An information theoretic learning framework based on Renyi’s α entropy for brain effective connectivity estimation

The interactions among neural populations distributed across different brain regions are at the core of cognitive and perceptual processing. Therefore, the ability of studying the flow of information within networks of connected neural assemblies is of fundamental importance to understand such proce...

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Autores:: De La Pava Panche , Ivan

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2021

Institución:: Universidad Tecnológica de Pereira

Repositorio:: Repositorio Institucional UTP

Idioma:: eng

Description
Summary:	The interactions among neural populations distributed across different brain regions are at the core of cognitive and perceptual processing. Therefore, the ability of studying the flow of information within networks of connected neural assemblies is of fundamental importance to understand such processes. In that regard, brain connectivity measures constitute a valuable tool in neuroscience. They allow assessing functional interactions among brain regions through directed or non-directed statistical dependencies estimated from neural time series. Transfer entropy (TE) is one such measure. It is an effective connectivity estimation approach based on information theory concepts and statistical causality premises. It has gained increasing attention in the literature because it can capture purely nonlinear directed interactions, and is model free. That is to say, it does not require an initial hypothesis about the interactions present in the data. These properties make it an especially convenient tool in exploratory analyses. However, like any information-theoretic quantity, TE is defined in terms of probability distributions that in practice need to be estimated from data. A challenging task, whose outcome can significantly affect the results of TE. Also, it lacks a standard spectral representation, so it cannot reveal the local frequency band characteristics of the interactions it detects.

An information theoretic learning framework based on Renyi’s α entropy for brain effective connectivity estimation

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