Identifying and Tracking the number of independent clusters of functionally interdependent neurons from biophysical models of population activity
Feilong Chen; El-Dawlatly, S.; Rong Jin; Oweiss, K.
Neural Engineering, 2007. CNE apos;07. 3rd International IEEE/EMBS Conference on
Volume , Issue , 2-5 May 2007 Page(s):542 - 545
Digital Object Identifier   10.1109/CNE.2007.369729
Summary: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

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