Gradient-Free Training of Spiking Neural Networks via Low-Rank Evolution Strategies 事件

Gradient-Free Training of Spiking Neural Networks via Low-Rank Evolution Strategies arXiv:2605.30361v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) offer compelling energy efficiency on neuromorphic hardware, yet their training remains challenging because the discrete spike threshold is non-differentiable. Surrogate-gradient methods sidestep this by approximating the derivative, but they impose backpropagation infrastructure that is incompatible with on-chip learning. Evolution