Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks

TL;DR

This paper reviews the development of spiking neural networks (SNNs), highlighting their biological inspiration, recent paradigms like DVS and DAS, and their potential in brain-computer interfaces and brain-inspired AI.

Contribution

It provides a comprehensive overview of recent advances in SNNs, emphasizing new paradigms, their biological basis, and applications in BCI and AI, proposing a future research direction.

Findings

SNNs are increasingly aligned with biological neural processes.

Recent paradigms like DVS and DAS enhance SNN applications.

SNNs offer advantages in energy efficiency and robustness for BCI.

Abstract

Spiking neural networks (SNNs) are gaining popularity in the computational simulation and artificial intelligence fields owing to their biological plausibility and computational efficiency. This paper explores the historical development of SNN and concludes that these two fields are intersecting and merging rapidly. Following the successful application of Dynamic Vision Sensors (DVS) and Dynamic Audio Sensors (DAS), SNNs have found some proper paradigms, such as continuous visual signal tracking, automatic speech recognition, and reinforcement learning for continuous control, that have extensively supported their key features, including spike encoding, neuronal heterogeneity, specific functional circuits, and multiscale plasticity. Compared to these real-world paradigms, the brain contains a spiking version of the biology-world paradigm, which exhibits a similar level of complexity and is usually considered a mirror of the real world. Considering the projected rapid development of invasive and parallel Brain-Computer Interface (BCI), as well as the new BCI-based paradigms that include online pattern recognition and stimulus control of biological spike trains, SNNs naturally leverage their advantages in energy efficiency, robustness, and flexibility. The biological brain has inspired the present study of SNNs and effective SNN machine-learning algorithms, which can help enhance neuroscience discoveries in the brain by applying them to the new BCI paradigm. Such two-way interactions with positive feedback can accelerate brain science research and brain-inspired intelligence technology.