Information-geometric measure of 3-neuron firing patterns characterizes scale-dependence in cortical networks.

ABSTRACT: To understand the functional connectivity of neural networks, it is important to develop simple and incisive descriptors of multineuronal firing patterns. Analysis at the pairwise level has proven to be a powerful approach in the retina, but it may not suffice to understand complex cortical networks. Here we address the problem of describing interactions among triplets of neurons. We consider two approaches: an information-geometric measure (Amari 2001), which we call the "strain," and the Kullback-Leibler divergence. While both approaches can be used to assess whether firing patterns differ from those predicted by a pairwise maximum-entropy model, the strain provides additional information. Specifically, when the observed firing patterns differ from those predicted by a pairwise model, th

SUBMITTER: Ohiorhenuan IE 

PROVIDER: S-EPMC2974058 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature