Assessment of synchrony in multiple neural spike trains using loglinear point process models

10.1214/10-AOAS429

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Main Authors: Kass, R.E., Kelly, R.C., Loh, W.-L.
Other Authors: STATISTICS & APPLIED PROBABILITY
Format: Article
Published: 2014
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Online Access:http://scholarbank.nus.edu.sg/handle/10635/105013
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Institution: National University of Singapore
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spelling sg-nus-scholar.10635-1050132024-11-13T15:37:43Z Assessment of synchrony in multiple neural spike trains using loglinear point process models Kass, R.E. Kelly, R.C. Loh, W.-L. STATISTICS & APPLIED PROBABILITY Discrete-time approximation Loglinear model Marked process Nonstationary point process Simultaneous events Spike train Synchrony detection 10.1214/10-AOAS429 Annals of Applied Statistics 5 2 B 1262-1292 2014-10-28T05:10:11Z 2014-10-28T05:10:11Z 2011-06 Article Kass, R.E., Kelly, R.C., Loh, W.-L. (2011-06). Assessment of synchrony in multiple neural spike trains using loglinear point process models. Annals of Applied Statistics 5 (2 B) : 1262-1292. ScholarBank@NUS Repository. https://doi.org/10.1214/10-AOAS429 19326157 http://scholarbank.nus.edu.sg/handle/10635/105013 000295453900007 Scopus
institution National University of Singapore
building NUS Library
continent Asia
country Singapore
Singapore
content_provider NUS Library
collection ScholarBank@NUS
topic Discrete-time approximation
Loglinear model
Marked process
Nonstationary point process
Simultaneous events
Spike train
Synchrony detection
spellingShingle Discrete-time approximation
Loglinear model
Marked process
Nonstationary point process
Simultaneous events
Spike train
Synchrony detection
Kass, R.E.
Kelly, R.C.
Loh, W.-L.
Assessment of synchrony in multiple neural spike trains using loglinear point process models
description 10.1214/10-AOAS429
author2 STATISTICS & APPLIED PROBABILITY
author_facet STATISTICS & APPLIED PROBABILITY
Kass, R.E.
Kelly, R.C.
Loh, W.-L.
format Article
author Kass, R.E.
Kelly, R.C.
Loh, W.-L.
author_sort Kass, R.E.
title Assessment of synchrony in multiple neural spike trains using loglinear point process models
title_short Assessment of synchrony in multiple neural spike trains using loglinear point process models
title_full Assessment of synchrony in multiple neural spike trains using loglinear point process models
title_fullStr Assessment of synchrony in multiple neural spike trains using loglinear point process models
title_full_unstemmed Assessment of synchrony in multiple neural spike trains using loglinear point process models
title_sort assessment of synchrony in multiple neural spike trains using loglinear point process models
publishDate 2014
url http://scholarbank.nus.edu.sg/handle/10635/105013
_version_ 1821200433850351616