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Queers in STEM social London
by Niedernhuber,Maria Oct. 29, 2024

Oct. 29, 2024

Dear all Representatives of LGBT university staff networks in London, as well as the organisation Pride in STEM, are organising a social for queer academics, focusing on STEM. Anyone is welcome. Since our social originated at a cognitive and computational neuroscience conference, queer neuroscientists are very well represented, and we hope that this will be a great opportunity to network for queer people in cognitive and computational neuroscience. This is the sign-up link: <https://www.outsavvy.com/event/23202/queers-in-stem-pub-social> https://www.outsavvy.com/event/23202/queers-in-stem-pub-social. If anyone here is interested in co-organising, please shoot me an email, and feel free to forward the sign-up link to anyone you know who might be interested. We are especially still looking for a contact person at the University of Greenwich and at Royal Holloway University of London, which are not represented in the organising team at the moment, to advertise the social in their local LGBT staff network. Feel free to email me to chat about the initiative, and how to get involved! Have a lovely week! Maria (they/them) --- Maria Niedernhuber Postdoctoral researcher in Cognitive Neuroscience Consciousness and Cognition Lab, University of Cambridge Sleep and Cognition Lab, QMUL

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[Newsletter] rTAIM Seminar #13 | Ethical Agent or Mere Tool?: A Cautioning Argument for “Moral” Artificial Intelligence in Healthcare | 30 October 2024, 15h00, Online
by steven gouveia Oct. 28, 2024

Oct. 28, 2024

rTAIM (Rebuilding Trust in AI Medicine) Monthly Seminars Seminar #13 Ethical Agent or Mere Tool?: A Cautioning Argument for “Moral” Artificial Intelligence in Healthcare Jordan Joseph Wadden (Unity Health Toronto - Canada) Following the 12th rTAIM Online Seminar (by Francesco Prinzi, University of Palermo), we are happy to announce the first seminar of this academic year 2024/2025, the 13th rTAIM Online Seminar, with the participation of Jordan Joseph Wadden (Unity Health Toronto, Canada), on the 30th October 2024, 15h00-16h30 Lisbon Time Zone, via Zoom. ONLINE | Link Zoom<https://videoconf-colibri.zoom.us/j/95294324802?pwd=Lt2o4boR0iHYWFtXvu6KdrD…> ID: 952 9432 4802 | Password: 506705 #Seminar 13: To talk of moral machines presupposes that researchers understand enough about how humans come to their own conceptions of the good. Moral epistemology and moral psychology have struggled to describe these facts and processes, and we still don’t have a generalizable conception. However, there appears to be an assumption that ethics can nonetheless be instilled directly into artificial intelligence. This is of special concern in healthcare because algorithms and systems are being designed to recommend treatments and pathways for patients. But finding the right treatment for a patient requires more than just an analysis of the medical facts. Healthcare teams must also weigh preferences, goals, psycho-social needs, religious and spiritual beliefs, quality of life, and so on. Each of these elements has a unique, and often individualized, moral element. This paper presents an argument that healthcare artificial intelligence ought to be viewed as a mere tool, rather than as a (quasi-)moral agent which could act alone in the clinical encounter. I argue that some researchers have become distracted by the AI and robot rights discussions, making them think it’s possible to code ethics into a system when humans still don’t understand how we come to believe our own moral beliefs. Short Bio: Jordan Joseph Wadden, MA, PhD, HEC-C, is a clinical ethicist at Unity Health Toronto in Canada. He is an assistant professor (status only) in the Department of Family and Community Medicine at the University of Toronto. His research focuses on the ethical implementation of artificial intelligence and machine learning in healthcare. He was the winner of the 2024 Ethics of AI Award at the 3rd International Conference on the Ethics of Artificial Intelligence. rTAIM Seminars: https://ifilosofia.up.pt/activities/rtaim-seminars https://trustaimedicine.weebly.com/rtaim-seminars.html Organisation: Steven S. Gouveia (MLAG/IF) Mind, Language and Action Group (MLAG) Instituto de Filosofia da Universidade do Porto – UIDB/00502/2020 Fundação para a Ciência e a Tecnologia (FCT) ____________________________________________ Instituto de Filosofia (UI&D 502) Faculdade de Letras da Universidade do Porto Via Panorâmica s/n 4150-564 Porto Tel. 22 607 71 80 E-mail: ifilosofia(a)letras.up.pt<mailto:ifilosofia@letras.up.pt> http://ifilosofia.up.pt/

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NIH-funded Post-doctoral fellowship - spatial navigation, gait, mobility, EEG, MRI, virtual reality - in biomedical engineering at the University of Florida
by Steve Weisberg Oct. 28, 2024

Oct. 28, 2024

A postdoctoral researcher position studying electrocortical activity during human navigation is available in the laboratory of Dr. Daniel Ferris ( https://faculty.eng.ufl.edu/human-neuromechanics-laboratory) This position is for an initial one-year appointment, and has the potential to be renewed for three additional years based on performance and funding availability. https://explore.jobs.ufl.edu/cw/en-us/job/533572/postdoc Join a team studying age- and ADRD-related changes in gait and navigation, including Rachael Seidler, Dan Ferris, and Steve Weisberg. -- --------------------------------------------- *Steven M. Weisberg, PhD * Pronouns: he/him/his Assistant Professor of Psychology University of Florida Director: SCANN Lab <http://scannlab.psych.ufl.edu>

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[comp-neuro] job advertisement | 1 fully funded PhD position (E13) | Application deadline: Nov.18, 2024
by Daniel Schmid Oct. 28, 2024

Oct. 28, 2024

Vacancy for dissertation/PhD project *Learning Search and Decision Mechanisms in Medical Diagnosis* We are looking for a new PhD student for a fully funded interdisciplinary research project investigating the computational mechanisms underlying the perceptual processes in medical imaging. *Brief project description and goals* Medical experts routinely screen images for signs of abnormality indicative of a disease. Experimental evidence shows that these experts, based on their training, are able to make a diagnose above chance level even after screening images for only a few seconds. Such visual inspection utilizes global ensemble scene statistics, or “scene gist”, to provide contextual guidance information. The goal of the PhD project is to investigate how such global, contextual gist information can be computed and incorporated in machine vision approaches, such as deep neural networks for natural or medical image processing. In particular, a goal is to develop neural mechanisms that can be integrated into existing pre-trained CNNs to compute scene gist for rapid global decision-making and contextual guidance in medical images. Such mechanisms should explain how such global feature compositions are learned to increase the classification specificity and, at the same time, do not equip an observer with the ability to precisely localize such feature compositions indicative of the evidence. At the implementation level, computational units (neurons) in hierarchical CNN architectures will be extended by integrating local bottom-up feature extraction mechanisms with top-down modulatory contextual fields. We focus on these mechanisms to learn the integration of information streams in counter-stream networks by employing novel two-point information integration units inspired by recent findings from neuroscience. These investigations will contribute to develop novel CNN architectures by integrating feedforward and feedback data streams that operate at different spatio-temporal scales and feature types inspired by computational mechanisms in biological vision. The combined global and local information is expected to provide more detailed explanations on a mechanistic level how radiologists steer their attention and search capacities and learn to improve such skills. *Requirements* - MSc degree in Computer Science, Engineering, Physics, Mathematics, Cognitive Systems/Science or equivalent relevant background - Experience in computational vision, neural networks, machine learning; with strong mathematical/physics foundation - Experience in developing biologically inspired models – or interested in learning how to develop such mechanisms and models - Experience in analysis of large data and evaluation of results - Strong coding skills - Personal skills: working in a team, but should be able to work independently, is strongly focused and enthusiastic about the project theme, is self-motivated and takes responsibility for reaching milestones - Experience and talent in scientific writing (with proficient written & oral English) *Contact & application* The project is part of the newly founded research training group “KEMAI” that focuses on integrating knowledge and learning based approaches for better medical AI (https://kemai.uni-ulm.de/ for more information). The offered PhD position is fully funded (E13 salary) and also benefits from an interdisciplinary setting, the ability to connect with peers from related projects and a clearly structured PhD program. Application materials are expected until November 18, 2024. They should contain a CV, statement of research interest and background in relation to the KEMAI training group and project theme C2. A cover letter should inform about the expected date of availability and name of two referees. Submit your application via the KEMAI website (preferred) or to Prof. Heiko Neumann (heiko.neumann(a)uni-ulm.de) or reach out for more information. Best regards, Daniel Schmid -- Daniel Schmid, M.Sc. Institute of Neural Information Processing Faculty of Engineering, Computer Science and Psychology Ulm University James-Frank-Ring D-89081 Ulm Germany e-mail: daniel-1.schmid(a)uni-ulm.de

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Justin Wood speaking on November 12 in Developing Minds global online lecture series
by Jochen Triesch Oct. 28, 2024

Oct. 28, 2024

Dear colleagues, On November 12, the Developing Minds global online lecture series is proud to host Justin Wood from Indiana University Bloomington, USA, speaking on: "Radical empiricism: The origins of knowledge as a mini-evolution“ Tuesday, November 12, 2024 9:00 am EST (Eastern Standard Time, US) 14:00 UTC (Universal Coordinated Time) 15:00 CET (Central European Time) 23:00 JST (Japan Standard Time) The zoom link/credentials are: https://uni-frankfurt.zoom-x.de/j/62980753356?pwd=0J6BtOaAy20YyanXX1gpkB3Ta… Meeting-ID: 629 8075 3356 Kenncode: 904159 Abstract: What are the origins of knowledge? A common theoretical strategy is to isolate core primitives of the mind, such as systems for reasoning about objects, space, number, agents, and language. The problem with this strategy is it disconnects brain development from general fitting principles that underlie chemistry, evolution, culture, and artificial intelligence. I argue that development can be understood as a “mini-evolution,” in which core mental skills are the products of generic evolution-like fitting processes. In evolution, life started from scratch, and species emerged as DNA adapted to the world via blind fitting. Likewise, in development, knowledge starts from scratch, and mental skills emerge as individual brains adapt to the world via blind fitting. Historically, the main roadblock for testing fitting theories has been the lack of benchmarks and models for measuring whether fitting models learn like brains. To directly compare learning across brains and models, both must be trained with the same data and tested on the same tasks. We propose a solution—called “Newborn Embodied Turing Tests” (NETTs)—in which newborn animals and fitting models are reared in the same environments and tested with the same tasks. I’ll describe a series of NETTs showing that generic fitting models (transformers) develop many core mental skills, including orientation selectivity, visual binding, shape-based vision, invariant object recognition, imprinting, collective behavior, and social preferences. These skills develop spontaneously when generic fitting models fit to prenatal and postnatal experiences. There is no need to postulate mysterious core primitives to explain the rapid development of domain-specific knowledge. Rather, core knowledge can be the product of generic fitting machinery: a radical empiricist view of the origins of knowledge. This mini-evolution view unifies chemistry, evolution, development, culture, and artificial intelligence under a common fitting framework, with shared general principles. Short Bio: Justin Wood is an Associate Professor of Informatics at Indiana University Bloomington. He is affiliated with programs in Animal Behavior, Neuroscience, Cognitive Science, and Psychological & Brain Sciences. He received his B.A. from University of Virginia and M.S. & Ph.D. from Harvard University. Dr. Wood has studied the psychological abilities of a range of populations, including human adults, children, infants, chimpanzees, wild monkeys, starlings, and newborn chicks. He works at the intersection of developmental psychology, neuroscience, virtual reality, and artificial intelligence to characterize the origins and computational foundations of intelligence. The talk will be recorded and made available for later viewing. For more information on the talk series and recordings of previous events, please visit: https://sites.google.com/view/developing-minds-series/home Best regards, Jochen Triesch -- Prof. Dr. Jochen Triesch Johanna Quandt Chair for Theoretical Life Sciences Frankfurt Institute for Advanced Studies and Goethe University Frankfurt http://fias.uni-frankfurt.de/~triesch/ Tel: +49 (0)69 798-47531 Fax: +49 (0)69 798-47611

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Join Us for the Statistical Physics of Cognition Workshop – Institute of Physics, London
by Rajpal, Hardik Oct. 25, 2024

Oct. 25, 2024

Dear Colleagues, We are excited to announce that the Statistical Physics of Cognition workshop will take place on the 25th and 26th of November 2024 at the Institute of Physics, King's Cross, London. This workshop will explore the intersection of physics and neuroscience to better understand cognition, bringing together leading researchers from various disciplines, including statistical mechanics, network science, self-organized criticality, information theory, and more. The programme for the workshop is now live and can be viewed here: https://iop.eventsair.com/spc2024/programme. While registrations open next week, we invite those interested in attending to fill out an expression of interest form in advance: https://iop.eventsair.com/spc2024/registration We especially encourage PhD students and Early Career Researchers to participate by submitting abstracts for poster presentations. This is a great opportunity to present your research, receive feedback from leading experts, and engage in exciting discussions. * Abstract submission deadline: 31st October 2024 * Submit your abstract here: https://iop.eventsair.com/spc2024/abstract-submission We look forward to welcoming you to what promises to be a thought-provoking event at the frontier of cognitive science and physics. Please feel free to share this announcement with your colleagues, students, and networks. If you have any questions, don’t hesitate to reach out. Best regards, Hardik Rajpal Research Associate Organizing Committee, Statistical Physics of Cognition Workshop Imperial College London

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World wide VVTNS series: Fifth season opening lecture, Wednesday, October 30, 2024, at 11:00 am EDT
by David Hansel Oct. 25, 2024

Oct. 25, 2024

*Important:* The seminar is at 11 am *EDT* Daylight saving time ends on Sunday, November 3, 2024 In Europe it ends on Sunday October 27, 2024 [image: VVTNS.png] https://www.wwtns.online <https://streaklinks.com/A9c7PbbpKY7PxB6PaAJWGD3-/https%3A%2F%2Fwww.wwtns.on…> - on twitter: wwtns@TheoreticalWide You are cordially invited to the fifth season opening lecture of the online VVTNS series *Karel Svoboda* Allen Institute on the topic of *Illuminating synaptic learning* The lecture will be held on zoom on October 30, 2023, at *11:00 am EDT * To receive the zoom link: https://www.wwtns.online/register-page *Abstract: *How do synapses in the middle of the brain know how to adjust their weight to advance a behavioral goal (i.e. learning)? This is referred to as the synaptic ‘credit assignment problem’. A large variety of synaptic learning rules have been proposed, mainly in the context of artificial neural networks. The most powerful learning rules (e.g. back-propagation of error) are thought to be biologically implausible, whereas the widely studied biological learning rules (Hebbian) are insufficient for goal-directed learning. I will describe ongoing work focused on understanding synaptic learning rules in the cortex in a brain-computer interface task. ᐧ -- *Il n'y a ni obligation ni pression à traiter ou répondre à ce mail en dehors des heures de travail* "В каждой шутке есть доля шутки" 'Life is good ..' (Carl van Vreeswijk, 1962-2022) --------------------------------------- David Hansel Directeur de Recherche au CNRS Co-Group leader Cerebral Dynamics Plasticity and Learning lab., CNRS 45 rue des Saints Peres 75270 Paris Cedex 06 Tel (Cell): +33 607508403 - Fax (33).1.49.27.90.62 *CONFIDENTIALITY AND PRIVACY NOTICE:* *This message and the documents that might be attached, are addressed exclusively to their(s) recipient(s) and may contain privileged or confidential information. The access to this information by people other than those designated is not authorized. If you are not the indicated recipient, you are notified that the use, disclosure and / or copying without authorization is prohibited under current legislation. If you have received this message in error, please kindly inform the sender immediately and proceed to its destruction.* ᐧ

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Software developer at Okinawa Institute of Science and Technology
by Erik De Schutter Oct. 25, 2024

Oct. 25, 2024

The Computational Neuroscience unit (https://www.oist.jp/research/research-units/cnu) at the Okinawa Institute of Science and Technology, Japan has an opening for a postdoctoral researcher or technician to contribute to the software development of the nanoscale simulator of neuronal electrophysiology and molecular properties STEPS https://steps.sourceforge.net/STEPS/default.php. Initial duration is two years with an option to extend. The software developer will join the STEPS team and contribute to maintenance and further expansion of the software capacities. Recent versions of STEPS improved its parallel performance (http://www.frontiersin.org/articles/10.3389/fninf.2022.883742/full) and added modeling of vesicles (https://www.nature.com/articles/s42003-024-06276-5) The ideal candidate will have a scientific background but also possess good programming skills, but experienced software engineers can also apply. Competences: For the postdoctoral position a recent PhD is required in a relevant discipline such as neuroscience, molecular biology, biophysics, biochemistry, and for a technician a masters degree is required in a software/computer science related discipline. For either position, we require a talented programmer with experience in C++ and Python. In satisfying these criteria, it is expected that the candidate has some experience of modeling neuronal or other biological processes computationally, but this is not essential if the candidate has a strong enough interest in moving into the field. Fluent English conversation and writing skills are required. Benefits / Offer: A competitive salary and relocation package are offered. This an ideal position to gain experience in a prominent computational neuroscience lab. Contributions of technicians are honored by authorship on relevant publications. Please contact Prof. Erik De Schutter (erik(a)oist.jp) for inquiries.

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Computational Psychology or Neuroscience Assistant Professor Position at the University of Arizona
by Cowen, Stephen Leigh - (scowen) Oct. 22, 2024

Oct. 22, 2024

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

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Re: Some scientific history that I experienced relevant to the recent Nobel Prizes to Hopfield and Hinton
by Grossberg, Stephen Oct. 22, 2024

Oct. 22, 2024

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 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

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