Higher-order spike correlations and neuronal information processing

LISANDRO MONTANGIE; FERNANDO MONTANI

Maceio, Brasil

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

Statistical modeling on biologically inspired neural populations are important for inferring the function of complex neural circuits. A neuronal pool is a group of interconnected neurons with specic functions, and the major cause of correlated ring in neural networks is common presynaptic input.We identify beyond-pairwise inputs across neurons as the biophysical mechanisms that generates higher order correlations within the joint distribution of ring and provide its exact analytical expression.Our approach can be of help to gain further insights of the role of higher-order correlations in information transmission,ideally of in populations up to several thousands of neurons, where the spike sorting methodologies are useless.