Non-parametric change point detection for spike trains

Thiago Mosqueiro; Martin Strube-Bloss; Rafael Tuma; Reynaldo Pinto; Brian Smith; Ramon Huerta

doi:10.1109/CISS.2016.7460561

Non-parametric change point detection for spike trains

Thiago Mosqueiro, Martin Strube-Bloss, Rafael Tuma, Reynaldo Pinto, Brian Smith, Ramon Huerta

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

Two techniques of non-parametric change point detection are applied to two different neuroscience datasets. In the first dataset, we show how the multivariate non-parametric change point detection can precisely estimate reaction times to input stimulation in the olfactory system using joint information of spike trains from several neurons. In the second example, we propose to analyze communication and sequence coding using change point formalism as a time segmentation of homogeneous pieces of information, revealing cues to elucidate directionality of the communication in electric fish. We are also sharing our software implementation Chapolins at GitHub.

Original language	English (US)
Title of host publication	2016 50th Annual Conference on Information Systems and Sciences, CISS 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	545-550
Number of pages	6
ISBN (Electronic)	9781467394574
DOIs	https://doi.org/10.1109/CISS.2016.7460561
State	Published - Apr 26 2016
Event	50th Annual Conference on Information Systems and Sciences, CISS 2016 - Princeton, United States Duration: Mar 16 2016 → Mar 18 2016

Publication series

Name	2016 50th Annual Conference on Information Systems and Sciences, CISS 2016

Other

Other	50th Annual Conference on Information Systems and Sciences, CISS 2016
Country/Territory	United States
City	Princeton
Period	3/16/16 → 3/18/16

Keywords

Communication
Electric fish
Non-parametric
Olfaction

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems

Access to Document

10.1109/CISS.2016.7460561

Cite this

Mosqueiro, T., Strube-Bloss, M., Tuma, R., Pinto, R., Smith, B., & Huerta, R. (2016). Non-parametric change point detection for spike trains. In 2016 50th Annual Conference on Information Systems and Sciences, CISS 2016 (pp. 545-550). [7460561] (2016 50th Annual Conference on Information Systems and Sciences, CISS 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS.2016.7460561

Non-parametric change point detection for spike trains. / Mosqueiro, Thiago; Strube-Bloss, Martin; Tuma, Rafael et al.

2016 50th Annual Conference on Information Systems and Sciences, CISS 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 545-550 7460561 (2016 50th Annual Conference on Information Systems and Sciences, CISS 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mosqueiro, T, Strube-Bloss, M, Tuma, R, Pinto, R, Smith, B & Huerta, R 2016, Non-parametric change point detection for spike trains. in 2016 50th Annual Conference on Information Systems and Sciences, CISS 2016., 7460561, 2016 50th Annual Conference on Information Systems and Sciences, CISS 2016, Institute of Electrical and Electronics Engineers Inc., pp. 545-550, 50th Annual Conference on Information Systems and Sciences, CISS 2016, Princeton, United States, 3/16/16. https://doi.org/10.1109/CISS.2016.7460561

Mosqueiro T, Strube-Bloss M, Tuma R, Pinto R, Smith B, Huerta R. Non-parametric change point detection for spike trains. In 2016 50th Annual Conference on Information Systems and Sciences, CISS 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 545-550. 7460561. (2016 50th Annual Conference on Information Systems and Sciences, CISS 2016). doi: 10.1109/CISS.2016.7460561

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abstract = "Two techniques of non-parametric change point detection are applied to two different neuroscience datasets. In the first dataset, we show how the multivariate non-parametric change point detection can precisely estimate reaction times to input stimulation in the olfactory system using joint information of spike trains from several neurons. In the second example, we propose to analyze communication and sequence coding using change point formalism as a time segmentation of homogeneous pieces of information, revealing cues to elucidate directionality of the communication in electric fish. We are also sharing our software implementation Chapolins at GitHub.",

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Non-parametric change point detection for spike trains

Abstract

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Keywords

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