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DTSTART;TZID=UTC:20230601T163000
DTEND;TZID=UTC:20230601T173000
DTSTAMP:20260501T232759
CREATED:20230127T222256Z
LAST-MODIFIED:20230828T170621Z
UID:10000044-1685637000-1685640600@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/2023-06-01/
LOCATION:Online
CATEGORIES:Discussion
ORGANIZER;CN="Arne Diehl":MAILTO:arne.diehl@donders.ru.nl
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230606T080000
DTEND;TZID=America/Los_Angeles:20230606T090000
DTSTAMP:20260501T232759
CREATED:20230606T211025Z
LAST-MODIFIED:20230606T211025Z
UID:10000236-1686038400-1686042000@www.neuropac.info
SUMMARY:INRC Forum: Kenneth Stewart
DESCRIPTION:Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing\nAbstract:Achieving real-time\, personalized intelligence at the edge with learning capabilities holds enormous promise to enhance our daily experiences and assist in decision-making\, planning\, and sensing. Yet\, today’s technology encounters difficulties with efficient and reliable learning at the edge\, due to a lack of personalized data\, insufficient hardware\, and the inherent challenges posed by online learning. Over time and across multiple developmental phases\, the brain has evolved to incorporate new knowledge by efficiently building on previous knowledge. We seek to emulate this remarkable process in digital neuromorphic technology through two interconnected stages of learning.\nInitially\, a meta-training phase fine-tunes the learning hardware’s hyperparameters for few-shot learning by deploying a differentiable simulation of three-factor learning in a neuromorphic chip. This meta-training process refines the synaptic plasticity and related hyperparameters to align with the specific dynamics inherent in the hardware and the given task domain. During the subsequent deployment stage\, these optimized hyperparameters enable accurate learning of new classes using the local three-factor synaptic plasticity updates.\nWe demonstrate our approach using event-driven vision sensor data and the Intel Loihi neuromorphic processor and the associated plasticity dynamics\, achieving state-of-the-art accuracy in learning new categories in one-shot in real-time among three task domains. Our methodology is versatile and can be applied to situations demanding quick learning and adaptation at the edge\, such as navigating unfamiliar environments or learning unexpected categories of data through user engagement. \nBio: Kenneth Stewart is a final year Ph.D. candidate in Computer Science at the University of California\, Irvine advised by professors Emre Neftci\, Nikil Dutt\, and Jeffery Krichmar. Throughout his Ph.D. Kenneth has investigated adaptive learning algorithms with Spiking Neural Networks that can be applied in Neuromorphic hardware for online\, on-chip learning. During his Ph.D. Kenneth has published several papers in the area and was a candidate for the IEEE AICAS’20 best paper award. In addition to papers\, Kenneth co-authored patents regarding adaptive edge learning for gesture and speech recognition applications with the Accenture Future Tech Lab. Kenneth is one of the leading members of Neurobench’s Few-shot Online Learning initiative trying to motivate further research into the area. After earning his degree at the end of the Summer Kenneth hopes to scale up his research to apply it to real-world problems. \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-kenneth-stewart/
LOCATION:Online
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230611
DTEND;VALUE=DATE:20230614
DTSTAMP:20260501T232759
CREATED:20230129T222826Z
LAST-MODIFIED:20230129T222826Z
UID:10000025-1686441600-1686700799@www.neuropac.info
SUMMARY:International Conference on Artificial Intelligence Circuits and Systems (AICAS) 2023
DESCRIPTION:The entire world and in particular China are massively investing in AI. China is hosting large ecosystems in AI\, as well as numerous conferences. Most of these activities are software oriented. Top universities\, academies\, and institutes are bringing support to motivate scientists to contribute. IEEE AICAS 2023 is intended to fill the hardware large gap. \nAICAS 2023 is currently planned as a hybrid event with in-person presentations along with an option for remote attendees. Speakers should plan to present in person at AICAS 2023. The safety of our speakers and audience remains a priority concern. We will monitor global pandemic conditions and update and adjust the conference format if needed. \nThe venue is in Hangzhou\, which is an ancient city with a history of 2200 years and one of the seven ancient capitals in China. It is located 200 km from Shanghai. Hangzhou is the center of science\, education\, and culture of Zhejiang Province\, and is a key national tourism city. Hangzhou is also renowned as “A Paradise on the Earth”\, with its West Lake scenic area widely known\, which is one of the most attractive tourism regions in China. \nThe AICAS’23 conference will be held in one of the best 5-star hotels in the center of the city\, and within a walking distance to the subway. The region would offer choices of cinemas\, supermarket\, restaurants and entertainment as well.
URL:https://www.neuropac.info/event/international-conference-on-artificial-intelligence-circuits-and-systems-aicas-2023/
LOCATION:Hangzhou\, Hangzhou\, China
CATEGORIES:Conference
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230615T163000
DTEND;TZID=UTC:20230615T173000
DTSTAMP:20260501T232759
CREATED:20230127T222256Z
LAST-MODIFIED:20230828T170621Z
UID:10000045-1686846600-1686850200@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/2023-06-15/
LOCATION:Online
CATEGORIES:Discussion
ORGANIZER;CN="Arne Diehl":MAILTO:arne.diehl@donders.ru.nl
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230618
DTEND;VALUE=DATE:20230623
DTSTAMP:20260501T232759
CREATED:20230129T222319Z
LAST-MODIFIED:20230129T222319Z
UID:10000023-1687046400-1687478399@www.neuropac.info
SUMMARY:Computer Vision and Pattern Recognition Conference (CVPR) 2023
DESCRIPTION:The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost\, it provides an exceptional value for students\, academics and industry researchers.
URL:https://www.neuropac.info/event/computer-vision-and-pattern-recognition-conference-cvpr-2023/
LOCATION:Vancouver Convention Center\, Vancouver\, Canada
CATEGORIES:Conference
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230618
DTEND;VALUE=DATE:20230624
DTSTAMP:20260501T232759
CREATED:20230129T221059Z
LAST-MODIFIED:20230129T221059Z
UID:10000021-1687046400-1687564799@www.neuropac.info
SUMMARY:International Joint Conference on Neural Networks (IJCNN)
DESCRIPTION: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. \nEach 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.
URL:https://www.neuropac.info/event/international-joint-conference-on-neural-networks-ijcnn/
LOCATION:Gold Coast Convention and Exhibition Centre\, Queensland\, Australia
CATEGORIES:Conference
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230619
DTEND;VALUE=DATE:20230620
DTSTAMP:20260501T232759
CREATED:20230129T163545Z
LAST-MODIFIED:20230129T163545Z
UID:10000019-1687132800-1687219199@www.neuropac.info
SUMMARY:Workshop on Event-based Vision @ CVPR 2023
DESCRIPTION:4th International Workshop on Event-Based Vision. \nHeld in conjunction with the IEEE Conference on Computer Vision and Pattern Recognition 2023\, as part of the track: CV for non-traditional modalities\n\nThis workshop is dedicated to event-based cameras\, smart cameras\, and algorithms processing data from these sensors. Event-based cameras are bio-inspired sensors with the key advantages of microsecond temporal resolution\, low latency\, very high dynamic range\, and low power consumption. Because of these advantages\, event-based cameras open frontiers that are unthinkable with standard frame-based cameras (which have been the main sensing technology for the past 60 years). These revolutionary sensors enable the design of a new class of algorithms to track a baseball in the moonlight\, build a flying robot with the agility of a bee\, and perform structure from motion in challenging lighting conditions and at remarkable speeds. These sensors became commercially available in 2008 and are slowly being adopted in computer vision and robotics. In recent years they have received attention from large companies\, e.g.\, the event-sensor company Prophesee collaborated with Intel and Bosch on a high spatial resolution sensor\, Samsung announced mass production of a sensor to be used on hand-held devices\, and they have been used in various applications on neuromorphic chips such as IBM’s TrueNorth and Intel’s Loihi. The workshop also considers novel vision sensors\, such as pixel processor arrays (PPAs)\, which perform massively parallel processing near the image plane. Because early vision computations are carried out on-sensor\, the resulting systems have high speed and low-power consumption\, enabling new embedded vision applications in areas such as robotics\, AR/VR\, automotive\, gaming\, surveillance\, etc. This workshop will cover the sensing hardware\, as well as the processing and learning methods needed to take advantage of the above-mentioned novel cameras.
URL:https://www.neuropac.info/event/workshop-on-event-based-vision-cvpr-2023/
LOCATION:Vancouver\, Canada\, Vancouver\, Canada
CATEGORIES:Conference,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230620T080000
DTEND;TZID=America/Los_Angeles:20230620T090000
DTSTAMP:20260501T232759
CREATED:20230618T010420Z
LAST-MODIFIED:20230618T010420Z
UID:10000237-1687248000-1687251600@www.neuropac.info
SUMMARY:INRC Forum: Wolfgang Maass\, Christoph Stoeckl & Yukun Yang
DESCRIPTION:Local prediction-learning in high-dimensional spaces enables neural networks to plan\nAbstract: Being able to plan a sequence of actions in order to reach a goal\, or more generally to solve a problem\, is a cornerstone of higher brain function. But compelling models which explain how the brain can achieve that are missing. We show that local synaptic plasticity enables a neural network to create high-dimensional representations of actions and sensory inputs so that they encode salient information about their relationship. In fact\, it can create a cognitive map that reduces planning to a simple geometric problem in a high-dimensional space that can easily be solved by a neural network. This method also explains how self-supervised learning enables a neural network to control a complex muscle system so that it can handle locomotion challenges that never occurred during learning. The underlying learning strategy bears some similarity to self-attention networks (Transformers). But it does not require non-local learning rules or very large datasets. Hence it is suitable for implementation in highly energy-efficient neuromorphic hardware\, in particular for on-chip learning on Loihi 2.\nOne goal of our presentation will be to initiate discussions about the relation of this learning-based use of large vectors to other VSA approaches\, its relation to Transformers\, and possible applications in robotics. \nBio: Wolfgang Maass is a Professor of Computer Science at Technische Universität Graz. He received his PhD (1974) and Habilitation (1978) in Mathematics from Ludwig-Maximilians-Universität in Munich. He conducted research at MIT\, the University of Chicago\, and UC Berkeley\, as a Heisenberg Fellow of the Deutsche Forschungsgemeinschaft. He has been the Editor of Machine Learning (1995-1997)\, Archive for Mathematical Logic (1987-2000)\, and Biological Cybernetics (2006-present). He was also a Sloan Fellow at the Computational Neurobiology Lab of the Salk Institute in La Jolla\, California from 1997-1998. Since 2005\, he has been an Adjunct Fellow of the Frankfurt Institute of Advanced Studies (FIAS).\nChristoph Stoeckl is a Postdoc researcher at Technische Universität Graz working in the intersection between computational neuroscience and AI. His research interests include neuromorphic hardware as well as exploring connections between Transformers and neural networks. Before joining the research lab of Prof. Maass\, he obtained a Master’s degree in Computer Science also at TU Graz.\nYukun Yang is a 1st-year Doctoral Student at Technische Universität Graz\, supervised by Prof. Wolfgang Maass. His primary research interest is at the intersection of AI and neuroscience\, with a focus on discovering the learning principles of the brain and its neuromorphic applications. Before joining TU Graz\, he earned M.S. in the ECE Department at Duke University in 2020. Earlier\, he received B.E. in Information Engineering from Xi’an Jiaotong University in 2018. \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-tu-graz/
LOCATION:Online
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230625
DTEND;VALUE=DATE:20230715
DTSTAMP:20260501T232759
CREATED:20230129T155215Z
LAST-MODIFIED:20230129T155215Z
UID:10000012-1687651200-1689379199@www.neuropac.info
SUMMARY:Telluride Neuromorphic Cognition Engineering Workshop
DESCRIPTION:The workshop is a 3-week project based meeting organized around specific topic areas to bring the organizing principles of neural cognition into machine intelligence\, and to use lessons and technology from machine intelligence to understand how brains work. \n 
URL:https://www.neuropac.info/event/telluride-neuromorphic-cognition-engineering-workshop/
LOCATION:TBA
CATEGORIES:Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230626
DTEND;VALUE=DATE:20230629
DTSTAMP:20260501T232759
CREATED:20230129T163038Z
LAST-MODIFIED:20230129T163038Z
UID:10000018-1687737600-1687996799@www.neuropac.info
SUMMARY:tinyML EMEA Innovation Forum 2023
DESCRIPTION:The tinyML EMEA Innovation Forum is accelerating the adoption of tiny machine learning across the region by connecting the efforts of the private sector with those of academia in pushing the boundaries of machine learning and artificial intelligence on ultra-low powered devices.
URL:https://www.neuropac.info/event/tinyml-emea-innovation-forum-2023/
LOCATION:Marriott Hotel\, Amsterdam\, Netherlands
CATEGORIES:Conference,Discussion,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230627T080000
DTEND;TZID=America/Los_Angeles:20230627T090000
DTSTAMP:20260501T232759
CREATED:20230626T220627Z
LAST-MODIFIED:20230626T220627Z
UID:10000238-1687852800-1687856400@www.neuropac.info
SUMMARY:INRC Forum: Robert Legenstein
DESCRIPTION:Memory-enriched computation and learning through synaptic and non-synaptic plasticity\nAbstract:Virtually any task faced by humans has a temporal component and therefore demands some form of memory. Consequently\, a variety of memory systems and mechanisms have been shown to exist in the brain of humans and other animals. These memory systems operate on a multitude of time scales\, from seconds to years. Yet\, it is still not well understood how memory is implemented in the brain and how cortical neuronal networks utilize these systems for computation. In this talk\, I will present some recent models that extend (spiking and non-spiking) neural network models with memory using Hebbian and non-Hebbian types of plasticity. I will discuss the similarities between these models and transformers\, arguably the most powerful models for sequence processing in the area of machine learning. I will show that Hebbian plasticity can significantly increase the computational and learning capabilities of spiking neural networks. Further\, I will show how neurons with non-synaptic plasticity can be utilized for memory and how networks of such neurons can be trained without the need to backpropagate errors through time. \nBio: Dr. Robert Legenstein received his PhD in computer science from the Graz University of Technology\, Graz\, Austria\, in 2002. He is a full professor at the Department of Computer Science\, TU Graz\, head of the Institute for Theoretical Computer Science\, and leading the Graz Center for Machine Learning. Dr. Legenstein has served as associate editor of IEEE Transactions on Neural Networks and Learning Systems (2012-2016). He is an action editor for Transactions on Machine Learning Research\, and he was on the program committee for NeurIPS and ICLR several times. His primary research interests are learning in models for biological networks of neurons and neuromorphic hardware\, probabilistic neural computation\, novel brain-inspired architectures for computation and learning\, and memristor-based computing concepts. \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-robert-legenstein/
LOCATION:Online
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230629T163000
DTEND;TZID=UTC:20230629T173000
DTSTAMP:20260501T232759
CREATED:20230127T222256Z
LAST-MODIFIED:20230828T170621Z
UID:10000046-1688056200-1688059800@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/2023-06-29/
LOCATION:Online
CATEGORIES:Discussion
ORGANIZER;CN="Arne Diehl":MAILTO:arne.diehl@donders.ru.nl
END:VEVENT
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