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.
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; Letizia Allegra Mascaro, Anna; Resta, Francesco; Pavone, Enea Francesco; Denker, Micheal; Stanislao Paolucci, Pier. - In: COMMUNICATIONS BIOLOGY. - ISSN 2399-3642. - (2021). [10.48550/arXiv.2104.07445]
Simulations approaching data: cortical slow waves in inferred models of the whole hemisphere of mouse
Cristiano Capone
Co-primo
;Chiara De LucaCo-primo
;Giulia De Bonis;Elena Pastorelli;Francesco Simula;Cosimo Lupo;Francesco Pavone;
2021
Abstract
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.File | Dimensione | Formato | |
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