  Brain-inspired spiking neural networks (SNNs) have gained prominence in the
field of neuromorphic computing owing to their low energy consumption during
feedforward inference on neuromorphic hardware. However, it remains an open
challenge how to effectively benefit from the sparse event-driven property of
SNNs to minimize backpropagation learning costs. In this paper, we conduct a
comprehensive examination of the existing event-driven learning algorithms,
reveal their limitations, and propose novel solutions to overcome them.
Specifically, we introduce two novel event-driven learning methods: the
spike-timing-dependent event-driven (STD-ED) and membrane-potential-dependent
event-driven (MPD-ED) algorithms. These proposed algorithms leverage precise
neuronal spike timing and membrane potential, respectively, for effective
learning. The two methods are extensively evaluated on static and neuromorphic
datasets to confirm their superior performance. They outperform existing
event-driven counterparts by up to 2.51% for STD-ED and 6.79% for MPD-ED on the
CIFAR-100 dataset. In addition, we theoretically and experimentally validate
the energy efficiency of our methods on neuromorphic hardware. On-chip learning
experiments achieved a remarkable 30-fold reduction in energy consumption over
time-step-based surrogate gradient methods. The demonstrated efficiency and
efficacy of the proposed event-driven learning methods emphasize their
potential to significantly advance the fields of neuromorphic computing,
offering promising avenues for energy-efficiency applications.
