Statistical modelling of neuronal activity for infinitely large number of neurons

PORTESI, M.; MONTANI, F.

Leipzig

Conferencia; 3rd Conference on Information Geometry and its Applications (IGAIA III); 2010

University of Leipzig

An important open question in mathematical neuroscience is how to evaluate the significance of high order spike correlations in the neural code through analytically solvable models. We investigate the thermodynamic limit of a widespread probability distribution of firing in a neuronal pool, within the information geometry framework, considering all possible contributions from high order correlations. This allows us to identify a deformation parameter accounting for the different regimes of firing within the probability distribution, and to investigate whether those regimes could saturate or increase information as the number of neurons goes to infinity.