Self-perturbation and homeostasis in embodied recurrent neural networks: A meta-model and some explorations with mechanisms for sensorimotor coordination

Abstract

We present a model of a recurrent neural network, embodied in a minimalist articulated agent with a single link and joint. The configuration of the agent defined by one angle (degree of freedom), is determined by the activation state of the neural network. This is done by contracting a muscle with many muscular fibers, whose contraction state needs to be coordinated to generate high amplitude link displacements. In networks without homeostasic (self-regulatory) mechanism the neural state dynamics and the configuration state dynamics converges to a fixed point. Introduction of random noise, shows that fixed points are meta-stable. When neural units are endowed with homeostasic mechanisms in the form of threshold adjustment, the dynamics of the configuration angle and neural state becomes aperiodic. Learning mechanisms foster functional and structural cluster formation, and modifies the distribution of the kinetic energy of the network. We also present a meta-model of embodied neural agents, that identifies self-perturbation as a mechanism for neural development without a teacher. © Springer-Verlag Berlin Heidelberg 2007.