Using animal data and neural dynamics to reverse engineer a neuromechanical rat model

Abstract

A baseline model for testing how afferent muscle feedback affects both timing and activation levels of muscle contractions has been constructed. We present an improved version of the neuromechanical model from our previous work [6]. This updated model has carefully tuned muscles, feedback pathways, and central pattern generators (CPGs). Kinematics and force plate data from trotting rats were used to better design muscles for the legs. A recent pattern generator topology [15] is implemented to better mimic the rhythm generation and pattern formation networks in the animal. Phase-space and numerical phase response analyses reveal the dynamics underlying CPG behavior, resulting in an oscillator that produces both robust cycles and favorable perturbation responses. Training methods were used to tune synapse properties to shape desired motor neuron activation patterns. The result is a model which is capable of self-propelled hind leg stepping and will serve as a baseline as we investigate the effects changes in afferent feedback have on muscle activation patterns.