Comparison of spiking neural networks with different topologies based on anti-disturbance ability under external noise

Abstract

The research on robustness of brain-like models contributes to promoting its neural information processing ability, and the understanding of bio-brain function. However, the biological rationality of the current brain-like models is inadequate. In addition, the effect of network topologies on the robustness of brain-like models has not been clarified. In this study, inspired by the topological characteristics of biological functional brain networks, we construct five kinds of spiking neural networks (SNNs), which have the same Izhikevich neuron model and the same synaptic plasticity model with time-delay, but different network topologies. Then, the robustness of the SNNs with different topologies is comparatively assessed based on the anti-disturbance ability under different noise. Further, by taking a speech recognition task as the case study, we investigate the anti-disturbance ability of these SNNs in application. Finally, the anti-disturbance mechanism of the SNNs is discussed. Our simulation consistently certifies that: (i) In terms of anti-disturbance indicators, the complex SNN outperforms the small-world SNN, the small-world SNN outperforms the scale-free SNN, and all of them outperform the random SNN and the regular SNN, which indicates that the SNNs with more biological rationality have the better anti-disturbance ability. (ii) In terms of speech recognition accuracy, the performance of SNNs with different topologies presents the consistent order with the anti-disturbance ability above. And the recognition accuracy of these SNNs under disturbance still remains almost the same, compared with these SNNs without disturbance. (iii) The evolution process of neural information is clarified, which hints that the synaptic plasticity is the intrinsic factor of anti-disturbance ability, and the network topology is a factor that affects the anti-disturbance ability at the level of performance. Our simulation results are conducive to the design of neuromorphic algorithms with robustness on analog or mixed-signal neuromorphic chips under complex noise environment.