[Comp-neuro] High level abstractions in concept cells; Single spiking neurons have "meaning" and are actually at the cognitive level?

Dorian Aur dorianaur at gmail.com
Fri Aug 19 20:17:32 CEST 2011


I feel that it is our goal to pursue *the truth in science*  and  the
"truth" should not be an "insult" to any scientist. A larger list can
increase interactions between scientists and a better chance to find the

To summarize the  previous discussion:
(i)*Quiroga et al. 2007 have "shown the concept" relationship* not Moran
Cerf (see Dr. Koch statement!)
(ii)The proposed *"concept* neuron" by Moran Cerf group  is similar to the *
gnostic* neuron model presented by Konorski *in the early 1960s (**La même
Jeannette* autrement coiffée) it is not a *novel discovery *

1. First there is no "Jennifer  Aniston" (JA) neuron. The same neuron  has
to respond to many other presented objects (otherwise is a *grandmother cell
*) . Therefore, Quiroga et al.,  have extensively filtered the data to
obtain this outcome (Quiroga et al. 2005) --details can be provided.  When
you record 10^3 neurons, have complex random temporal patterns  and use
statistics with  an ambiguous measure (firing rate) that *provides little
information*  everything becomes possible.
2. Even when this neuron responds to JA *it may in fact respond to different
other features *that are present in those images, however the firing rate
measure *is not sensitive enough to detect these subtle aspect*s
3. Very important, a strong firing rate to JA may indicate a reorganization
triggered   when JA is repeatedly  presented not JA concept

4. To get *reliable semantics* from experimental data a direct  relationship
with *"memory*" has to be extracted . Spike directivity  provides directly
this relationship since it relates specific information  with the  *topography
of analyzed** neuron .* See the difference between the spider presentation
and JA presentation  (
specific  parts of the neuron are active . Based on firing rate one cannot
distinguish between spider and JA (the firing rate is 8Hz ) in both cases
5. The reorganization always occurs in repetitive tasks and can change the
neuronal response. It is unlikely to extract from an experiment *the
entire*"blond women" (
*the concept*) from a single neuron.

2011/8/19 Asim Roy <ASIM.ROY at asu.edu>

> It is really unethical for Dorian Aur to post a part of a private
> discussion on concept cells and spiking neurons based on some perhaps
> breakthrough findings in the lab of Dr. Izthak Fried at UCLA and which
> involves Dr. Christof Koch and others at Caltech. We have been discussing
> these findings in our own discussion list for over two months now and to
> take one such email and post it on several mailing lists is an insult to all
> of us. I would appeal to all mailing lists that have not posted this note
> yet to desist from doing so. Dorian Aur didn’t get our permission to post
> this. I wish Comp-neuro, being a moderated list, had checked with us since
> we are cited on this note. Dorian Aur is just trying to promote his point of
> view in this post. We have had a full discussion of these issues directly
> with him for quite some time and he seems to have a losing argument that
> firing rates (inter-spike interval, temporal code) has no useful
> information.****
> ** **
> Asim Roy****
> Arizona State University****
> www.lifeboat.com/ex/bios.asim.roy****
> ** **
> ** **
> *From:* comp-neuro-bounces at neuroinf.org [mailto:
> comp-neuro-bounces at neuroinf.org] *On Behalf Of *Dorian Aur
> *Sent:* Thursday, August 11, 2011 12:54 PM
> *To:* Connectionists at cs.cmu.edu; cogsci at psych.colorado.edu;
> agents at cs.umbc.edu; ai at dcs.qmul.ac.uk;
> aiia_announcements at penelope.csd.auth.gr; COGPSY at listserv.tamu.edu;
> comp-neuro at neuroinf.org; spp-misc at philebus.tamu.edu;
> comp-neuro-owner at neuroinf.org
> *Subject:* [Comp-neuro] High level abstractions in concept cells; Single
> spiking neurons have "meaning" and are actually at the cognitive level?***
> *
> ** **
> Asim, your idea to add together various scientists with similar interests
> represents an important step to trigger a debate that can  indeed solve
> current issues regarding data analysis and the nature of neural code.****
> I’ve had myself the privilege to analyze some  excellent recordings from Dr
> Fried lab. In general it is an exception to get four good neurons where
> spike directivity (SD) can be  computed (the electrodes need to be together
> in a tetrode configuration). Dr Fried I’m really impressed, excellent
> recordings! Therefore  in an attempt of  *pursuing the truth *I have
> analyzed some of these recordings from a different perspective *and *took
> the liberty to *post it here the outcome.*****
> *Dorian’s summary of some issues in interpreting temporal coding -
> includes notes from **Dr. Itzhak Fried and Christof Koch, **Quian Quiroga,
> **Walter Freeman*****
> *1.*       Work on brain-machine interfaces based on single-neuron
> activity is quite standard now and being pursued by a number of groups.***
> *
> *Itzhak Fried: **I would not describe BMI using single neuron activity as
> **quite standard. Most of the existing data is with frontal/motor or
> parietal neuron.*****
> *Christof:** **Yes. Most such BMI operate in motor, pre-motor and parietal
> cortex. *****
> *Dorian :*There are several issues in interpreting single neuron activity
> using the  firing rate or interspike interval. Several  important details
> are  missing  in temporal patterns (see
> http://dx.doi.org./10.1016/j.jneumeth.2006.05.003;
> http://dx.doi.org./10.1016/j.jneumeth.2005.05.006  and  the book
> Neuroelectrodynamics: Understanding the Brain Language,
> http://dx.doi.org/10.3233/978-1-60750-473-3-i for a model of computation)
> which are important in information coding****
> *2.*       Concept cells are comparable to place cells in rodents (concept
> cells = place cells) and therefore not a finding that surprises the
> neuroscience community.****
> *Itzhak Fried: **Concept cells are not place cells but I proposed that
> they  can be viewed as "place cells" in a different "attribute or feature
> space".  They do share with place cells coding properties, that is:
> specificity , invariance, sparseness and the explicit nature of the code.
> One can speculate that the mechanism developed for coding of space in rodent
> hippocampus  has evolved to accommodate more elaborate abstraction in
> humans. As for "surprises", it is difficult to surprise the neuroscience
> community, but for us the explicit nature of the code on the single neuron
> level was a surprise.*****
> *Christof:** **There are some similarities to place cells in rodents.
> However, we find these highly selective cells in all regions of the MTL, not
> just the hippocampus.  How far this comparison goes is not clear (what, for
> example, is the analog of grid cells in the entorhinal cortex?)*****
> *Dorian:** *Different experiments in rats or humans show strong
> similarities that can provide meaningful explanation for these data.
> However, an understanding of presented examples cannot solely come from a
> firing rate analysis. A relevant example shows how neurons operate during  a
> T-maze procedural learning task. (D Aur, and M Jog,  Reading the Neural
> Code: What do Spikes Mean for Behavior?. Available from Nature Precedings <
> http://dx.doi.org/10.1038/npre.2007.61.1, 2007)****
> The "expert" neurons in striatum during  T-maze learning provide a similar
> behavior,  however *they  extensively fire only before learning*. In order
> to understand the meaning of their firing activity a different measure was
> computed and analyzed. *Spike directivity* is a vector that reflects the
> distribution of electrical patterns in recorded spikes. During learning
> these  "expert" neurons reduce their firing rate. After one week of training
> the neurons generate only few spikes between the tone and  turn starts.
>  This represents the critical moment when the decision regarding turning is
> taken.  After training these “expert” neurons show less random  spike
> directivities  (a preferred direction of AP propagation)than before
> training . The delivered spikes after  the tone  predict the turning
> direction on the T-maze. In many cases the firing rate cannot be estimated
> (one spike in single trials)
> *The first counterexample: *When it fires the same neuron can code for the
> left turn or for the right turn depending on the context (high or low  tone,
> the T-maze task,
> http://neuroelectrodynamics.blogspot.com/p/spike-directivity.html ).  ****
> *The second counterexample:* The same neuron responds with the same firing
> rate for two different  objects (spider, Jennifer Aniston) the difference
> occurs  in the preferred spike directivity (see
> http://neuroelectrodynamics.blogspot.com/p/spike-directivity.html).****
> The outcome in spike directivity is a counterexample for temporal coding.
> *The spikes cannot be added since they provide different semantics (apples
> and oranges). That’s the beauty of counterexamples. You only need one single
> counterexample to throw down a "solid"theoretical construct  of temporal
> coding. No need  for other examples that reinforce the  temporal coding!**
> ***
> In this case:
> (i) During learning several options are explored and the  *strong firing
> rate reflects uncertainty in these “expert” neurons.  Therefore, we
> hypothesized that strong firing (with strong variability of spike
> directivity) represents a way * to search for a  correct solution during
> learning
> (ii) After learning all these cells provide an efficient response with only
> few spikes for the same event.****
> (iii) The *reduction in uncertainty generates a meaningful outcome* that
> can be observed in a preferred direction of spike directivity
> Therefore, *a decrease of uncertainty  is reflected in a reduced number of
> spikes delivered by a cell, an efficient response*. Contrary to current
> belief an increase in the firing rate *may show uncertainty*,  a
> searching  process required to deliver a solution.****
> Following  a similar analysis,  the cells from MTL can display a similar
> behavior. If two different objects are presented they can be separated  fast
> in these neurons  since they generate different spike
> directivity orientations (see
> http://neuroelectrodynamics.blogspot.com/p/spike-directivity.html). ****
> *3.*       The concept cells were found in different regions. For example,
> “James Brolin” in right hippocampus, “Venus Williams” in left hippocampus,
> “Marilyn Monroe” in left parahippocampal cortex, “Michael Jackson” in right
> amygdala.****
> *Itzhak Fried: **Yes, but they may represent different levels of
> abstraction or invariance in each of these regions. Although they were found
> in different MTL regions , the highest degree of invariance (across
> modalities) was in hippocampus and entorhinal cortex . Also remember the
> latency of the response,  usually around 350 msec.*****
> *Christof:** **Yes*****
> *Dorian :I*ntracellularly  within molecular structure specific information
> is "read" and "written" in these cells  during AP generation . In order to
> have the concept of "Jennifer Aniston" information from many cells is
> inferred synaptically and non-synaptically. Therefore, many neurons fire
> almost simultaneously in different brain regions  (MTL hippocampus ,
> entorhinal cortex) ****
> (i)                 However, a  too strong  increase of firing rate may
> show high uncertainty - a searching process required to identify the
> presented  object. ****
> (ii)               In order to represent a particular feature associated
> with  a certain presented image (e.g. Jennifer Aniston) these neuron can
> generate low firing rate with consistent  preferred spike directivity****
> (iii)             The semantics do not appear  in the firing rate!!! (ISI)
> therefore statistical analyzes  of firing rate (ISI)  can be highly
> irrelevant to determine the meaning of firing. The same neuron can code for
> different features in different spikes depending on presented context.****
>  ****
> *4.*       The sister cells (e.g. other Jennifer Aniston concept cells)
> are not necessarily in contiguous locations in the brain. They could be in
> different hemispheres and different regions within a hemisphere. (“The
> subject most likely activated a large pool of neurons selective to ‘Johnny
> Cash’ even though the feedback was only based on just one such unit. We
> identified 8 such units in a total of 7 subjects.”)****
> *Itzhak Fried: **The sister cell may be  a confusing term, but a major
> point is that organization of "concept cells"  is not columnar or
> topographic. Given their sparse and nontopographic distribution  it would be
> difficult to trace them on fMRI.*****
> *Dorian: *There is little information in the temporal code (firing rate,
> ISI)
> Based solely on firing rate (ISI) analyses it is very hard to figure out
>  the role of certain spikes ( see the counterexample where the same neuron
> provides different semantics when it fires
> http://precedings.nature.com/documents/61/version/1) .* *****
> *5.*       Even though a million cells are activated by an image of
> Jennifer Aniston, and say 12 of them are Jennifer Aniston concept cells, in
> your experiments, you tracked only one such concept cell and* **that was
> good enough*.* **There was no need to “read out” other Jennifer Aniston
> concept cells*, wherever they were,* **as would be required in a
> distributed representation framework*.****
> *Itzhak Fried: **Yes. But I  suspect more than a million cells are
> activated by Jennifer Aniston and they could probably be arranged on a
> variance scale with our "concept cells" at the extreme low. Still it is
> easier to find a concept cell than a Higg's boson.*****
> *Christof:** **We have no idea whether J. Aniston activates a million of
> such cells. Yes, the movie of the superimposed images was based on four
> selective units of a presumably much larger pool. It is well possible that
> if we had recorded from more sister neurons, control would have been swifter
> or more precise or more reliable.*****
> *Dorian: *****
> (i)These cells do not fire only for Jennifer Aniston as presented in
> Quiroga et al., 2005. (ii) In different contexts they should   fire for
> different presented objects (see
> http://precedings.nature.com/documents/5345/version/2).
> (iii) A strong  increase  in the firing rate  may show a different process
> (iv)If spike directivity points randomly in space then the  strong increase
> in firing rate display uncertainty, an ongoing  “searching” process to
> associate different presented features
> (v)The efficient coding of a particular feature associated with Jennifer
> Aniston  needs to provide a consistent preferred spike directivity. This
> outcome is determined by a  consistent intracellular location of particular
> "memories" and is revealed using spike directivity or imaging the spike.
> (see http://precedings.nature.com/documents/5345/version/2 or
> http://neuroelectrodynamics.blogspot.com/p/spike-directivity.html)****
> 6.       In your image control experiments, where the subject focused on
> one of two images on a computer screen to enhance its visibility (a target
> vs. a distractor image)* **by “thinking” about it* (the target image),* **the
> subjects were able to control and modulate the activity of the concept units
> selective to specific images*. “Thinking” in this case might simply imply
> invoking some images from memory of the target concept (e.g. Jennifer
> Aniston) and that might also imply the “internal assignment” of meaning to
> the target concept cells. (This is a tenuous argument. Wish we could also
> say that the concept cell activates the “memories,” thereby providing
> linkage both ways.)****
> *Itzhak Fried:  **Read the Science 2008 paper by Gelbard -Sagiv, Mukamel,
> Harel, Malach and Fried where you will see how such cell (firing selectively
> to the actual sight of a 10 sec video of the Simpson's, as one example ) is
> reactivated just before (1.2 sec)  the patient reports the recollection.**
> ***
> *Christof:** **The cognitive or neuronal processes underlying the
> voluntary control seen in these fading experiments are unclear. Personally,
> I think they are closer to object based attention than to memory but this
> remains to be **proven.*****
> *Dorian: *I was particularly interested by  the *Simpson *example,  even
> stopped the presentation to show that  this specific cell  has fired  for
> different other images with low firing rate. If during the increase in
> firing rate the computed  spike directivities show  outcome then this case
> can be a typical example where the neuron  is “searching” for a solution.
> Since  other* different  neurons may activate the recollection* *the
>  re-searching process can be triggered.  I feel that this neuron will not
> provide  stable high firing rate longer time in this case (*over one week
> of repeated Simpson presentation*).    **If spike directivity is less
>  random then indeed this particular neuron can embed some  features
>  associated to Simpson. Information  is “read” or “written” in this cell
> during these spikes and  the cell  may contribute to form the Simpson
> abstraction( however not alone!).*****
> 7.       Here’s an interesting conclusion from Waydo, Kraskov, Quiroga,
> Fried and Koch (The Journal of Neuroscience, 2006):
> “Instead, it would imply that rather than a single neuron responding to
> dozens of stimuli out of a universe of tens of thousands,* **such a neuron
> might respond to only one or a few stimuli out of perhaps hundreds currently
> being tracked by this memory system*, still with millions of neurons being
> activated by a typical stimulus. These results are consistent with Barlow’s
> (1972) claim that “at the upper levels of the hierarchy,* **a relatively
> small proportion [of neurons] are active, and each of these says a lot when
> it is active*,” and his further speculation that the “*aim of information
> processing in higher sensory centers is to represent the input as completely
> as possible by activity in as few neurons as possible*” (Barlow, 1972).”**
> **
> *Itzhak Fried: **When I proposed the term "concept cells" for the unique
> group of cells we found in hippocampus and neighbouring MTL structures it
> was with the intention of provoking such diuscussions, but using the
> nomenclature we should not be carried away by the hype of the terminology
> and lose sight of the data.(I do agree with Freeman's cautionary note re
> "meaning"). Do not forget that these cells are at the heart of the
> declarative memory system of MTL and thus  signify the transformation of
> percepts into what can be later consciously recollected.* ****
> *The intriguing question is how these cells are formed and change. We know
> patients form these cells to the experimenters over a day or so.  We are
> currently completing  a study which will provide some relevant data.*****
> *Christof:** **Yes, Horace Barlow's 1972 paper was very forward looking
> and deserves to be widely read and cited. *The efficiency in processing
> information is the main goal of the brain, therefore the process of  object
> recognition is optimized in these cells that respond  which a decrease in
> firing rate and a “specialization” of involved neurons that carries specific
> features ****
> *Quian Quiroga*****
> Asim, thanks for triggering this interesting discussion.****
> Yes, I do believe these cells encode meaning. We say this explicitly in a
> TiCS paper (at the end of the section before the Conclusion).****
> *From Walter Freeman*****
> Your paraphrase is ambiguous. "Concept cells" certainly have meaning for
> observers, but do they express and transmit meaning within the brain of the
> subject to other parts of the brain? In other words, how in a small fraction
> of a second does the output of the "concept cell" capture and control
> attention and the neural machinery leading to Sherrington's "final common
> path"?****
> I conceive the "concept cell" as one of ~10^5 neurons forming a Hebbian
> assembly, which provides the key to a global attractor and the energy needed
> to  trigger a phase transition. In this view the meaning is expressed by the
> attractor involving ~10^9 neurons. The spikes of the sampled "concept cell"
> (in concert with ~10^5 - 1 other cells) are an essential sign, neural
> correlate, and agency mediating the construction of meaning from the memory
> (synaptic matrix) selected by a stimu****
>   ****
> *Dorian: *The efficiency of information processing  seems to be  the main
> reason of changes in the dynamics of firing. The T-maze learning shows a
> process of optimization. The firing rate is reduced when  a certain
> semantics is acquired in single cells. Here, in these recordings  it seem to
> be a similar process. If the objects are presented several times the
> increase in  “specialization”  occurs
> http://neuroelectrodynamics.blogspot.com/p/spike-directivity.html*)*.****
> I agree with Dr Freeman.  The temporal coding is ambiguous (see both
> counterexamples) the meaning  seems to be a result of electrical inference
> (not of temporal patterns)
> http://precedings.nature.com/documents/5345/version/2) - .****
> The *Horace Barlow's 1972 paper was an inspiration to  develop the new
>  computational model – NeuroElectroDynamics (NED). This computational
> model shows that information is integrated across different  scales  in the
> brain using electrical activity  (not temporal patterns) in order to
> generate the *“the *final common path” (* see a small network of four
> neurons http://precedings.nature.com/documents/5345/version/2) *. *Many
> scientists have previously envisioned and described different (non-Turing)
> forms of computations. *Computing by physical interaction in neurons ( in
> the brain)  generates a powerful (non-Turing)  model of computation. *****
> I’m  always  interested to analyze good recordings and really  delighted
> that  with these  new techniques the mystery of neural code can be solved
> http://neuroelectrodynamics.blogspot.com/p/cracking-neural-code.html “Cracking” the neural code was not the main goal goal. The result occurred
> in response to  other different questions- Why artificial intelligence
> cannot move beyond  capabilities of a two-and-a-half year old child? Why
> brain computations are so powerful? ****
> We found that several controversies in the field were generated by keeping
> alive the temporal coding paradigm. I value the contribution of all
> scientists that have worked in this field; they kept our interest focused on
> fundamental issues and our success in understanding how the brain computes
> (see NED) reflects  their long-standing  effort in this area. ****
> *"We have seen a little further by standing on the shoulders of Giants
> .... not because our sight is superior or because we are taller than they,
> but because they raise us up, and by their great stature add to ours”*****
> *Isaac Newton*****
> Therefore, I'm actively interacting   to clarify several issues regarding
> temporal coding.
> neuroelectrodynamics.blogspot.com/****
> http://neuronline.sfn.org/SFN/SFN/Home/Default.aspx (require membership)
> www.linkedin.com/groups/Computational-Neuroscience-1376707 (require
> membership)
>  Dorian Aur****
> ** **
> ** **
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