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Friedrich Miescher Institute for Biomedical Research (FMI)
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Schedule
Wednesday, November 3, 2021
3:55 PM Europe/Berlin
Recording provided by the organiser.
Domain
Host
SNUFA
Duration
70 minutes
Recent advances in neuromorphic hardware and Surrogate Gradient (SG) learning highlight the potential of Spiking Neural Networks (SNNs) for energy-efficient signal processing and learning. Like in Artificial Neural Networks (ANNs), training performance in SNNs strongly depends on the initialization of synaptic and neuronal parameters. While there are established methods of initializing deep ANNs for high performance, effective strategies for optimal SNN initialization are lacking. Here, we address this gap and propose flexible data-dependent initialization strategies for SNNs.
Julia Gygax
Friedrich Miescher Institute for Biomedical Research (FMI)
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