Classification of state transition by frequent method with Fuzzy logic and Self Organization Map in a living neuronal networks

Abstract

Neuronal networks, composed by a lot of mutually connected neurons, contribute to highly functions of brain such as memory, learning and so on. Neuronal activity has reproducible spatio-temporal patterns and analyzing transition of the dynamical patterns is considered to critical for elucidation of information processing in brain. Modification of neuronal activity patterns after an electrical input stimulation by external world possibly linked to the transition of internal states in a neuronal networks. In this study, we attempted to classify spatial-temporal patterns of cultured rat hippocampal network on a multi electrode array (MEA) dish that has 64 planner electrodes on the bottom. We calculated compatibility degrees for 64 dimensional vectors from recorded neuronal electrical spikes by using fuzzy logic. Compatibility degrees were used for the criteria for a winner node in a 2-dimentional output layer of a Self-Organization Map, instead of Euclidean distances. We analyzed the transition of spatial patterns of spontaneous and evoked electrical activity in cultured neuronal networks, using this type of Fuzzy-SOM algorithm.