S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks

Spiking Neural Networks (SNNs) are biologically plausible models that have been identified as potentially apt for the deployment for energy-efficient intelligence at the edge, particularly for sequential learning tasks. However, training of SNNs poses a significant challenge due to the necessity for precise temporal and spatial credit assignment. Back-propagation through time (BPTT) algorithm, whilst being the most widely used method for addressing these issues, incurs a high computational cost due to its temporal dependency. Moreover, BPTT and its approximations solely utilize causal information derived from the spiking activity to compute the synaptic updates, thus neglecting non-causal relationships. In this work, we propose S-TLLR, a novel three-factor temporal local learning rule inspired by the Spike-Timing Dependent Plasticity (STDP) mechanism, aimed at training SNNs on event-based learning tasks. S-TLLR considers both causal and non-causal relationships between pre and post-synaptic activities, achieving performance comparable to BPTT and enhancing performance relative to methods using only causal information. Furthermore, S-TLLR has low memory and time complexity, which is independent of the number of time steps, rendering it suitable for online learning on low-power devices. To demonstrate the scalability of our proposed method, we have conducted extensive evaluations on event-based datasets spanning a wide range of applications, such as image and gesture recognition, audio classification, and optical flow estimation. In all the experiments, S-TLLR achieved high accuracy with a reduction in the number of computations between $1.1-10\times$.