INRC Forum: Thomas Nowotny
2023-03-14 08:00 - 09:00 (America/Los_Angeles) | Online
Loss shaping enhances exact gradient learning with EventProp in Spiking Neural Networks
Abstract: In a recent paper Wunderlich and Pehle (2021) introduced the EventProp algorithm that enables training spiking neural networks by gradient descent on exact gradients. In this talk I will present extensions of EventProp to support a wider class of loss functions and an implementation in the GPU enhanced neuronal networks framework (GeNN) which exploits sparsity. The GPU acceleration allows us to test EventProp extensively on more challenging learning benchmarks. We find that EventProp performs well on some tasks but for others there are issues where learning is slow or fails entirely. We have discovered that the problems relate to the exact gradient of the loss function not providing information about loss changes due to spike creation or spike deletion. Depending on the details of the task and loss function, descending the exact gradient with EventProp can lead to the deletion of important spikes and so to an inadvertent increase of the loss and decrease of classification accuracy and hence a failure to learn. In other situations, the lack of knowledge about the benefits of creating additional spikes can lead to a lack of gradient flow into earlier layers, slowing down learning. We are trying to overcome these problems in the form of `loss shaping’, where we introduce a suitable weighting function into an integral loss to increase gradient flow from the output layer towards earlier layers. I will show example result for the Spiking Heidelberg Digits and sequential spiking MNIST where we achieve (close to) state-of-the-art performance.
Bio. Prof. Thomas Nowotny has a background in theoretical physics. After his PhD from Leipzig University in 2001 he started working in Computational Neuroscience and bio-inspired AI at the Institute for non-linear Science at UCSD. He is now a Professor in Informatics at the University of Sussex and the head of the AI research group. His interests include olfaction, hybrid systems, spiking neural networks and their efficient simulation, bio-inspired AI and algorithms for neuromorphic computing.
For the meeting link, see the full INRC Forum Spring 2023 Schedule (accessible only to INRC Affiliates and Fully Engaged Members).

Computational and Systems Neuroscience (COSYNE) 2023
2023-03-09 - 2023-03-14 | Montreal, Montreal, Canada
The annual COSYNE conference provides an inclusive forum for the exchange of experimental and theoretical approaches to problems in systems neuroscience, in order to understand how neural systems are built and function.

INRC Forum: Bruno Olshausen
2023-03-07 08:00 - 09:00 (America/Los_Angeles) | Online
Computing with Dynamics
Abstract: Is the brain a computer? Or is it a dynamical system? While computation serves as a useful metaphor for cognitive processes, as we delve into the neuroanatomical circuits and physiological properties of brains we encounter structures and phenomena that seem foreign and unfamiliar in terms of standard computing models. Highly recurrent circuits with massive interconnectivity, attractor dynamics, oscillations, traveling waves, and active sensing are all hallmarks of biological neural systems. How do we make sense of these things in terms of “computation?” Or are we working with the wrong metaphor? Here I shall present a number of recent findings from neuroscience that challenge us to think in new ways about the underlying physical processes governing perception and cognition.
Bio: Bruno OIshausen is Professor of Neuroscience and Optometry at the University of California, Berkeley. He also serves as Director of the Redwood Center for Theoretical Neuroscience, an interdisciplinary research group focusing on mathematical and computational models of brain function. He received B.S. and M.S. degrees in Electrical Engineering from Stanford University, and a Ph.D. in Computation and Neural Systems from the California Institute of Technology. Prior to Berkeley he was a member of the Departments of Psychology and Neurobiology, Physiology & Behavior at UC Davis. During postdoctoral work with David Field at Cornell he developed the sparse coding model of visual cortex which provides a linking principle between natural scene statistics and the response properties of visual neurons. Olshausen’s current research aims to understand the information processing strategies employed by the brain for doing tasks such as object recognition and scene analysis. This work seeks not only to advance our understanding of the brain, but also to discover new algorithms for scene analysis based on how brains work.
For the meeting link, see the full INRC Forum Spring 2023 Schedule (accessible only to INRC Affiliates and Fully Engaged Members).

Hands-on session with snnTorch
2023-03-02 18:00 - 19:30 UTC | Online
Bio: Jason K. Eshraghian is an Assistant Professor at the Department of Electrical and Computer Engineering at UC Santa Cruz, CA, USA. Prior to that, he was a Post-Doctoral Researcher at the Department of Electrical Engineering and Computer Science, University of Michigan in Ann Arbor. He received the Bachelor of Engineering (Electrical and Electronic) and the Bachelor of Laws degrees from The University of Western Australia, WA, Australia in 2016, where he also completed his Ph.D. Degree.
Professor Eshraghian was awarded the 2019 IEEE VLSI Best Paper Award, the Best Paper Award at 2019 IEEE Artificial Intelligence CAS Conference, and the Best Live Demonstration Award at 2020 IEEE ICECS for his work on neuromorphic vision and in-memory computing using RRAM. He currently serves as the secretary-elect of the IEEE Neural Systems and Applications Committee, and was a recipient of the Fulbright Future Fellowship (Australian-America Fulbright Commission), the Forrest Research Fellowship (Forrest Research Foundation), and the Endeavour Fellowship (Australian Government).

EBRAINS - a solution-driven approach to enable brain science and technologies
2023-02-28 10:00 - 11:30 UTC | Online
There will be great opportunities for data scientists looking to expand their knowledge, companies looking for project partnerships, cognitive psychologists who need the inspiration to improve their technology and more.
Programme:

10h00 – 10h05: Introduction by France Nivelle
10h05 – 10h20: EBRAINS Introduction and Scientific vision by Prof. dr. Viktor Jirsa, Chief Science Officer at EBRAINS, Professor at Aix-Marseille Université, INS Director / DR CNRS
10h20 – 10h35: EBRAINS Neurotechnology approach by Prof. Dr. Pieter Roelfsema, MD, PhD, Director of the Netherlands Institute for Neuroscience
10h35 – 10h50: The future of Brain Health within the EBRAINS Research Infrastructure by Univ.-Prof. Dr. med. Petra Ritter, BIH Johanna Quandt Professor for Brain Simulation, Director Brain Simulation Section, Berlin Institute of Health & Dept. of Neurology, Charité University Hospital Berlin
10h50 – 11h05: A solution approach for EBRAINS Research Infrastructureby Prof. dr. ir. Liesbet Peeters, Professor at the Biomedical Research Institute and Data Science Institute of Hasselt University, Multiple Sclerosis Data Alliance Core Partner
11h05 – 11h10: Concluding remarks by France Nivelle
11h10 – 11h30: Q&A session

Click here to register.

Theory of Neuromorphic Computing
2023-02-23 16:30 - 17:30, 2023-02-09 16:30 - 17:30, 2023-01-26 16:30 - 17:30 UTC | Online
Recurring discussion meeting by researchers interested in the theory of neuromorphic computing.
Hosted by Arne Diehl and Johan Kwisthout of Radboud University. To join the meetings, please contact Arne Diehl: arne.diehl@donders.ru.nl.

Deadline: CapoCaccia Workshop 2023
2023-02-19 | Online
Workshop theme for 2023: “Lessons from machine learning and neuroscience for building efficient intelligent systems”
For more information, see the official website.

Giorgia Dellaferrera: PEPITA - A forward-forward alternative to backpropagation
2023-02-14 18:00 - 19:30 UTC | Online
Bio: Giorgia Dellaferrera has completed her PhD in computational neuroscience at the Institute of Neuroinformatics (ETH Zurich and the University of Zurich) and IBM Research Zurich with Prof. Indiveri, Prof. Eleftheriou and Dr. Pantazi. Her doctoral thesis focused on the interplay between neuroscience and artificial intelligence, with an emphasis on learning mechanisms in brains and machines. During her PhD, she visited the lab of Prof. Kreiman at the Harvard Medical School (US), where she developed a biologically inspired training strategy for artificial neural networks. Before her PhD, Giorgia obtained a master in Applied Physics at the Swiss Federal Institute of Technology Lausanne (EPFL) and worked as an intern at the Okinawa Institute of Science and Technology, Logitech, Imperial College London, and EPFL.

Deadline: International Joint Conference on Neural Networks (IJCNN)
2023-01-31 | Online
The International Joint Conference on Neural Networks is organized jointly by the International Neural Network Society and the IEEE Computational Intelligence Society, and is the premiere international meeting for researchers and other professionals in neural networks and related areas.
Each year, the conference features invited plenary talks by world-renowned speakers in the areas of neural network theory and applications, computational neuroscience, robotics, and distributed intelligence. In addition to regular technical sessions with oral and poster presentations, the conference program will include special sessions, competitions, tutorials, and workshops on topics of current interest.

Anatomy and function of the prefrontal cortex across species
2023-01-29 08:00 - 17:00 UTC | Paris, Paris, France
Understanding the human brain relies for a large part on work in animal models, which necessitates a careful cross-species comparison. The prefrontal cortex (PFC) is particularly relevant in this regard as its function is poorly understood and potential cross-species differences remain highly debated. This workshop brings together experimental and computational scientists whose work allows a comparison of the anatomy and function of the PFC between species (in particular between humans, monkeys and mice).
EBRAINS is uniquely suited to provide a platform to compare anatomy, physiology and behaviour between species, due to its atlases, numerous datasets, as well as the whole brain models from different species. The interactive format of the workshop will allow speakers as well as non-speakers from largely separated fields to interact and discuss, which is expected to create novel insights as well as lead to more coherence and clarity in the relevant terminology, formats and important research directions in the field.
Register here

Nengo - Applied Brain Research
2023-01-26 18:00 - 19:30 UTC | Online
Bio: Trevor Bekolay’s primary research interest is in learning and memory. In his Master’s degree, he explored how to do supervised, unsupervised, and reinforcement learning in networks of biologically plausible spiking neurons. In his PhD, he applied this knowledge to the domain of speech to explore how sounds coming into the ear become high-level linguistic representations, and how those representations become sequences of vocal tract movements that produce speech.
Trevor is also passionate about reproducible science, particularly when complex software pipelines are involved. In 2013, he started a development effort to reimplement the Nengo neural simulator from scratch in Python, which has now grown to a project with over 20 contributors around the world.
About the host: Open Neuromorphic
ONM is an organization that aims at providing one place to reference all relevant open-source project in the neuromorphic research domain.
