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Journal Abstract Search

213 related items for PubMed ID: 27008191

  • 1. Flexible models for spike count data with both over- and under- dispersion.
    Stevenson IH.
    J Comput Neurosci; 2016 Aug; 41(1):29-43. PubMed ID: 27008191
    [Abstract] [Full Text] [Related]

  • 2. Modeling stimulus-dependent variability improves decoding of population neural responses.
    Ghanbari A, Lee CM, Read HL, Stevenson IH.
    J Neural Eng; 2019 Oct 25; 16(6):066018. PubMed ID: 31404915
    [Abstract] [Full Text] [Related]

  • 3. Dynamic Modeling of Spike Count Data With Conway-Maxwell Poisson Variability.
    Wei G, Stevenson IH.
    Neural Comput; 2023 Jun 12; 35(7):1187-1208. PubMed ID: 37187169
    [Abstract] [Full Text] [Related]

  • 4. Bayesian estimation of stimulus responses in Poisson spike trains.
    Lehky SR.
    Neural Comput; 2004 Jul 12; 16(7):1325-43. PubMed ID: 15165392
    [Abstract] [Full Text] [Related]

  • 5. Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability.
    Charles AS, Park M, Weller JP, Horwitz GD, Pillow JW.
    Neural Comput; 2018 Apr 12; 30(4):1012-1045. PubMed ID: 29381442
    [Abstract] [Full Text] [Related]

  • 6. Including long-range dependence in integrate-and-fire models of the high interspike-interval variability of cortical neurons.
    Jackson BS.
    Neural Comput; 2004 Oct 12; 16(10):2125-95. PubMed ID: 15333210
    [Abstract] [Full Text] [Related]

  • 7. Application of the Conway-Maxwell-Poisson generalized linear model for analyzing motor vehicle crashes.
    Lord D, Guikema SD, Geedipally SR.
    Accid Anal Prev; 2008 May 12; 40(3):1123-34. PubMed ID: 18460381
    [Abstract] [Full Text] [Related]

  • 8. Using Tweedie distributions for fitting spike count data.
    Moshitch D, Nelken I.
    J Neurosci Methods; 2014 Mar 30; 225():13-28. PubMed ID: 24440773
    [Abstract] [Full Text] [Related]

  • 9. Distortion of neural signals by spike coding.
    Goldberg DH, Andreou AG.
    Neural Comput; 2007 Oct 30; 19(10):2797-839. PubMed ID: 17716013
    [Abstract] [Full Text] [Related]

  • 10. Linear-nonlinear-time-warp-poisson models of neural activity.
    Lawlor PN, Perich MG, Miller LE, Kording KP.
    J Comput Neurosci; 2018 Dec 30; 45(3):173-191. PubMed ID: 30294750
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  • 12. Decoding spike trains instant by instant using order statistics and the mixture-of-Poissons model.
    Wiener MC, Richmond BJ.
    J Neurosci; 2003 Mar 15; 23(6):2394-406. PubMed ID: 12657699
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  • 14. Testing the odds of inherent vs. observed overdispersion in neural spike counts.
    Taouali W, Benvenuti G, Wallisch P, Chavane F, Perrinet LU.
    J Neurophysiol; 2016 Jan 01; 115(1):434-44. PubMed ID: 26445864
    [Abstract] [Full Text] [Related]

  • 15. Multiscale analysis of neural spike trains.
    Ramezan R, Marriott P, Chenouri S.
    Stat Med; 2014 Jan 30; 33(2):238-56. PubMed ID: 23996238
    [Abstract] [Full Text] [Related]

  • 16. Information transmission using non-poisson regular firing.
    Koyama S, Omi T, Kass RE, Shinomoto S.
    Neural Comput; 2013 Apr 30; 25(4):854-76. PubMed ID: 23339613
    [Abstract] [Full Text] [Related]

  • 17. Application of the Hyper-Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes.
    Khazraee SH, Sáez-Castillo AJ, Geedipally SR, Lord D.
    Risk Anal; 2015 May 30; 35(5):919-30. PubMed ID: 25385093
    [Abstract] [Full Text] [Related]

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  • 19. The time-rescaling theorem and its application to neural spike train data analysis.
    Brown EN, Barbieri R, Ventura V, Kass RE, Frank LM.
    Neural Comput; 2002 Feb 30; 14(2):325-46. PubMed ID: 11802915
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