  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. To this end, we consider the
spike count as the discrete neural representation in the SNN, and design ANN
neuronal activation function that can effectively approximate the spike count
of the coupled SNN. The proposed tandem learning rule demonstrates competitive
pattern recognition and regression capabilities on both the conventional
frame-based and event-based vision datasets, with at least an order of
magnitude reduced inference time and total synaptic operations over other
state-of-the-art SNN implementations. Therefore, the proposed tandem learning
rule offers a novel solution to training efficient, low latency, and high
accuracy deep SNNs with low computing resources.
