Evolving virtual neuronal morphologies: A case study in genetic L-systems programming

Abstract

Theoretical and Experimental Neurobiology Unit, Okinawa Institute of Science and Technology, Japan Virtual neurons are digitized representations of biological neurons, with an emphasis on their morphology. In previous research we presented a proof of principle of reconstructing virtual neuronal morphologies by means of Genetic L-Systems Programming (GLP) [13]. However, the results were limited due to a hard evolutionary search process and a minimalistic fitness function. In this work we analyzed the search process and optimized the GLP configuration to enhance the search process. In addition, we designed a neuron type-specific fitness function which provides an incremental assessment of the evolved structures. The results are significantly better and relevant issues are discussed. © Springer-Verlag Berlin Heidelberg 2007.