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Proceedings Paper

Data clustering via temporal segmentation of spiking neurons
Author(s): Irit Opher; David Horn
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Paper Abstract

We present a novel method for data clustering using temporal segmentation of spiking neurons. We use arrays of neurons whose pulse coupled interactions reflect the internal structure of the data set. The dynamical development of this system leads to temporal grouping of neurons that belong to the same cluster, while different clusters fire at different times. Grouping is achieved via two mechanisms: intra cluster synchrony and desynchronization between clusters. The former is induced by either instantaneous excitatory connections or delayed inhibitory ones, and the latter is induced by instantaneous inhibitory competition. We apply our method to a synthetic sum of gaussians and to the iris data set, demonstrating its capabilities.

Paper Details

Date Published: 22 March 1999
PDF: 9 pages
Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); doi: 10.1117/12.343070
Show Author Affiliations
Irit Opher, Tel Aviv Univ. (Israel)
David Horn, Tel Aviv Univ. (Israel)


Published in SPIE Proceedings Vol. 3728:
Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks
Thomas Lindblad; Mary Lou Padgett; Jason M. Kinser, Editor(s)

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