A Biologically Plausible Spiking Neural Network for Decoding Kinematics in the Hippocampus and Premotor Cortex

Abstract

This work presents a spiking neural network for predicting kinematics from neural data towards accurate and energy-efficient brain machine interface. A brain machine interface is a technological system that interprets neural signals to allow motor impaired patients to control prosthetic devices. Spiking neural networks have the potential to improve brain machine interface technology due to their low power cost and close similarity to biological neural structures. The SNN in this study uses the leaky integrate-and-fire model to simulate the behavior of neurons, and learns using a local learning method that uses surrogate gradient to learn the parameters of the network. The network implements a novel continuous time output encoding scheme that allows for regression-based learning. The SNN is trained and tested offline on neural and kinematic data recorded from the premotor cortex of a primate and the hippocampus of a rat. The model is evaluated by finding the correlation between the predicted kinematic data and true kinematic data, and achieves peak Pearson Correlation Coefficients of 0.77 for the premotor cortex recordings and 0.80 for the hippocampus recordings. The accuracy of the model is benchmarked against a Kalman filter decoder and a LSTM network, as well as a spiking neural network trained with backpropagation to compare the effects of local learning.