Relationship between the brain and the central pattern generator based on recurrent neural network

Abstract

In animals, the command centers in the brain can drive the locomotion. However, it remains unclear how the brain modulates the locomotor central pattern generator (CPG). In this paper, a novel model is established to describe the relation between the brain and the CPG with time delay. The artificial recurrent neural network (RNN) consists of various computational modules that are used to model the brain. The brain synchronization under amplitude and frequency variations of the CPG and the effect of the RNN parameters variations on the CPG are investigated. In the paper, the excitatory neuron probability and average connections number are parameter space of RNN and the parameter space of CPG, which include frequency and amplitude. According to the simulation results, the best RNN synchronization could be obtained by finding the optimum parameters space between the RNN and the CPG. I propose that the parameter space of some CPGs is related to the parameter space of the brain. This leads to a brain load decrement that facilities the control action. The results are meaningful to investigate how to study the relationship between the brain and the locomotion.