Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects.

Abstract

Temporal processing is fundamental for biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking neural networks (SNNs) are well-suited for efficient temporal processing owing to their rich neuronal dynamics and event-driven computation. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this article, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of four tasks characterized by rich temporal dependencies across multiple timescales. Utilizing this benchmark suite, we comprehensively evaluate recently introduced SNN approaches. Our results demonstrate substantial progress in neuron models, normalization methods, and neural architectures, while also uncovering challenges for online learning and long-range temporal dependencies compared to nonspiking sequence models. These findings establish a comprehensive understanding of the current state of SNNs in temporal processing and lay the foundation for the advancement of neuromorphic algorithms toward efficient operation in dynamic environments.