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A dendrogram approach to the structure of spike trains

A dendrogram approach to the structure of spike trains Houghton BMC Neuroscience 2011, 12(Suppl 1):P153 http://www.biomedcentral.com/1471-2202/12/S1/P153 POSTER PRESENTATION Open Access A dendrogram approach to the structure of spike trains Conor Houghton From Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011 A novel mathematical description for the temporal http://neurodatabase.org. The data consist of single cell structure of spike trains is presented. This works by recordings from the nucleus of the solitary tract in rat mapping the spike train to a dendrogram produced by during presentation of taste stimuli. With a clear separa- hierarchical clustering. The branch-length structure of tion between the stimulus timescale and the temporal the dendrogram is equivalent to the distribution of scale of spiking, these data are chosen to present a kind inter-spike intervals, but morphological descriptions of of worst-case scenario for the proposal. Nonetheless, the dendrogram can be used to quantify other aspects when the real data is compared to artificial data pro- of the temporal structure of spike trains. In this way, it duced by shuffling the inter-spike intervals, the Mann– is shown that example sets of spike trains have more Whitney U test shows that real data has a significantly structure than is accounted for by the distribution of higher value of the morphological parameter introduced inter-spike intervals. The goal is to segregate the loose here. notion of “sparseness” into two different aspects of tem- poral structure, the distribution of inter-spike intervals Acknowledgements and the morphology of the dendrogram constructed Thanks to Patricia Di Lorenzo , Jonathan Victor and http://neurodatabase.org during clustering and to provide a tool for analyzing for making the data analyzed here available; Science Foundation Ireland for grant 08/RFP/MTH1280 and the James S McDonnell Foundation for an and comparing spike train properties. Understanding Human Cognition Scholar award. Roughly, the distribution of inter-spike intervals fails to describe the temporal granularity caused by the clus- Published: 18 July 2011 tering of spikes. The difficulty with quantifying this is in References deciding how close two spikes need to be for them to 1. Fionn Murtagh: An empirical study of the coeffcients for measuring the be agglomerated in clusters. The approach taken here is structure of hierarchic classification. In Data Analysis and Informatics III. to avoid that choice by performing a hierarchical clus- Amsterdam: Elsevier;E. Diday et al. 1984:385-393. tering: the spikes are agglomerated into larger and larger groups as a timescale is increased, this is illustrated in Figure 1. Quantifying morphological properties like the bushiness of the resulting dendrogram then describes the temporal structure of spike trains. In fact, hierarchi- cal clustering is of interest to computer scientists and this provides a number of candidate methods for quanti- fying the morphology of dendrograms, the one used here was introduced in [1] and scores dendrograms from zero, for the leggiest, to one, for the bushiest. This quantification is applied to an example spike- train data set described in [2] and made available on Figure 1 The spikes are clustered at different time scales to give an agglomerative dendrogram. Correspondence: houghton@maths.tcd.ie School of Mathematics, Trinity College Dublin, Dublin, Ireland Full list of author information is available at the end of the article © 2011 Houghton; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Houghton BMC Neuroscience 2011, 12(Suppl 1):P153 Page 2 of 2 http://www.biomedcentral.com/1471-2202/12/S1/P153 2. Di Lorenzo Patricia, Victor Jonathan: Taste Response Variability and Temporal Coding in the Nucleus of the Solitary Tract of the Rat. J Neurophysiology 2003, 90:1418-1431. doi:10.1186/1471-2202-12-S1-P153 Cite this article as: Houghton: A dendrogram approach to the structure of spike trains. BMC Neuroscience 2011 12(Suppl 1):P153. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BMC Neuroscience Springer Journals

A dendrogram approach to the structure of spike trains

BMC Neuroscience , Volume 12 (1) – Jul 18, 2011

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References (5)

Publisher
Springer Journals
Copyright
Copyright © 2011 by Houghton; licensee BioMed Central Ltd.
Subject
Biomedicine; Neurosciences; Neurobiology; Animal Models
eISSN
1471-2202
DOI
10.1186/1471-2202-12-S1-P153
Publisher site
See Article on Publisher Site

Abstract

Houghton BMC Neuroscience 2011, 12(Suppl 1):P153 http://www.biomedcentral.com/1471-2202/12/S1/P153 POSTER PRESENTATION Open Access A dendrogram approach to the structure of spike trains Conor Houghton From Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011 A novel mathematical description for the temporal http://neurodatabase.org. The data consist of single cell structure of spike trains is presented. This works by recordings from the nucleus of the solitary tract in rat mapping the spike train to a dendrogram produced by during presentation of taste stimuli. With a clear separa- hierarchical clustering. The branch-length structure of tion between the stimulus timescale and the temporal the dendrogram is equivalent to the distribution of scale of spiking, these data are chosen to present a kind inter-spike intervals, but morphological descriptions of of worst-case scenario for the proposal. Nonetheless, the dendrogram can be used to quantify other aspects when the real data is compared to artificial data pro- of the temporal structure of spike trains. In this way, it duced by shuffling the inter-spike intervals, the Mann– is shown that example sets of spike trains have more Whitney U test shows that real data has a significantly structure than is accounted for by the distribution of higher value of the morphological parameter introduced inter-spike intervals. The goal is to segregate the loose here. notion of “sparseness” into two different aspects of tem- poral structure, the distribution of inter-spike intervals Acknowledgements and the morphology of the dendrogram constructed Thanks to Patricia Di Lorenzo , Jonathan Victor and http://neurodatabase.org during clustering and to provide a tool for analyzing for making the data analyzed here available; Science Foundation Ireland for grant 08/RFP/MTH1280 and the James S McDonnell Foundation for an and comparing spike train properties. Understanding Human Cognition Scholar award. Roughly, the distribution of inter-spike intervals fails to describe the temporal granularity caused by the clus- Published: 18 July 2011 tering of spikes. The difficulty with quantifying this is in References deciding how close two spikes need to be for them to 1. Fionn Murtagh: An empirical study of the coeffcients for measuring the be agglomerated in clusters. The approach taken here is structure of hierarchic classification. In Data Analysis and Informatics III. to avoid that choice by performing a hierarchical clus- Amsterdam: Elsevier;E. Diday et al. 1984:385-393. tering: the spikes are agglomerated into larger and larger groups as a timescale is increased, this is illustrated in Figure 1. Quantifying morphological properties like the bushiness of the resulting dendrogram then describes the temporal structure of spike trains. In fact, hierarchi- cal clustering is of interest to computer scientists and this provides a number of candidate methods for quanti- fying the morphology of dendrograms, the one used here was introduced in [1] and scores dendrograms from zero, for the leggiest, to one, for the bushiest. This quantification is applied to an example spike- train data set described in [2] and made available on Figure 1 The spikes are clustered at different time scales to give an agglomerative dendrogram. Correspondence: houghton@maths.tcd.ie School of Mathematics, Trinity College Dublin, Dublin, Ireland Full list of author information is available at the end of the article © 2011 Houghton; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Houghton BMC Neuroscience 2011, 12(Suppl 1):P153 Page 2 of 2 http://www.biomedcentral.com/1471-2202/12/S1/P153 2. Di Lorenzo Patricia, Victor Jonathan: Taste Response Variability and Temporal Coding in the Nucleus of the Solitary Tract of the Rat. J Neurophysiology 2003, 90:1418-1431. doi:10.1186/1471-2202-12-S1-P153 Cite this article as: Houghton: A dendrogram approach to the structure of spike trains. BMC Neuroscience 2011 12(Suppl 1):P153. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit

Journal

BMC NeuroscienceSpringer Journals

Published: Jul 18, 2011

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