Brain machine interface decoding method based on spiking neural network

Abstract

The present invention discloses a brain machine interface decoding method based on spiking neural network, comprising: (1) constructing a liquid state machine model based on a spiking neural network, the liquid state machine model consists of an input layer, an middle layer and an output layer, wherein, a connection weight from the input layer to the middle layer is W, a loop connection weight inside the middle layer is W, a readout weight from the middle layer to the output layer is W; (2) Inputting a neuron spike train signal, and training each weight with the following strategy: (2-1) Using STDP without supervision to train the connection weight Wfrom the input layer to the middle layer; (2-2) Setting the loop connection weight Winside the middle layer by means of distance model and random connection, and obtaining a middle layer liquid information R(t); (2-3) Using ridge regression with supervision to train the readout weight Wfrom the middle layer to the output layer, and establishing a mapping between the middle layer liquid information R(t) and the output motion information, and finally outputting a predicted motion trajectory. The present invention can quickly train a model in a relatively short time, predict the arm motion trajectory in real time, and achieve an improvement in efficiency and accuracy.