A Quadrupedal Locomotion Central Pattern Generator Based on Oscillatory Building Block Networks

Abstract

This work presents the mimicking of the main rhythmic gait patterns of a quadrupedal animal as an illustration of a novel approach to the prediction and generation of coupled neural oscillation. Based on Scheduling by Multiple Edge Reversal (SMER), a simple though powerful distributed algorithm, it is shown how oscillatory building blocks (OBBs) can be created for the generation of complex rhythmic patterns and implemented as asymmetric Hopfield neural networks. The network is able to reproduce the different firing rates observed among pairs of biological motor neurons working during different gait patterns.