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Decoding Neuronal Networks: A Reservoir Computing Approach for Predicting Connectivity and Functionality (2311.03131v3)
Published 6 Nov 2023 in q-bio.QM, cs.LG, physics.bio-ph, eess.SP, and physics.comp-ph
Abstract: In this study, we address the challenge of analyzing electrophysiological measurements in neuronal networks. Our computational model, based on the Reservoir Computing Network (RCN) architecture, deciphers spatio-temporal data obtained from electrophysiological measurements of neuronal cultures. By reconstructing the network structure on a macroscopic scale, we reveal the connectivity between neuronal units. Notably, our model outperforms common methods like Cross-Correlation and Transfer-Entropy in predicting the network's connectivity map. Furthermore, we experimentally validate its ability to forecast network responses to specific inputs, including localized optogenetic stimuli.
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