Modularity Facilitates Classification Performance of Spiking Neural Networks for Decoding Cortical Spike Trains. - Tengjun Liu; Yuxiao Ning; Pengfu Liu; Yiwei Zhang; Yansong Chua; Weidong Chen; Shaomin Zhang (EMBC, 2023)

Emergent Bio-Functional Similarities in a Cortical-Spike-Train-Decoding Spiking Neural Network Facilitate Predictions of Neural Computation. - Tengjun Liu; Yansong Chua; Yiwei Zhang; Yuxiao Ning; Pengfu Liu; Guihua Wan; Zijun Wan; Shaomin Zhang; Weidong Chen 002 (CoRR, 2023)

The influence of non-stationarity of spike signals on decoding performance in intracortical brain-computer interface: a simulation study. - Zijun Wan; Tengjun Liu; Xingchen Ran; Pengfu Liu; Weidong Chen 002; Shaomin Zhang (Frontiers Comput. Neurosci., 2023)

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 002; Shaomin Zhang (Biomed. Signal Process. Control., 2025)