Adaptation through a stochastic evolutionary neuron migration process (SENMP)

Abstract

Mimicking of the growth and adaptation of a biological neural circuit in an artificial medium is a challenging task. In this paper, we propose a phenomenological developmental model based on a stochastic evolutionary neuron migration process (SENMP). Employing a spatial encoding scheme with lateral interaction of neurons for artificial neural networks representing candidate solutions within a neural ensemble, neurons of the ensemble form problem-specific geometrical structures as they migrate under selective pressure. The approach is applied to gain new insights into the development, adaptation and plasticity in artificial neural networks and to evolve purposeful behavior for autonomous robots. We demonstrate the feasibility and advantages of the approach by. using a simulator to evolve a robust navigation behavior for a mobile robot and by verifying the results in a real office environment. We also present some preliminary results regarding the behavior of the adapting neural ensemble and, particularly, a phenomenon exhibiting Hebbian dynamics.