Optimal initialization strategies for Deep Spiking Neural Networks
Wednesday, November 3, 2021
14:55 UTC
Julia Gygax
Friedrich Miescher Institute for Biomedical Research (FMI)
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Hosted by SNUFA70 minutes
Abstract
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.
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