Training a Dendritic Neural Model with Genetic Algorithm for Classification Problems

Abstract

Recently, more neuroscience researches focus on the role of dendritic structure during the neural computation. Inspired by the specified topologies of numerous dendritic trees, we proposed a single neural model with a particular dendritic structure. The dendrites are composed of several branches, and these branches correspond to three distributions in coordinate, which are used to classify the training data as required. Genetic algorithm is used as the training algorithm. Experimental results based on two benchmark classification problems verify the effectiveness of the proposed method, and the distributions of trained dendritic structures are also presented.