Context trees applied to the statistical modeling of neural spike trains

• Grant number: 16/24676-1
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: March 01, 2017
• End date: May 19, 2019
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Applied Probability and Statistics
• Principal Investigator: Jefferson Antonio Galves
• Grantee: Ludmila Brochini Rodrigues
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat, AP.CEPID

Abstract

We propose to model neuronal networks through interacting stochastic systems with memory of variable length introduced by Galves and Locherbah (2013) which hydrodynamic behavior was examined by De Masi et al. (2014). In order to study models of this class under realistic biological conditions we will model spike train data from experimental electrophysiological in vivo recordings of insect antennal lobe neurons that process olfactory information (Pouzat et al., 2002) as well as freely moving electric (Forlim et al., 2014) performing electrocomunication. We will use statistical model selection in the class of stochastic processes of variable length (context tree) to reveal the underlying probabilistic structure of spike trains and test, through an interactive experimental protocol, whether such structure is affected by a designed source. Moreover, we will be able to infer the interaction graph of recorded neurons by using the estimator proposed with proven consistency by Duarte et al. 2016, which will allow us to compare functional differences in the same system of different animal models. We believe that the use of these innovative mathematical tools of solid analytical ground will further the understanding of the aforementioned Neurobiological systems and has the potential to unveil properties that cannot be observed through statistical analyses traditionally used in Neurobiology. (AU)

Scientific publications

(4)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

COSTA, ARIADNE A.; BROCHINI, LUDMILA; KINOUCHI, OSAME. Self-Organized Supercriticality and Oscillations in Networks of Stochastic Spiking Neurons. Entropy, v. 19, n. 8, AUG 2017. (16/24676-1, 13/07699-0, 16/20945-8, 16/00430-3)

GIRARDI-SCHAPPO, MAURICIO; BROCHINI, LUDMILA; COSTA, ARIADNE A.; CARVALHO, TAWAN T. A.; KINOUCHI, OSAME. Synaptic balance due to homeostatically self-organized quasicritical dynamics. PHYSICAL REVIEW RESEARCH, v. 2, n. 1, FEB 20 2020. (16/24676-1, 13/07699-0, 19/12746-3, 16/00430-3, 18/09150-9, 16/20945-8)

KINOUCHI, OSAME; BROCHINI, LUDMILA; COSTA, ARIADNE A.; FERREIRA CAMPOS, JOAO GUILHERME; COPELLI, MAURO. Stochastic oscillations and dragon king avalanches in self-organized quasi-critical systems. SCIENTIFIC REPORTS, v. 9, MAR 7 2019. (16/00430-3, 16/24676-1, 13/07699-0)

GIRARDI-SCHAPPO, MAURICIO; GALERA, EMILIO F.; CARVALHO, TAWAN T. A.; BROCHINI, LUDMILA; KAMIJI, NILTON L.; ROQUE, ANTONIO C.; KINOUCHI, OSAME. A unified theory of E/I synaptic balance, quasicritical neuronal avalanches and asynchronous irregular spiking. JOURNAL OF PHYSICS-COMPLEXITY, v. 2, n. 4, DEC 2021. (19/12746-3, 18/09150-9, 18/20277-0, 16/24676-1, 13/07699-0, 15/50122-0, 16/03855-5)