fMRI-based spiking neural network verified by anti-damage capabilities under random attacks

Lei Guo, Chengjun Liu, Youxi Wu and Guizhi Xu

Chaos, Solitons & Fractals, 2023, vol. 176, issue C

Abstract: Research on brain-like models with bio-rationality promotes the development of artificial intelligence. However, the topology of a brain-like model still lacks bio-rationality. Bio-brains have self-adaptive robustness. The purpose of this paper is to investigate a more bio-rational brain-like model verified by anti-damage capabilities. Thus, this paper proposes a new spiking neural network (SNN) constrained by a functional brain network based on human-brain functional magnetic resonance imaging (fMRI-SNN). Then, we investigate the anti-damage capabilities of our fMRI-SNN, and discuss the mechanism of these anti-damage capabilities. Our results indicate the following: (i) The fMRI-SNN has anti-damage capabilities under random attacks evaluated by two anti-damage indicators. Based on the relevance analysis, our discussion implies that the intrinsic element of the anti-damage capabilities is the synaptic plasticity. (ii) The anti-damage capabilities of our fMRI-SNN outperform those of the scale-free SNN and the small-world SNN. Our discussion on dynamic topological characteristics further implies that the network topology is an element that impacts the performance level of the anti-damage capabilities.

Keywords: Spiking neural network; Functional magnetic resonance imaging; Anti-damage capabilities; Random attack; Synaptic plasticity; Dynamic topological characteristics (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:176:y:2023:i:c:s0960077923009840

DOI: 10.1016/j.chaos.2023.114083

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