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<div dir="ltr">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. </div>
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<div dir="ltr">First and foremost, representational redundancy make sense for an agent that can suffer physical damage<span style="font-size: inherit;">. 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).<span></span></span></div>
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<div dir="ltr"><span style="font-size: inherit;">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.<span></span></span></div>
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</span></div>
<div dir="ltr"><span style="font-size: inherit;">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. </span></div>
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</span></div>
<div dir="ltr"><span style="font-size: inherit;">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).</span></div>
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<div dir="ltr"><span style="font-size: inherit;">Danny Silver</span></div>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif"><b>From:</b> Connectionists <connectionists-bounces@mailman.srv.cs.cmu.edu> on behalf of Jeffrey Bowers <J.Bowers@bristol.ac.uk><br>
<b>Sent:</b> Saturday, February 24, 2024 5:06 PM<br>
<b>To:</b> Grossberg, Stephen <steve@bu.edu>; KENTRIDGE, ROBERT W. <robert.kentridge@durham.ac.uk>; Gary Marcus <gary.marcus@nyu.edu>; Laurent Mertens <laurent.mertens@kuleuven.be><br>
<b>Cc:</b> connectionists@mailman.srv.cs.cmu.edu <connectionists@mailman.srv.cs.cmu.edu><br>
<b>Subject:</b> Re: Connectionists: Early history of symbolic and neural network approaches to AI
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<span style="font-size:11.0pt">I think this is where terminology is confusing things. I agree that ART (and all
</span><span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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).
</span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">So, just as grandmother cells can easy be falsified if you define a grandmother cell that only
<b>responds</b> 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).</span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">Jeff</span><span style="font-size:11.0pt"></span></p>
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<b><span lang="EN-US" style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">From:
</span></b><span lang="EN-US" style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">Grossberg, Stephen <steve@bu.edu><br>
<b>Date: </b>Saturday, 24 February 2024 at 16:46<br>
<b>To: </b>Jeffrey Bowers <J.Bowers@bristol.ac.uk>, KENTRIDGE, ROBERT W. <robert.kentridge@durham.ac.uk>, Gary Marcus <gary.marcus@nyu.edu>, Laurent Mertens <laurent.mertens@kuleuven.be><br>
<b>Cc: </b>connectionists@mailman.srv.cs.cmu.edu <connectionists@mailman.srv.cs.cmu.edu>, Grossberg, Stephen <steve@bu.edu><br>
<b>Subject: </b>Re: Connectionists: Early history of symbolic and neural network approaches to AI</span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">Dear Jeff,</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">Thanks for your supportive remark.</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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. </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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.</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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.</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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.</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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.
</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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.</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">I have attached a couple of the figures that summarize the ARTSCAN Search architecture that tries to explain and simulate these interactions.</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">This neural architecture is far from being a “grandmother cell”!</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">My Magnum Opus provides a lot more modeling explanations and data about these issues:</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"><a href="https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/dp/0190070552">https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/dp/0190070552</a></span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">Best again,
</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">Steve</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<b><span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">From:
</span></b><span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">Jeffrey Bowers <J.Bowers@bristol.ac.uk><br>
<b>Date: </b>Saturday, February 24, 2024 at 4:38</span><span style="font-size: 12pt; font-family: Arial, sans-serif; color: black;"> </span><span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">AM<br>
<b>To: </b>Grossberg, Stephen <steve@bu.edu>, KENTRIDGE, ROBERT W. <robert.kentridge@durham.ac.uk>, Gary Marcus <gary.marcus@nyu.edu>, Laurent Mertens <laurent.mertens@kuleuven.be><br>
<b>Cc: </b>connectionists@mailman.srv.cs.cmu.edu <connectionists@mailman.srv.cs.cmu.edu>, Grossberg, Stephen <steve@bu.edu><br>
<b>Subject: </b>Re: Connectionists: Early history of symbolic and neural network approaches to AI</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">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</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt"> </span><span lang="EN-US"></span></p>
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<b><span lang="EN-US" style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">From:
</span></b><span lang="EN-US" style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">Grossberg, Stephen <steve@bu.edu><br>
<b>Date: </b>Friday, 23 February 2024 at 18:12<br>
<b>To: </b>Jeffrey Bowers <J.Bowers@bristol.ac.uk>, KENTRIDGE, ROBERT W. <robert.kentridge@durham.ac.uk>, Gary Marcus <gary.marcus@nyu.edu>, Laurent Mertens <laurent.mertens@kuleuven.be><br>
<b>Cc: </b>connectionists@mailman.srv.cs.cmu.edu <connectionists@mailman.srv.cs.cmu.edu>, Grossberg, Stephen <steve@bu.edu><br>
<b>Subject: </b>Re: Connectionists: Early history of symbolic and neural network approaches to AI</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">Dear Jeff et al.,</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">The term Grandmother Cell was a good heuristic but, as has been noted in this email thread, is also ill-defined.</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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.</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">There are also view-specific categories in posterior Inferotemporal Cortex (ITp) that do not have such broad invariance.</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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.</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">Just a scan of the article titles illustrates that there has been a lot of work on this topic.</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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. <i>Cognitive Psychology</i>, <b>58</b>, 1-48. </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"><a href="https://sites.bu.edu/steveg/files/2016/06/FazGroMin2008.pdf">https://sites.bu.edu/steveg/files/2016/06/FazGroMin2008.pdf</a></span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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? <i>Neural
Networks</i>, <b>24</b>, 1050-1061.</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"><a href="https://sites.bu.edu/steveg/files/2016/06/NN2853.pdf">https://sites.bu.edu/steveg/files/2016/06/NN2853.pdf</a></span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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. <i>Neural Networks</i>, <b>24</b>, 1036-1049.</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"><a href="https://sites.bu.edu/steveg/files/2016/06/GroMarCao2011TR.pdf">https://sites.bu.edu/steveg/files/2016/06/GroMarCao2011TR.pdf</a></span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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. <i>Neural
Networks</i>, <b>24</b>, 686-692. </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"><a href="https://sites.bu.edu/steveg/files/2016/06/GroSriYaz2011TR.pdf">https://sites.bu.edu/steveg/files/2016/06/GroSriYaz2011TR.pdf</a></span><span lang="EN-US"></span></p>
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</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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. <i>Cognitive
Psychology</i>, <b>65</b>, 77-117.</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"><a href="https://sites.bu.edu/steveg/files/2016/06/FolGroMin2012.pdf">https://sites.bu.edu/steveg/files/2016/06/FolGroMin2012.pdf</a></span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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. <i>Frontiers in Psychology: Perception Science, </i>doi: 10.3389/fpsyg.2014.01457</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"><a href="https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2014.01457/full">https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2014.01457/full</a></span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">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.</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">Best,</span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif"> </span><span lang="EN-US"></span></p>
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<span lang="EN-US" style="font-size:11.0pt; font-family:"Aptos",sans-serif">Steve</span><span lang="EN-US"></span></p>
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<b><span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">From:
</span></b><span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">Connectionists <connectionists-bounces@mailman.srv.cs.cmu.edu> on behalf of Jeffrey Bowers <J.Bowers@bristol.ac.uk><br>
<b>Date: </b>Thursday, February 22, 2024 at 11:11</span><span style="font-size: 12pt; font-family: Arial, sans-serif; color: black;"> </span><span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">AM<br>
<b>To: </b>KENTRIDGE, ROBERT W. <robert.kentridge@durham.ac.uk>, Gary Marcus <gary.marcus@nyu.edu>, Laurent Mertens <laurent.mertens@kuleuven.be><br>
<b>Cc: </b>connectionists@mailman.srv.cs.cmu.edu <connectionists@mailman.srv.cs.cmu.edu><br>
<b>Subject: </b>Re: Connectionists: Early history of symbolic and neural network approaches to AI</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">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.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">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.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Jeff </span><span lang="EN-US"></span></p>
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<b><span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">From:
</span></b><span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">KENTRIDGE, ROBERT W. <robert.kentridge@durham.ac.uk><br>
<b>Date: </b>Wednesday, 21 February 2024 at 20:56<br>
<b>To: </b>Jeffrey Bowers <J.Bowers@bristol.ac.uk>, Gary Marcus <gary.marcus@nyu.edu>, Laurent Mertens <laurent.mertens@kuleuven.be><br>
<b>Cc: </b>connectionists@mailman.srv.cs.cmu.edu <connectionists@mailman.srv.cs.cmu.edu><br>
<b>Subject: </b>Re: Connectionists: Early history of symbolic and neural network approaches to AI</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">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!</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">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,
</span><a href="https://jov.arvojournals.org/article.aspx?articleid=2552242"><span style="font-size:11.0pt">https://jov.arvojournals.org/article.aspx?articleid=2552242</span></a><span style="font-size:11.0pt">), where a person’s judgment is indicative of the
state of a high level consumer of orientation information.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">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.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">cheers,</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Bob</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt"><img border="0" width="57" height="57" id="Picture_x0020_12" src="cid:image001.jpg@01DA64AF.5BBBA710" alt="Image result for university of durham logo" style="width:.5937in; height:.5937in">
<img border="0" width="118" height="55" id="Picture_x0020_11" src="cid:image002.png@01DA64AF.5BBBA710" alt="signature_2025328812" style="width:1.2291in; height:.5729in"> <img border="0" width="94" height="55" id="Picture_x0020_10" src="cid:image003.png@01DA64AF.5BBBA710" alt="signature_824875734" style="width:.9791in; height:.5729in"> <img border="0" width="46" height="56" id="Picture_x0020_9" src="cid:image004.jpg@01DA64AF.5BBBA710" alt="Image result for durham cvac" style="width:.4791in; height:.5833in"></span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Professor of Psychology, University of Durham.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Durham PaleoPsychology Group.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Durham Centre for Vision and Visual Cognition.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Durham Centre for Visual Arts and Culture.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt"> </span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt"><img border="0" width="49" height="49" id="Picture_x0020_8" src="cid:image005.jpg@01DA64AF.5BBBA710" alt="9k=" style="width:.5104in; height:.5104in"></span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Fellow. </span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Canadian Institute for Advanced Research, </span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Brain, Mind & Consciousness Programme.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Department of Psychology,</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">University of Durham,</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Durham DH1 3LE, UK.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">p: +44 191 334 3261</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">f: +44 191 334 3434</span><span lang="EN-US"></span></p>
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<b><span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">From:
</span></b><span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">Jeffrey Bowers <J.Bowers@bristol.ac.uk><br>
<b>Date: </b>Wednesday, 21 February 2024 at 12:31<br>
<b>To: </b>KENTRIDGE, ROBERT W. <robert.kentridge@durham.ac.uk>, Gary Marcus <gary.marcus@nyu.edu>, Laurent Mertens <laurent.mertens@kuleuven.be><br>
<b>Cc: </b>connectionists@mailman.srv.cs.cmu.edu <connectionists@mailman.srv.cs.cmu.edu><br>
<b>Subject: </b>Re: Connectionists: Early history of symbolic and neural network approaches to AI</span><span lang="EN-US"></span></p>
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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:<span lang="EN-US"></span></p>
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<span style="font-family: Arial, sans-serif; background: white; color: rgb(34, 34, 34);">Bowers, J. S. (2009). On the biological plausibility of grandmother cells: implications for neural network theories in psychology and neuroscience.<span class="apple-converted-space"> </span></span><i><span style="font-family: Arial, sans-serif; color: rgb(34, 34, 34);">Psychological
review</span></i><span style="font-family: Arial, sans-serif; background: white; color: rgb(34, 34, 34);">,<span class="apple-converted-space"> </span></span><i><span style="font-family: Arial, sans-serif; color: rgb(34, 34, 34);">116</span></i><span style="font-family: Arial, sans-serif; background: white; color: rgb(34, 34, 34);">(1),
220.</span><span lang="EN-US"></span></p>
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<b><span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">From:
</span></b><span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">Connectionists <connectionists-bounces@mailman.srv.cs.cmu.edu> on behalf of KENTRIDGE, ROBERT W. <robert.kentridge@durham.ac.uk><br>
<b>Date: </b>Wednesday, 21 February 2024 at 11:48<br>
<b>To: </b>Gary Marcus <gary.marcus@nyu.edu>, Laurent Mertens <laurent.mertens@kuleuven.be><br>
<b>Cc: </b>connectionists@mailman.srv.cs.cmu.edu <connectionists@mailman.srv.cs.cmu.edu><br>
<b>Subject: </b>Re: Connectionists: Early history of symbolic and neural network approaches to AI</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">I agree – empirical evidence is just what we need in this super-interesting discussion.
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<span style="font-size:11.0pt">I should point out a few things about the Quiroga et al 2005 ‘Jennifer Aniston cell’ finding (<i>Nature</i>, <b>435</b>. 1102 - 1107 ).
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<span style="font-size:11.0pt">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 *<b>only</b>* of that individual, meaning that one can infer that this individual is being perceived, thought of, etc. whenever that cell is active.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">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 *<b>only</b>* 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.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">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.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">cheers</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Bob</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt"><img border="0" width="57" height="57" id="Picture_x0020_5" src="cid:image001.jpg@01DA64AF.5BBBA710" alt="Image result for university of durham logo" style="width:.5937in; height:.5937in">
<img border="0" width="118" height="55" id="Picture_x0020_4" src="cid:image002.png@01DA64AF.5BBBA710" alt="signature_2975123418" style="width:1.2291in; height:.5729in"> <img border="0" width="94" height="55" id="Picture_x0020_3" src="cid:image003.png@01DA64AF.5BBBA710" alt="signature_2364801924" style="width:.9791in; height:.5729in"> <img border="0" width="46" height="56" id="Picture_x0020_2" src="cid:image004.jpg@01DA64AF.5BBBA710" alt="Image result for durham cvac" style="width:.4791in; height:.5833in"></span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Professor of Psychology, University of Durham.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Durham PaleoPsychology Group.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Durham Centre for Vision and Visual Cognition.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Durham Centre for Visual Arts and Culture.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt"><img border="0" width="49" height="49" id="Picture_x0020_1" src="cid:image005.jpg@01DA64AF.5BBBA710" alt="9k=" style="width:.5104in; height:.5104in"></span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Fellow. </span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Canadian Institute for Advanced Research, </span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Brain, Mind & Consciousness Programme.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Department of Psychology,</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">University of Durham,</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Durham DH1 3LE, UK.</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">p: +44 191 334 3261</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">f: +44 191 334 3434</span><span lang="EN-US"></span></p>
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<b><span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">From:
</span></b><span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">Connectionists <connectionists-bounces@mailman.srv.cs.cmu.edu> on behalf of Gary Marcus <gary.marcus@nyu.edu><br>
<b>Date: </b>Wednesday, 21 February 2024 at 05:49<br>
<b>To: </b>Laurent Mertens <laurent.mertens@kuleuven.be><br>
<b>Cc: </b>connectionists@mailman.srv.cs.cmu.edu <connectionists@mailman.srv.cs.cmu.edu><br>
<b>Subject: </b>Re: Connectionists: Early history of symbolic and neural network approaches to AI</span><span lang="EN-US"></span></p>
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<strong><span style="font-size: 12pt; font-family: Calibri, sans-serif; background: rgb(255, 254, 207); color: black;">[EXTERNAL EMAIL]</span></strong><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">Deeply disappointing that someone would try to inject actual empirical evidence into this discussion.
</span><span style="font-size:11.0pt; font-family:"Apple Color Emoji"">😂</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt">On Feb 20, 2024, at 08:41, Laurent Mertens <laurent.mertens@kuleuven.be> wrote:</span><span lang="EN-US"></span></p>
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<span style="font-size:11.0pt"> </span><span lang="EN-US"></span></p>
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<span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">Reacting to your statement:</span><span lang="EN-US"></span></p>
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<span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">"However, inside the skull of my brain, there are not any neurons that have a one-to-one correspondence to the symbol."</span><span lang="EN-US"></span></p>
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<span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">What about the Grandmother/Jennifer Aniston/Halle Berry neuron?</span><span lang="EN-US"></span></p>
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<span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">(See, e.g.,
</span><a href="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="><span style="font-size:12.0pt; font-family:"Aptos",sans-serif">https://www.caltech.edu/about/news/single-cell-recognition-halle-berry-brain-cell-1013</span></a><span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">)</span><span lang="EN-US"></span></p>
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<span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">KR,</span><span lang="EN-US"></span></p>
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<span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">Laurent</span><span lang="EN-US"></span></p>
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<b><span style="font-size: 11pt; color: black;">From:</span></b><span style="font-size: 11pt; color: black;"> Connectionists <connectionists-bounces@mailman.srv.cs.cmu.edu> on behalf of Weng, Juyang <weng@msu.edu><br>
<b>Sent:</b> Monday, February 19, 2024 11:11 PM<br>
<b>To:</b> Michael Arbib <arbib@usc.edu>; connectionists@mailman.srv.cs.cmu.edu <connectionists@mailman.srv.cs.cmu.edu><br>
<b>Subject:</b> Re: Connectionists: Early history of symbolic and neural network approaches to AI</span><span style="font-size:11.0pt">
</span><span lang="EN-US"></span></p>
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<span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">Dear Michael,</span><span lang="EN-US"></span></p>
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<span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;"> You wrote, "Your brain did not deal with symbols?"</span><span lang="EN-US"></span></p>
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<span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;"> I have my Conscious Learning (DN-3) model that tells me:<br>
My brain "deals with symbols" that are sensed from the extra-body world by the brain's sensors and effecters.</span><span lang="EN-US"></span></p>
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<span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;"> 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.</span><span lang="EN-US"></span></p>
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<span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;"> This is my educated hypothesis. The DN-3 brain does not need any symbol inside the skull.</span><span lang="EN-US"></span></p>
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<span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;"> 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. </span><span lang="EN-US"></span></p>
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<span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;"> Best regards,</span><span lang="EN-US"></span></p>
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<span style="font-size: 12pt; font-family: Aptos, sans-serif; color: black;">-John </span><span lang="EN-US"></span></p>
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<b><span style="font-size: 11pt; color: black;">From:</span></b><span style="font-size: 11pt; color: black;"> Michael Arbib <arbib@usc.edu><br>
<b>Sent:</b> Monday, February 19, 2024 1:28 PM<br>
<b>To:</b> Weng, Juyang <weng@msu.edu>; connectionists@mailman.srv.cs.cmu.edu <connectionists@mailman.srv.cs.cmu.edu><br>
<b>Subject:</b> RE: Connectionists: Early history of symbolic and neural network approaches to AI</span><span style="font-size:11.0pt">
</span><span lang="EN-US"></span></p>
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<p style="margin:0cm">So you believe that, as you wrote out these words, the neural networks in your brain did not deal with symbols?<span lang="EN-US"></span></p>
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<p style="margin:0cm"><b>From:</b> Connectionists <connectionists-bounces@mailman.srv.cs.cmu.edu>
<b>On Behalf Of </b>Weng, Juyang<br>
<b>Sent:</b> Monday, February 19, 2024 8:07 AM<br>
<b>To:</b> connectionists@mailman.srv.cs.cmu.edu<br>
<b>Subject:</b> Connectionists: Early history of symbolic and neural network approaches to AI<span lang="EN-US"></span></p>
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<p style="margin:0cm"><span style="color: black;">I do not agree with <span style="background: white;">
Newell and Simon</span> 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.</span><span lang="EN-US"></span></p>
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