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<p class="MsoNormal">Hi Tsvi,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">You will find definitions of localist and distributed representation in this paper. I refer to more than one source and they essentially mean the same. It’s a fairly standard definition used in cognitive and neuroscience.<o:p></o:p></p>
<p class="MsoNormal"><a href="https://www.frontiersin.org/articles/10.3389/fpsyg.2012.00551/full">https://www.frontiersin.org/articles/10.3389/fpsyg.2012.00551/full</a><o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">This paper has some definitions of grandmother cells:<o:p></o:p></p>
<p class="MsoNormal"><a href="https://www.frontiersin.org/articles/10.3389/fpsyg.2013.00300/full">https://www.frontiersin.org/articles/10.3389/fpsyg.2013.00300/full</a><o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Your argument about rehearsal is interesting. Perhaps do a video explaining the difference between your method and the globalist method. Rehearsal is definitely computationally expensive. If there’s a smarter way to do the same, that would
definitely be of interest.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Juergen, sorry that this discussion deviates from what you started. Improper credit assignment is indeed a problem in this field. I had this note about 10 years back from one of the SVM gurus:<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal" style="margin-left:.5in"><i>“Your RBF nets are very close to Support Vector Machines with kernels,
<u><span style="background:yellow;mso-highlight:yellow">so you were ahead of the curve</span></u>. Now SVM are one of the most successful learning methodologies.”
<o:p></o:p></i></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">The problem in this field shows up in many other forms, one being fear of dominant personalities and their theories. I have seen that first hand in my private debates over the years.
<o:p></o:p></p>
<p class="MsoNormal"> <o:p></o:p></p>
<p class="MsoNormal">Best,<o:p></o:p></p>
<p class="MsoNormal">Asim<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal"><b>From:</b> Tsvi Achler <achler@gmail.com> <br>
<b>Sent:</b> Saturday, November 6, 2021 10:23 PM<br>
<b>To:</b> Asim Roy <ASIM.ROY@asu.edu><br>
<b>Cc:</b> Adam Krawitz <akrawitz@uvic.ca>; connectionists <connectionists@cs.cmu.edu>; Schmidhuber Juergen <juergen@idsia.ch>; Levine, Daniel S <levine@uta.edu>; Juyang Weng <juyang.weng@gmail.com><br>
<b>Subject:</b> Re: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Hi Asim,<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">I love your globalist-localist dichotomy as it refers to learning. In localist learning only the neuron that is associated with the learning label needs to be modified to learn. Feed forward methods are globalist because if you want to
modify a label you have to rehearse with all the old and the new and many weights may change anywhere within the network.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Unfortunately when I try to publish papers using this distinction I find globalist and localist can mean many things to related literatures. Do you have good references that nail down the definitions and history from the beginning of connectionism?
I would love to put that up and clarify it within a general search, maybe even place it on Wikipedia.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">I find the localist-globalist distinction very important.. Regulatory feedback learns in a localist way. This is why it does not need the same rehearsal as feed forward networks. <o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Regulatory feedback may also help address the criticism by those who subscribe to globalist methods and criticise the need of non-distributed single grandmother cells. Regulatory feedback can support a localist method that is also distributed.<o:p></o:p></p>
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<p class="MsoNormal">Due to regulatory feedback, if multiple neurons encode the same thing then they create a linearly dependent type of situation. You can create many neurons that encode the same thing (let's say grandmother) and get the same result as if
you had one neuron. In this paradigm all of the neurons that encode the same thing can simply be summed up and they act as a single neuron. Thus any combination of neurons can be used in total to represent the same thing and if one neuron dies this does
not affect the sum at all.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">This linearly dependent situation also yields interesting results especially when you put a regulatory feedback network into the paradigm of LTP and LTD experiments. Within LTP & LTD activation and induction, you can get changes where
a different linearly dependent neuron takes priority, looking like synaptic change but occurring without any synaptic change. This can greatly change our understanding of brain recognition and design of experiments.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Although I may not expect the community to immediately adopt this paradigm and results, I do expect it to be accepted as a possibility to be thought about and analyzed, and placed in literature where it will be read. Instead it is snuffed
out.<o:p></o:p></p>
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<p class="MsoNormal">You won't be able to read it in a publication.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">For example, I had a conference paper on this linear dependence accepted a while ago, but since I did not have funding, I could not provide the $500 the conference demanded. As a pay-to-play paywall journal they did not care about the situation.
Moreover as a long term non-funded researcher I am no longer willing to put my time and efforts into publishing work where it will not be read or where a cost to the author is demanded even if there is no funding. If the connectionist community and academia
really wants to support new ideas, it has to equitably provide funding, make publications free, or make things less political. <o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">If there are enough people interested and subscribed to view videos I can make a video to show the dynamics that create this situation just like I did for regulatory feedback.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Sincerely<o:p></o:p></p>
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<p class="MsoNormal">-Tsvi<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">On Sat, Nov 6, 2021, 1:57 AM Asim Roy <<a href="mailto:ASIM.ROY@asu.edu" target="_blank">ASIM.ROY@asu.edu</a>> wrote:<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Over a period of more than 25 years, I have had the opportunity to argue about the brain in both public forums and private discussions. And they included very well-known scholars
such as Walter Freeman (UC-Berkeley), Horace Barlow (Cambridge; great grandson of Charles Darwin), Jay McClelland (Stanford), Bernard Baars (Neuroscience Institute), Christof Koch (Allen Institute), Teuvo Kohonen (Finland) and many others, some of whom are
on this list. And many became good friends through these debates. <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">We argued about many issues over the years, but the one that baffled me the most was the one about localist vs. distributed representation. Here’s the issue. As far as I know, although
all the Nobel prizes in the field of neurophysiology – from Hubel and Wiesel (simple and complex cells) and Moser and O’Keefe (grid and place cells) to the current one on discovery of temperature and touch sensitive receptors and neurons - are about finding
“meaning” in single or a group of dedicated cells, the distributed representation theory has yet to explain these findings of “meaning.” Contrary to the assertion that the field is open-minded, I think most in this field are afraid the to cross the red line.
<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Horace Barlow was the exception. He was perhaps the only neuroscientist who was willing to cross the red line and declare that “grandmother cells will be found.” After a debate
on this issue in 2012, which included Walter Freeman and others, Horace visited me in Phoenix at the age of 91 for further discussion.
<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">If the field is open minded, would love to hear how distributed representation is compatible with finding “meaning” in the activations of single or a dedicated group of cells.<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Asim Roy<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Professor, Arizona State University<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><a href="https://urldefense.com/v3/__https:/lifeboat.com/ex/bios.asim.roy__;!!IKRxdwAv5BmarQ!MmZHsjQisJKynaUiIzDFMs7M9QdB-2H7C3NjXmdhJPZtYlUSaU_dYQGzfYCX86c$" target="_blank">Lifeboat
Foundation Bios: Professor Asim Roy</a><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b>From:</b> Connectionists <<a href="mailto:connectionists-bounces@mailman.srv.cs.cmu.edu" target="_blank">connectionists-bounces@mailman.srv.cs.cmu.edu</a>>
<b>On Behalf Of </b>Adam Krawitz<br>
<b>Sent:</b> Friday, November 5, 2021 10:01 AM<br>
<b>To:</b> <a href="mailto:connectionists@cs.cmu.edu" target="_blank">connectionists@cs.cmu.edu</a><br>
<b>Subject:</b> Re: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc.<o:p></o:p></p>
</div>
</div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Tsvi,<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">I’m just a lurker on this list, with no skin in the game, but perhaps that gives me a more neutral perspective. In the spirit of progress:</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
<ol start="1" type="1">
<li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l0 level1 lfo1">
<span lang="EN-CA">If you have a neural network approach that you feel provides a new and important perspective on cognitive processes, then write up a paper making that argument clearly, and I think you will find that the community is incredibly open to that.
Yes, if they see holes in the approach they will be pointed out, but that is all part of the scientific exchange. Examples of this approach include: Elman (1990) Finding Structure in Time, Kohonen (1990) The Self-Organizing Map, Tenenbaum et al. (2011) How
to Grow a Mind: Statistics, Structure, and Abstraction (not neural nets, but a “new” approach to modelling cognition). I’m sure others can provide more examples.</span><o:p></o:p></li><li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l0 level1 lfo1">
<span lang="EN-CA">I’m much less familiar with how things work on the applied side, but I have trouble believing that Google or anyone else will be dismissive of a computational approach that actually works. Why would they? They just want to solve problems
efficiently. Demonstrate that your approach can solve a problem more effectively (or at least as effectively) as the existing approaches, and they will come running. Examples of this include: Tesauro’s TD-Gammon, which was influential in demonstrating the
power of RL, and LeCun et al.’s convolutional NN for the MNIST digits.</span><o:p></o:p></li></ol>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Clearly communicate the novel contribution of your approach and I think you will find a receptive audience.</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Thanks,</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Adam</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b>From:</b> Connectionists <<a href="mailto:connectionists-bounces@mailman.srv.cs.cmu.edu" target="_blank">connectionists-bounces@mailman.srv.cs.cmu.edu</a>>
<b>On Behalf Of </b>Tsvi Achler<br>
<b>Sent:</b> November 4, 2021 9:46 AM<br>
<b>To:</b> <a href="mailto:gary@ucsd.edu" target="_blank">gary@ucsd.edu</a><br>
<b>Cc:</b> <a href="mailto:connectionists@cs.cmu.edu" target="_blank">connectionists@cs.cmu.edu</a><br>
<b>Subject:</b> Re: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc.<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Lastly Feedforward methods are predominant in a large part because they have financial backing from large companies with advertising and clout like Google and
the self-driving craze that never fully materialized. </span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Feedforward methods are not fully connectionist unless rehearsal for learning is implemented with neurons. That means storing all patterns, mixing them randomly
and then presenting to a network to learn. As far as I know, no one is doing this in the community, so feedforward methods are only partially connectionist. By allowing popularity to predominate and choking off funds and presentation of alternatives we are
cheating ourselves from pursuing other more rigorous brain-like methods.</span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Sincerely,</span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">-Tsvi</span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">On Tue, Nov 2, 2021 at 7:08 PM Tsvi Achler <<a href="mailto:achler@gmail.com" target="_blank">achler@gmail.com</a>> wrote:</span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Gary- Thanks for the accessible online link to the book. </span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">I looked especially at the inhibitory feedback section of the book which describes an Air Conditioner AC type feedback. </span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">It then describes a general field-like inhibition based on all activations in the layer. It also describes the role of inhibition in sparsity and feedforward
inhibition,</span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">The feedback described in Regulatory Feedback is similar to the AC feedback but occurs for each neuron individually, vis-a-vis its inputs.</span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Thus for context, regulatory feedback is not a field-like inhibition, it is very directed based on the neurons that are activated and their inputs. This sort
of regulation is also the foundation of Homeostatic Plasticity findings (albeit with changes in Homeostatic regulation in experiments occurring in a slower time scale). The regulatory feedback model describes the effect and role in recognition of those regulated
connections in real time during recognition.</span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">I would be happy to discuss further and collaborate on writing about the differences between the approaches for the next book or review.</span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">And I want to point out to folks, that the system is based on politics and that is why certain work is not cited like it should, but even worse these politics
are here in the group today and they continue to very strongly influence decisions in the connectionist community and holds us back.</span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Sincerely,</span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">-Tsvi</span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">On Mon, Nov 1, 2021 at 10:59 AM
<a href="mailto:gary@ucsd.edu" target="_blank">gary@ucsd.edu</a> <<a href="mailto:gary@eng.ucsd.edu" target="_blank">gary@eng.ucsd.edu</a>> wrote:</span><o:p></o:p></p>
</div>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA" style="font-size:18.0pt;font-family:"Times New Roman",serif">Tsvi - While I think
<a href="https://urldefense.com/v3/__https:/www.amazon.com/dp/0262650541/__;!!IKRxdwAv5BmarQ!P43fgF97h1EkMmUyqwIyGb3BiM6QvDDIayyZy_zt_11O7NVqPb6YiU7U4snDyk0$" target="_blank">
Randy and Yuko's book </a>is actually somewhat better than the online version (and buying choices on amazon start at $9.99), there
<b>is</b> <a href="https://urldefense.com/v3/__https:/compcogneuro.org/__;!!IKRxdwAv5BmarQ!P43fgF97h1EkMmUyqwIyGb3BiM6QvDDIayyZy_zt_11O7NVqPb6YiU7UH2qn4go$" target="_blank">
an online version.</a> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA" style="font-size:18.0pt;font-family:"Times New Roman",serif">Randy & Yuko's models take into account feedback and inhibition. </span><o:p></o:p></p>
</div>
</div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
<div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">On Mon, Nov 1, 2021 at 10:05 AM Tsvi Achler <<a href="mailto:achler@gmail.com" target="_blank">achler@gmail.com</a>> wrote:</span><o:p></o:p></p>
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<blockquote style="border:none;border-left:solid #CCCCCC 1.0pt;padding:0in 0in 0in 6.0pt;margin-left:4.8pt;margin-top:5.0pt;margin-right:0in;margin-bottom:5.0pt">
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Daniel,</span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Does your book include a discussion of Regulatory or Inhibitory Feedback published in several low impact journals between 2008 and 2014 (and in videos subsequently)?</span><o:p></o:p></p>
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<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">These are networks where the primary computation is inhibition back to the inputs that activated them and may be very counterintuitive given today's trends.
You can almost think of them as the opposite of Hopfield networks.</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">I would love to check inside the book but I dont have an academic budget that allows me access to it and that is a huge part of the problem with how information
is shared and funding is allocated. I could not get access to any of the text or citations especially Chapter 4: "Competition, Lateral Inhibition, and Short-Term Memory", to weigh in.</span><o:p></o:p></p>
</div>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">I wish the best circulation for your book, but even if the Regulatory Feedback Model is in the book, that does not change the fundamental problem if the book
is not readily available. </span><o:p></o:p></p>
</div>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">The same goes with Steve Grossberg's book, I cannot easily look inside. With regards to Adaptive Resonance I dont subscribe to lateral inhibition as a predominant
mechanism, but I do believe a function such as vigilance is very important during recognition and Adaptive Resonance is one of a very few models that have it. The Regulatory Feedback model I have developed (and Michael Spratling studies a similar model as
well) is built primarily using the vigilance type of connections and allows multiple neurons to be evaluated at the same time and continuously during recognition in order to determine which (single or multiple neurons together) match the inputs the best without
lateral inhibition.</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Unfortunately within conferences and talks predominated by the Adaptive Resonance crowd I have experienced the familiar dismissiveness and did not have an opportunity
to give a proper talk. This goes back to the larger issue of academic politics based on small self-selected committees, the same issues that exist with the feedforward crowd, and pretty much all of academia.</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Today's information age algorithms such as Google's can determine relevance of information and ways to display them, but hegemony of the journal systems and the
small committee system of academia developed in the middle ages (and their mutual synergies) block the use of more modern methods in research. Thus we are stuck with this problem, which especially affects those that are trying to introduce something new and
counterintuitive, and hence the results described in the two National Bureau of Economic Research articles I cited in my previous message.</span><o:p></o:p></p>
</div>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA" style="color:black">Thomas, I am happy to have more discussions and/or start a different thread.</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Sincerely,</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Tsvi Achler MD/PhD</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
</div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
<div>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">On Sun, Oct 31, 2021 at 12:49 PM Levine, Daniel S <<a href="mailto:levine@uta.edu" target="_blank">levine@uta.edu</a>> wrote:</span><o:p></o:p></p>
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<blockquote style="border:none;border-left:solid #CCCCCC 1.0pt;padding:0in 0in 0in 6.0pt;margin-left:4.8pt;margin-top:5.0pt;margin-right:0in;margin-bottom:5.0pt">
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA" style="font-size:12.0pt;color:black">Tsvi,</span><o:p></o:p></p>
</div>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA" style="font-size:12.0pt;color:black"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA" style="font-size:12.0pt;color:black">While deep learning and feedforward networks have an outsize popularity, there are plenty of published sources that cover
a much wider variety of networks, many of them more biologically based than deep learning. A treatment of a range of neural network approaches, going from simpler to more complex cognitive functions, is found in my textbook
<i>Introduction to Neural and Cognitive Modeling</i> (3rd edition, Routledge, 2019). Also Steve Grossberg's book
<i>Conscious Mind, Resonant Brain</i> (Oxford, 2021) emphasizes a variety of architectures with a strong biological basis.</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA" style="font-size:12.0pt;color:black"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA" style="font-size:12.0pt;color:black"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA" style="font-size:12.0pt;color:black">Best,</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA" style="font-size:12.0pt;color:black"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA" style="font-size:12.0pt;color:black"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA" style="font-size:12.0pt;color:black">Dan Levine</span><o:p></o:p></p>
</div>
<div class="MsoNormal" align="center" style="text-align:center"><span lang="EN-CA">
<hr size="2" width="98%" align="center">
</span></div>
<div id="gmail-m_-1200202972266866444m_1470554579056945204gmail-m_-3449454411413463923gmail-m_-99491911606122447gmail-m_-1743973086040584978m_-7977962918574028451m_3158366256145482305m_-7622355768744816385m_-898643676068276388gmail-m_-7809501330019106925gmail-m_8996447038276730094gmail-m_-2305817410909496922gmail-m_7665975300539281535divRplyFwdMsg">
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b><span lang="EN-CA" style="color:black">From:</span></b><span lang="EN-CA" style="color:black"> Connectionists <<a href="mailto:connectionists-bounces@mailman.srv.cs.cmu.edu" target="_blank">connectionists-bounces@mailman.srv.cs.cmu.edu</a>>
on behalf of Tsvi Achler <<a href="mailto:achler@gmail.com" target="_blank">achler@gmail.com</a>><br>
<b>Sent:</b> Saturday, October 30, 2021 3:13 AM<br>
<b>To:</b> Schmidhuber Juergen <<a href="mailto:juergen@idsia.ch" target="_blank">juergen@idsia.ch</a>><br>
<b>Cc:</b> <a href="mailto:connectionists@cs.cmu.edu" target="_blank">connectionists@cs.cmu.edu</a> <<a href="mailto:connectionists@cs.cmu.edu" target="_blank">connectionists@cs.cmu.edu</a>><br>
<b>Subject:</b> Re: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc.</span><span lang="EN-CA">
</span><o:p></o:p></p>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
</div>
<div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Since the title of the thread is Scientific Integrity, I want to point out some issues about trends in academia and then especially focusing on the connectionist
community. </span><o:p></o:p></p>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">In general analyzing impact factors etc the most important progress gets silenced until the mainstream picks it up <a href="https://urldefense.com/v3/__https:/nam12.safelinks.protection.outlook.com/?url=https*3A*2F*2Fwww.nber.org*2Fsystem*2Ffiles*2Fworking_papers*2Fw22180*2Fw22180.pdf*3Ffbclid*3DIwAR1zHhU4wmkrHASTaE-6zwIs6gI9-FxZcCED3BETxUJlMsbN_2hNbmJAmOA&data=04*7C01*7Clevine*40uta.edu*7Cb1a267e3b6a64ada666208d99ca37f6d*7C5cdc5b43d7be4caa8173729e3b0a62d9*7C1*7C0*7C637713048300122043*7CUnknown*7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0*3D*7C1000&sdata=9o*2FzcYY8gZVZiAwyEL5SVI9TEzBWfKf7nfhdWWg8LHU*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUl!!IKRxdwAv5BmarQ!P43fgF97h1EkMmUyqwIyGb3BiM6QvDDIayyZy_zt_11O7NVqPb6YiU7UD9hRGNg$" target="_blank">Impact
Factiors in novel research www.nber.org/.../working_papers/w22180/w22180.pdf</a> and often this may take a generation <a href="https://urldefense.com/v3/__https:/nam12.safelinks.protection.outlook.com/?url=https*3A*2F*2Fwww.nber.org*2Fdigest*2Fmar16*2Fdoes-science-advance-one-funeral-time*3Ffbclid*3DIwAR1Lodsf1bzje-yQU9DvoZE2__S6R7UPEgY1_LxZCSLdoAYnj-uco0JuyVk&data=04*7C01*7Clevine*40uta.edu*7Cb1a267e3b6a64ada666208d99ca37f6d*7C5cdc5b43d7be4caa8173729e3b0a62d9*7C1*7C0*7C637713048300132034*7CUnknown*7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0*3D*7C1000&sdata=DgxnJTT7MsN5KCzZlA7VAHKrHXVsRsYhopJv0FCwbtw*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUl!!IKRxdwAv5BmarQ!P43fgF97h1EkMmUyqwIyGb3BiM6QvDDIayyZy_zt_11O7NVqPb6YiU7UapVS1t0$" target="_blank">https://www.nber.org/.../does-science-advance-one-funeral...</a> .</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">The connectionist field is stuck on feedforward networks and variants such as with inhibition of competitors (e.g. lateral inhibition), or other variants that
are sometimes labeled as recurrent networks for learning time where the feedforward networks can be rewound in time.</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">This stasis is specifically occuring with the popularity of deep learning. This is often portrayed as neurally plausible connectionism but requires an implausible
amount of rehearsal and is not connectionist if this rehearsal is not implemented with neurons (see video link for further clarification).</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Models which have true feedback (e.g. back to their own inputs) cannot learn by backpropagation but there is plenty of evidence these types of connections exist
in the brain and are used during recognition. Thus they get ignored: no talks in universities, no featuring in "premier" journals and no funding. </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">But they are important and may negate the need for rehearsal as needed in feedforward methods. Thus may be essential for moving connectionism forward.</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">If the community is truly dedicated to brain motivated algorithms, I recommend giving more time to networks other than feedforward networks.</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Video: <a href="https://urldefense.com/v3/__https:/nam12.safelinks.protection.outlook.com/?url=https*3A*2F*2Fwww.youtube.com*2Fwatch*3Fv*3Dm2qee6j5eew*26list*3DPL4nMP8F3B7bg3cNWWwLG8BX-wER2PeB-3*26index*3D2&data=04*7C01*7Clevine*40uta.edu*7Cb1a267e3b6a64ada666208d99ca37f6d*7C5cdc5b43d7be4caa8173729e3b0a62d9*7C1*7C0*7C637713048300132034*7CUnknown*7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0*3D*7C1000&sdata=EaEp5zLZ7HkDhsBHmP3x3ObPl8j14B8*2BFcOkkNEWZ9w*3D&reserved=0__;JSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUl!!IKRxdwAv5BmarQ!P43fgF97h1EkMmUyqwIyGb3BiM6QvDDIayyZy_zt_11O7NVqPb6YiU7UzMnNL04$" target="_blank">https://www.youtube.com/watch?v=m2qee6j5eew&list=PL4nMP8F3B7bg3cNWWwLG8BX-wER2PeB-3&index=2</a></span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Sincerely,</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Tsvi Achler</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
</div>
</div>
</div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA"> </span><o:p></o:p></p>
<div>
<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">On Wed, Oct 27, 2021 at 2:24 AM Schmidhuber Juergen <<a href="mailto:juergen@idsia.ch" target="_blank">juergen@idsia.ch</a>> wrote:</span><o:p></o:p></p>
</div>
<blockquote style="border:none;border-left:solid #CCCCCC 1.0pt;padding:0in 0in 0in 6.0pt;margin-left:4.8pt;margin-top:5.0pt;margin-right:0in;margin-bottom:5.0pt">
<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:12.0pt"><span lang="EN-CA">Hi, fellow artificial neural network enthusiasts!<br>
<br>
The connectionists mailing list is perhaps the oldest mailing list on ANNs, and many neural net pioneers are still subscribed to it. I am hoping that some of them - as well as their contemporaries - might be able to provide additional valuable insights into
the history of the field.<br>
<br>
Following the great success of massive open online peer review (MOOR) for my 2015 survey of deep learning (now the most cited article ever published in the journal Neural Networks), I've decided to put forward another piece for MOOR. I want to thank the many
experts who have already provided me with comments on it. Please send additional relevant references and suggestions for improvements for the following draft directly to me at
<a href="mailto:juergen@idsia.ch" target="_blank">juergen@idsia.ch</a>:<br>
<br>
<a href="https://urldefense.com/v3/__https:/nam12.safelinks.protection.outlook.com/?url=https*3A*2F*2Fpeople.idsia.ch*2F*juergen*2Fscientific-integrity-turing-award-deep-learning.html&data=04*7C01*7Clevine*40uta.edu*7Cb1a267e3b6a64ada666208d99ca37f6d*7C5cdc5b43d7be4caa8173729e3b0a62d9*7C1*7C0*7C637713048300142030*7CUnknown*7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0*3D*7C1000&sdata=mW3lH7SqKg4EuJfDwKcC2VhwEloC3ndh6kI5gfQ2Ofw*3D&reserved=0__;JSUlJX4lJSUlJSUlJSUlJSUlJQ!!IKRxdwAv5BmarQ!P43fgF97h1EkMmUyqwIyGb3BiM6QvDDIayyZy_zt_11O7NVqPb6YiU7UNznV_Qo$" target="_blank">https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html</a><br>
<br>
The above is a point-for-point critique of factual errors in ACM's justification of the ACM A. M. Turing Award for deep learning and a critique of the Turing Lecture published by ACM in July 2021. This work can also be seen as a short history of deep learning,
at least as far as ACM's errors and the Turing Lecture are concerned.<br>
<br>
I know that some view this as a controversial topic. However, it is the very nature of science to resolve controversies through facts. Credit assignment is as core to scientific history as it is to machine learning. My aim is to ensure that the true history
of our field is preserved for posterity.<br>
<br>
Thank you all in advance for your help! <br>
<br>
Jürgen Schmidhuber<br>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">--
</span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Gary Cottrell 858-534-6640 FAX: 858-534-7029</span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:12.0pt"><span lang="EN-CA">Computer Science and Engineering 0404<br>
IF USING FEDEX INCLUDE THE FOLLOWING LINE: <br>
CSE Building, Room 4130<br>
University of California San Diego -<br>
9500 Gilman Drive # 0404<br>
La Jolla, Ca. 92093-0404</span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA">Email:
<a href="mailto:gary@ucsd.edu" target="_blank">gary@ucsd.edu</a><br>
Home page: <a href="https://urldefense.com/v3/__http:/www-cse.ucsd.edu/*gary/__;fg!!IKRxdwAv5BmarQ!P43fgF97h1EkMmUyqwIyGb3BiM6QvDDIayyZy_zt_11O7NVqPb6YiU7U-G68mLE$" target="_blank">
http://www-cse.ucsd.edu/~gary/</a></span><o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span lang="EN-CA" style="font-size:9.5pt">Schedule: <a href="https://urldefense.com/v3/__http:/tinyurl.com/b7gxpwo__;!!IKRxdwAv5BmarQ!P43fgF97h1EkMmUyqwIyGb3BiM6QvDDIayyZy_zt_11O7NVqPb6YiU7UcMz40H8$" target="_blank">http://tinyurl.com/b7gxpwo</a></span><o:p></o:p></p>
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<p><i><span lang="EN-CA" style="font-size:11.5pt;font-family:"Book Antiqua",serif;color:black">Listen carefully,</span></i><i><span lang="EN-CA" style="font-size:14.0pt;font-family:"Book Antiqua",serif;color:black"><br>
</span></i><i><span lang="EN-CA" style="font-size:11.5pt;font-family:"Book Antiqua",serif;color:black">Neither the Vedas</span></i><i><span lang="EN-CA" style="font-size:14.0pt;font-family:"Book Antiqua",serif;color:black"><br>
</span></i><i><span lang="EN-CA" style="font-size:11.5pt;font-family:"Book Antiqua",serif;color:black">Nor the Qur'an</span></i><i><span lang="EN-CA" style="font-size:14.0pt;font-family:"Book Antiqua",serif;color:black"><br>
</span></i><i><span lang="EN-CA" style="font-size:11.5pt;font-family:"Book Antiqua",serif;color:black">Will teach you this:</span></i><i><span lang="EN-CA" style="font-size:14.0pt;font-family:"Book Antiqua",serif;color:black"><br>
</span></i><i><span lang="EN-CA" style="font-size:11.5pt;font-family:"Book Antiqua",serif;color:black">Put the bit in its mouth,</span></i><i><span lang="EN-CA" style="font-size:14.0pt;font-family:"Book Antiqua",serif;color:black"><br>
</span></i><i><span lang="EN-CA" style="font-size:11.5pt;font-family:"Book Antiqua",serif;color:black">The saddle on its back,</span></i><i><span lang="EN-CA" style="font-size:14.0pt;font-family:"Book Antiqua",serif;color:black"><br>
</span></i><i><span lang="EN-CA" style="font-size:11.5pt;font-family:"Book Antiqua",serif;color:black">Your foot in the stirrup,</span></i><i><span lang="EN-CA" style="font-size:14.0pt;font-family:"Book Antiqua",serif;color:black"><br>
</span></i><i><span lang="EN-CA" style="font-size:11.5pt;font-family:"Book Antiqua",serif;color:black">And ride your wild runaway mind</span></i><i><span lang="EN-CA" style="font-size:14.0pt;font-family:"Book Antiqua",serif;color:black"><br>
</span></i><i><span lang="EN-CA" style="font-size:11.5pt;font-family:"Book Antiqua",serif;color:black">All the way to heaven.</span></i><o:p></o:p></p>
<p><i><span lang="EN-CA" style="font-size:14.0pt;font-family:"Book Antiqua",serif;color:black">-- Kabir</span></i><o:p></o:p></p>
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