Self-organized Synaptic Learning of Gaits in Virtual Creatures: A neural simulation study within Connectology

Abstract

The theory of Connectology sets forth three psychologically founded synaptic learning mechanisms that may describe all aspects of animal learning. Of particular interest to this thesis is the learning of animal motion behavior, or, more specifically, the development of synchronized and repetitive movement patterns - gaits.Computer simulations are performed according to the methodology of computational neuroethology: Artificial neural networks are simulated operating in a tight feedback loop with a structurally simple but mechanically realistic body and a physically realistic environment. Neural network learning is purely synaptical and is performed solely within the lifetime of one such ANN-controlled system. Additionally, the configuration parameter space is searched by means of genetic algorithms.Simulation results show examples of synchronized and repetitive movement patterns developing when neuronal and mechanical model parameters are appropriately specified. These simulations thereby provide the first examples known to us of a fully unsupervised and self-organized artificial neural system that synaptically learns synchronized and repetitive motor control. In spite of limited mechanical model complexity, the most efficient movement patterns to some degree resemble the gaits seen in nature.