<div dir="ltr"><div>This entire thread of discussion reminds me of this famous quote: </div><div><br></div><div>"Academic Politics Are So Vicious Because the Stakes Are So Small"</div><div>(there is no agreement on who to attribute the quote to)</div><div> </div><div>For me personally, the message is: Take it easy. None of this is as big of a deal as it may seem at moments. Have more fun. Worry less.</div><div><br></div><div>Danko</div><div><br></div><br clear="all"><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr">Dr. Danko Nikolić<br><a href="http://www.danko-nikolic.com" target="_blank">www.danko-nikolic.com</a><br><a href="https://www.linkedin.com/in/danko-nikolic/" target="_blank">https://www.linkedin.com/in/danko-nikolic/</a><div>--- A progress usually starts with an insight ---</div></div></div></div><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Fri, Nov 5, 2021 at 8:09 AM Tsvi Achler <<a href="mailto:achler@gmail.com">achler@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div>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. </div><div><br></div><div>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.</div><div><br></div><div>Sincerely,</div><div>-Tsvi</div><div><br></div><div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Tue, Nov 2, 2021 at 7:08 PM Tsvi Achler <<a href="mailto:achler@gmail.com" target="_blank">achler@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">Gary- Thanks for the accessible online link to the book. <div><br></div><div>I looked especially at the inhibitory feedback section of the book which describes an Air Conditioner AC type feedback. </div><div>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,</div><div><br></div><div>The feedback described in Regulatory Feedback is similar to the AC feedback but occurs for each neuron individually, vis-a-vis its inputs.</div><div>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.</div><div><br></div><div>I would be happy to discuss further and collaborate on writing about the differences between the approaches for the next book or review.</div><div><br></div><div>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.</div><div><br></div><div>Sincerely,</div><div>-Tsvi</div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">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:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div class="gmail_default" style="font-family:"times new roman",serif;font-size:large">Tsvi - While I think <a href="https://www.amazon.com/dp/0262650541/" 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://compcogneuro.org/" target="_blank">an online version.</a> </div><div class="gmail_default" style="font-family:"times new roman",serif;font-size:large">Randy & Yuko's models take into account feedback and inhibition. </div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Mon, Nov 1, 2021 at 10:05 AM Tsvi Achler <<a href="mailto:achler@gmail.com" target="_blank">achler@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div dir="ltr">Daniel,<div><br></div><div>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)?</div><div>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.</div><div><br></div><div>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.</div><div><br></div><div>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. </div><div><br></div><div>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.</div><div><br></div><div>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.</div><div><br></div><div>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.</div><div><br></div><div><span style="color:rgb(0,0,0)">Thomas, I am happy to have more discussions and/or start a different thread.</span><br></div><div><br></div><div>Sincerely,</div><div>Tsvi Achler MD/PhD</div><div><br></div><div><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">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:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
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Tsvi,</div>
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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.</div>
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Best,</div>
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<div style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">
Dan Levine</div>
<div id="gmail-m_-6394725210988801100gmail-m_-3449454411413463923gmail-m_-99491911606122447gmail-m_-1743973086040584978m_-7977962918574028451m_3158366256145482305m_-7622355768744816385m_-898643676068276388gmail-m_-7809501330019106925gmail-m_8996447038276730094gmail-m_-2305817410909496922gmail-m_7665975300539281535appendonsend"></div>
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<div id="gmail-m_-6394725210988801100gmail-m_-3449454411413463923gmail-m_-99491911606122447gmail-m_-1743973086040584978m_-7977962918574028451m_3158366256145482305m_-7622355768744816385m_-898643676068276388gmail-m_-7809501330019106925gmail-m_8996447038276730094gmail-m_-2305817410909496922gmail-m_7665975300539281535divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" style="font-size:11pt" color="#000000"><b>From:</b> 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.</font>
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<div dir="ltr">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.
<div><br>
<div>In general analyzing impact factors etc the most important progress gets silenced until the mainstream picks it up <a href="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" rel="nofollow noopener" role="link" 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://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" rel="nofollow noopener" role="link" target="_blank">https://www.nber.org/.../does-science-advance-one-funeral...</a> .</div>
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<div>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.</div>
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<div>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).</div>
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<div>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. </div>
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<div>But they are important and may negate the need for rehearsal as needed in feedforward methods. Thus may be essential for moving connectionism forward.</div>
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<div>If the community is truly dedicated to brain motivated algorithms, I recommend giving more time to networks other than feedforward networks.</div>
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<div>Video: <a href="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" target="_blank">https://www.youtube.com/watch?v=m2qee6j5eew&list=PL4nMP8F3B7bg3cNWWwLG8BX-wER2PeB-3&index=2</a></div>
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<div>Sincerely,</div>
<div>Tsvi Achler</div>
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<div dir="ltr">On Wed, Oct 27, 2021 at 2:24 AM Schmidhuber Juergen <<a href="mailto:juergen@idsia.ch" target="_blank">juergen@idsia.ch</a>> wrote:<br>
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<blockquote style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
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://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" rel="noreferrer" 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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</blockquote></div><br clear="all"><div><br></div>-- <br><div dir="ltr"><div dir="ltr"><div><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div>Gary Cottrell 858-534-6640 FAX: 858-534-7029<br></div><div>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<br><br></div><div>Email: <a href="mailto:gary@ucsd.edu" target="_blank">gary@ucsd.edu</a><br>Home page: <a href="http://www-cse.ucsd.edu/~gary/" target="_blank">http://www-cse.ucsd.edu/~gary/</a></div><div><span style="font-size:12.8px">Schedule: </span><span style="font-size:12.8px"><a href="http://tinyurl.com/b7gxpwo" style="font-size:12.8px" target="_blank">http://tinyurl.com/b7gxpwo</a></span><br></div><div><div style="font-size:12.8px"><br></div><div style="font-size:12.8px"><p style="font-family:"Book Antiqua";font-style:oblique;font-size:1.2em;color:rgb(0,0,0)">Listen carefully,<br style="font-style:oblique;font-size:1.2em">Neither the Vedas<br style="font-style:oblique;font-size:1.2em">Nor the Qur'an<br style="font-style:oblique;font-size:1.2em">Will teach you this:<br style="font-style:oblique;font-size:1.2em">Put the bit in its mouth,<br style="font-style:oblique;font-size:1.2em">The saddle on its back,<br style="font-style:oblique;font-size:1.2em">Your foot in the stirrup,<br style="font-style:oblique;font-size:1.2em">And ride your wild runaway mind<br style="font-style:oblique;font-size:1.2em">All the way to heaven.</p><p style="font-family:"Book Antiqua";font-style:oblique;font-size:1.2em;color:rgb(0,0,0)"><span style="font-size:1.2em;font-style:oblique">-- Kabir</span></p></div></div>
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