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arXiv · 2311.03131

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

Ilya Auslender·Giorgio Letti·Yasaman Heydari·Clara Zaccaria·Lorenzo Pavesi
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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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Ilya Auslender, Giorgio Letti, Yasaman Heydari, Clara Zaccaria, Lorenzo Pavesi. 2023-11-06. Decoding Neuronal Networks: A Reservoir Computing Approach for Predicting Connectivity and Functionality. https://doi.org/10.1016/j.neunet.2024.107058

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