Contemporary implementations of spiking bio-inspired neural networks

TL;DR

This paper reviews current hardware implementations of bio-inspired spiking neural networks across various domains, emphasizing their significance for understanding neural activity and improving information processing.

Contribution

It provides a comprehensive survey of neuromorphic hardware approaches in semiconductor, superconductor, and optical domains, highlighting hybrid solutions.

Findings

Hardware specifics influence neural simulation accuracy.

Different physical domains offer unique advantages for implementation.

Hybrid approaches can enhance performance and versatility.

Abstract

The extensive development of the field of spiking neural networks has led to many areas of research that have a direct impact on people's lives. As the most bio-similar of all neural networks, spiking neural networks not only allow the solution of recognition and clustering problems (including dynamics), but also contribute to the growing knowledge of the human nervous system. Our analysis has shown that the hardware implementation is of great importance, since the specifics of the physical processes in the network cells affect their ability to simulate the neural activity of living neural tissue, the efficiency of certain stages of information processing, storage and transmission. This survey reviews existing hardware neuromorphic implementations of bio-inspired spiking networks in the "semiconductor", "superconductor" and "optical" domains. Special attention is given to the possibility of effective "hybrids" of different approaches