Dynamical analysis of recurrent neural circuits in articulated limb controllers for tool use
Han Wang, Qinbo Li, Jaewook Yoo, Yoonsuck Choe · 2016
Recurrent neural networks (RNNs) often show very complicated temporal behavior. In this paper, we investigate the dynamics of a simple recurrent neural network used in a nontrivial articulated limb control task in a tool use domain. The RNN for the task is evolved by the NeuroEvolution of Augmenting Topologies (NEAT) algorithm. As a non-linear dynamical system, RNNs exhibit strong correlation between their external behavior and internal dynamics. Discovery of the fixed points and limit cycles in the system dynamics will help us understand the roles of neurons and connections in the neural network, and provide insights on how to analyze the function of biology-based nervous systems. We hope our results can provide new criteria to the evaluation of the evolved RNNs, and help the diagnosis of the failures in control problems using neuroevolution.