A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks

Spiking neural networks (SNNs) have demonstrated excellent capabilities invarious intelligent scenarios. Most existing methods for training SNNs arebased on the concept of synaptic plasticity; however, learning in the realisticbrain also utilizes intrinsic non-synaptic mechanisms of neurons. The spikethreshold of biological neurons is a critical intrinsic neuronal feature thatexhibits rich dynamics on a millisecond timescale and has been proposed as anunderlying mechanism that facilitates neural information processing. In thisstudy, we develop a novel synergistic learning approach that involvessimultaneously training synaptic weights and spike thresholds in SNNs. SNNstrained with synapse-threshold synergistic learning~(STL-SNNs) achievesignificantly superior performance on various static and neuromorphic datasetsthan SNNs trained with two degenerated single-learning models. During training,the synergistic learning approach optimizes neural thresholds, providing thenetwork with stable signal transmission via appropriate firing rates. Furtheranalysis indicates that STL-SNNs are robust to noisy data and exhibit lowenergy consumption for deep network structures. Additionally, the performanceof STL-SNN can be further improved by introducing a generalized joint decisionframework. Overall, our findings indicate that biologically plausible synergiesbetween synaptic and intrinsic non-synaptic mechanisms may provide a promisingapproach for developing highly efficient SNN learning methods.