{"oa":1,"publisher":"arXiv","project":[{"call_identifier":"H2020","grant_number":"754411","name":"ISTplus - Postdoctoral Fellowships","_id":"260C2330-B435-11E9-9278-68D0E5697425"},{"name":"Efficient coding with biophysical realism","_id":"626c45b5-2b32-11ec-9570-e509828c1ba6","grant_number":"P34015"}],"publication_status":"submitted","doi":"10.48550/ARXIV.2108.06686","_id":"10912","month":"08","title":"Quantifying the coexistence of neuronal oscillations and avalanches","external_id":{"arxiv":["2108.06686"]},"oa_version":"Preprint","citation":{"ieee":"F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, and D. De Martino, “Quantifying the coexistence of neuronal oscillations and avalanches.” arXiv.","mla":"Lombardi, Fabrizio, et al. Quantifying the Coexistence of Neuronal Oscillations and Avalanches. arXiv, doi:10.48550/ARXIV.2108.06686.","chicago":"Lombardi, Fabrizio, Selver Pepic, Oren Shriki, Gašper Tkačik, and Daniele De Martino. “Quantifying the Coexistence of Neuronal Oscillations and Avalanches.” arXiv, n.d. https://doi.org/10.48550/ARXIV.2108.06686.","ista":"Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence of neuronal oscillations and avalanches. 10.48550/ARXIV.2108.06686.","ama":"Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence of neuronal oscillations and avalanches. doi:10.48550/ARXIV.2108.06686","apa":"Lombardi, F., Pepic, S., Shriki, O., Tkačik, G., & De Martino, D. (n.d.). Quantifying the coexistence of neuronal oscillations and avalanches. arXiv. https://doi.org/10.48550/ARXIV.2108.06686","short":"F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, D. De Martino, (n.d.)."},"type":"preprint","date_updated":"2022-03-22T07:53:18Z","abstract":[{"lang":"eng","text":"Brain dynamics display collective phenomena as diverse as neuronal oscillations and avalanches. Oscillations are rhythmic, with fluctuations occurring at a characteristic scale, whereas avalanches are scale-free cascades of neural activity. Here we show that such antithetic features can coexist in a very generic class of adaptive neural networks. In the most simple yet fully microscopic model from this class we make direct contact with human brain resting-state activity recordings via tractable inference of the model's two essential parameters. The inferred model quantitatively captures the dynamics over a broad range of scales, from single sensor fluctuations, collective behaviors of nearly-synchronous extreme events on multiple sensors, to neuronal avalanches unfolding over multiple sensors across multiple time-bins. Importantly, the inferred parameters correlate with model-independent signatures of \"closeness to criticality\", suggesting that the coexistence of scale-specific (neural oscillations) and scale-free (neuronal avalanches) dynamics in brain activity occurs close to a non-equilibrium critical point at the onset of self-sustained oscillations."}],"status":"public","date_created":"2022-03-21T11:41:28Z","day":"17","ddc":["570"],"year":"2021","date_published":"2021-08-17T00:00:00Z","article_processing_charge":"No","language":[{"iso":"eng"}],"acknowledgement":"FL acknowledges support from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 754411. GT\r\nacknowledges the support of the Austrian Science Fund (FWF) under Stand-Alone Grant\r\nNo. P34015.","author":[{"last_name":"Lombardi","id":"A057D288-3E88-11E9-986D-0CF4E5697425","full_name":"Lombardi, Fabrizio","orcid":"0000-0003-2623-5249","first_name":"Fabrizio"},{"first_name":"Selver","full_name":"Pepic, Selver","last_name":"Pepic","id":"F93245C4-C3CA-11E9-B4F0-C6F4E5697425"},{"last_name":"Shriki","first_name":"Oren","full_name":"Shriki, Oren"},{"last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gašper","full_name":"Tkačik, Gašper"},{"last_name":"De Martino","full_name":"De Martino, Daniele","first_name":"Daniele"}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2108.06686"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","page":"37","ec_funded":1,"department":[{"_id":"GaTk"}]}