Estimating Post-Synaptic Effects for Online Training of Feed-Forward SNNs

Facilitating online learning in spiking neural networks (SNNs) is a key step in developing event-based models that can adapt to changing environments and learn from continuous data streams in real-time. Although forward-mode differentiation enables online learning, its computational requirements res...

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Vydané v:	2024 International Conference on Neuromorphic Systems (ICONS) s. 264 - 271
Hlavní autori:	Summe, Thomas M., Schaefer, Clemens JS, Joshi, Siddharth
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 30.07.2024
Predmet:	Adaptation models Approximation algorithms Computational modeling Encoding gradient approximation Neuromorphics online learning Real-time systems Scalability Spiking neural networks Streams Training
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