Spiking Neural Network Model for Brain-like Computing and Progress of Its Learning Algorithm

Abstract

With the increasingly prominent limitations of deep neural networks in practical applications,brain-like computing spiking neural networks with biological interpretability have become the focus of research.The uncertainty and complex diversity of application scenarios pose new challenges to researchers,requiring brain-like computing spiking neural networks with multi-scale architectures similar to biological brain organizations to realize the perception and decision-making function of multi-modal and uncertain information.This paper mainly introduces the multi-scale biological rational brain-like computing spiking neural network model and its learning algorithm for multi-modal information representation and uncertainty information perception,analyzing and discussing two key technical issues that the spiking neural network based on the interconnection of memristors can rea-lize multi-scale architecture brain-like computing,namely:the consistency problem of multi-modal and uncertain information with spike timing representation,and the computing fault-tolerant problem for the multi-scale spiking neural network with different learning algorithms.Finally,this paper analyzes and forecasts the further research direction of brain-like computing spiking neural network.

Keywords

• brain-liked computing|spiking neural network|multi-scale network model|multi-modal information|uncertain information perception|decision fusion|learning algorithm|stdp