Decoding neuronal networks: A Reservoir Computing approach for predicting connectivity and functionality
Year: 2024
Type: article
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.
Source: Neural Networks
Institution: University of Trento
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,350
Funders: European Commission, Università degli Studi di Trento, Università degli Studi di Genova, Fondazione Cassa Di Risparmio Di Trento E Rovereto, Horizon 2020 Framework Programme, European Research Council, Horizon 2020, H2020 European Research Council, H2020 Marie Skłodowska-Curie Actions, HORIZON EUROPE European Research Council
Awards: An integrated setup for in-vitro optogenetic experiments using AI to localize stimulation with a feedback of electrophysiological signals, G3288148890, An integrated setup for in-vitro optogenetic experiments using AI to localize stimulation with a feedback of electrophysiological signals, G434337789, G8633196619, Unveiling the relationship between brain connectivity and function by integrated photonics
OpenAlex ID: W4405801861