In this paper we present a computational model which decodes the spatio-temporal data from electrophysiological measurements of neuronal networks and reconstructs the network structure on a macroscopic domain, representing the connectivity between neuronal units. The model is based on reservoir computing network (RCN) approach, where experimental data is used as training and validation data. Consequently, the model can be used to study the functionality of different neuronal cultures and simulate the network response to external stimuli.
Reservoir Computing Model For Multi-Electrode Electrophysiological Data Analysis / Auslender, Ilya; Pavesi, Lorenzo. - (2023), pp. 151-156. (Intervento presentato al convegno 20th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB) tenutosi a Eindhoven, NETHERLANDS nel 29-31 August 2023) [10.1109/cibcb56990.2023.10264895].
Reservoir Computing Model For Multi-Electrode Electrophysiological Data Analysis
Auslender, Ilya;Pavesi, Lorenzo
2023-01-01
Abstract
In this paper we present a computational model which decodes the spatio-temporal data from electrophysiological measurements of neuronal networks and reconstructs the network structure on a macroscopic domain, representing the connectivity between neuronal units. The model is based on reservoir computing network (RCN) approach, where experimental data is used as training and validation data. Consequently, the model can be used to study the functionality of different neuronal cultures and simulate the network response to external stimuli.File | Dimensione | Formato | |
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