Event-Driven Learning for Spiking Neural Networks

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.

Spike-Timing-Dependent Back Propagation in Deep Spiking Neural Networks

The success of Deep Neural Networks (DNNs) can be attributed to its deep structure, that learns invariant feature representation at multiple levels of abstraction. Brain-inspired Spiking Neural Networks (SNNs) use spatiotemporal spike patterns to encode and transmit information, which is biologically realistic, and suitable for ultra-low-power event-driven neuromorphic implementation. Therefore, Deep Spiking Neural Networks (DSNNs) represent a promising direction in artificial intelligence, with the potential to benefit from the best of both worlds. However, the training of DSNNs is challenging because standard error back-propagation (BP) algorithms are not directly applicable. In this paper, we first establish an understanding of why error back-propagation does not work well in DSNNs. To address this problem, we propose a simple yet efficient Rectified Linear Postsynaptic Potential function (ReL-PSP) for spiking neurons and propose a Spike-Timing-Dependent Back-Propagation (STDBP) learning algorithm for DSNNs. In the proposed learning algorithm, the timing of individual spikes is used to carry information (temporal coding), and learning (back-propagation) is performed based on spike timing in an event-driven manner. Experimental results demonstrate that the proposed learning algorithm achieves state-of-the-art performance in spike time based learning algorithms of SNNs. This work investigates the contribution of dynamics in spike timing to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DSNNs.