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

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, the strain indicates the nature of this difference – whether there is an excess or a deficit of synchrony – while the Kullback-Leibler divergence only indicates the magnitude of the difference...

• Type: Journal
• Year: 2011
• Where: JCNS
• Authors: Ifije E. Ohiorhenuan, Jonathan D. Victor
• Keywords: Firing Patterns, JCNS 2011, Multineuronal Firing Patterns, Pairwise Model