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

In three linesEGGROLL, a low-rank factorization of Evolution Strategies perturbations, reduces memory complexity from O(mn) to O(r(m+n)) for gradient-free training of Spiking Neural Networks. On N-MNIST, the method achieves 79.21% test accuracy with 2.23× speedup versus full-rank ES, enabling on-chip learning on neuromorphic hardware without surrogate gradients.

Summary generated by Claude — human-verified