Evolving dendritic morphology and parameters in biologically realistic model neurons for pattern recognition

Abstract

This paper addresses the problem of how dendritic topology and other properties of a neuron can determine its pattern recognition performance. In this study, dendritic trees were evolved using an evolutionary algorithm, which varied both morphologies and other parameters. Based on these trees, we constructed multi-compartmental conductance-based models of neurons. We found that dendritic morphology did have a considerable effect on pattern recognition performance. The results also revealed that the evolutionary algorithm could find effective morphologies, with a performance that was five times better than that of hand-tuned models.