Training Deep Spiking Neural Networks using Backpropagation

Understand

Deep spiking neural networks (SNNs) hold great potential for improving the latency and energy efficiency of deep neural networks through event-based computation.

• However, training such networks is difficult due to the non-differentiable nature of asynchronous spike events.
• In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are only considered as noise.
• This enables an error backpropagation mechanism for deep SNNs, which works directly on spike signals and membrane potentials.