{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/simulations-approaching-data-cortical-slow","title":"Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse","arxiv_id":"2104.07445","date":"2021-04-15","proceeding":null,"authors":["Cristiano Capone","Chiara De Luca","Giulia De Bonis","Robin Gutzen","Irene Bernava","Elena Pastorelli","Francesco Simula","Cosimo Lupo","Leonardo Tonielli","Anna Letizia Allegra Mascaro","Francesco Resta","Francesco Pavone","Micheal Denker","Pier Stanislao Paolucci"],"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.","url_abs":"https://arxiv.org/abs/2104.07445v3","url_pdf":"https://arxiv.org/pdf/2104.07445v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"simulations-approaching-data-cortical-slow","repo_url":"https://github.com/ape-group/corticalsw_inference","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"simulations-approaching-data-cortical-slow","repo_url":"https://github.com/APE-group/InteractiveExplorationBrainStates","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}