Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse
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arXiv
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| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2021
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| _version_ | 1866914461873340416 |
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| author | Capone, Cristiano De Luca, Chiara De Bonis, Giulia Gutzen, Robin Bernava, Irene Pastorelli, Elena Simula, Francesco Lupo, Cosimo Tonielli, Leonardo Mascaro, Anna Letizia Allegra Resta, Francesco Pavone, Francesco Denker, Micheal Paolucci, Pier Stanislao |
| author_facet | Capone, Cristiano De Luca, Chiara De Bonis, Giulia Gutzen, Robin Bernava, Irene Pastorelli, Elena Simula, Francesco Lupo, Cosimo Tonielli, Leonardo Mascaro, Anna Letizia Allegra Resta, Francesco Pavone, Francesco Denker, Micheal Paolucci, Pier Stanislao |
| contents | Thanks to novel, powerful brain activity recording techniques, we can create data-driven models from thousands of recording channels and large portions of the cortex, which can improve our understanding of brain-states neuromodulation and the related richness of traveling waves dynamics.
We investigate the inference of data-driven models and the comparison among experiments and simulations, through the characterization of the spatio-temporal features of cortical waves in experimental recordings and simulations. Inference is built in two steps: the inner loop that optimizes by likelihood maximization a mean-field model, and the outer loop that optimizes a periodic neuro-modulation by relying on direct comparison of observables apt for the characterization of cortical slow waves. The model is capable to reproduce most of the features of the non-stationary and non-linear dynamics displayed by the high-resolution recording of the in-vivo mouse brain obtained by wide-field calcium imaging techniques. The proposed approach is of interest for both experimental and computational neuroscientists. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2104_07445 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse Capone, Cristiano De Luca, Chiara De Bonis, Giulia Gutzen, Robin Bernava, Irene Pastorelli, Elena Simula, Francesco Lupo, Cosimo Tonielli, Leonardo Mascaro, Anna Letizia Allegra Resta, Francesco Pavone, Francesco Denker, Micheal Paolucci, Pier Stanislao Neurons and Cognition Dynamical Systems Thanks to novel, powerful brain activity recording techniques, we can create data-driven models from thousands of recording channels and large portions of the cortex, which can improve our understanding of brain-states neuromodulation and the related richness of traveling waves dynamics. We investigate the inference of data-driven models and the comparison among experiments and simulations, through the characterization of the spatio-temporal features of cortical waves in experimental recordings and simulations. Inference is built in two steps: the inner loop that optimizes by likelihood maximization a mean-field model, and the outer loop that optimizes a periodic neuro-modulation by relying on direct comparison of observables apt for the characterization of cortical slow waves. The model is capable to reproduce most of the features of the non-stationary and non-linear dynamics displayed by the high-resolution recording of the in-vivo mouse brain obtained by wide-field calcium imaging techniques. The proposed approach is of interest for both experimental and computational neuroscientists. |
| title | Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse |
| topic | Neurons and Cognition Dynamical Systems |
| url | https://arxiv.org/abs/2104.07445 |