Spike Neural Network of Motor Cortex Model for Arm Reaching Controlling

Abstract

Motor cortex modeling is crucial for understanding movement planning and execution. While interconnected recurrent neural networks have successfully described the dynamics of neural population activity, most existing methods utilize continuous signal-based neural networks, which do not reflect the biological spike neural signal. To address this limitation, we propose a recurrent spike neural network to simulate motor cortical activity during an arm-reaching task. Specifically, our model is built upon integrate-and-fire spiking neurons with conductance-based synapses. We carefully designed the interconnections of neurons with two different firing time scales - "fast" and "slow" neurons. Experimental results demonstrate the effectiveness of our method, with the models neuronal activity in good agreement with monkeys motor cortex data at both single-cell and population levels. Quantitative analysis reveals a correlation coefficient 0.89 between the models and real data. These results suggest the possibility of multiple timescales in motor cortical control.