Clustering with spiking neurons

Irit Opher*, David Horn, Brigitte Quenet

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We present a novel neural method for data clustering using temporal segmentation of spiking neurons. Our clustering algorithm relies only on distances between data points. Each point is associated with a neuron, and the distances are used to determine the synaptic weights between those neurons. The dynamical development of this system leads to synchronous firing of neurons that belong to the same cluster, while different clusters fire at different times. Such dynamic behavior is called temporal segmentation. It is achieved via two mechanisms - intra cluster synchrony, induced by excitatory connections within each cluster, and desynchronization between clusters induced by inhibitory competition. We test our clustering method on the iris data set. For problems that do not have a unique clustering solution, we construct a pair-correlation matrix on the basis of multiple clustering solutions. By performing a second clustering algorithm, this time on the pair-correlation matrix, we are able to define second order clusters of the original distance matrix. This method is demonstrated on a biological data set.

Original languageEnglish
Title of host publicationIEE Conference Publication
Number of pages6
ISBN (Print)0852967217, 9780852967218
StatePublished - 1999
EventProceedings of the 1999 the 9th International Conference on 'Artificial Neural Networks (ICANN99)' - Edinburgh, UK
Duration: 7 Sep 199910 Sep 1999

Publication series

NameIEE Conference Publication
ISSN (Print)0537-9989


ConferenceProceedings of the 1999 the 9th International Conference on 'Artificial Neural Networks (ICANN99)'
CityEdinburgh, UK


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