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A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule
[article]
2019
arXiv
pre-print
Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally categorized into two basic classes, i.e., backpropagation-like training methods and plasticity-based learning methods. The former methods are dependent on energy-inefficient real-valued computation and non-local transmission, as also required in
arXiv:1812.06574v2
fatcat:gj4f4jm5d5atbatrsab6pvvgua