A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks

Understand

Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures.

• However, due to the non-differentiable nature of spiking neuronal functions, the standard error back-propagation algorithm is not directly applicable to SNNs.
• In this work, we propose a tandem learning framework, that consists of an SNN and an Artificial Neural Network (ANN) coupled through weight sharing.
• The ANN is an auxiliary structure that facilitates the error back-propagation for the training of the SNN at the spike-train level.