Complex spiking neural network with synaptic time delay evaluated by anti-damage capabilities under random attacks

External attack can affect the normal functioning of electronic equipment including neuromorphic hardware, which leads to failure. Research on the brain -like model with robustness is beneficial to obtain its stable performance under external attack. Synaptic time delay (STD) is highly correlated with bio-brain function. However, the synaptic plasticity of brain -like models still lacks bio-rationality. Inspired by the bio-synaptic time delay, the purpose of this paper is to investigate a bio-rational brain -like model with bio-consistent STD evaluated by the anti -damage capabilities. In this paper, we propose a spiking neural network (SNN) with the topology of a complex network called the ComSNN, in which the topology has both the SWP and SFP conforming to biological functional brain networks, the nodes are Izhikevich neuron models, and the edges are synaptic plasticity models with random time delay conforming to the dynamic range of bio-synaptic time delay. Then, the anti -damage capabilities of the ComSNNs with different types of STDs under random attacks are evaluated based on the two anti -damage indicators. Further, taking a speech recognition task as the case study, the anti -damage capabilities of these ComSNNs are verified in application. Finally, the anti -damage mechanism of the ComSNN with STD is discussed. Our results indicate the following: (i) In terms of two antidamage indicators, the ComSNN with random STD is superior to the ComSNN with fixed STD; in turn, the ComSNN with fixed STD is superior to the ComSNN without STD. (ii) Compared with the ComSNN without random attacks, the speech recognition accuracy of the ComSNN with random STD under random attacks still remains almost the same, which indicates the ComSNN has anti -damage capabilities in application; the recognition accuracies of ComSNNs with different types of STDs present the consistent order with the results based on the two anti -damage indicators. (iii) A correlation between the mean synaptic weight and the antidamage capabilities implies that the intrinsic factor of the anti -damage capabilities is the synaptic plasticity; synaptic plasticity can change dynamic topological characteristics of SNNs, the analysis results of dynamic topological characteristics imply that the STD impacts the anti -damage capabilities of the network.