Prototype Construction for Clustering of Point Processes based on Imprecise Synchrony

DOI

10.2991/eusflat.2013.55How to use a DOI?

Keywords

clustering prototype distance point process spike train analysis temporal imprecision

Abstract

We consider the task to cluster realizations of point processes, that is, lists of points in time. Our guiding principle is that two such lists are the more similar, the more (approximately) synchronous points they contain. This task occurs in the analysis of parallel spike trains in neurobiology, where it arises from the desire to detect assemblies of neurons, which are characterized by the synchronous spiking activity they exhibit. While earlier approaches along similar lines employed mainly hierarchical agglomerative clustering, based on distance measures for spike trains, we try to make prototype-based clustering approaches (like (fuzzy-)c-means clustering) applicable by proposing a method to construct cluster prototypes. For this we draw on an idea that is inspired by mountain clustering. In addition, we adapt a method that was originally developed for outlier detection in order to actually single out relevant groups of related realizations of point processes in front of a background of noise, and thus to identify neuron assemblies in parallel spike train data.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Christian Borgelt
AU  - Christian Braune
PY  - 2013/08
DA  - 2013/08
TI  - Prototype Construction for Clustering of Point Processes based on Imprecise Synchrony
BT  - Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)
PB  - Atlantis Press
SP  - 387
EP  - 394
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2013.55
DO  - 10.2991/eusflat.2013.55
ID  - Borgelt2013/08
ER  -