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DTSTART;VALUE=DATE:20230430
DTEND;VALUE=DATE:20230515
DTSTAMP:20260417T211533
CREATED:20230127T225007Z
LAST-MODIFIED:20230127T225007Z
UID:10000011-1682812800-1684108799@www.neuropac.info
SUMMARY:CapoCaccia Workshop 2023
DESCRIPTION:Workshop theme for 2023: “Lessons from machine learning and neuroscience for building efficient intelligent systems” \nIt is an exciting era of significant progress in the quest for implementing intelligence in artificial systems. This stems from major breakthroughs in our understanding of natural intelligence\, thanks to new tools for better data collection and analysis from the brain; in the development of machine learning algorithms for solving real-world problems; and in the availability of scalable computing substrates that are smaller\, denser\, faster and feature parallel processing capabilities. \nIn this workshop\, we combine all the above towards a more efficient and powerful implementation of intelligent systems. Specifically\, our objective is to pinpoint what current ideas from machine learning and neuroscience can lead to practical designs for implementing low-power and miniaturized neuromorphic intelligent systems. To do so\, in a setting that fosters brainstorming and cross-fertilization\, we stimulate exchange of ideas on topics in which biology\, modeling\, and engineering are dealt with simultaneously. These topics will range from fundamental principles such as learning\, memory and the neurobiology of time; to high-level functions such as navigation\, embodiment and active sensing. \nThe mission of the CapoCaccia Workshops for Neuromorphic Intelligence is to understand the principles of biological intelligence and apply this knowledge in technologies\, for the good of all mankind. \nThe workshop features open and highly interactive discussion sessions in the morning; hands-on projects\, tutorials\, and hardware and software jamming sessions during the day; and free-form discussions in the evenings. \nThe workshop is open to everyone\, but since resources are limited\, we can accept only a limited number of registrations. Due to the limited number of hotel rooms\, Ph.D. students are expected to pair up and share rooms. All participants are encouraged to stay for the full two week period\, but can stay for less if necessary
URL:https://www.neuropac.info/event/capocaccia-workshop-2023/
LOCATION:Alghero\, Sardinia\, Italy\, Alghero\, Sardinia\, Italy
CATEGORIES:Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230501
DTEND;VALUE=DATE:20230506
DTSTAMP:20260417T211533
CREATED:20230129T222127Z
LAST-MODIFIED:20230129T222127Z
UID:10000022-1682899200-1683331199@www.neuropac.info
SUMMARY:International Conference on Learning Representations (ICLR) 2023
DESCRIPTION:
URL:https://www.neuropac.info/event/international-conference-on-learning-representations-iclr-2023/
LOCATION:Kigali Convention Center\, Kigali\, Rwanda
CATEGORIES:Conference
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230502T080000
DTEND;TZID=America/Los_Angeles:20230502T090000
DTSTAMP:20260417T211533
CREATED:20230430T102520Z
LAST-MODIFIED:20230430T102520Z
UID:10000233-1683014400-1683018000@www.neuropac.info
SUMMARY:INRC Forum: Jeff Orchard
DESCRIPTION:Hyperdimensional Algorithms using Spiking Phasors\nAbstract: Hyperdimensional (HD) computing offers a powerful framework for representing compositional reasoning. Such algorithms lend themselves to neural-network implementations\, allowing us to create neural networks that can perform cognitive functions\, like spatial reasoning\, arithmetic\, and symbolic logic. But the vectors involved can be quite large. Advances in neuromorphic hardware hold the promise of reducing the running time and energy footprint of neural networks by orders of magnitude. In this talk\, I will extend some pioneering work to run HD algorithms on a substrate of spiking neurons\, implementing examples in spatial memory\, function representation\, and temporal memory. \nBio: Jeff Orchard received degrees in applied mathematics from the University of Waterloo (BMath) and the University of British Columbia (MSc)\, and received his PhD in Computing Science from Simon Fraser University in 2003. Since then\, he has been a faculty member at the Cheriton School of Computer Science at the University of Waterloo in Canada. Prof. Orchard’s research focuses on computational neuroscience\, using mathematical models and computer simulations of neural networks in an effort to understand how the brain works. Guided by both theory and anatomy\, he is building neural networks based on computational theories of the brain — such as predictive coding — to uncover the way we perceive the world. His research also includes Vector Symbolic Architectures and Algebras\, spatial navigation\, and population coding. He is a core member of the Centre for Theoretical Neuroscience. \nFor the meeting link\, see the full INRC Forum Spring 2023 Schedule (accessible only to INRC Affiliates and Fully Engaged Members).
URL:https://www.neuropac.info/event/inrc-forum-jeff-orchard/
LOCATION:Online
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230504T163000
DTEND;TZID=UTC:20230504T173000
DTSTAMP:20260417T211533
CREATED:20230127T222256Z
LAST-MODIFIED:20230828T170621Z
UID:10000042-1683217800-1683221400@www.neuropac.info
SUMMARY:Theory of Neuromorphic Computing
DESCRIPTION:Recurring discussion meeting by researchers interested in the theory of neuromorphic computing. \nHosted by Arne Diehl and Johan Kwisthout of Radboud University. To join the meetings\, please contact Arne Diehl: arne.diehl@donders.ru.nl.
URL:https://www.neuropac.info/event/theory-of-neuromorphic-computing/2023-05-04/
LOCATION:Online
CATEGORIES:Discussion
ORGANIZER;CN="Arne Diehl":MAILTO:arne.diehl@donders.ru.nl
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230505T110000
DTEND;TZID=America/New_York:20230505T120000
DTSTAMP:20260417T211533
CREATED:20230423T184025Z
LAST-MODIFIED:20230423T185121Z
UID:10000232-1683284400-1683288000@www.neuropac.info
SUMMARY:Frances Chance - Modeling Coordinate Transformations in Neural and Neuromorphic Systems
DESCRIPTION:Hosted by the Perception and Robotics Group Seminar Series on Robotics and Computer Vision at the University of Maryland. \nAbstract. Animals excel at a wide range behaviors\, many of which are essential for survival. For example\, dragonflies are aerial predators\, known for both their speed and high success rate\, that must perform fast\, accurate\, and efficient calculations to survive. I will present a neural network model\, inspired by the dragonfly nervous system\, that calculates turning for successful prey interception. The model relies upon a coordinate transformation from eye-coordinates to body-coordinates\, an operation that must be performed by almost any animal nervous system relying upon sensory information to interact with the external world. I will discuss how I and collaborators are combining neuroscience experiments\, modeling studies\, and exploration of neuromorphic architectures to understand how the biological dragonfly nervous system performs coordinate transformations and to develop novel approaches for efficient neural- inspired computation. \nBio. As a computational neuroscientist\, Frances Chance has always been fascinated by how neural circuits compute information. Her current research focuses on applying knowledge of how neural systems operate towards the development of novel neuro-inspired algorithms and brain- based architectures. Frances Chance received her PhD and MS from Brandeis University and her BS from the California Institute of Technology. Currently she is a Principal Member of the Technical Staff at Sandia National Laboratories.
URL:https://www.neuropac.info/event/frances-chance-modeling-coordinate-transformations-in-neural-and-neuromorphic-systems/
LOCATION:University of Maryland\, 8125 Paint Branch Dr (Room IRB 4105)\, College Park\, MD\, 20740\, United States
CATEGORIES:Talk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230509T080000
DTEND;TZID=America/Los_Angeles:20230509T090000
DTSTAMP:20260417T211533
CREATED:20230509T065534Z
LAST-MODIFIED:20230509T065534Z
UID:10000234-1683619200-1683622800@www.neuropac.info
SUMMARY:INRC Forum: Bradley Theilman
DESCRIPTION:Stochastic Neuromorphic Circuits for Solving MAXCUT\nAbstract: Finding the maximum cut of a graph (MAXCUT) is a classic optimization problem that has motivated parallel algorithm development. In this talk\, I will present two neuromorphic circuits that transform a source of randomness into computationally useful correlations for approximating solutions to graph MAXCUT. Neuromorphic computing has been successfully applied to various graph algorithms\, by exploiting the analogy between a graph and the connectivity of a neural circuit. However\, the physical constraints of neuromorphic hardware make translating an arbitrary graph into the neuromorphic domain challenging. Neuromorphic computing is also beginning to explore stochastic devices as efficient sources of randomness for large-scale stochastic algorithms. Graph MAXCUT is a well-known NP-complete discrete optimization problem with the best-known approximate solutions being stochastic algorithms\, such as the Goemans-Williamson algorithm. I will show how to combine large-scale sources of intrinsic randomness with neuromorphic principles to implement two classes of stochastic approximations to graph MAXCUT in neuromorphic hardware. These approaches have architectural advantages over other neuromorphic graph algorithms and benefit from the theoretical performance guarantees of their algorithmic inspirations. I will show results from simulations of these circuits as well as results from an implementation of one of these circuits on Intel’s Loihi neuromorphic system. This work opens a new direction for stochastic neuromorphic circuits applied to discrete optimization. \nBio: Bradley Theilman is a postdoctoral appointee at Sandia National Laboratories. His research focuses on applying neuroscientific principles to neuromorphic computing. He earned a Ph.D. in computational neuroscience in 2021 from UC San Diego\, where he worked on topological approaches to understanding neural population activity in the auditory regions of songbird brains in the laboratory of Dr. Tim Gentner. \nFor the meeting link\, see the full INRC Forum Spring 2023 Schedule (accessible only to INRC Affiliates and Fully Engaged Members).
URL:https://www.neuropac.info/event/inrc-forum-bradley-theilman/
LOCATION:Online
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230518T163000
DTEND;TZID=UTC:20230518T173000
DTSTAMP:20260417T211533
CREATED:20230127T222256Z
LAST-MODIFIED:20230828T170621Z
UID:10000043-1684427400-1684431000@www.neuropac.info
SUMMARY:Theory of Neuromorphic Computing
DESCRIPTION:Recurring discussion meeting by researchers interested in the theory of neuromorphic computing. \nHosted by Arne Diehl and Johan Kwisthout of Radboud University. To join the meetings\, please contact Arne Diehl: arne.diehl@donders.ru.nl.
URL:https://www.neuropac.info/event/theory-of-neuromorphic-computing/2023-05-18/
LOCATION:Online
CATEGORIES:Discussion
ORGANIZER;CN="Arne Diehl":MAILTO:arne.diehl@donders.ru.nl
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230521
DTEND;VALUE=DATE:20230526
DTSTAMP:20260417T211533
CREATED:20230129T222609Z
LAST-MODIFIED:20230129T222609Z
UID:10000024-1684627200-1685059199@www.neuropac.info
SUMMARY:International Symposium on Circuits and Systems (ISCAS) 2023
DESCRIPTION:The IEEE International Symposium on Circuits and Systems (ISCAS) is the flagship conference of the IEEE Circuits and Systems (CAS) Society and the world’s premiere forum for researchers in the active fields of theory\, design and implementation of circuits and systems.
URL:https://www.neuropac.info/event/international-symposium-on-circuits-and-systems-iscas-2023/
LOCATION:Monterey\, Monterey\, CA\, United States
CATEGORIES:Conference
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230530T080000
DTEND;TZID=America/Los_Angeles:20230530T090000
DTSTAMP:20260417T211533
CREATED:20230527T011911Z
LAST-MODIFIED:20230527T011911Z
UID:10000235-1685433600-1685437200@www.neuropac.info
SUMMARY:INRC Forum: Jason Eshraghian & Ruijie Zhu
DESCRIPTION:Scaling up SNNs with SpikeGPT\nAbstract: If we had a dollar for every time we heard “It will never scale!”\, then neuromorphic engineers would be billionaires. This presentation will be centered on SpikeGPT\, the first large-scale language model (LLM) using spiking neural nets (SNNs)\, and possibly the largest SNN that has been trained using error backpropagation.\nThe need for lightweight language models is more pressing than ever\, especially now that we are becoming increasingly reliant on them from word processors and search engines\, to code troubleshooting and academic grant writing. Our dependence on a single LLM means that every user is potentially pooling sensitive data into a singular database\, which leads to significant security risks if breached.\nSpikeGPT was built to move towards addressing the privacy and energy consumption challenges we presently run into using Transformer blocks. Our approach decomposes self-attention down into a recurrent form that is compatible with spiking neurons\, along with dynamical weight matrices where the dynamics are learnable\, rather than the parameters as with conventional deep learning.\nWe will provide an overview of what SpikeGPT does\, how it works\, and what it took to train it successfully. We will also provide a demo on how users can download pre-trained models available on HuggingFace so that listeners are able to experiment with them. \nBio: Dr. Jason Eshraghian is an assistant professor of Electrical and Computer Engineering at UC Santa Cruz. He is the developer of snnTorch\, a widely adopted Python library used to train and model brain-inspired spiking neural networks. He was awarded the IEEE TCAS-I Darlington’23\, IEEE TVLSI’19\, and IEEE AICAS’19 best paper awards\, and the best live demonstration award at IEEE ICECS’20. He was the recipient of the Fulbright Future Fellowship (Australian-America Fulbright Commission)\, the Forrest Research Fellowship (Forrest Research Foundation)\, and the Endeavour Fellowship (Australian Government). He leads the UCSC Neuromorphic Computing Group which focuses on porting principles from neuroscience into building more effective learning algorithms in software and hardware. Dr. Eshraghian is the Secretary of the IEEE Neural Systems and Applications Committee and an Associate Editor with APL Machine Learning.\nRuijie Zhu is commencing his Ph.D. in Electrical and Computer Engineering at UC Santa Cruz in the Fall of 2023. He recently completed his Bachelor Degree in Computer Science at the University of Electronic Science and Technology of China\, where he became a regular contributor to open-source neuromorphic projects\, including snnTorch\, SpikingJelly\, and led the development of SpikeGPT\, the first spiking neural network generative language model. He was elected as the chair of the 2020 Students Open-Source Conference (SOSConf)\, which attracted over 3\,000 online participants. His research focus is on enabling the development of large-scale spiking neural networks. \nFor the meeting link\, see the full INRC Forum Spring 2023 Schedule (accessible only to INRC Affiliates and Fully Engaged Members).
URL:https://www.neuropac.info/event/inrc-forum-eshraghian-zhu/
LOCATION:Online
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