cat” it does so in the context of hearing, seeing, chasing, touching, smelling the animal over a variety of experiences.  I suspect this is related to Steve’s clarification of the complexity of what we see happening in the human nervous system when representing a concept.


Also note that, when you ask a child if the animal in front of her is a “cat” her response verbally or in writing is a complex sequence of motor signals that are more like a song than a single representation.   This is quite different from the simple one-hot encodings output by current ANNs. Such a complex output sequence could be activated by a signal neuron, but that is certainly not a requirement, nor does a grandmother cell seem likely if the encoding of a concept is based on several sensory modalities that must deal with perceptual variations over time and space.


My question, to those who believe that symbols and the concepts to which they refer are represented in a complex distributed manner, is the following: Are such representations likely to be static in nature (e.g. a single activation within a small region of an embedding space),  or are they likely to be dynamic in nature (e.g. a series of activations within a more complex temporal-spatial manifold of an emedding space).


Danny Silver


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From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu> on behalf of Jeffrey Bowers <J.Bowers at bristol.ac.uk>
Sent: Saturday, February 24, 2024 5:06 PM
To: Grossberg, Stephen <steve at bu.edu>; KENTRIDGE, ROBERT W. <robert.kentridge at durham.ac.uk>; Gary Marcus <gary.marcus at nyu.edu>; Laurent Mertens <laurent.mertens at kuleuven.be>
Cc: connectionists at mailman.srv.cs.cmu.edu <connectionists at mailman.srv.cs.cmu.edu>
Subject: Re: Connectionists: Early history of symbolic and neural network approaches to AI

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I think this is where terminology is confusing things.  I agree that ART (and all other neural architectures) is “far from being a ‘grandmother cell’”.  The question is whether a neural architecture includes grandmother cells – that is, a unit high in a hierarchy of units that is used to classify objects. On distributed systems there is no such unit at any level of a hierarchy – it is patterns of activation all the way up. By contrast, on grandmother cell theories, there is an architecture that does include units that code for an (abstract) category.  Indeed, even all current fashionable DNNs include grandmother cells whenever they use “one hot encoding” of categories (which they almost always do).

So, just as grandmother cells can easy be falsified if you define a grandmother cell that only responds to one category of input, you can falsify a grandmother cells by claiming that it requires only one cell to be active in a network.  The classic question was whether simple cells mapped onto complex cells, that mapped onto more complex cells, that eventually mapped onto singe neurons that code for one category.  I’m a big fan of ART models, and in my way of thinking, your models include grandmother cells (other than perhaps your distributed ART model, that I’m not so familiar with – but I’m thinking that does not include a winner-take-all dynamic).


Jeff

From: Grossberg, Stephen <steve at bu.edu>
Date: Saturday, 24 February 2024 at 16:46
To: Jeffrey Bowers <J.Bowers at bristol.ac.uk>, KENTRIDGE, ROBERT W. <robert.kentridge at durham.ac.uk>, Gary Marcus <gary.marcus at nyu.edu>, Laurent Mertens <laurent.mertens at kuleuven.be>
Cc: connectionists at mailman.srv.cs.cmu.edu <connectionists at mailman.srv.cs.cmu.edu>, Grossberg, Stephen <steve at bu.edu>
Subject: Re: Connectionists: Early history of symbolic and neural network approaches to AI
Dear Jeff,

Thanks for your supportive remark.

One thing to keep in mind is that, even if a recognition category has a compressed representation using a small, compact population of cells, a much larger population of cells is needed for that category to work.

For starters, even a compact category representation is activated by a distributed pattern of activation across the network of feature-selective cells with which the category resonates via excitatory feedback signals when it is chosen.

In the case of invariant object categories, a widespread neural architecture is needed to learn it, including modulatory signals from the dorsal, or Where, cortical stream to the ventral, or What, cortical stream where the category is being learned.

These modulatory signals are needed to ensure that the invariant object category binds together only views that belong to that object, and not irrelevant features that may be distributed across the scene.

These modulatory signals also maintain spatial attention on the invariant category as it is being learned. I call the resonance that accomplishes this a surface-shroud resonance. I propose that it occurs between cortical areas V4 and PPC and triggers a system-wide resonance at earlier and later cortical areas.

Acting in space on the object that is recognized by the invariant category requires reciprocal What-to-Where stream interactions. These interactions embody a proposed solution of the Where’s Waldo Problem.

I have attached a couple of the figures that summarize the ARTSCAN Search architecture that tries to explain and simulate these interactions.

This neural architecture is far from being a “grandmother cell”!

My Magnum Opus provides a lot more modeling explanations and data about these issues:
https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/dp/0190070552

Best again,

Steve



From: Jeffrey Bowers <J.Bowers at bristol.ac.uk>
Date: Saturday, February 24, 2024 at 4:38 AM
To: Grossberg, Stephen <steve at bu.edu>, KENTRIDGE, ROBERT W. <robert.kentridge at durham.ac.uk>, Gary Marcus <gary.marcus at nyu.edu>, Laurent Mertens <laurent.mertens at kuleuven.be>
Cc: connectionists at mailman.srv.cs.cmu.edu <connectionists at mailman.srv.cs.cmu.edu>, Grossberg, Stephen <steve at bu.edu>
Subject: Re: Connectionists: Early history of symbolic and neural network approaches to AI
Dear Steve, I agree, the grandmother cell theory is ill defined, and it is often defined in such a way that it is false.  But then people conclude from that that the brain encodes information in a distributed manner, with each unit (neuron) coding for multiple different things.  That conclusion is unjustified.  I think your ART models provide an excellent example of one way to implement grandmother cell theories.  ART can learn localist codes where a single unit encodes an object in an abstract way.  The Jennifer Aniston neuron results are entirely consistent with your models, even though a given neuron might respond above baseline to other inputs (at least prior to settling into a resonance).  Jeff

From: Grossberg, Stephen <steve at bu.edu>
Date: Friday, 23 February 2024 at 18:12
To: Jeffrey Bowers <J.Bowers at bristol.ac.uk>, KENTRIDGE, ROBERT W. <robert.kentridge at durham.ac.uk>, Gary Marcus <gary.marcus at nyu.edu>, Laurent Mertens <laurent.mertens at kuleuven.be>
Cc: connectionists at mailman.srv.cs.cmu.edu <connectionists at mailman.srv.cs.cmu.edu>, Grossberg, Stephen <steve at bu.edu>
Subject: Re: Connectionists: Early history of symbolic and neural network approaches to AI
Dear Jeff et al.,

The term Grandmother Cell was a good heuristic but, as has been noted in this email thread, is also ill-defined.

It is known that there are cells in anterior Inferotemporal Cortex (ITa) that may be called invariant object recognition categories because they respond to a visually perceived object from multiple views, sizes, and positions.

There are also view-specific categories in posterior Inferotemporal Cortex (ITp)  that do not have such broad invariance.

I list below several of our articles that model how invariant object categories and view-specific categories may be learned. We also use the modeling results to explain a lot of data.

Just a scan of the article titles illustrates that there has been a lot of work on this topic.

Fazl, A., Grossberg, S., and Mingolla, E. (2009). View-invariant object category learning, recognition, and search: How spatial and object attention are coordinated using surface-based attentional shrouds. Cognitive Psychology, 58, 1-48.
https://sites.bu.edu/steveg/files/2016/06/FazGroMin2008.pdf

Cao, Y., Grossberg, S., and Markowitz, J. (2011). How does the brain rapidly learn and reorganize view- and positionally-invariant object representations in inferior temporal cortex? Neural Networks, 24, 1050-1061.
https://sites.bu.edu/steveg/files/2016/06/NN2853.pdf

Grossberg, S., Markowitz, J., and Cao, Y. (2011). On the road to invariant recognition: Explaining tradeoff and morph properties of cells in inferotemporal cortex using multiple-scale task-sensitive attentive learning. Neural Networks, 24, 1036-1049.
https://sites.bu.edu/steveg/files/2016/06/GroMarCao2011TR.pdf

Grossberg, S., Srinivasan, K., and Yazdabakhsh, A. (2011). On the road to invariant object recognition: How cortical area V2 transforms absolute to relative disparity during 3D vision. Neural Networks, 24, 686-692.
https://sites.bu.edu/steveg/files/2016/06/GroSriYaz2011TR.pdf

Foley, N.C., Grossberg, S. and Mingolla, E. (2012). Neural dynamics of object-based multifocal visual spatial attention and priming: Object cueing, useful-field-of-view, and crowding. Cognitive Psychology, 65, 77-117.
https://sites.bu.edu/steveg/files/2016/06/FolGroMin2012.pdf

Grossberg, S., Srinivasan, K., and Yazdanbakhsh, A. (2014). Binocular fusion and invariant category learning due to predictive remapping during scanning of a depthful scene with eye movements. Frontiers in Psychology: Perception Science, doi: 10.3389/fpsyg.2014.01457
https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2014.01457/full

More articles on related topics can be found on my web page sites.bu.edu/steveg, including how humans can search for an object at an expected position in space, even though its invariant object category representation cannot be used to do so.

Best,

Steve
From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu> on behalf of Jeffrey Bowers <J.Bowers at bristol.ac.uk>
Date: Thursday, February 22, 2024 at 11:11 AM
To: KENTRIDGE, ROBERT W. <robert.kentridge at durham.ac.uk>, Gary Marcus <gary.marcus at nyu.edu>, Laurent Mertens <laurent.mertens at kuleuven.be>
Cc: connectionists at mailman.srv.cs.cmu.edu <connectionists at mailman.srv.cs.cmu.edu>
Subject: Re: Connectionists: Early history of symbolic and neural network approaches to AI
Good point, I should not have used simple cells as an example of grandmother cells.  In fact, I agree that some sort of population coding is likely supporting our perception of orientation.  For example, simple cells are oriented in steps of about 5 degrees, but we can perceive orientations at a much finer granularity, so it must be a combination of cells driving our perception.

The other reason I should have not used simple cells is that grandmother cells are a theory about how we identify familiar categories of objects (my grandmother, or a dog or a cat).  Orientation is a continuous dimension where distributed coding may be more suitable.  The better example I gave is the word representation DOG in the IA model.  The fact that the DOG detector is partly activated by the input CAT does not falsify the hypothesis that DOG is locally coded. Indeed, it has hand-wired to be localist.  In the same way, the fact that a Jennifer Aniston neuron might be weakly activated by another face does not rule out the hypothesis that the neuron selectively codes for Jennifer Aniston.  I agree it is not strong evidence for a grandmother cell – there may be other images that drive the neuron even more, we just don’t know given the limited number of images presented to the patient.  But it is interesting that there are various demonstrations that artificial networks learn grandmother cells under some conditions – when you can test the model on all the familiar categories it has seen.  So, I would not rule out grandmother cells out of hand.

Jeff

From: KENTRIDGE, ROBERT W. <robert.kentridge at durham.ac.uk>
Date: Wednesday, 21 February 2024 at 20:56
To: Jeffrey Bowers <J.Bowers at bristol.ac.uk>, Gary Marcus <gary.marcus at nyu.edu>, Laurent Mertens <laurent.mertens at kuleuven.be>
Cc: connectionists at mailman.srv.cs.cmu.edu <connectionists at mailman.srv.cs.cmu.edu>
Subject: Re: Connectionists: Early history of symbolic and neural network approaches to AI
Again, it is great to be examining the relationship between ‘real’ neural coding and the ins and outs of representation in ANNs. I’m really pleased to be able to make a few contributions to a list which I’ve lurked on since the late 1980s!

I feel I should add an alternative interpretation of orientation coding in primary visual cortex to that so clearly explained by Jeffrey. It is, indeed, tempting to think of orientation tuned cells as labelled lines or grandmother cells where we read off activity in individual cells as conveying the presence of a line segment with a specific orientation at a particular location in the visual field. As neuroscientists we can certainly do this. The key question is whether brain areas outside primary visual cortex, which are consumers of information coded in primary visual cortex, also do this. The alternative view of orientation coding is that orientation is represented by a population code where orientation is represented as the vector sum of orientation preferences in cells with many different orientation tunings, weighted by their levels of activity, and that it is this population code that is read by areas that are consumers of orientation information. The notion of neural population coding of orientation was first tested electrophysiologically by Georgopoulos in 1982, examining population coding of the direction of arm movements in primary motor cortex. There is more recent psychophysical evidence that people’s confidence in their judgements of the orientation of a visual stimulus can be predicted on the basis of a population coding scheme (Bays, 2016, A signature of neural coding at human perceptual limits. Journal of Vision, https://jov.arvojournals.org/article.aspx?articleid=2552242), where a person’s judgment is indicative of the state of a high level consumer of orientation information.

So again, I’d err on the side of suggesting that although we can conceive of single neurons in primary visual cortex as encoding information (maybe not really symbols in this case anyway), it isn’t our ability to interpret things like this that matters, rather, it is the way the rest of the brain interprets information delivered by primary visual cortex.

cheers,

Bob


[Image result for university of durham logo]   [signature_2025328812]     [signature_824875734]    [Image result for durham cvac]
Professor of Psychology, University of Durham.
Durham PaleoPsychology Group.
Durham Centre for Vision and Visual Cognition.
Durham Centre for Visual Arts and Culture.

[9k=]
Fellow.
Canadian Institute for Advanced Research,
Brain, Mind & Consciousness Programme.



Department of Psychology,
University of Durham,
Durham DH1 3LE, UK.

p: +44 191 334 3261
f: +44 191 334 3434





From: Jeffrey Bowers <J.Bowers at bristol.ac.uk>
Date: Wednesday, 21 February 2024 at 12:31
To: KENTRIDGE, ROBERT W. <robert.kentridge at durham.ac.uk>, Gary Marcus <gary.marcus at nyu.edu>, Laurent Mertens <laurent.mertens at kuleuven.be>
Cc: connectionists at mailman.srv.cs.cmu.edu <connectionists at mailman.srv.cs.cmu.edu>
Subject: Re: Connectionists: Early history of symbolic and neural network approaches to AI
[EXTERNAL EMAIL]
It is possible to define a grandmother cell in a way that falsifies them.  For instance, defining grandmother cells as single neurons that only *respond* to inputs from one category.  Another definition that is more plausible is single neurons that only *represent* one category.  In psychology there are “localist” models that have single units that represent one category (e.g., there is a unit in the Interactive Activation Model that codes for the word DOG).  And a feature of localist codes is that they are partly activated by similar inputs. So a DOG detector is partly activated by the input HOG by virtue of sharing two letters.  But that partial activation of the DOG unit from HOG is no evidence against a localist or grandmother cell representation of the word DOG in the IA model.  Just as a simple cell of a vertical line is partly activated by a line 5 degrees off vertical – that does not undermine the hypothesis that the simple cell *represents* vertical lines.   I talk about the plausibility of Grandmother cells and discuss the Aniston cells in a paper I wrote sometime back:

Bowers, J. S. (2009). On the biological plausibility of grandmother cells: implications for neural network theories in psychology and neuroscience. Psychological review, 116(1), 220.


From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu> on behalf of KENTRIDGE, ROBERT W. <robert.kentridge at durham.ac.uk>
Date: Wednesday, 21 February 2024 at 11:48
To: Gary Marcus <gary.marcus at nyu.edu>, Laurent Mertens <laurent.mertens at kuleuven.be>
Cc: connectionists at mailman.srv.cs.cmu.edu <connectionists at mailman.srv.cs.cmu.edu>
Subject: Re: Connectionists: Early history of symbolic and neural network approaches to AI
I agree – empirical evidence is just what we need in this super-interesting discussion.

I should point out a few things about the Quiroga et al 2005 ‘Jennifer Aniston cell’ finding (Nature, 435. 1102 - 1107 ).

Quiroga et al themselves are at pains to point out that whilst the cells they found responded to a wide variety of depictions of specific individuals they were not ‘Grandmother cells’ as defined by Jerry Lettvin – that is, specific cells that respond to a broad range of depictions of an individual and *only* of that individual, meaning that one can infer that this individual is being perceived, thought of, etc. whenever that cell is active.

The cells Quiroga found do, indeed, respond to remarkably diverse ranges of stimuli depicting individuals, including not just photos in different poses, at different ages, in different costumes (including Hale Berry as Catwoman for the Hale Berry cell), but also names presented as text (e.g. ‘HALE BERRY’). Quiroga et al only presented stimuli representing a relatively small range of individuals and so it is unsafe to conclude that the cells they found respond *only* to the specific individuals they found. Indeed, they report that the Jennifer Aniston cell also responded strongly to an image of a different actress, Lisa Kudrow, who appeared in ‘Friends’ along with Jennifer Aniston.

So, the empirical evidence is still on the side of activity in sets of neurons as representing specific symbols (including those standing for specific individuals) rather than individual cells standing for specific symbols.

cheers

Bob


[Image result for university of durham logo]   [signature_2975123418]     [signature_2364801924]    [Image result for durham cvac]
Professor of Psychology, University of Durham.
Durham PaleoPsychology Group.
Durham Centre for Vision and Visual Cognition.
Durham Centre for Visual Arts and Culture.

[9k=]
Fellow.
Canadian Institute for Advanced Research,
Brain, Mind & Consciousness Programme.



Department of Psychology,
University of Durham,
Durham DH1 3LE, UK.

p: +44 191 334 3261
f: +44 191 334 3434





From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu> on behalf of Gary Marcus <gary.marcus at nyu.edu>
Date: Wednesday, 21 February 2024 at 05:49
To: Laurent Mertens <laurent.mertens at kuleuven.be>
Cc: connectionists at mailman.srv.cs.cmu.edu <connectionists at mailman.srv.cs.cmu.edu>
Subject: Re: Connectionists: Early history of symbolic and neural network approaches to AI
[EXTERNAL EMAIL]
Deeply disappointing that someone would try to inject actual empirical evidence into this discussion. 😂

On Feb 20, 2024, at 08:41, Laurent Mertens <laurent.mertens at kuleuven.be> wrote:

Reacting to your statement:
"However, inside the skull of my brain, there are not any neurons that have a one-to-one correspondence to the symbol."

What about the Grandmother/Jennifer Aniston/Halle Berry neuron?
(See, e.g., https://www.caltech.edu/about/news/single-cell-recognition-halle-berry-brain-cell-1013<https://urldefense.proofpoint.com/v2/url?u=https-3A__www.caltech.edu_about_news_single-2Dcell-2Drecognition-2Dhalle-2Dberry-2Dbrain-2Dcell-2D1013&d=DwMFAw&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=it3XOFrc2yBru1bmF9dud4UoT60mjmur8mR3zGu365JPKmtWSuFnJTxRJOV4WSpa&s=kh-rqxQw6qcxbM8bhUYTHNaJHN5jtc3SLI5RXC5XgWA&e=>)

KR,
Laurent

________________________________
From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu> on behalf of Weng, Juyang <weng at msu.edu>
Sent: Monday, February 19, 2024 11:11 PM
To: Michael Arbib <arbib at usc.edu>; connectionists at mailman.srv.cs.cmu.edu <connectionists at mailman.srv.cs.cmu.edu>
Subject: Re: Connectionists: Early history of symbolic and neural network approaches to AI

Dear Michael,
    You wrote, "Your brain did not deal with symbols?"
    I have my Conscious Learning (DN-3) model that tells me:
    My brain "deals with symbols" that are sensed from the extra-body world by the brain's sensors and effecters.
     However, inside the skull of my brain, there are not any neurons that have a one-to-one correspondence to the symbol.   In this sense,  the brain does not have any symbol in the skull.
    This is my educated hypothesis.  The DN-3 brain does not need any symbol inside the skull.
    In this sense, almost all neural network models are flawed about the brain, as long as they have a block diagram where each block corresponds to a function concept in the extra-body world.  I am sorry to say that, which may make many enemies.
    Best regards,
-John
________________________________
From: Michael Arbib <arbib at usc.edu>
Sent: Monday, February 19, 2024 1:28 PM
To: Weng, Juyang <weng at msu.edu>; connectionists at mailman.srv.cs.cmu.edu <connectionists at mailman.srv.cs.cmu.edu>
Subject: RE: Connectionists: Early history of symbolic and neural network approaches to AI


So you believe that, as you wrote out these words, the neural networks in your brain did not deal with symbols?



From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu> On Behalf Of Weng, Juyang
Sent: Monday, February 19, 2024 8:07 AM
To: connectionists at mailman.srv.cs.cmu.edu
Subject: Connectionists: Early history of symbolic and neural network approaches to AI



I do not agree with Newell and Simon if they wrote that.   Otherwise, images and video are also symbols.  They probably were not sophisticated enough in 1976 to realize why neural networks in the brain should not contain or deal with symbols.


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