@inproceedings{4d4dfa24a6524d08b301560d520f602a,
title = "Online learning in SNNs with e-prop and Neuromorphic Hardware",
abstract = "Online learning in neural networks has the potential to transform AI research. By enabling new information to be assimilated into existing systems, platforms can be adaptive to unseen data and can personalise performance to an individual. A common approach in providing AI to a user is to send queries to a remote cloud service which processes the information and sends back a response. Neuromorphic hardware offers an alternate solution by providing a dedicated computing platform from which neural networks can be run locally and efficiently. This work explores the potential of the SpiNNaker neuromorphic hardware to run the eligibility propagation (e-prop) algorithm on chip whilst learning online in real time.",
keywords = "neuromorphic hardware, online learning, spiking neural networks",
author = "Adam Perrett and Sara Summerton and Andrew Gait and Oliver Rhodes",
note = "Funding Information: The authors are grateful to Guillaume Bellec (Swiss Federal Institute of Technology Lausanne), Franz Scherr and Wolfgang Maass (Graz University of Technology), for technical support and helpful discussions on the e-prop algorithm. This work was supported by the EU ICT Flagship Human Brain Project (H2020 785907 and 945539). Publisher Copyright: {\textcopyright} 2022 ACM.; 2022 Annual Neuro-Inspired Computational Elements Conference, NICE 2022 ; Conference date: 28-03-2022 Through 01-04-2022",
year = "2022",
month = may,
day = "3",
doi = "10.1145/3517343.3517352",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "32--39",
editor = "Murat Okandan and Aimone, {James B.}",
booktitle = "Proceedings of the 2022 Annual Neuro-Inspired Computational Elements Conference, NICE 2022",
address = "United States",
}