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 proceedingConference 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 languageEnglish (US)
Title of host publication2016 50th Annual Conference on Information Systems and Sciences, CISS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages545-550
Number of pages6
ISBN (Electronic)9781467394574
DOIs
StatePublished - Apr 26 2016
Event50th Annual Conference on Information Systems and Sciences, CISS 2016 - Princeton, United States
Duration: Mar 16 2016Mar 18 2016

Other

Other50th Annual Conference on Information Systems and Sciences, CISS 2016
CountryUnited States
CityPrinceton
Period3/16/163/18/16

Keywords

  • Communication
  • Electric fish
  • Non-parametric
  • Olfaction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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  • 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] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS.2016.7460561