Plenary Talk: "Neural population activity: finding simplicity in complexity"

FERNANDO MONTANI

Maceio, Brasil

Conferencia; XVIII Conference on Nonequilibrium Statistical Mechanics and Nonlinear Physics; 2014

Most standard analytical methods are appropriate for analyzing two neurons at the same time. However, to understand how large numbers of neurons interact, advanced statistical methods, are needed to interpret these large-scale neural recordings. We develop a simple mathematically tractable model that makes it feasible to account for higher-order spike correlations in a neuronal pool with highly interconnected common inputs beyond second order statistics. We show how input nonlinearities can shape higher-order correlations and enhance coding performance by neural populations. Moreover to investigate the neural network dynamics, we estimate the Bandt and Pompe probability distribution function associated to the interspike intervals and EEGs. This allows us to investigate how the information of the system might saturate to a finite value as the degree of interconnectivity across neurons grows, inferring in this way the emergent dynamical properties of the system.