Assessment of synchrony in multiple neural spike trains using loglinear point process models
10.1214/10-AOAS429
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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 |
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Discrete-time approximation Loglinear model Marked process Nonstationary point process Simultaneous events Spike train Synchrony detection |
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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 |
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10.1214/10-AOAS429 |
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STATISTICS & APPLIED PROBABILITY |
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STATISTICS & APPLIED PROBABILITY Kass, R.E. Kelly, R.C. Loh, W.-L. |
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Article |
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Kass, R.E. Kelly, R.C. Loh, W.-L. |
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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 |
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Assessment of synchrony in multiple neural spike trains using loglinear point process models |
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Assessment of synchrony in multiple neural spike trains using loglinear point process models |
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assessment of synchrony in multiple neural spike trains using loglinear point process models |
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2014 |
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http://scholarbank.nus.edu.sg/handle/10635/105013 |
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