An Information Geometry Approach to Investigate High-order Spike Correlations

FERNANDO MONTANI; ELENA PHOKA; MARIELA PORTESI; SIMON R SCHULTZ

Workshop; International Workshop on Anomalous Statistics, Generalized Entropies, and Information Geometry; 2012

We consider the spiking activity of very large neuronal ensembles to investigate how noise correlationis processed in the brain. Within the information geometry framework, we develop a statistical model toquantify the degree of the correlation in a pool of neurons. More specically, we make use of the extendedcentral limit theorem to consider all possible contributions, higher than two, in the spike correlations. Thisallows us, through the identication of a deformation parameter, to deal with the problem of correlationsin a pool of neurons in the thermodynamic limit. We estimate the probability distribution of ring andapply our formalism to a data set obtained from mouse barrel cortex.