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SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks
2018
Neural Computation
A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a
doi:10.1162/neco_a_01086
pmid:29652587
pmcid:PMC6118408
fatcat:ytfksmhbgrdrtgghycbvovmuqu