- Comp-neuro - lists.cnsorg.org

2024 Bonsai Developer Conference

by Joaquin Rapela

Hello, We are excited to invite you to the 2024 Bonsai Developer Conference, taking place from December 2nd to 4th, 2024, at the Sainsbury Wellcome Centre in London, UK. This event brings together neuroscience researchers, computational scientists, and software engineers with a shared interest in the Bonsai visual reactive programming language. The conference program and additional details can be found at https://conference.bonsai-rx.org/2024/ About Bonsai ------------ Bonsai https://bonsai-rx.org/ is an open-source reactive visual programming language widely use for neuroscience experimental control. Differently from standard programming languages where control flow is specified by composing sequences of instructions, and checking conditional loops for the occurrence of events, in reactive languages https://introtorx.com/ the control flow is specified by composing event streams. By placing asynchronous data streams front and centre, it becomes much easier to succinctly build complex programs manipulating hundreds of parallel data sources and controllers while retaining interoperability and modularity. Bonsai is a visual programming language where programs are laid out graphically. Each node represents a data stream, and edges represent input dependencies between streams. Parameters can be manipulated dynamically and every stream can be inspected at runtime. This graphical approach allows users with no programming experience to create complex asynchronous programs, and experienced Bonsai users to very quickly deploy and manage entire systems and applications. Bonsai is able to interface with a large number of experimental hardware for neuroscience experimental control, allowing researchers to easily build sophisticated experiments. We look forward to see you in London! Best regards, Goncalo Lopes g.lopes(a)neurogears.org Nicholas Guilbeault n.guilbeault(a)ucl.ac.uk Joaquin Rapela j.rapela(a)ucl.ac.uk

Oct. 22, 2024

Computational Psychology or Neuroscience Assistant Professor Position at the University of Arizona

by Cowen, Stephen Leigh - (scowen)

Hello everyone, The University of Arizona Psychology Department is hiring a tenure-track Assistant Professor in computational psychology, computational cognitive science, and/or computational neuroscience. A complete description of the position can be found below. We are looking for an individual with expertise and a publication history in domains such as modeling complex systems and behavior using approaches such as deep learning, machine learning, Bayesian methods, artificial neural networks, reinforcement learning, dynamic systems modelling, network analysis, and large language models. Preference will be given to applicants with the potential to collaborate across disciplines in Cognitive Science and Psychology. See the full posting and apply using the following link: https://arizona.csod.com/ux/ats/careersite/4/home/requisition/20668?c=arizo… The first batch of applications will be reviewed on Nov. 12. Applications will be accepted until the position is filled. Best, Stephen ------------------------------------------ Stephen Cowen, Ph.D. Associate Professor, Psychology Evelyn F. McKnight Brian Institute Interim Director: Cognition and Neural Systems Graduate interdisciplinary member: Neuroscience, Physiological Sciences, Applied Biosciences, and Cognitive Science The University of Arizona Life Sciences North, Rm 347 1501 N. Campbell Ave. Tucson, AZ 85724-5115 520-626-2615

Oct. 22, 2024

Postdoctoral Opportunity in My Research Group at University of Tennessee Knoxville

by Maroulas, Vasileios

Dear Colleagues, I wanted to bring to your attention a postdoctoral opening in my research group at the National Institute for Mathematical and Biological Synthesis (NIMBioS) and the Department of Mathematics at the University of Tennessee, Knoxville. The position is at the intersection of topological deep learning and computational neuroscience and it is a collaboration with the US Army Research lab. Please feel free to share the following link with anyone who might be interested in the position: https://www.mathjobs.org/jobs/list/25406 Thank you in advance for helping spread the word. Best regards, Vasileios Vasileios Maroulas, Ph.D. Associate Vice Chancellor Director, AI Tennessee Initiative Professor of Mathematics The University of Tennessee, Knoxville Office of Research, Innovation, & Economic Development <https://research.utk.edu/oried/> Andy Holt Tower 1331 Circle Park Drive Knoxville, TN 37996-0100 Department of Mathematics<https://math.utk.edu/> Ayres Hall 1403 Circle Dr. Knoxville, TN 37996 email: vasileios.maroulas(a)utk.edu<mailto:vasileios.maroulas@utk.edu> office: 865-974-4302 LinkedIn: Vasileios Maroulas| LinkedIn<https://www.linkedin.com/in/vasileios-maroulas-8721643/>

Oct. 22, 2024

Re: Some scientific history that I experienced relevant to the recent Nobel Prizes to Hopfield and Hinton

by Julie Goulet

Oct. 22, 2024

Re: Some scientific history that I experienced relevant to the recent Nobel Prizes to Hopfield and Hinton

by Sara A. Solla

Stephen, You have an explanation for everything. I was at the NIPS (now NeurIPS) meeting when you were giving a talk, but instead of talking about your work you spent the time allotted to you to complain about Hopfield stealing your ideas. I do not quite remember the year, but I am sure you do - late 1980s? So, yes, you have been complaining about this for as long as I know you. Word limits allow you to mention Hopfield’s 84 paper but not his 82 paper??!! It takes as many characters to write 82 as to write 84. The 82 paper was published at a time before ArXiv, preprints, and online postings, that is to say an actually printed copy became available, and that was in April 82. Your paper, the one where you did it all before, was submitted in August 82. You submitted your paper a few months after the 1982 paper by Hopfield appeared in print in PNAS. Anyone can verify this by looking at the printed versions of these two papers. What nobody can verify is that ‘someone told you that Hopfield knew about your paper’ before writing his. What cannot be verified is that there was a version of your paper that you tried to submit in 1980 to a journal that just happened to disappear. The article from 1971 that I referred to is the one you cited in your first tirade. The paper is about "Pavlovian pattern learning by nonlinear neural networks". According to Google Scholar, it has received 118 citations. And it is NOT because it appeared in a journal that has ceased to exist. It appeared in PNAS. I will assume that the 118 citations is an accurate measure of the interest that the paper has triggered in the 53 years since it was published. That you wrote a review in 1988 and that Michael Cohen wrote an article in 1992 does not give either of you precedence over Hopfield’s 1982 and 1984 papers. The Group on Statistical Physics and Nonlinear Dynamics of the American Physical Society organized a session to discuss the Nobel Prize given to Hopfield and Hinton. You can watch it on YouTube, https://www.youtube.com/watch?v=inMeFnIhuA8. My advice to you is to watch this discussion, read the 1982 and 1984 papers by Hopfield, then read your own papers, and try to see what it is that he figured out and that you missed. More than forty years later, it is time for you to look into these papers and acknowledge the crucial differences. If you still do not see it, I will be glad to try to explain it to you. Then you get to Hinton. Once I reminded you that the Prize citation is about the Boltzmann Machine and not about Back Propagation, you stopped criticizing Back Propagation to instead advertise work by James Williamson published in 1996 and 1997. These papers seem not to precede but to follow the 1985 publication of the Boltzmann Machine paper by 11 and 12 years. As for what was the atmosphere of intellectual intimidation in your group, you say that I would not know because I was not there. This is factually incorrect. I was there. I visited. I saw the group dynamics, and I was shocked. I was a young scientist, just a few years beyond my PhD, but I had never seen anything like it. One had to believe, not just agree with but believe in ART to work in your group. I ask you again: if ART provides a solution to every problem, as you advertised then and seem to continue to advertise today, how come there are still unsolved problems? The long note you have published in response to the Physics Nobel Prize is just pathetic. Sara On Mon, Oct 21, 2024 at 6:22 PM Grossberg, Stephen <steve(a)bu.edu> wrote: > Dear Sara, > > > > I reply below to your claims. > > > > *From: *Sara A. Solla <sasolla(a)gmail.com> > *Date: *Monday, October 21, 2024 at 5:12 PM > *To: *Grossberg, Stephen <steve(a)bu.edu> > *Cc: *André Fabio Kohn via Comp-neuro <comp-neuro(a)lists.cnsorg.org>, > Stephen Grossberg <steve(a)cns.bu.edu> > *Subject: *Re: [Comp-neuro] Re: Some scientific history that I > experienced relevant to the recent Nobel Prizes to Hopfield and Hinton > > You don't often get email from sasolla(a)gmail.com. Learn why this is > important <https://aka.ms/LearnAboutSenderIdentification> > > Stephen, > > You have been complaining about not getting enough credit since I first > met you in the mid 1980s. > > > > That is not true. > > > > You refer to publishing before Hopfield's 1984 paper. You deliberately > ignore his 1982 paper, which received 27946 citations: > Neural networks and physical systems with emergent collective > computational abilities, JJ Hopfield > PNAS, April 15, 1982, 79 (8) 2554-2558 > > > > My post was originally on a web site with strict word limits. That is why > I mentioned Hopfield’s 1984 article, but not his 1982 article. > > > > Section 6 of my 1988 review article: > > > > Grossberg, S. (1988) Nonlinear neural networks: Principles, mechanisms, > and architectures. *Neural Networks*, *1 *, 17-61. > > https://sites.bu.edu/steveg/files/2016/06/Gro1988NN.pdf > > > > reviews Binary, Linear, and Continuous-Nonlinear classical neural network > models by many authors. > > > > This article has been cited 2410 times. > > > > Section 6 shows that the 1982 equation that Hopfield used is a variant of > the 1943 McCulloch-Pitts model. > > > > The Liapunov function for it is just a discrete version of the Liapunov > function for the Additive Model. > > > In contrast, the 1971 student paper you mentioned received 118 citations. > > > > I assume that you mean: > > > > Grossberg, S. (1971). Embedding fields: Underlying philosophy, > mathematics, and applications to psychology, physiology, and anatomy. *Journal > of Cybernetics*, 1, 28-50. > > https://sites.bu.edu/steveg/files/2016/06/Gro1971JoC.pdf > > > > This was a review article published in a journal that ceased to exist in > 1980 (see below) and thus could not be searched well by Google, which began > in 1998. > > > > The article discussed Generalized Additive Models (see its Section 6). > > > > I was able to get articles published proving global limit and oscillation > theorems for Generalized Additive Models in the* Proceedings of the > National Academy of Sciences* between 1967 and 1971; e.g., > > > > Grossberg, S. (1971). Pavlovian pattern learning by nonlinear neural > networks. *Proceedings of the National Academy of Sciences*, 68, 828-831. > > https://sites.bu.edu/steveg/files/2016/06/Gro1971ProNatAcaSci.pdf > > > > See Equations 1, 2, 3, and 4 etc. > > > > Your 1983 paper with Michael Cohen in IEEE Transaction of Man, Systems, > and Cybernetics was received on August 1, 1982, several months after the > 1982 paper by Hopfeld had appeared in print. You did get a good number of > citations on this paper, 3344, but not as good as 27946. > > > > Michael Cohen and I tried to submit it to the *Journal of Cybernetics* in > 1980. > > > > But there was a glitch in the submission process due to the fact that the *Journal > of Cybernetics* unexpectedly stopped publishing that year and our article > got lost in the shuffle, unknown to us for a while. > > > > We resubmitted in 1982, which is why it was published in 1983. > > > > Our results pre-dated Hopfield 1982 and 1984 by two years. > > > > About the difference in citations: > > > > There is a difference between discovery and marketing. I was told that > Hopfield knew about my work before and with Michael Cohen before he went > around the country lecturing about work that we had previously published, > without citation. > > > > You mention a 1982 paper with Michael Cohen; I have not been able to find > it. > > > > I think that was a typo. Michael published the following article in 1992: > > > > Cohen, M. A. (1992). The construction of arbitrary stable dynamics in > nonlinear neural networks. > > *Neural Networks*, 5, 83 – 103. > > https://www.sciencedirect.com/science/article/abs/pii/S0893608005800085 > > > > Here is the Abstract: > > > > “In this paper, two methods for constructing systems of ordinary > differential equations realizing any fixed finite set of equilibria in any > fixed finite dimension are introduced; no spurious equilibria are possible > for either method. By using the first method, one can construct a system > with the fewest number of equilibria, given a fixed set of attractors. > Using a strict *Lyapunov function [boldface mine] *for each of these > differential equations, a large class of systems with the same set of > equilibria is constructed. A method of fitting these nonlinear systems to > trajectories is proposed. In addition, a general method which will produce > an arbitrary number of periodic orbits of shapes of arbitrary complexity is > also discussed. A more general second method is given to construct a > differential equation which converges to a fixed given finite set of > equilibria. This technique is much more general in that it allows this set > of equilibria to have any of a large class of indices which are consistent > with the Morse Inequalities. It is clear that this class is not universal, > because there is a large class of additional vector fields with convergent > dynamics which cannot be constructed by the above method. The easiest way > to see this is to enumerate the set of Morse indices which can be obtained > by the above method and compare this class with the class of Morse indices > of arbitrary differential equations with convergent dynamics. The former > set of indices are a proper subclass of the latter, therefore, the above > construction cannot be universal. In general, it is a difficult open > problem to construct a specific example of a differential equation with a > given fixed set of equilibria, permissible Morse indices, and permissible > connections between stable and unstable manifolds. A strict Lyapunov > function is given for this second case as well. This strict *Lyapunov > function [boldface mine] *as above enables construction of a large class > of examples consistent with these more complicated dynamics and indices. > The determination of all the basins of attraction in the general9 case for > these systems is also difficult and open.” > > > > I have never heard anybody referring to you as 'the father of AI'. > > > > As an acolyte of Hopfield, you wouldn’t. > > > > What I do remember is a visit to your group at BU many, many years ago - > in the late 80s or early 90s. I was amazed at finding out that you closely > supervised every word in every slide that anybody in your group was allowed > to show when invited to give a talk. Everybody was under a lot of pressure > to 'stay on message', where the 'message' was your view on things. I had > never encountered a scientific group run as a cult. It made an impression, > but not a positive one. So much for training over 100 people - by teaching > them not to think by themselves. > > > > You describe my devoted teaching as something ugly, even ad hominem. > > > > When I was a student, no one told me how to write an article or how to > prepare and give a talk or poster. I developed a method to enable my > students to grow intellectually and become independent scholars. > > > > When I started working with a new PhD student or postdoc, we discussed > what topics would potentially interest them. Then we read hundreds of > psychological and neurobiological articles together about that topic and > began to discuss the results in them. > > > > For students interested in engineering and AI, we studied all the methods > that were available to solve problems in a targeted problem domain. > > > > I encouraged my students to advance their own concepts about what might be > the underlying mechanisms that generated an article’s data or benchmarks. > Because we studied large amounts of data, favorite concepts often hit a > brick wall and had to be discarded. > > > > We developed a modeling method that I like to call the Method of Minimal > Anatomies. See Figure 2.37 in my 2021 Magnum Opus > > > > https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/dp/0190070552 > > > > This Method is just Occam’s Razor applied to neural networks. > > > > As the Figure summarizes, it “Operationalizes the proper level of > abstraction” and “that you cannot ‘derive a brain’ in one step”. Our models > have been getting incrementally developed in a principled way to the > present time. > > > > Your statement that “the message was your [my] view on things” is the > opposite of how we worked. > > > > And how would you know? You were not there. > > > > The proof of the pudding is that all my students got positions that they > wanted after they earned their PhD’s with me. > > > > I was also told by multiple famous scientists that our department sent > them their best postdoctoral fellows. > > > > In fact, various major labs would contact me to let me know that they had > an open position and asked if one of my students was about to graduate. > > > > My students have gone on to successful careers after their postdocs and > other first post-PhD positions. > > > > Around half of them went into academe, and the other half into > engineering, technology, and AI. > > > > Groups of us have periodically gotten together for happy social events, > either at international conferences or in the Boston area. > > > > And they sent me information about their accomplishments and growing > families for many years. > > > > As for back-propagation, this is not what Hinton is cited for in the Nobel > Prize citation. It is for the Boltzmann machine. > > > > Yes, I know that, and I have also noted that my PhD student, James > Williamson, developed statistical neural network models that did not have > problems related to deep Learning. I have elsewhere noted that: > > > Restricted Boltzmann Machines (RBMS) also have analogs in the Adaptive > Resonance Theory (ART) family of models, but without the problems that > follow from using variants of Deep Learning. > > Here are two of them, both developed by my PhD student, James R. > Williamson: > > Gaussian ARTMAP: A Neural Network for Fast Incremental Learning of Noisy > Multidimensional Maps > https://www.sciencedirect.com/science/article/pii/0893608095001158 > > A Constructive, Incremental Learning Network for Mixture Modeling and > Classification > > http://techlab.bu.edu/files/resources/articles_tt/A%20constructive,%20incre… > > > > Finally, I always wondered: If ART solves all problems, why are there > ML/AI problems that remain to be solved? > > > > I would never claim that. I have always taught that science is never done. > See the Method of Minimal Anatomies. > > > > The fact that you can even think that about my work shows, sad to say, how > biased you have become. > > > > On Mon, Oct 21, 2024 at 7:49 AM Grossberg, Stephen via Comp-neuro < > comp-neuro(a)lists.cnsorg.org> wrote: > > Dear Comp-neuro colleagues, > > > > Here are some short summaries of the history of neural network > discoveries, as I experienced it, that are relevant to the recent Nobel > Prizes to Hopfield and Hinton: > > ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++THETHE THE NOBEL > PRIZES IN PHYSICS TO HOPFIELD AND HINTON > FOR MODELS THEY DID NOT DISCOVER: THE CASE OF HOPFIELD > > Here I summarize my concerns about the Hopfield award. > > I published articles in 1967 – 1972 in the Proceedings of the National > Academy of Sciences that introduced the Additive Model that Hopfield used > in 1984. My articles proved global theorems about the limits and > oscillations of my Generalized Additive Models. See sites.bu.edu/steveg for > these articles. > > For example: > > Grossberg, S. (1971). Pavlovian pattern learning by nonlinear neural > networks. Proceedings of the National Academy of Sciences, 68, 828-831. > https://lnkd.in/emzwx4Tw > > This article illustrates that my mathematical results were part of a > research program to develop biological neural networks that provide > principled mechanistic explanations of psychological and neurobiological > data. > > Later, Michael Cohen and I published a Liapunov function that included the > Additive Model and generalizations thereof in 1982 and 1983 before Hopfield > (1984) appeared. > > For example, > > Cohen, M.A. and Grossberg, S. (1983). Absolute stability of global pattern > formation and parallel memory storage by competitive neural networks. IEEE > Transactions on Systems, Man, and Cybernetics, SMC-13, 815-826. > https://lnkd.in/eAFAdvbu > > I was told that Hopfield knew about my work before he published his 1984 > article, without citation. > > Recall that I started my neural networks research in 1957 as a Freshman at > Dartmouth College. > > That year, I introduced the biological neural network paradigm, as well as > the short-term memory (STM), medium-term memory (MTM), and long-term memory > (LTM) laws that are used to this day, including in the Additive Model, to > explain data about how brains make minds. > > See the review in https://lnkd.in/gJZJtP_W . > > When I started in 1957, I knew no one else who was doing neural networks. > That is why my colleagues call me the Father of AI. > > I then worked hard to create a neural networks community, notably a > research center, academic department, the International Neural Network > Society, the journal Neural Networks, multiple international conferences on > neural networks, and Boston-area research centers, while training over 100 > gifted PhD students, postdocs, and faculty to do neural network research. > See the Wikipedia page. > > That is why I did not have time or strength to fight for priority of my > models. > > Recently, I was able to provide a self-contained and non-technical > overview and synthesis of some of my scientific discoveries since 1957, as > well as explanations of the work of many other scientists, in my 2021 > Magnum Opus > > Conscious Mind, Resonant Brain: How Each Brain Makes a Mind > > https://lnkd.in/eiJh4Ti > > ++++++++++++++++++++++++++++++++++++++++++++++++++++ > > THE NOBEL PRIZES IN PHYSICS TO HOPFIELD AND HINTON > FOR MODELS THEY DID NOT DISCOVER: THE CASE OF HINTON > > Here I summarize my concerns about the Hinton award. > > Many authors developed Back Propagation (BP) before Hinton; e.g., Amari > (1967), Werbos (1974), Parker (1982), all before Rumelhart, Hinton, & > Williams (1986). > > BP has serious computational weaknesses: > > It is UNTRUSTWORTHY (because it is UNEXPLAINABLE). > > It is UNRELIABLE (because it can experience CATASTROPHIC FORGETTING. > > It should thus never be used in financial or medical applications. > > BP learning is also SLOW and uses non-biological NONLOCAL WEIGHT TRANSPORT. > > See Figure, right column, top. > > In 1988, I published 17 computational problems of BP: > https://lnkd.in/erKJvXFA > > BP gradually grew out of favor because other models were better. > > Later, huge online databases and supercomputers enabled Deep Learning to > use BP to learn. > > My 1988 article contrasted BP with Adaptive Resonance Theory (ART) which I > first published in 1976: > https://lnkd.in/evkfq22G > > See Figure, right column, bottom. > > ART never had BP’s problems. > > ART is now the most advanced cognitive and neural theory that explains HOW > HUMANS LEARN TO ATTEND, RECOGNIZE, and PREDICT events in a changing world. > > ART also explains and simulates data from hundreds of psychological and > neurobiological experiments. > > In 1980, I derived ART from a THOUGHT EXPERIMENT about how ANY system can > AUTONOMOUSLY learn to correct predictive errors in a changing world: > https://lnkd.in/eGWE8kJg > > The thought experiment derives ART from a few facts of life that do not > mention mind or brain. > > ART is thus a UNIVERSAL solution of the problem of autonomous error > correction in a changing world. > > That is why ART models can be used in designs for AUTONOMOUS ADAPTIVE > INTELLIGENCE in engineering, technology, and AI. > > ART also proposes a solution of the classical MIND-BODY PROBLEM: > > HOW, WHERE in our brains, and WHY from a deep computational perspective, > we CONSCIOUSLY SEE, HEAR, FEEL, and KNOW about the world, and use our > conscious states to PLAN and ACT to realize VALUED GOALS. > > For details, see > > Conscious Mind, Resonant Brain: How Each Brain Makes a Mind > > https://lnkd.in/eiJh4Ti > > > +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ > > > > _______________________________________________ > Comp-neuro mailing list -- comp-neuro(a)lists.cnsorg.org > Mailing list webpage (to subscribe or view archives): > https://www.cnsorg.org/comp-neuro-mailing-list > > To contact admin/moderators, send an email to: > comp-neuro-owner(a)lists.cnsorg.org > To unsubscribe, send an email to comp-neuro-leave(a)lists.cnsorg.org > >

Oct. 22, 2024

[Deadline Nov. 1st][Call for Data & Papers] 2nd International Whole Brain Architecture Workshop (Feb. 24th, 2025)

by Hiroshi Yamakawa

Dear Colleagues, << Abstract submission site is now open; data paper templates are available! >> We are pleased to announce the Second International Whole Brain Architecture Workshop, building on the remarkable success of our inaugural event. (The first online workshop, held in July 2024, attracted over 100 participants and generated significant enthusiasm in the field.) This second workshop will be held on February 24th, 2025 ( https://wba-initiative.org/en/24521/ ). Registration for presentations opens on October 20th, 2024, and we eagerly anticipate your submissions of BRA data. About This Workshop: The development of brain-inspired artificial intelligence, which aims to understand and simulate the computational functions of the brain, is a promising approach to reproducing human-like problem-solving abilities. This ambitious goal requires knowledge from diverse research fields, including neuroscience and cognitive science. Given the vast amount of knowledge needed, it is crucial to accumulate and share standardized design information as open knowledge. This workshop serves as a platform for cooperation and sharing of standardized design information (BRA data) essential for developing brain-inspired AI. Additionally, it provides an opportunity to enhance the academic value of submitted datasets through expert discussions and peer review. Why Participate: • Contribute to the cutting-edge field of brain-inspired AI • Share your research with a global audience of experts • Gain insights from interdisciplinary discussions • Potential for your work to be published on the BRA Editorial System (BRAES) https://ggle.io/6ybj • Access to standardized data paper templates to ensure high-quality submissions We invite submissions of BRA data and accompanying papers. Accepted data and papers will be published on BRAES, with paper DOIs shared on the workshop program page. We believe that this sharing and integration of brain-inspired AI design information will significantly advance the field. Date and Location (hybrid): • Date: February 24th, 2025 (Monday) • Venue: Tokyo, Japan (on-site) and online (details TBA) Important Dates: • BRA Data submission opens: October 20th, 2024 (JST) • Paper abstract submission deadline: November 1st, 2024 (JST) • BRA Data submission deadline: December 20th, 2024 (JST) • Full Paper submission deadline: January 4th, 2025 (JST) • Completed BRA Data submission deadline: January 4th, 2025 (JST) • Paper notification: January 24th, 2025 (JST) New Updates: The submission site for data paper abstracts is now open and accessible at https://ggle.io/79e3 The data paper template is now available for download at https://ggle.io/79e5 For detailed submission guidelines, please visit: https://wba-initiative.org/en/24521/ We look forward to welcoming you and your colleagues to present data and papers and to engage in stimulating discussions that will shape the future of brain-inspired AI. If you have any questions, please don't hesitate to contact the Workshop organizers at bra-support(a)wba-initiative.org. Join us in advancing the frontier of brain-inspired artificial intelligence! Best regards, BRA Support Team

Oct. 22, 2024

Re: Some scientific history that I experienced relevant to the recent Nobel Prizes to Hopfield and Hinton

by Grossberg, Stephen

Dear Sara, I reply below to your claims. From: Sara A. Solla <sasolla(a)gmail.com> Date: Monday, October 21, 2024 at 5:12 PM To: Grossberg, Stephen <steve(a)bu.edu> Cc: André Fabio Kohn via Comp-neuro <comp-neuro(a)lists.cnsorg.org>, Stephen Grossberg <steve(a)cns.bu.edu> Subject: Re: [Comp-neuro] Re: Some scientific history that I experienced relevant to the recent Nobel Prizes to Hopfield and Hinton You don't often get email from sasolla(a)gmail.com. Learn why this is important<https://aka.ms/LearnAboutSenderIdentification> Stephen, You have been complaining about not getting enough credit since I first met you in the mid 1980s. That is not true. You refer to publishing before Hopfield's 1984 paper. You deliberately ignore his 1982 paper, which received 27946 citations: Neural networks and physical systems with emergent collective computational abilities, JJ Hopfield PNAS, April 15, 1982, 79 (8) 2554-2558 My post was originally on a web site with strict word limits. That is why I mentioned Hopfield’s 1984 article, but not his 1982 article. Section 6 of my 1988 review article: Grossberg, S. (1988) Nonlinear neural networks: Principles, mechanisms, and architectures. Neural Networks, 1 , 17-61. https://sites.bu.edu/steveg/files/2016/06/Gro1988NN.pdf reviews Binary, Linear, and Continuous-Nonlinear classical neural network models by many authors. This article has been cited 2410 times. Section 6 shows that the 1982 equation that Hopfield used is a variant of the 1943 McCulloch-Pitts model. The Liapunov function for it is just a discrete version of the Liapunov function for the Additive Model. In contrast, the 1971 student paper you mentioned received 118 citations. I assume that you mean: Grossberg, S. (1971). Embedding fields: Underlying philosophy, mathematics, and applications to psychology, physiology, and anatomy. Journal of Cybernetics, 1, 28-50. https://sites.bu.edu/steveg/files/2016/06/Gro1971JoC.pdf This was a review article published in a journal that ceased to exist in 1980 (see below) and thus could not be searched well by Google, which began in 1998. The article discussed Generalized Additive Models (see its Section 6). I was able to get articles published proving global limit and oscillation theorems for Generalized Additive Models in the Proceedings of the National Academy of Sciences between 1967 and 1971; e.g., Grossberg, S. (1971). Pavlovian pattern learning by nonlinear neural networks. Proceedings of the National Academy of Sciences, 68, 828-831. https://sites.bu.edu/steveg/files/2016/06/Gro1971ProNatAcaSci.pdf See Equations 1, 2, 3, and 4 etc. Your 1983 paper with Michael Cohen in IEEE Transaction of Man, Systems, and Cybernetics was received on August 1, 1982, several months after the 1982 paper by Hopfeld had appeared in print. You did get a good number of citations on this paper, 3344, but not as good as 27946. Michael Cohen and I tried to submit it to the Journal of Cybernetics in 1980. But there was a glitch in the submission process due to the fact that the Journal of Cybernetics unexpectedly stopped publishing that year and our article got lost in the shuffle, unknown to us for a while. We resubmitted in 1982, which is why it was published in 1983. Our results pre-dated Hopfield 1982 and 1984 by two years. About the difference in citations: There is a difference between discovery and marketing. I was told that Hopfield knew about my work before and with Michael Cohen before he went around the country lecturing about work that we had previously published, without citation. You mention a 1982 paper with Michael Cohen; I have not been able to find it. I think that was a typo. Michael published the following article in 1992: Cohen, M. A. (1992). The construction of arbitrary stable dynamics in nonlinear neural networks. Neural Networks, 5, 83 – 103. https://www.sciencedirect.com/science/article/abs/pii/S0893608005800085 Here is the Abstract: “In this paper, two methods for constructing systems of ordinary differential equations realizing any fixed finite set of equilibria in any fixed finite dimension are introduced; no spurious equilibria are possible for either method. By using the first method, one can construct a system with the fewest number of equilibria, given a fixed set of attractors. Using a strict Lyapunov function [boldface mine] for each of these differential equations, a large class of systems with the same set of equilibria is constructed. A method of fitting these nonlinear systems to trajectories is proposed. In addition, a general method which will produce an arbitrary number of periodic orbits of shapes of arbitrary complexity is also discussed. A more general second method is given to construct a differential equation which converges to a fixed given finite set of equilibria. This technique is much more general in that it allows this set of equilibria to have any of a large class of indices which are consistent with the Morse Inequalities. It is clear that this class is not universal, because there is a large class of additional vector fields with convergent dynamics which cannot be constructed by the above method. The easiest way to see this is to enumerate the set of Morse indices which can be obtained by the above method and compare this class with the class of Morse indices of arbitrary differential equations with convergent dynamics. The former set of indices are a proper subclass of the latter, therefore, the above construction cannot be universal. In general, it is a difficult open problem to construct a specific example of a differential equation with a given fixed set of equilibria, permissible Morse indices, and permissible connections between stable and unstable manifolds. A strict Lyapunov function is given for this second case as well. This strict Lyapunov function [boldface mine] as above enables construction of a large class of examples consistent with these more complicated dynamics and indices. The determination of all the basins of attraction in the general case for these systems is also difficult and open.” I have never heard anybody referring to you as 'the father of AI'. As an acolyte of Hopfield, you wouldn’t. What I do remember is a visit to your group at BU many, many years ago - in the late 80s or early 90s. I was amazed at finding out that you closely supervised every word in every slide that anybody in your group was allowed to show when invited to give a talk. Everybody was under a lot of pressure to 'stay on message', where the 'message' was your view on things. I had never encountered a scientific group run as a cult. It made an impression, but not a positive one. So much for training over 100 people - by teaching them not to think by themselves. You describe my devoted teaching as something ugly, even ad hominem. When I was a student, no one told me how to write an article or how to prepare and give a talk or poster. I developed a method to enable my students to grow intellectually and become independent scholars. When I started working with a new PhD student or postdoc, we discussed what topics would potentially interest them. Then we read hundreds of psychological and neurobiological articles together about that topic and began to discuss the results in them. For students interested in engineering and AI, we studied all the methods that were available to solve problems in a targeted problem domain. I encouraged my students to advance their own concepts about what might be the underlying mechanisms that generated an article’s data or benchmarks. Because we studied large amounts of data, favorite concepts often hit a brick wall and had to be discarded. We developed a modeling method that I like to call the Method of Minimal Anatomies. See Figure 2.37 in my 2021 Magnum Opus https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/dp/0190070552 This Method is just Occam’s Razor applied to neural networks. As the Figure summarizes, it “Operationalizes the proper level of abstraction” and “that you cannot ‘derive a brain’ in one step”. Our models have been getting incrementally developed in a principled way to the present time. Your statement that “the message was your [my] view on things” is the opposite of how we worked. And how would you know? You were not there. The proof of the pudding is that all my students got positions that they wanted after they earned their PhD’s with me. I was also told by multiple famous scientists that our department sent them their best postdoctoral fellows. In fact, various major labs would contact me to let me know that they had an open position and asked if one of my students was about to graduate. My students have gone on to successful careers after their postdocs and other first post-PhD positions. Around half of them went into academe, and the other half into engineering, technology, and AI. Groups of us have periodically gotten together for happy social events, either at international conferences or in the Boston area. And they sent me information about their accomplishments and growing families for many years. As for back-propagation, this is not what Hinton is cited for in the Nobel Prize citation. It is for the Boltzmann machine. Yes, I know that, and I have also noted that my PhD student, James Williamson, developed statistical neural network models that did not have problems related to deep Learning. I have elsewhere noted that: Restricted Boltzmann Machines (RBMS) also have analogs in the Adaptive Resonance Theory (ART) family of models, but without the problems that follow from using variants of Deep Learning. Here are two of them, both developed by my PhD student, James R. Williamson: Gaussian ARTMAP: A Neural Network for Fast Incremental Learning of Noisy Multidimensional Maps https://www.sciencedirect.com/science/article/pii/0893608095001158 A Constructive, Incremental Learning Network for Mixture Modeling and Classification http://techlab.bu.edu/files/resources/articles_tt/A%20constructive,%20incre… Finally, I always wondered: If ART solves all problems, why are there ML/AI problems that remain to be solved? I would never claim that. I have always taught that science is never done. See the Method of Minimal Anatomies. The fact that you can even think that about my work shows, sad to say, how biased you have become. On Mon, Oct 21, 2024 at 7:49 AM Grossberg, Stephen via Comp-neuro <comp-neuro(a)lists.cnsorg.org<mailto:comp-neuro@lists.cnsorg.org>> wrote: Dear Comp-neuro colleagues, Here are some short summaries of the history of neural network discoveries, as I experienced it, that are relevant to the recent Nobel Prizes to Hopfield and Hinton: ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++THETHE THE NOBEL PRIZES IN PHYSICS TO HOPFIELD AND HINTON FOR MODELS THEY DID NOT DISCOVER: THE CASE OF HOPFIELD Here I summarize my concerns about the Hopfield award. I published articles in 1967 – 1972 in the Proceedings of the National Academy of Sciences that introduced the Additive Model that Hopfield used in 1984. My articles proved global theorems about the limits and oscillations of my Generalized Additive Models. See sites.bu.edu/steveg<http://sites.bu.edu/steveg> for these articles. For example: Grossberg, S. (1971). Pavlovian pattern learning by nonlinear neural networks. Proceedings of the National Academy of Sciences, 68, 828-831. https://lnkd.in/emzwx4Tw This article illustrates that my mathematical results were part of a research program to develop biological neural networks that provide principled mechanistic explanations of psychological and neurobiological data. Later, Michael Cohen and I published a Liapunov function that included the Additive Model and generalizations thereof in 1982 and 1983 before Hopfield (1984) appeared. For example, Cohen, M.A. and Grossberg, S. (1983). Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Transactions on Systems, Man, and Cybernetics, SMC-13, 815-826. https://lnkd.in/eAFAdvbu I was told that Hopfield knew about my work before he published his 1984 article, without citation. Recall that I started my neural networks research in 1957 as a Freshman at Dartmouth College. That year, I introduced the biological neural network paradigm, as well as the short-term memory (STM), medium-term memory (MTM), and long-term memory (LTM) laws that are used to this day, including in the Additive Model, to explain data about how brains make minds. See the review in https://lnkd.in/gJZJtP_W . When I started in 1957, I knew no one else who was doing neural networks. That is why my colleagues call me the Father of AI. I then worked hard to create a neural networks community, notably a research center, academic department, the International Neural Network Society, the journal Neural Networks, multiple international conferences on neural networks, and Boston-area research centers, while training over 100 gifted PhD students, postdocs, and faculty to do neural network research. See the Wikipedia page. That is why I did not have time or strength to fight for priority of my models. Recently, I was able to provide a self-contained and non-technical overview and synthesis of some of my scientific discoveries since 1957, as well as explanations of the work of many other scientists, in my 2021 Magnum Opus Conscious Mind, Resonant Brain: How Each Brain Makes a Mind https://lnkd.in/eiJh4Ti ++++++++++++++++++++++++++++++++++++++++++++++++++++ THE NOBEL PRIZES IN PHYSICS TO HOPFIELD AND HINTON FOR MODELS THEY DID NOT DISCOVER: THE CASE OF HINTON Here I summarize my concerns about the Hinton award. Many authors developed Back Propagation (BP) before Hinton; e.g., Amari (1967), Werbos (1974), Parker (1982), all before Rumelhart, Hinton, & Williams (1986). BP has serious computational weaknesses: It is UNTRUSTWORTHY (because it is UNEXPLAINABLE). It is UNRELIABLE (because it can experience CATASTROPHIC FORGETTING. It should thus never be used in financial or medical applications. BP learning is also SLOW and uses non-biological NONLOCAL WEIGHT TRANSPORT. See Figure, right column, top. In 1988, I published 17 computational problems of BP: https://lnkd.in/erKJvXFA BP gradually grew out of favor because other models were better. Later, huge online databases and supercomputers enabled Deep Learning to use BP to learn. My 1988 article contrasted BP with Adaptive Resonance Theory (ART) which I first published in 1976: https://lnkd.in/evkfq22G See Figure, right column, bottom. ART never had BP’s problems. ART is now the most advanced cognitive and neural theory that explains HOW HUMANS LEARN TO ATTEND, RECOGNIZE, and PREDICT events in a changing world. ART also explains and simulates data from hundreds of psychological and neurobiological experiments. In 1980, I derived ART from a THOUGHT EXPERIMENT about how ANY system can AUTONOMOUSLY learn to correct predictive errors in a changing world: https://lnkd.in/eGWE8kJg The thought experiment derives ART from a few facts of life that do not mention mind or brain. ART is thus a UNIVERSAL solution of the problem of autonomous error correction in a changing world. That is why ART models can be used in designs for AUTONOMOUS ADAPTIVE INTELLIGENCE in engineering, technology, and AI. ART also proposes a solution of the classical MIND-BODY PROBLEM: HOW, WHERE in our brains, and WHY from a deep computational perspective, we CONSCIOUSLY SEE, HEAR, FEEL, and KNOW about the world, and use our conscious states to PLAN and ACT to realize VALUED GOALS. For details, see Conscious Mind, Resonant Brain: How Each Brain Makes a Mind https://lnkd.in/eiJh4Ti +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ _______________________________________________ Comp-neuro mailing list -- comp-neuro(a)lists.cnsorg.org<mailto:comp-neuro@lists.cnsorg.org> Mailing list webpage (to subscribe or view archives): https://www.cnsorg.org/comp-neuro-mailing-list To contact admin/moderators, send an email to: comp-neuro-owner(a)lists.cnsorg.org<mailto:comp-neuro-owner@lists.cnsorg.org> To unsubscribe, send an email to comp-neuro-leave(a)lists.cnsorg.org<mailto:comp-neuro-leave@lists.cnsorg.org>

Oct. 22, 2024

Re: Some scientific history that I experienced relevant to the recent Nobel Prizes to Hopfield and Hinton

by Sara A. Solla

Stephen, You have been complaining about not getting enough credit since I first met you in the mid 1980s. You refer to publishing before Hopfield's 1984 paper. You deliberately ignore his 1982 paper, which received 27946 citations: Neural networks and physical systems with emergent collective computational abilities, JJ Hopfield PNAS, April 15, 1982, 79 (8) 2554-2558 In contrast, the 1971 student paper you mentioned received 118 citations. Your 1983 paper with Michael Cohen in IEEE Transaction of Man, Systems, and Cybernetics was received on August 1, 1982, several months after the 1982 paper by Hopfeld had appeared in print. You did get a good number of citations on this paper, 3344, but not as good as 27946. You mention a 1982 paper with Michael Cohen; I have not been able to find it. I have never heard anybody referring to you as 'the father of AI'. What I do remember is a visit to your group at BU many, many years ago - in the late 80s or early 90s. I was amazed at finding out that you closely supervised every word in every slide that anybody in your group was allowed to show when invited to give a talk. Everybody was under a lot of pressure to 'stay on message', where the 'message' was your view on things. I had never encountered a scientific group run as a cult. It made an impression, but not a positive one. So much for training over 100 people - by teaching them not to think by themselves. As for back-propagation, this is not what Hinton is cited for in the Nobel Prize citation. It is for the Boltzmann machine. Finally, I always wondered: If ART solves all problems, why are there ML/AI problems that remain to be solved? On Mon, Oct 21, 2024 at 7:49 AM Grossberg, Stephen via Comp-neuro < comp-neuro(a)lists.cnsorg.org> wrote: > Dear Comp-neuro colleagues, > > > > Here are some short summaries of the history of neural network > discoveries, as I experienced it, that are relevant to the recent Nobel > Prizes to Hopfield and Hinton: > > ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++THETHE THE NOBEL > PRIZES IN PHYSICS TO HOPFIELD AND HINTON > FOR MODELS THEY DID NOT DISCOVER: THE CASE OF HOPFIELD > > Here I summarize my concerns about the Hopfield award. > > I published articles in 1967 – 1972 in the Proceedings of the National > Academy of Sciences that introduced the Additive Model that Hopfield used > in 1984. My articles proved global theorems about the limits and > oscillations of my Generalized Additive Models. See sites.bu.edu/steveg for > these articles. > > For example: > > Grossberg, S. (1971). Pavlovian pattern learning by nonlinear neural > networks. Proceedings of the National Academy of Sciences, 68, 828-831. > https://lnkd.in/emzwx4Tw > > This article illustrates that my mathematical results were part of a > research program to develop biological neural networks that provide > principled mechanistic explanations of psychological and neurobiological > data. > > Later, Michael Cohen and I published a Liapunov function that included the > Additive Model and generalizations thereof in 1982 and 1983 before Hopfield > (1984) appeared. > > For example, > > Cohen, M.A. and Grossberg, S. (1983). Absolute stability of global pattern > formation and parallel memory storage by competitive neural networks. IEEE > Transactions on Systems, Man, and Cybernetics, SMC-13, 815-826. > https://lnkd.in/eAFAdvbu > > I was told that Hopfield knew about my work before he published his 1984 > article, without citation. > > Recall that I started my neural networks research in 1957 as a Freshman at > Dartmouth College. > > That year, I introduced the biological neural network paradigm, as well as > the short-term memory (STM), medium-term memory (MTM), and long-term memory > (LTM) laws that are used to this day, including in the Additive Model, to > explain data about how brains make minds. > > See the review in https://lnkd.in/gJZJtP_W . > > When I started in 1957, I knew no one else who was doing neural networks. > That is why my colleagues call me the Father of AI. > > I then worked hard to create a neural networks community, notably a > research center, academic department, the International Neural Network > Society, the journal Neural Networks, multiple international conferences on > neural networks, and Boston-area research centers, while training over 100 > gifted PhD students, postdocs, and faculty to do neural network research. > See the Wikipedia page. > > That is why I did not have time or strength to fight for priority of my > models. > > Recently, I was able to provide a self-contained and non-technical > overview and synthesis of some of my scientific discoveries since 1957, as > well as explanations of the work of many other scientists, in my 2021 > Magnum Opus > > Conscious Mind, Resonant Brain: How Each Brain Makes a Mind > > https://lnkd.in/eiJh4Ti > > ++++++++++++++++++++++++++++++++++++++++++++++++++++ > > THE NOBEL PRIZES IN PHYSICS TO HOPFIELD AND HINTON > FOR MODELS THEY DID NOT DISCOVER: THE CASE OF HINTON > > Here I summarize my concerns about the Hinton award. > > Many authors developed Back Propagation (BP) before Hinton; e.g., Amari > (1967), Werbos (1974), Parker (1982), all before Rumelhart, Hinton, & > Williams (1986). > > BP has serious computational weaknesses: > > It is UNTRUSTWORTHY (because it is UNEXPLAINABLE). > > It is UNRELIABLE (because it can experience CATASTROPHIC FORGETTING. > > It should thus never be used in financial or medical applications. > > BP learning is also SLOW and uses non-biological NONLOCAL WEIGHT TRANSPORT. > > See Figure, right column, top. > > In 1988, I published 17 computational problems of BP: > https://lnkd.in/erKJvXFA > > BP gradually grew out of favor because other models were better. > > Later, huge online databases and supercomputers enabled Deep Learning to > use BP to learn. > > My 1988 article contrasted BP with Adaptive Resonance Theory (ART) which I > first published in 1976: > https://lnkd.in/evkfq22G > > See Figure, right column, bottom. > > ART never had BP’s problems. > > ART is now the most advanced cognitive and neural theory that explains HOW > HUMANS LEARN TO ATTEND, RECOGNIZE, and PREDICT events in a changing world. > > > ART also explains and simulates data from hundreds of psychological and > neurobiological experiments. > > In 1980, I derived ART from a THOUGHT EXPERIMENT about how ANY system can > AUTONOMOUSLY learn to correct predictive errors in a changing world: > https://lnkd.in/eGWE8kJg > > The thought experiment derives ART from a few facts of life that do not > mention mind or brain. > > ART is thus a UNIVERSAL solution of the problem of autonomous error > correction in a changing world. > > That is why ART models can be used in designs for AUTONOMOUS ADAPTIVE > INTELLIGENCE in engineering, technology, and AI. > > ART also proposes a solution of the classical MIND-BODY PROBLEM: > > HOW, WHERE in our brains, and WHY from a deep computational perspective, > we CONSCIOUSLY SEE, HEAR, FEEL, and KNOW about the world, and use our > conscious states to PLAN and ACT to realize VALUED GOALS. > > For details, see > > Conscious Mind, Resonant Brain: How Each Brain Makes a Mind > > https://lnkd.in/eiJh4Ti > > > +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ > > > _______________________________________________ > Comp-neuro mailing list -- comp-neuro(a)lists.cnsorg.org > Mailing list webpage (to subscribe or view archives): > https://www.cnsorg.org/comp-neuro-mailing-list > > To contact admin/moderators, send an email to: > comp-neuro-owner(a)lists.cnsorg.org > To unsubscribe, send an email to comp-neuro-leave(a)lists.cnsorg.org >

Oct. 21, 2024

[Jobs] Postdoc Available in Theoretical Neuroscience

by Michael Furlong

Job Ad – Postdoctoral Researcher in Theoretical Neuroscience Prof Chris Eliasmith, who holds the Canada Research Chair in Theoretical Neuroscience and heads the Computational Neuroscience Research Group (CNRG) in the Centre for Theoretical Neuroscience (CTN) at the University of Waterloo, is seeking a postdoctoral researcher in Theoretical Neuroscience for a fixed-term appointment. Description: The postdoctoral position will be hosted in the CNRG, with a principal focus on neural modeling to build the next version of the Spaun brain model, the world’s largest functional brain model. The project integrates spiking deep neural networks, motor control, probabilistic inference, navigation, perception and cognition to develop a state-of-the-art, large-scale, spiking, whole-brain model. Applicants should have a PhD, with demonstrated skills in at least one of those areas and a willingness to learn about the others. This project leverages the CNRG’s existing expertise in using neural networks for large-scale brain modeling, originally demonstrated in 2012 with the first version of Spaun. A subsequent version in 2018 significantly extended performance. The latest version currently being built by the CNRG will again break new barriers in the scale and sophistication of whole brain models. Unlike past models, it will be embedded in a sophisticated 3D environment, yet retain the ability to perform a wide variety of tasks, from simple perceptual and motor tasks to challenging intelligence tests. Overall, the long-term goal of the project is to advance the state-of-the-art in large-scale brain models. Additional Information: The successful applicant will be housed in the vibrant Centre for Theoretical Neuroscience (CTN), which includes 7 core and 7 affiliated labs focussed on neural modeling and improving our basic understanding of neural computation. The CTN hosts monthly seminars, a yearly ‘Brain Day’ event, a summer school, and regular social activities. The position is for a fixed term of one year, eight months, with extensions contingent on performance and securing additional funding. Salary is $45,000 - 65,000 CAD per year. To apply, please send a full CV, 3 references and cover letter explaining your background and fit for the job to: Dr. Chris Eliasmith at celiasmith(a)uwaterloo.ca.

Oct. 21, 2024

Re: "Experienced" history of Neural Networks

by James Bower

argg I am sorry, typing too fast Could you change: Any equally interesting conflict I witnessed first hand (with no horse in the race), which even at that time was full of similar claims of precedence. I could tell that story too, but instead, I will simply say that Stephen's recent posting made me a bit nostalgic for the days when you were either were in the “email from Stephen claiming prescience club” or not - I thankfully never was. To An equally interesting conflict I witnessed first hand (with no horse in the race), which even at that time was full of similar claims of precedence. I could tell that story too, but instead, I will simply say that Stephen's recent posting made me a bit nostalgic for the days when you were either in the “email from Stephen claiming prescience club” or not - I thankfully never was. Or I can resubmit Sorry for the extra work Jim Dr. James M. Bower Ph.D. 541-499-7502 Simulating a 17th century landed gentry scientist. Also: Affiliate Professor of Biology Southern Oregon University Visiting Professor of Computational Neuroscience Biocomputation Research Group School of Physics, Engineering and Computer Science University of Hertfordshire, UK Linked in <https://www.linkedin.com/in/james-m-bower-130163/> Wikipedia <https://en.wikipedia.org/wiki/James_M._Bower> > On Oct 21, 2024, at 11:15 AM, James Bower <bowerj(a)sou.edu> wrote: > > Any equally interesting conflict I witnessed first hand (with no horse in the race), which even at that time was full of similar claims of precedence. > > I could tell that story too, but instead, I will simply say that Stephen's recent posting made me a bit nostalgic for the days when you were either were in the “email from Stephen claiming prescience club” or not - I thankfully never was.

Oct. 21, 2024