Adaptation through a Stochastic Evolutionary Neuron Migration Process

• Main Author: Haverinen, J. (Janne)
• Format: Doctoral Thesis
• Language: English
• Published: University of Oulu 2004
• Subjects: Hebbian-like dynamics; SENMP; adaptation; artificial life; lateral interaction; mobile robot; neural networks; neuron migration; selective pressure
• Online Access: http://urn.fi/urn:isbn:9514273079; http://nbn-resolving.de/urn:isbn:9514273079

Abstract Artificial Life is an interdisciplinary scientific and engineering enterprise investigating the fundamental properties of living systems through the simulation and synthesis of life-like processes in artificial media. One of the avenues of investigation is autonomous robots and agents. Mimicking of the growth and adaptation of a biological neural circuit in an artificial medium is a challenging task owing to our limited knowledge of the complex process taking place in a living organism. By combining several developmental mechanisms, including the chemical, mechanical, genetic, and electrical, researchers have succeeded in developing networks with interesting topology, morphology, and function within Artificial Computational Chemistry. However, most of these approaches still fail to create neural circuits able to solve real problems in perception and robot control. In this thesis a phenomenological developmental model called a Stochastic Evolutionary Neuron Migration Process (SENMP) is proposed. Employing a spatial encoding scheme with lateral interaction of neurons for artificial neural networks, which represent candidate solutions within a neural network ensemble, neurons of the ensemble form problem-specific spatial patterns with the desired dynamics as they migrate under the selective pressure. The approach is applied to gain new insights into development, adaptation and plasticity in neural networks and to evolve purposeful behaviors for mobile robots. In addition, the approach is used to study the relationship of spatial patterns, composed of interacting entities, and their dynamics. The feasibility and advantages of the approach are demonstrated by evolving neural controllers for solving a non-Markovian double pole balancing problem and by evolving controllers that exhibit navigation behavior for simulated and real mobile robots in complex environments. Preliminary results regarding the behavior of the adapting neural network ensemble are also shown and, particularly, a phenomenon exhibiting Hebbian-like dynamics. This thesis is a step toward a long range goal that aims to create an intelligent robot that is capable of learning complex skills and adapts rapidly to environmental changes.