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DTSTART;VALUE=DATE:20230430
DTEND;VALUE=DATE:20230515
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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
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20230501
DTEND;VALUE=DATE:20230506
DTSTAMP:20260418T105427
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
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230502T080000
DTEND;TZID=America/Los_Angeles:20230502T090000
DTSTAMP:20260418T105427
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
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BEGIN:VEVENT
DTSTART;TZID=UTC:20230504T163000
DTEND;TZID=UTC:20230504T173000
DTSTAMP:20260418T105427
CREATED:20230127T222256Z
LAST-MODIFIED:20230828T170621Z
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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:20260418T105427
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
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