Anti-injury function of complex spiking neural networks under targeted attack

Abstract

Brain-like intelligence is to simulate the structure and function of the biological brain as much as possible based on the latest findings in brain science. The biological brain has strong robustness under external attacks. In this study, two complex spiking neural networks (CSNNs) are constructed: a spiking neural network (SNN) with small-world topology and a SNN with scale-free topology, in which the nodes are Izhikevich neuron model, and the edges are the synaptic plasticity model including excitatory and inhibitory synapses. For the targeted attack, the anti-injury function of two CSNNs is comparatively analyzed. On this basis, the anti-injury mechanism of CSNN is explored. The experimental results show that: (1) Small-world SNN (SWSNN) has better performance than scale-free SNN (SFSNN) in the anti-injury ability under targeted attack on high-degree nodes, and they are similar in the anti-injury ability under targeted attack on intermediate and low-degree nodes; the results of the robustness of topology are consistent with the results of anti-injury function of CSNNs, which indicates that the anti-injury ability of two kinds of CSNNs is affected by topology. (2) The dynamic evolutions of neuron firing, synaptic weight, and topological characteristic in the information processing of CSNN have a linkage effect under targeted attack; the dynamic evolution of synaptic weight is significantly related to the anti-injury ability of CSNNs, which clues synaptic plasticity is the intrinsic factor of the anti-injury function of CSNNs.