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BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20250929T200000
DTEND;TZID=Europe/Helsinki:20250929T213000
DTSTAMP:20260607T112619
CREATED:20250924T110105Z
LAST-MODIFIED:20250924T111208Z
UID:10000358-1759176000-1759181400@www.neuropac.info
SUMMARY:Tonic: Deep Dive & Maintainer Transition Workshop
DESCRIPTION:Description: \nAn incredible opportunity to lead a foundational project in the neuromorphic ecosystem! Gregor Lenz\, co-founder of Open Neuromorphic and creator of Tonic\, is passing the torch to a new maintainer for this essential open-source library. \nThis deep-dive workshop will cover: \n\nChallenges of handling neuromorphic data and Tonic’s design philosophy\nWalkthrough of dataset loading\, transformations\, and caching\nThe future roadmap and how the community can contribute\n\nThis is a must-attend for researchers and developers working with event-based data—and a perfect chance for someone to step up as a new maintainer of Tonic. \n  \nSpeaker: Gregor Lenz
URL:https://www.neuropac.info/event/tonic-deep-dive-maintainer-transition-workshop/
LOCATION:Online – Live on the Open Neuromorphic YouTube Channel
CATEGORIES:Tutorial,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241220
DTEND;VALUE=DATE:20241221
DTSTAMP:20260607T112619
CREATED:20241202T111936Z
LAST-MODIFIED:20241202T111936Z
UID:10000309-1734652800-1734739199@www.neuropac.info
SUMMARY:ONM Student Talk: Ram Gaurav @ Virginia Tech
DESCRIPTION:Ramashish Gaurav (Ram) is a 3rd year Ph.D. student at Virginia Tech\, USA. He is supervised by Prof. Yang (Cindy) Yi in her BRICC Lab\, ECE @ VT. Of late\, Ram has been working on reservoir-based spiking models for Time Series Classification (TSC). Reservoir Computing is a well-established domain for time-series processing where a reservoir of statically (and recurrently) connected neurons compute high-dimensional temporal features\, over which a linear readout layer learns the mapping to the output. \nIn his recent work [1]\, Ram designed the Legendre-SNN (LSNN)\, a simple – yet high performing SNN model (for univariate TSC) where he has used the Legendre Delay Network (LDN) [2] as a non-spiking reservoir (in fact\, the LDN in LSNN is implemented with just basic matrix-operations). In a subsequent work (currently under review)\, he extended his LSNN to DeepLSNN that accounts for multivariate time-series signals too; upon experimenting with it\, he found that DeepLSNN models outperform a popular (and complex) LSTM-Conv integrated model [3] on more than 30% of 101 TSC datasets. His latest work is on the evaluation of Legendre-SNN on the Loihi-2 chip [4] — on which this talk is focused at. \nTalk details here\, time TBA.
URL:https://www.neuropac.info/event/onm-student-talk-ram-gaurav-virginia-tech/
LOCATION:Online
CATEGORIES:Talk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240504T110000
DTEND;TZID=America/New_York:20240504T121500
DTSTAMP:20260607T112619
CREATED:20240428T081014Z
LAST-MODIFIED:20240428T081014Z
UID:10000285-1714820400-1714824900@www.neuropac.info
SUMMARY:Sangyeob Kim @ ONM - C-DNN and C-Transformer: Mixing ANNs and SNNs for the Best of Both Worlds
DESCRIPTION:From the Open Neuromorphic website. \nSangyeob and his team have developed a C-DNN processor that effectively processes object recognition workloads\, achieving 51.3% higher energy efficiency compared to the previous state-of-the-art processor. Subsequently\, they have applied C-DNN not only to image classification but also to other applications\, and have developed the C-Transformer\, which applies this technique to a Large Language Model (LLM). As a result\, they demonstrate that the energy consumed in LLM can be reduced by 30% to 72% using the C-DNN technique\, compared to the previous state-of-the-art processor. In this talk\, we will introduce the processor developed for C-DNN and C-Transformer\, and discuss how neuromorphic computing can be used in actual applications in the future. \n\n\nAbout the Speaker\nSangyeob Kim (Student Member\, IEEE) received the B.S.\, M.S. and Ph.D. degrees from the School of Electrical Engineering\, Korea Advanced Institute of Science and Technology (KAIST)\, Daejeon\, South Korea\, in 2018\, 2020 and 2023\, respectively. He is currently a Post-Doctoral Associate with the KAIST. His current research interests include energy-efficient system-on-chip design\, especially focused on deep neural network accelerators\, neuromorphic hardware\, and computing-in-memory accelerators.
URL:https://www.neuropac.info/event/sangyeob-kim-onm-c-dnn-and-c-transformer-mixing-anns-and-snns-for-the-best-of-both-worlds/
LOCATION:Online
CATEGORIES:Talk,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20240418T230000
DTEND;TZID=Europe/Berlin:20240419T003000
DTSTAMP:20260607T112619
CREATED:20231103T153206Z
LAST-MODIFIED:20240428T080746Z
UID:10000267-1713481200-1713486600@www.neuropac.info
SUMMARY:Tobias Fischer @ ONM - Advances in Neuromorphic Visual Place Recognition
DESCRIPTION:From the Open Neuromorphic website. \nAbout the Speaker\nTobias conducts interdisciplinary research at the intersection of intelligent robotics\, computer vision\, and computational cognition. My main goal is to develop high-performing\, bio-inspired computer vision algorithms that simultaneously examine animals/humans and robots’ perceptional capabilities. He is a Lecturer (Assistant Professor) in Queensland University of Technology’s Centre for Robotics. He joined the Centre as an Associate Investigator and Research Fellow in January 2020. Previously\, he was a postdoctoral researcher in the Personal Robotics Lab at Imperial College London. He received a PhD from Imperial College in January 2019. His thesis was awarded the UK Best Thesis in Robotics Award 2018 and the Eryl Cadwaladr Davies Award for the best thesis in Imperial’s EEE Department in 2017-2018. He previously received an M.Sc. degree (distinction) in Artificial Intelligence from The University of Edinburgh in 2014 and a B.Sc. degree in Computer Engineering from Ilmenau University of Technology\, Germany\, in 2013. His works have attracted two best poster awards\, one best paper award\, and he was the senior author of the winning submission to the Facebook Mapillary Place Recognition Challenge 2020.
URL:https://www.neuropac.info/event/tobias-fischer-onm-advances-in-neuromorphic-visual-place-recognition/
LOCATION:Online
CATEGORIES:Talk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20240305T180000
DTEND;TZID=Europe/Berlin:20240305T193000
DTSTAMP:20260607T112619
CREATED:20240130T001106Z
LAST-MODIFIED:20240308T111533Z
UID:10000277-1709661600-1709667000@www.neuropac.info
SUMMARY:Maxence Ernoult @ ONM: Accelerating Neuromorphic Inference and Training at the Edge @ Rain
DESCRIPTION:From the Open Neuromorphic website. \n\n\nMaxence will present us Rain’s vision and technological roadmap to build hardware optimized for inference and training at the edge including both the hardware and algorithm aspects with an emphasis on why physical and mathematical principles matter more to him than biological inspiration. \n\n\n\n\n\nAbout the Speaker\nMaxence Ernoult graduated from Ecole Polytechnique and the University of Cambridge in 2016\, specializing in applied mathematics and theoretical physics. His PhD research was conducted in neuromorphic computing at Sorbonne University\, in collaboration with Mila. During this time\, he specialized in developing hardware-friendly alternatives to backpropagation and played a significant role in scaling up several of these alternatives\, including Equilibrium Propagation and Difference Target Propagation. This work was undertaken alongside notable figures such as Ben Scellier\, Blake Richards\, and Yoshua Bengio. In 2021\, Maxence joined IBM Research\, focusing on AI safety. Subsequently\, in 2022\, he began a new position at Rain.
URL:https://www.neuropac.info/event/maxence-ernoult-onm-accelerating-neuromorphic-inference-and-training-at-the-edge-rain/
LOCATION:Online
CATEGORIES:Talk,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Zurich:20240227T180000
DTEND;TZID=Europe/Zurich:20240227T193000
DTSTAMP:20260607T112619
CREATED:20240105T080743Z
LAST-MODIFIED:20240308T111522Z
UID:10000275-1709056800-1709062200@www.neuropac.info
SUMMARY:Aaron Spieler @ ONM - The ELM Neuron: An Efficient and Expressive Cortical Neuron Model Can Solve Long-Horizon Tasks
DESCRIPTION:From the Open Neuromorphic website. \nBiological cortical neurons are remarkably sophisticated computational devices\, temporally integrating their vast synaptic input over an intricate dendritic tree\, subject to complex\, nonlinearly interacting internal biological processes. \nWith the aim to explore the computational implications of leaky memory units and nonlinear dendritic processing\, we introduce the Expressive Leaky Memory (ELM) neuron model\, a biologically inspired phenomenological model of a cortical neuron. Remarkably\, by exploiting a few such slowly decaying memory-like hidden states and two-layered nonlinear integration of synaptic input\, our ELM neuron can accurately match the aforementioned input-output relationship with under ten-thousand trainable parameters. \nWe evaluate the model on various tasks with demanding temporal structures\, including the Long Range Arena (LRA) datasets\, as well as a novel neuromorphic dataset based on the Spiking Heidelberg Digits dataset (SHD-Adding). The ELM neuron reliably outperforms the classic Transformer or Chrono-LSTM architectures on these tasks\, even solving the Pathfinder-X task with over 70% accuracy (16k context length). \n\n\n\n\n\nAbout the Speaker\nAaron Spieler is a computational neuroscientist passionate about exploring the intersection of deep learning and neuroscience. After earning his Bachelor’s in Computer Science from the University of Potsdam\, he undertook an extended internship at Amazon Web Services working in deep learning based forecasting\, before further specializing with a Master’s in Computational Neuroscience at the University of Tübingen. Throughout his Master’s thesis and a subsequent internship at the Max Planck Institute for Intelligent Systems\, Aaron focused on phenomenological neuron modeling with applications to long-range prediction tasks. Pursuing this work allowed him to collaborate with excellent researchers from diverse backgrounds\, including Prof. Bernhard Schölkopf and Prof. Anna Levina.
URL:https://www.neuropac.info/event/aaron-spieler-onm-the-elm-neuron-an-efficient-and-expressive-cortical-neuron-model-can-solve-long-horizon-tasks/
LOCATION:Online
CATEGORIES:Talk,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Zurich:20240205T180000
DTEND;TZID=Europe/Zurich:20240205T200000
DTSTAMP:20260607T112619
CREATED:20240105T080554Z
LAST-MODIFIED:20240105T080554Z
UID:10000274-1707156000-1707163200@www.neuropac.info
SUMMARY:Jens E. Pedersen @ ONM - NIR: A Unified Instruction Set for Brain-Inspired Computing
DESCRIPTION:Have you wondered how to use neuromorphic hardware platforms? \nAre you depressed by your power bill after you bought your >400W GPU rig? \nThen you came to the right place! \nIn this workshop\, we will show you how to move models from your favourite framework directly to neuromorphic hardware with 1-2 lines of code! We will present the technology behind\, the Neuromorphic Intermediate Representation \, and demonstrate how we can use it to run a live spiking convnet on the Speck chip. \nNIR is currently supported by Intel Loihi \, Speck \, SpiNNaker2 \, Xylo and a host of simulators\, including Norse \, snnTorch \, and Spyx . \nJoin us on the 5th of February to get your own hands-on experience with NIR and neuromorphic hardware! \nAll it requires is a computer and a bit of Python knowledge. \nAgenda: \n\n18:00 – 19:00: NIR introduction\n\nMotivation: coupling neuromorphic hardware and software\nDemonstrating NIR: from PyTorch to Speck\nQ&A\n\n\n19:00 – 20:00: Workshop\n\nHands-on experience with NIR via Jupyter Notebooks or custom models\nQ&A and collaborative discussions\n\n\n\nSpeakers: \n\nJens E. Pedersen \, PhD at the Neurocomputing Systems lab at KTH Royal Institute of Technology\, Sweden\n\nNote: The event will be hosted virtually. Stay tuned for the video link and further updates. \n\n\n\n\n\nAbout the Speaker\nJens is a computer scientist studying his PhD in neuromorphic computing at the KTH Royal Institute of Technology. Jens co-authored the Norse simulator and the AEStream event-based streaming library.
URL:https://www.neuropac.info/event/jens-e-pedersen-onm-nir-a-unified-instruction-set-for-brain-inspired-computing/
LOCATION:Online
CATEGORIES:Talk,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Zurich:20240125T180000
DTEND;TZID=Europe/Zurich:20240125T193000
DTSTAMP:20260607T112619
CREATED:20240105T080357Z
LAST-MODIFIED:20240105T080357Z
UID:10000273-1706205600-1706211000@www.neuropac.info
SUMMARY:Carlos Ortega-Otero @ ONM - IBM NorthPole: Neural Inference at the Frontier of Energy\, Space\, and Time
DESCRIPTION:Abstract \nComputing\, since its inception\, has been processor-centric\, with memory separated from compute. Inspired by the organic brain and optimized for inorganic silicon\, NorthPole is a neural inference architecture that blurs this boundary by eliminating off-chip memory\, intertwining compute with memory on-chip\, and appearing externally as an active memory chip. NorthPole is a low-precision\, massively parallel\, densely interconnected\, energy-efficient\, and spatial computing architecture with a co-optimized\, high-utilization programming model. \nOn the ResNet50 benchmark image classification network\, relative to a graphics processing unit (GPU) that uses a comparable 12-nanometer technology process\, NorthPole achieves a 25 times higher energy metric of frames per second (FPS) per watt\, a 5 times higher space metric of FPS per transistor\, and a 22 times lower time metric of latency. Similar results are reported for the Yolo-v4 detection network. \nNorthPole outperforms all prevalent architectures\, even those that use more-advanced technology processes. \nAbout the Speaker \n\n\n\n\n\nDr. Carlos Ortega-Otero is an Sr. Research Staff Member at IBM driven by a passion in Circuit Design\, Neuromorphic Chip Architectures\, Low-Power Circuits and Physical Design optimizations. He earned his Ph.D. from Cornell University under the guidance of Prof. Rajit Manohar. \nThroughout his career\, he has worked in groundbreaking projects\, including Ultra-Low Power Asynchronous Sensor Network nodes\, Medical Implantable Wireless Sensors\, The TrueNorth Brain-Inspired Chip\, and the NorthPole Project. At IBM\, Carlos works under the leadership of Dr. Dharmendra Modha in the Brain-Inspired Computing Group. \nHe plays key roles in Architecture\, Specification\, Digital Implementation\, Physical Design\, Timing Signoff\, and Manufacturing teams of the NorthPole Project. Carlos is proud to be part of the Brain-Inspired Computing Group at IBM that continues to shape the future of Integrated Circuits and AI.
URL:https://www.neuropac.info/event/carlos-ortega-otero-onm-ibm-northpole-neural-inference-at-the-frontier-of-energy-space-and-time/
LOCATION:Online
CATEGORIES:Talk,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Zurich:20240115T180000
DTEND;TZID=Europe/Zurich:20240115T193000
DTSTAMP:20260607T112619
CREATED:20240105T080205Z
LAST-MODIFIED:20240105T080205Z
UID:10000272-1705341600-1705347000@www.neuropac.info
SUMMARY:Cristian Axenie @ ONM - Hybrid Learning for Event-Based Visual Motion Detection and Tracking of Pedestrians
DESCRIPTION:The Vision Zero Program’s purpose is to reduce traffic-related fatalities and serious injuries while promoting equitable\, safe\, and healthy mobility for all. Ultimately\, the challenge is to detect pedestrians during the day and especially at night in order to implement safety measures. \nThe current study introduces an award-winning low-power solution employing neuromorphic visual sensing and hybrid neuro-statistical processing developed by the Technische Hochschule Nürnberg team for the TinyML Vision Zero San Jose Competition. The solution proposes a novel neuromorphic edge fusion of spiking neural networks and event-based expectation maximization for the detection and tracking of pedestrians and bicyclists. \nWe provide a deployment-ready evaluation of the detection performance along with robustness\, energy footprint\, and weatherization while emphasizing the advantages of the neuro-statistical edge solution and its city-level scaling capabilities. \n\n\nAbout the Speaker\nDr. Axenie is Professor of Artificial Intelligence and Research Group Leader in Cognitive Neurocomputing at the Technische Hochschule Nürnberg Georg Simon Ohm in Germany. \nAfter earning a Dr. Eng. Sc. in Neuroscience and Robotics from the Technical University of Munich in 2016\, Dr. Axenie joined the Huawei Research Center in Munich. Between 2017 and 2023 Dr. Axenie was Staff Research Engineer with Huawei Research Center. At the same time\, Dr. Axenie was the Principal Investigator and Head of the Audi Konfuzius-Institut Ingolstadt Laboratory at the Technische Hochschule Ingolstadt. \nDr. Axenie is a seasoned researcher with more than 15 years of academic research and more than 10 years of industrial research experience. His research was disseminated in more than 50 peer-reviewed publications and more than 10 patents. Currently Dr. Axenie focuses on sustainable and efficient deployment of intelligent algorithms for sensor fusion and closed-loop control.
URL:https://www.neuropac.info/event/cristian-axenie-onm-hybrid-learning-for-event-based-visual-motion-detection-and-tracking-of-pedestrians/
LOCATION:Online
CATEGORIES:Talk,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20231219T180000
DTEND;TZID=Europe/Berlin:20231219T193000
DTSTAMP:20260607T112619
CREATED:20231103T152925Z
LAST-MODIFIED:20231103T152925Z
UID:10000265-1703008800-1703014200@www.neuropac.info
SUMMARY:Brad Aimone @ ONM - Programming Scalable Neuromorphic Algorithms With Fugu
DESCRIPTION:From the Open Neuromorphic website \nExplore neural-inspired computing with Brad Aimone\, a leading neuroscientist at Sandia Labs. Join us for insights into next-gen technology and neuroscience.
URL:https://www.neuropac.info/event/brad-aimone-onm-programming-scalable-neuromorphic-algorithms-with-fugu/
LOCATION:Online
CATEGORIES:Talk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20231213T060000
DTEND;TZID=Europe/Berlin:20231213T080000
DTSTAMP:20260607T112619
CREATED:20231130T123001Z
LAST-MODIFIED:20231130T123015Z
UID:10000271-1702447200-1702454400@www.neuropac.info
SUMMARY:Kade Heckel @ ONM - Neuromorphic Hackathon with Spyx
DESCRIPTION:From the open-neuromorphic.org website: \nJoin us on December 13th for an exciting Spyx hackathon and ONM talk! Learn how to use and contribute to Spyx \, a high-performance spiking neural network library\, and gain insights into the latest developments in neuromorphic frameworks. The session will cover Spyx’s utilization of memory and GPU to maximize training throughput\, along with discussions on the evolving landscape of neuromorphic computing. \nDon’t miss this opportunity to engage with experts\, collaborate on cutting-edge projects\, and explore the potential of Spyx in shaping the future of neuromorphic computing. Whether you’re a seasoned developer or just curious about the field\, this event promises valuable insights and hands-on experience. \nAgenda: \n\n18:00 – 19:00: Spyx Introduction\n\nDive into Spyx\, its features\, and how to contribute\nHands-on session: Explore Spyx functionalities and tackle real-world challenges\nQ&A and collaborative discussions\n\n\n19:00 – 20:00: Hackathon\n\nCollaborate on cutting-edge projects and explore the potential of Spyx\nQ&A and collaborative discussions\n\n\n\nSpeakers: \n\nKade Heckel\n\nNote: The event will be hosted virtually. Stay tuned for the video link and further updates. Let’s come together to push the boundaries of neuromorphic computing!
URL:https://www.neuropac.info/event/kade-heckel-onm-neuromorphic-hackathon-with-spyx/
LOCATION:Online
CATEGORIES:Talk,Tutorial,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20231116T180000
DTEND;TZID=Europe/Berlin:20231116T190000
DTSTAMP:20260607T112619
CREATED:20231103T152759Z
LAST-MODIFIED:20231103T152928Z
UID:10000264-1700157600-1700161200@www.neuropac.info
SUMMARY:Timoleon Moraitis @ ONM - Making Neuromorphic Computing Mainstream
DESCRIPTION:From the Open Neuromorphic website \nJoin us for a workshop with Timoleon Moraitis\, research group leader in neuromorphic computing\, at the interface of computational neuroscience with artificial intelligence.
URL:https://www.neuropac.info/event/timoleon-moraitis-onm-making-neuromorphic-computing-mainstream/
LOCATION:Online
CATEGORIES:Talk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20230926T180000
DTEND;TZID=Europe/Berlin:20230926T193000
DTSTAMP:20260607T112619
CREATED:20230925T101429Z
LAST-MODIFIED:20230925T101429Z
UID:10000246-1695751200-1695756600@www.neuropac.info
SUMMARY:Giulia D’Angelo: What’s catching your eye? The visual attention mechanism
DESCRIPTION:Abstract\nEvery agent\, whether animal or robotic\, needs to process its visual sensory input in an efficient way\, to allow understanding of\, and interaction with\, the environment. The process of filtering revelant information out of the continuous bombardment of complex sensory data is called selective attention. Visual attention is the result of the complex interplay between bottom-up and top-down mechanisms to perceptually organise and understand the scene. Giulia will describe how to approach visual attention using bio-inspired models emulating the human visual system to allow robots to interact with their surroundings. \nSpeaker’s bio\nGiulia D’Angelo is a postdoctoral researcher in neuroengineering in the EDPR laboratory at the Italian Institute of Technology. She obtained a B.Sc. in biomedical engineering and an M.Sc. in neuroengineering\, developing a neuromorphic visual system at the King’s College of London. She successfully defended her Ph.D. VIVA in 2022 at the university of Manchester\, proposing a biologically plausible model for event-driven saliency-based visual attention. She is currently working on bio-inspired visual algorithms exploiting neuromorphic platforms.
URL:https://www.neuropac.info/event/giulia-dangelo-whats-catching-your-eye-the-visual-attention-mechanism/
LOCATION:Online
CATEGORIES:Talk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230426T180000
DTEND;TZID=UTC:20230426T193000
DTSTAMP:20260607T112619
CREATED:20230127T223705Z
LAST-MODIFIED:20230127T223705Z
UID:10000009-1682532000-1682537400@www.neuropac.info
SUMMARY:Hands-on session with Xylo and Rockpool
DESCRIPTION:Speaker bio: Dylan Muir is the Vice President for Global Research Operations; Director for Algorithms and Applications; and Director for Global Business Development at SynSense. Dr. Muir is a specialist in architectures for neural computation. He has published extensively in computational and experimental neuroscience. At SynSense he is responsible for the company research vision\, and directing development of neural architectures for signal processing. Dr. Muir holds a Doctor of Science (PhD) from ETH Zurich\, and undergraduate degrees (Masters) in Electronic Engineering and in Computer Science from QUT\, Australia.
URL:https://www.neuropac.info/event/hands-on-session-with-xylo-and-rockpool/
LOCATION:Online
CATEGORIES:Tutorial
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230404T180000
DTEND;TZID=UTC:20230404T193000
DTSTAMP:20260607T112619
CREATED:20230127T223552Z
LAST-MODIFIED:20230127T223552Z
UID:10000008-1680631200-1680636600@www.neuropac.info
SUMMARY:Hands-on session with Sinabs and Speck
DESCRIPTION:Speaker bio: Gregor Lenz graduated with a Ph.D. in neuromorphic engineering from Sorbonne University. He thinks that technology can learn a thing or two from how biological systems process information. \nHis main interests are event cameras that are inspired by the human retina and spiking neural networks that mimic human brain in an effort to teach machines to compute a bit more like humans do. At the very least there are some power efficiency gains to be made\, but hopefully more! Also he loves to build open source software for spike-based machine learning. You can find more information on his personal website. \nHe is the maintainer of two open source projects in the field of neuromorphic computing\, Tonic and expelliarmus.
URL:https://www.neuropac.info/event/hands-on-session-with-sinabs-and-speck/
LOCATION:Online
CATEGORIES:Tutorial
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230302T180000
DTEND;TZID=UTC:20230302T193000
DTSTAMP:20260607T112619
CREATED:20230127T223200Z
LAST-MODIFIED:20230127T223200Z
UID:10000007-1677780000-1677785400@www.neuropac.info
SUMMARY:Hands-on session with snnTorch
DESCRIPTION: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. \nProfessor 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).
URL:https://www.neuropac.info/event/hands-on-session-with-snntorch-2/
LOCATION:Online
CATEGORIES:Tutorial
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230214T180000
DTEND;TZID=UTC:20230214T193000
DTSTAMP:20260607T112619
CREATED:20230127T223002Z
LAST-MODIFIED:20230127T223002Z
UID:10000006-1676397600-1676403000@www.neuropac.info
SUMMARY:Giorgia Dellaferrera: PEPITA - A forward-forward alternative to backpropagation
DESCRIPTION: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.
URL:https://www.neuropac.info/event/giorgia-dellaferrera-pepita-a-forward-forward-alternative-to-backpropagation/
LOCATION:Online
CATEGORIES:Talk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230126T180000
DTEND;TZID=UTC:20230126T193000
DTSTAMP:20260607T112619
CREATED:20230127T222756Z
LAST-MODIFIED:20230127T222756Z
UID:10000005-1674756000-1674761400@www.neuropac.info
SUMMARY:Nengo - Applied Brain Research
DESCRIPTION: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.\nTrevor 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. \nAbout the host: Open Neuromorphic\nONM is an organization that aims at providing one place to reference all relevant open-source project in the neuromorphic research domain.
URL:https://www.neuropac.info/event/nengo-applied-brain-research/
LOCATION:Online
CATEGORIES:Tutorial
END:VEVENT
END:VCALENDAR