Advanced Computing and Related Applications Leveraging Brain-inspired Spiking Neural Networks

TL;DR

This paper reviews the current state of spiking neural networks, including models, topologies, learning algorithms, and neuromorphic hardware, highlighting their potential in brain-inspired AI and real-time processing.

Contribution

It provides a comprehensive summary of neuronal models, network topologies, learning algorithms, and neuromorphic chips, offering guidance for newcomers to the field.

Findings

Analysis of five neuronal models and their strengths and weaknesses.

Summary of unsupervised and supervised learning algorithms for SNNs.

Review of current neuromorphic chip research worldwide.

Abstract

In the rapid evolution of next-generation brain-inspired artificial intelligence and increasingly sophisticated electromagnetic environment, the most bionic characteristics and anti-interference performance of spiking neural networks show great potential in terms of computational speed, real-time information processing, and spatio-temporal information processing. Data processing. Spiking neural network is one of the cores of brain-like artificial intelligence, which realizes brain-like computing by simulating the structure and information transfer mode of biological neural networks. This paper summarizes the strengths, weaknesses and applicability of five neuronal models and analyzes the characteristics of five network topologies; then reviews the spiking neural network algorithms and summarizes the unsupervised learning algorithms based on synaptic plasticity rules and four types of supervised learning algorithms from the perspectives of unsupervised learning and supervised learning; finally focuses on the review of brain-like neuromorphic chips under research at home and abroad. This paper is intended to provide learning concepts and research orientations for the peers who are new to the research field of spiking neural networks through systematic summaries.