Quantifying neuronal network dynamics through coarse-grained event trees.

ABSTRACT: Animals process information about many stimulus features simultaneously, swiftly (in a few 100 ms), and robustly (even when individual neurons do not themselves respond reliably). When the brain carries, codes, and certainly when it decodes information, it must do so through some coarse-grained projection mechanism. How can a projection retain information about network dynamics that covers multiple features, swiftly and robustly? Here, by a coarse-grained projection to event trees and to the event chains that comprise these trees, we propose a method of characterizing dynamic information of neuronal networks by using a statistical collection of spatial-temporal sequences of relevant physiological observables (such as sequences of spiking multiple neurons). We demonstrate, through idealized

SUBMITTER: Rangan AV 

PROVIDER: S-EPMC2504773 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature