Decoding Neuronal Networks: A Reservoir Computing Approach for Predicting Connectivity and Functionality
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910823256948736 |
|---|---|
| author | Auslender, Ilya Letti, Giorgio Heydari, Yasaman Zaccaria, Clara Pavesi, Lorenzo |
| author_facet | Auslender, Ilya Letti, Giorgio Heydari, Yasaman Zaccaria, Clara Pavesi, Lorenzo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_03131 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Decoding Neuronal Networks: A Reservoir Computing Approach for Predicting Connectivity and Functionality Auslender, Ilya Letti, Giorgio Heydari, Yasaman Zaccaria, Clara Pavesi, Lorenzo Quantitative Methods Machine Learning Signal Processing Biological Physics Computational Physics 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. |
| title | Decoding Neuronal Networks: A Reservoir Computing Approach for Predicting Connectivity and Functionality |
| topic | Quantitative Methods Machine Learning Signal Processing Biological Physics Computational Physics |
| url | https://arxiv.org/abs/2311.03131 |