Reservoir Computing Model For Multi-Electrode Electrophysiological Data Analysis

TL;DR

This paper introduces a reservoir computing model that decodes multi-electrode electrophysiological data to reconstruct neuronal network structures and analyze their functionality, enabling simulation of network responses to stimuli.

Contribution

The novel aspect is applying reservoir computing to electrophysiological data for network reconstruction and functional analysis of neuronal cultures.

Findings

Successfully reconstructs network connectivity from electrophysiological data

Allows simulation of neuronal network responses to stimuli

Provides a tool for studying neuronal network functionality

Abstract

In this paper we present a computational model which decodes the spatio-temporal data from electro-physiological measurements of neuronal networks and reconstructs the network structure on a macroscopic domain, representing the connectivity between neuronal units. The model is based on reservoir computing network (RCN) approach, where experimental data is used as training and validation data. Consequently, the model can be used to study the functionality of different neuronal cultures and simulate the network response to external stimuli.