类脑计算脉冲神经网络模型及其学习算法研究进展

doi:10.11896/jsjkx.220100058

Abstract

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.

Key words: Brain-liked computing, Spiking neural network, Multi-scale network model, Multi-modal information, Uncertain information perception, Decision fusion, Learning algorithm, STDP

CLC Number:

TP183

HUANG Zenan, LIU Xiaojie, ZHAO Chenhui, DENG Yabin, GUO Donghui. Spiking Neural Network Model for Brain-like Computing and Progress of Its Learning Algorithm[J].Computer Science, 2023, 50(1): 229-242.

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

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