A Stochastic Evolutionary Neuron Migration Process with Emerged Hebbian Dynamics

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 network ensemble, neurons of the ensemble form problem-specific geometrical structures as they migrate under selective pressure. The SENMP is applied to evolve purposeful behaviors for autonomous robots and to gain new insights into the development, adaptation and plasticity in artificial neural networks. We demonstrate the feasibility and advantages of the approach by evolving a robust navigation behavior for a mobile robot. We also present some preliminary results regarding the behavior of the adapting neural network ensemble and, particularly, a phenomenon exhibiting Hebbian dynamics.