Decoding Neuronal Networks: A Reservoir Computing Approach for Predicting Connectivity and Functionality

• URL: http://arxiv.org/abs/2311.03131v3
• Date: Tue, 5 Mar 2024 10:25:03 GMT
• Title: Decoding Neuronal Networks: A Reservoir Computing Approach for Predicting Connectivity and Functionality
• Authors: Ilya Auslender, Giorgio Letti, Yasaman Heydari, Clara Zaccaria, Lorenzo Pavesi
• Abstract summary: Our model deciphers data obtained from electrophysiological measurements of neuronal cultures. Notably, our model outperforms common methods like Cross-Correlation and Transfer-Entropy in predicting the network's connectivity map.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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