Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc.

Mon Nov 1 08:07:50 EDT 2021

Tsvi,

My book does not include the regulatory feedback you mention, but includes a lot of recurrent networks dating as far back as 1973 (some of them in high-impact journals).  It is indeed readily available, in fact it was announced on Connectionists about two years ago.  The link is
https://www.routledge.com/Introduction-to-Neural-and-Cognitive-Modeling-3rd-Edition/Levine/p/book/9781848726482 .  It is organized primarily by problems and secondarily by approaches.

Dan
________________________________
From: Tsvi Achler <achler at gmail.com>
Sent: Monday, November 1, 2021 4:23 AM
To: Levine, Daniel S <levine at uta.edu>
Cc: Schmidhuber Juergen <juergen at idsia.ch>; connectionists at cs.cmu.edu <connectionists at cs.cmu.edu>
Subject: Re: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc.

Daniel,

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)?
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.

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.

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.

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.

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.

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.

Thomas, I am happy to have more discussions and/or start a different thread.

Sincerely,
Tsvi Achler MD/PhD

On Sun, Oct 31, 2021 at 12:49 PM Levine, Daniel S <levine at uta.edu<mailto:levine at uta.edu>> wrote:
Tsvi,

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 Introduction to Neural and Cognitive Modeling (3rd edition, Routledge, 2019).  Also Steve Grossberg's book Conscious Mind, Resonant Brain (Oxford, 2021) emphasizes a variety of architectures with a strong biological basis.

Best,

Dan Levine
________________________________
From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu<mailto:connectionists-bounces at mailman.srv.cs.cmu.edu>> on behalf of Tsvi Achler <achler at gmail.com<mailto:achler at gmail.com>>
Sent: Saturday, October 30, 2021 3:13 AM
To: Schmidhuber Juergen <juergen at idsia.ch<mailto:juergen at idsia.ch>>
Cc: connectionists at cs.cmu.edu<mailto:connectionists at cs.cmu.edu> <connectionists at cs.cmu.edu<mailto:connectionists at cs.cmu.edu>>
Subject: Re: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc.

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.

In general analyzing impact factors etc the most important progress gets silenced until the mainstream picks it up Impact Factiors in novel research www.nber.org/.../working_papers/w22180/w22180.pdf<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%7Cd65ad5550fec40aafaca08d99d195386%7C5cdc5b43d7be4caa8173729e3b0a62d9%7C1%7C0%7C637713554370922823%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=uaVCVLcTNDHAyNwWQ%2BQikujb0vblzcwqtis2bxhpxnE%3D&reserved=0>  and often this may take a generation https://www.nber.org/.../does-science-advance-one-funeral...<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%7Cd65ad5550fec40aafaca08d99d195386%7C5cdc5b43d7be4caa8173729e3b0a62d9%7C1%7C0%7C637713554370932818%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=h6zq2Liyu%2FKWlZcIwD0sIoO6VsrUK755vriOKoSfCts%3D&reserved=0>  .

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.

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).

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.

But they are important and may negate the need for rehearsal as needed in feedforward methods.  Thus may be essential for moving connectionism forward.

If the community is truly dedicated to brain motivated algorithms, I recommend giving more time to networks other than feedforward networks.

Video: https://www.youtube.com/watch?v=m2qee6j5eew&list=PL4nMP8F3B7bg3cNWWwLG8BX-wER2PeB-3&index=2<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%7Cd65ad5550fec40aafaca08d99d195386%7C5cdc5b43d7be4caa8173729e3b0a62d9%7C1%7C0%7C637713554370932818%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=BqKoG5yltWfdFMAG2%2BMCTxzbfmLAz4Tsjdwy1ZjhRwE%3D&reserved=0>

Sincerely,
Tsvi Achler

On Wed, Oct 27, 2021 at 2:24 AM Schmidhuber Juergen <juergen at idsia.ch<mailto:juergen at idsia.ch>> wrote:
Hi, fellow artificial neural network enthusiasts!

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.

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 juergen at idsia.ch<mailto:juergen at idsia.ch>:

https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html<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%7Cd65ad5550fec40aafaca08d99d195386%7C5cdc5b43d7be4caa8173729e3b0a62d9%7C1%7C0%7C637713554370942814%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=BgJJl4D36%2BZ7ExQDaBh9lcJJFJZ5Byd0V7xuyvOjGuI%3D&reserved=0>

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.

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.

Thank you all in advance for your help!

Jürgen Schmidhuber

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