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Journal Abstract Search

115 related items for PubMed ID: 37187169

  • 21. Identification of time-varying neural dynamics from spike train data using multiwavelet basis functions.
    Xu S, Li Y, Guo Q, Yang XF, Chan RHM.
    J Neurosci Methods; 2017 Feb 15; 278():46-56. PubMed ID: 28062244
    [Abstract] [Full Text] [Related]

  • 22. Decoding neural spike trains: calculating the probability that a spike train and an external signal are related.
    Sanger TD.
    J Neurophysiol; 2002 Mar 15; 87(3):1659-63. PubMed ID: 11877538
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  • 23. A flexible approach to modelling over-, under- and equidispersed count data in IRT: The Two-Parameter Conway-Maxwell-Poisson Model.
    Beisemann M.
    Br J Math Stat Psychol; 2022 Nov 15; 75(3):411-443. PubMed ID: 35678959
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  • 24. Finite mixtures of mean-parameterized Conway-Maxwell-Poisson models.
    Zhan D, Young DS.
    Stat Pap (Berl); 2023 May 19; ():1-24. PubMed ID: 37360788
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  • 25. Poisson-like spiking in circuits with probabilistic synapses.
    Moreno-Bote R.
    PLoS Comput Biol; 2014 Jul 19; 10(7):e1003522. PubMed ID: 25032705
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  • 26. State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data.
    Shimazaki H, Amari S, Brown EN, Grün S.
    PLoS Comput Biol; 2012 Jul 19; 8(3):e1002385. PubMed ID: 22412358
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  • 28. Spike-train variability of auditory neurons in vivo: dynamic responses follow predictions from constant stimuli.
    Schaette R, Gollisch T, Herz AV.
    J Neurophysiol; 2005 Jun 19; 93(6):3270-81. PubMed ID: 15689392
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  • 32. Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons.
    Zhang WH, Wu S, Josić K, Doiron B.
    Nat Commun; 2023 Nov 04; 14(1):7074. PubMed ID: 37925497
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  • 33. Design strategies for dynamic closed-loop optogenetic neurocontrol in vivo.
    Bolus MF, Willats AA, Whitmire CJ, Rozell CJ, Stanley GB.
    J Neural Eng; 2018 Apr 04; 15(2):026011. PubMed ID: 29300002
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  • 34. Statistical assessment of time-varying dependency between two neurons.
    Ventura V, Cai C, Kass RE.
    J Neurophysiol; 2005 Oct 04; 94(4):2940-7. PubMed ID: 16160097
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  • 35. Neural population codes.
    Sanger TD.
    Curr Opin Neurobiol; 2003 Apr 04; 13(2):238-49. PubMed ID: 12744980
    [Abstract] [Full Text] [Related]

  • 36. Nonlinear dynamic modeling of spike train transformations for hippocampal-cortical prostheses.
    Song D, Chan RH, Marmarelis VZ, Hampson RE, Deadwyler SA, Berger TW.
    IEEE Trans Biomed Eng; 2007 Jun 04; 54(6 Pt 1):1053-66. PubMed ID: 17554824
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  • 37. Drawing inferences from Fano factor calculations.
    Eden UT, Kramer MA.
    J Neurosci Methods; 2010 Jun 30; 190(1):149-52. PubMed ID: 20416340
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  • 40. Multisensory Interactions Influence Neuronal Spike Train Dynamics in the Posterior Parietal Cortex.
    VanGilder P, Shi Y, Apker G, Dyson K, Buneo CA.
    PLoS One; 2016 Jun 30; 11(12):e0166786. PubMed ID: 28033334
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