motorSRNN: A spiking recurrent neural network inspired by brain topology for the effective and efficient decoding of cortical spike trains

Tengjun Liu, Yansong Chua, Yuxiao Ning, Pengfu Liu, Yiwei Zhang, Tuoru Li, Guihua Wan, Zijun Wan, Weidong Chen, Shaomin Zhang

Abstract: Highlights•In this study, we proposed motorSRNN, a recurrent spiking neural network (SNN) that draws inspiration from the neural motor circuit of primates.•The motorSRNN advanced the performance of previously reported SNN method in similar CST-decoding tasks, a feedforward SNN (fSNN), by over 25%.•The energy efficiency of motorSRNN was theoretically approximately 1/50 compared to traditional GRU and LSTM architectures.•The motorSRNN outperformed fSNN, GRU, and LSTM in terms of early-classification capabilities from 2 ms to the end in the 50-ms sample duration.•The motorSRNN elucidates a plausible rationale for the biologically employed topology: to enhance the resilience against Poisson noise from adjacent neurons.