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| Autori principali: | , |
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| Natura: | Preprint |
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2023
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| Accesso online: | https://arxiv.org/abs/2309.06297 |
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| _version_ | 1866912230611615744 |
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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 |