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Autori principali: Auslender, Ilya, Pavesi, Lorenzo
Natura: Preprint
Pubblicazione: 2023
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Accesso online:https://arxiv.org/abs/2309.06297
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author Auslender, Ilya
Pavesi, Lorenzo
author_facet Auslender, Ilya
Pavesi, Lorenzo
contents In this paper we present a computational model which decodes the spatio-temporal data from electro-physiological 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.
format Preprint
id arxiv_https___arxiv_org_abs_2309_06297
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Reservoir Computing Model For Multi-Electrode Electrophysiological Data Analysis
Auslender, Ilya
Pavesi, Lorenzo
Quantitative Methods
Emerging Technologies
Biological Physics
Neurons and Cognition
In this paper we present a computational model which decodes the spatio-temporal data from electro-physiological 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.
title Reservoir Computing Model For Multi-Electrode Electrophysiological Data Analysis
topic Quantitative Methods
Emerging Technologies
Biological Physics
Neurons and Cognition
url https://arxiv.org/abs/2309.06297