High-performance deep spiking neural networks with 0.3 spikes per neuron.

ABSTRACT: Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical results provide exact mapping algorithms from artificial to spiking neural networks with time-to-first-spike coding. In this paper we analyze in theory and simulation the learning dynamics of time-to-first-spike-networks and identify a specific instance of the vanishing-or-exploding gradient problem. While two choices of spiking neural network mappings solve this problem at initialization, only the one with a constant slope of the neuron membrane potential at threshold guarantees the equivalence of the training trajectory between spiking and a

SUBMITTER: Stanojevic A 

PROVIDER: S-EPMC11315905 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature