Identifying neuronal assemblies with local and global connectivity with scale space spectral clustering

Authors:
Karim Oweiss;Rong Jin;Yasir Suhail
Affiliations:
Department of Electrical & Computer Engineering, Michigan State University, 2120 EB, East Lansing, MI 48824-1226, USA;Department of Computer Science & Engineering, Michigan State University, 2120 EB, East Lansing, MI 48824-1226, USA;Department of Biomedical Engineering, Johns Hopkins University, MD, USA
Venue:
Neurocomputing
Year:
2007

Citing 2
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Abstract

A nonparametric approach is proposed to identify clusters of functionally interdependent neurons, independent of the time scale at which they are maximally correlated. The neural point processes are represented in a N-dimensional scale space using the Haar wavelet transform. A similarity measure between any given pair of neurons is defined in the scale space. Clusters of ''similar'' neurons are identified by first reducing the N-dimensional scale space representation using principal components to obtain a Q-dimensional space. The weighted principal components are subsequently used to connect each neuron to the others in a graph representation. A probabilistic spectral clustering algorithm is used to perform graph partitioning by maximizing cluster compactness. Performance is compared to that of the k-means and the expectation-maximization algorithms for 120 neurons with time-varying intensity functions consisting of spontaneous background activity and phased response elicited at distinct time scales.