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IJCNN
2007
IEEE

Spectral Clustering of Synchronous Spike Trains

10 years 5 months ago
Spectral Clustering of Synchronous Spike Trains
— In this paper a clustering algorithm that learns the groups of synchronized spike trains directly from data is proposed. Clustering of spike trains based on the presence of synchronous neural activity is of high relevance in neurophysiological studies. In this context such activity is thought to be associated with functional structures in the brain. In addition, clustering has the potential to analyze large volumes of data. The algorithm couples a distance between two spike trains recently proposed in the literature with spectral clustering. Finally, the algorithm is illustrated in sets of computer generated spike trains and analyzed for the dependence on its parameters and accuracy with respect to features of interest.
António R. C. Paiva, Sudhir Rao, Il Park, J
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where IJCNN
Authors António R. C. Paiva, Sudhir Rao, Il Park, Jose C. Principe
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