  Spiking Neural Networks (SNNs) are biologically plausible models that have
been identified as potentially apt for deploying energy-efficient intelligence
at the edge, particularly for sequential learning tasks. However, training of
SNNs poses significant challenges due to the necessity for precise temporal and
spatial credit assignment. Back-propagation through time (BPTT) algorithm,
whilst the most widely used method for addressing these issues, incurs a high
computational cost due to its temporal dependency. 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 deep SNNs
on event-based learning tasks. Furthermore, S-TLLR is designed to have low
memory and time complexities, which are independent of the number of time
steps, rendering it suitable for online learning on low-power edge 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, comparable
to BPTT, with a reduction in memory between $5-50\times$ and
multiply-accumulate (MAC) operations between $1.3-6.6\times$.
