Stochastic chains with unbounded memory applied in neuroscience

Ricardo Felipe Ferreira is a mathematician who, during his doctoral research, was guided by the RIDC NeuroMat associated investigator Alexsandro Giacomo Grimbert Gallo (UFSCar). During the first semester of 2019, Ferreira defended his doctoral thesis, in which NeuroMat research is a highlight.

FERREIRA, R. F. Stochastic chains with unbounded memory applied in neuroscience. 2019. 80 p. Tese (Doutorado em Estatística – Programa Interinstitucional de Pós-Graduação em Estatística) – Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos – SP, 2019.

Abstract

Stochastic chains with unbounded memory are a natural generalization of Markov chains, in the sense that the transition probabilities may depend on the whole past. These process, introduced independently by Onicescu and Mihoc in 1935 and Doeblin and Fortet in 1937, have been receiving increasing attention in the probabilistic literature, because they form a class richer than the Markov chains and have practical capabilities modelling of scientific data in several areas, from biology to linguistics. In this work, we use them to model interactions between spike trains. Our main goal is to develop new mathematical results about stochastic chains with unbounded memory. First, we study conditions that guarantee the existence and uniqueness of stationary chains compatible with a discontinuous family of transition probabilities. Then, we address the understanding of the phenomenology of spike trains and we propose to use directed information to quantify the information flow from one neuron to another. In this occasion, we fix concentration bounds for directed information estimation.

Read more about Ricardo Felipe Ferreira here.

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