Cadenes de Markov i la seva aplicació a la neurofı́sica

Abstract

Markov chains are primarily characterized by the probability distribution of their next state solely depending on their current one. This specific trait has enabled Markov chains to be applied in various fields of study, such as chemistry, economics and music. By expanding the theory of Markov chains to higher order Markov chains, where the probability distribution of their next state depends on their current one as well as on past ones, they also have an application in neurophysics. This work will study the theory behind Markov chains, going through higher order Markov chains and finally arriving to the expression of a powerful mathematical tool called transfer entropy, which quantifies the directionality and strength of connections among neurons, helping to shed some light into the mysteries of the brain.