Advancing brain-inspired computing with hybrid neural networks

TL;DR

This paper explores hybrid neural networks, which combine brain-like computing with artificial intelligence to improve tasks like perception and learning.

Contribution

The paper provides a comprehensive review of hybrid neural networks, focusing on their origin, framework, and future research directions.

Findings

Hybrid neural networks integrate artificial and spiking neural networks for improved performance.

The paper outlines the biological basis and construction framework of hybrid neural networks.

Potential research directions are suggested to advance brain-inspired computing.

Abstract

Brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain–computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network (HNN), which integrates computer-science-oriented artificial neural networks (ANNs) with neuroscience-oriented spiking neural networks (SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.

Discover the fascinating world of hybrid neural networks revolutionizing artificial intelligence by gaining insights into their origin, construction framework, and supporting systems, along with potential future research directions.

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