Reconstruction scheme for excitatory and inhibitory dynamics with quenched disorder: application to zebrafish imaging
Anno: 2021
Autori: Chicchi L., Cecchini G., Adam I., de Vito G., Livi R., Pavone FS., Silvestri L., Turrini L., Vanzi F., Fanelli D.
Affiliazione autori: Univ Florence, Dept Phys & Astron, Florence, Italy; Univ Florence, CSDC, Florence, Italy; Univ Florence, Dept Informat Engn, Florence, Italy; European Lab Nonlinear Spect, Florence, Italy; Univ Florence, Dept Neurosci Psychol Drug Res & Child Hlth, Florence, Italy; INFN Sez Firenze, Florence, Italy; Natl Res Councily, Natl Inst Opt, Florence, Italy; Univ Florence, Dept Biol, Florence, Italy.
Abstract: An inverse procedure is developed and tested to recover functional and structural information from global signals of brains activity. The method assumes a leaky-integrate and fire model with excitatory and inhibitory neurons, coupled via a directed network. Neurons are endowed with a heterogenous current value, which sets their associated dynamical regime. By making use of a heterogenous mean-field approximation, the method seeks to reconstructing from global activity patterns the distribution of in-coming degrees, for both excitatory and inhibitory neurons, as well as the distribution of the assigned currents. The proposed inverse scheme is first validated against synthetic data. Then, time-lapse acquisitions of a zebrafish larva recorded with a two-photon light sheet microscope are used as an input to the reconstruction algorithm. A power law distribution of the in-coming connectivity of the excitatory neurons is found. Local degree distributions are also computed by segmenting the whole brain in sub-regions traced from annotated atlas.
Giornale/Rivista: JOURNAL OF COMPUTATIONAL NEUROSCIENCE
Volume: 49 (2) Da Pagina: 159 A: 174
Maggiori informazioni: Open Access funding provided by Universita degli Studi di FirenzeParole chiavi: Network reconstruction; Neuroscience; Heterogeneous mean field approximation; Leak integrate and fire; Zebrafish larvaDOI: 10.1007/s10827-020-00774-1Citazioni: 5dati da “WEB OF SCIENCE” (of Thomson Reuters) aggiornati al: 2025-07-13Riferimenti tratti da Isi Web of Knowledge: (solo abbonati) Link per visualizzare la scheda su IsiWeb: Clicca quiLink per visualizzare la citazioni su IsiWeb: Clicca qui