Evaluation of the anti-disturbance capability of fMRI-based spiking neural network based on speech recognition

Abstract

The exterior electromagnetic noise can degrade the performance of neuromorphic hardware based on brain-inspired model. Therefore, enhancing the robustness of a brain-inspired model is a critical issue. However, the topology of a brain-inspired model lacks bio-plausibility. The purpose of this paper is to enhance the anti-disturbance capability of brain-inspired model under exterior electromagnetic noise by improving its bio-plausibility. In this paper, we propose a new spiking neural network (SNN) as a brain-inspired model called fMRI-SNN, in which the topology is constrained by functional magnetic resonance imaging (fMRI) data from the human brain, the nodes are Izhikevich neuron models, and the edges are synaptic plasticity models (SPMs) with time delay co-regulated by excitatory synapses and inhibitory synapses. Then, taken speech recognition (SR) as a case study, the recognition performance of fMRI-SNN is certified. To evaluate its anti-disturbance capability, the SR accuracy of fMRI-SNN under exterior electromagnetic noise is investigated, and is compared with SNNs with alternative topologies. To reveal its anti-disturbance mechanism, the neuroelectric characteristics, adaptive adjustment of synaptic plasticity, and dynamic topological characteristics of fMRI-SNN under exterior electromagnetic noise are discussed. The results indicate that the SR accuracy of fMRI-SNN under exterior electromagnetic noise is higher than that of SNNs with alternative topologies, and our discussion elucidates its anti-damage mechanism. Our results prompt that the brain-inspired model with bio-plausibility can enhance its robustness.