Analysis of neural interaction in motor cortex during reach-to-grasp task based on Dynamic Bayesian Networks

In this work, we took the analysis of neural interaction based on the data recorded from the motor cortex of a monkey, when it was trained to complete multi-targets reach-to-grasp tasks. As a recently proved effective tool, Dynamic Bayesian Network (DBN) was applied to model and infer interactions of dependence between neurons. In the results, the gained networks of neural interactions, which correspond to different tasks with different directions and orientations, indicated that the target information was not encoded in simple ways by neuronal networks. We also explored the difference of neural interactions between delayed period and peri-movement period during reach-to-grasp task. We found that the motor control process always led to relatively more complex neural interaction networks than the plan thinking process.