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Identifying and Tracking the number of independent clusters of functionally interdependent neurons from biophysical models of population activity

Clustering analysis is an important tool to study the functional interdependency among large ensembles of neurons from the observed spiking activity. An important question is how to determine the number of independent clusters when neuronal ensembles are dynamically recruited to process and store information, for e.g. during learning and behavior. In this paper, we propose a new approach based on graph theory to determine the number of clusters of functionally interdependent neurons from spontaneous spiking activity resulting from a dynamic biophysical model of the population. We show that the approach is capable of detecting the dynamic change in connectivity between otherwise independent clusters of neurons subject to distinct firing thresholds, resting potentials, post-spike potentials, and membrane voltage noise statistics