کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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408484 | 679031 | 2007 | 7 صفحه PDF | دانلود رایگان |

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.
Journal: Neurocomputing - Volume 70, Issues 10–12, June 2007, Pages 1728–1734