Decoding neuronal networks: A Reservoir Computing approach for predicting connectivity and functionality.
Journal:
Neural networks : the official journal of the International Neural Network Society
PMID:
39733702
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 such as Cross-Correlation, Transfer-Entropy, and a recently developed related algorithm in predicting the network's connectivity map. Furthermore, we experimentally validate its ability to forecast network responses to specific inputs, including localized optogenetic stimuli.