Decoding Neuronal Networks: A Reservoir Computing Approach for Predicting Connectivity and Functionality
This work addresses the problem of understanding neuronal connectivity and function for researchers in neuroscience, representing an incremental improvement over existing computational methods.
The study tackled the challenge of analyzing electrophysiological data in neuronal networks by using a Reservoir Computing Network model to reconstruct connectivity and predict functionality, outperforming methods like Cross-Correlation and Transfer-Entropy in connectivity prediction and validating its ability to forecast responses to stimuli.
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.