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Journal Abstract Search

155 related items for PubMed ID: 29381442

  • 1. 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; 30(4):1012-1045. PubMed ID: 29381442
    [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
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  • 3. Bayesian active learning of neural firing rate maps with transformed gaussian process priors.
    Park M, Weller JP, Horwitz GD, Pillow JW.
    Neural Comput; 2014 Aug 25; 26(8):1519-41. PubMed ID: 24877730
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  • 4. Flexible models for spike count data with both over- and under- dispersion.
    Stevenson IH.
    J Comput Neurosci; 2016 Aug 25; 41(1):29-43. PubMed ID: 27008191
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  • 5. Including long-range dependence in integrate-and-fire models of the high interspike-interval variability of cortical neurons.
    Jackson BS.
    Neural Comput; 2004 Oct 25; 16(10):2125-95. PubMed ID: 15333210
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  • 6. Fano factor estimation.
    Rajdl K, Lansky P.
    Math Biosci Eng; 2014 Feb 25; 11(1):105-23. PubMed ID: 24245675
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  • 7. Fitting a stochastic spiking model to neuronal current injection data.
    Shinomoto S.
    Neural Netw; 2010 Aug 25; 23(6):764-9. PubMed ID: 20478693
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  • 13. Efficient, adaptive estimation of two-dimensional firing rate surfaces via Gaussian process methods.
    Rad KR, Paninski L.
    Network; 2010 Aug 25; 21(3-4):142-68. PubMed ID: 21138363
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  • 14. Using Tweedie distributions for fitting spike count data.
    Moshitch D, Nelken I.
    J Neurosci Methods; 2014 Mar 30; 225():13-28. PubMed ID: 24440773
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  • 15. Dimensionality reduction in neural models: an information-theoretic generalization of spike-triggered average and covariance analysis.
    Pillow JW, Simoncelli EP.
    J Vis; 2006 Apr 28; 6(4):414-28. PubMed ID: 16889478
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  • 16. Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation.
    Kim H, Shinomoto S.
    Math Biosci Eng; 2014 Feb 28; 11(1):49-62. PubMed ID: 24245682
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  • 18. Nonlinear Modeling of Neural Interaction for Spike Prediction Using the Staged Point-Process Model.
    Qian C, Sun X, Zhang S, Xing D, Li H, Zheng X, Pan G, Wang Y.
    Neural Comput; 2018 Dec 28; 30(12):3189-3226. PubMed ID: 30314427
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