BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

203 related articles for article (PubMed ID: 24922500)

  • 1. A semiparametric Bayesian model for detecting synchrony among multiple neurons.
    Shahbaba B; Zhou B; Lan S; Ombao H; Moorman D; Behseta S
    Neural Comput; 2014 Sep; 26(9):2025-51. PubMed ID: 24922500
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Analyzing short-term noise dependencies of spike-counts in macaque prefrontal cortex using copulas and the flashlight transformation.
    Onken A; Grünewälder S; Munk MH; Obermayer K
    PLoS Comput Biol; 2009 Nov; 5(11):e1000577. PubMed ID: 19956759
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Efficient Markov chain Monte Carlo methods for decoding neural spike trains.
    Ahmadian Y; Pillow JW; Paninski L
    Neural Comput; 2011 Jan; 23(1):46-96. PubMed ID: 20964539
    [TBL] [Abstract][Full Text] [Related]  

  • 4. An application of reversible-jump Markov chain Monte Carlo to spike classification of multi-unit extracellular recordings.
    Nguyen DP; Frank LM; Brown EN
    Network; 2003 Feb; 14(1):61-82. PubMed ID: 12617059
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Sequential Monte Carlo point-process estimation of kinematics from neural spiking activity for brain-machine interfaces.
    Wang Y; Paiva AR; Príncipe JC; Sanchez JC
    Neural Comput; 2009 Oct; 21(10):2894-930. PubMed ID: 19548797
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Efficient, adaptive estimation of two-dimensional firing rate surfaces via Gaussian process methods.
    Rad KR; Paninski L
    Network; 2010; 21(3-4):142-68. PubMed ID: 21138363
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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; 26(8):1519-41. PubMed ID: 24877730
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation.
    Linderman SW; Johnson MJ; Wilson MA; Chen Z
    J Neurosci Methods; 2016 Apr; 263():36-47. PubMed ID: 26854398
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Detection of hidden structures in nonstationary spike trains.
    Takiyama K; Okada M
    Neural Comput; 2011 May; 23(5):1205-33. PubMed ID: 21299427
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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; 8(3):e1002385. PubMed ID: 22412358
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Bayesian population decoding of motor cortical activity using a Kalman filter.
    Wu W; Gao Y; Bienenstock E; Donoghue JP; Black MJ
    Neural Comput; 2006 Jan; 18(1):80-118. PubMed ID: 16354382
    [TBL] [Abstract][Full Text] [Related]  

  • 12. An overview of Bayesian methods for neural spike train analysis.
    Chen Z
    Comput Intell Neurosci; 2013; 2013():251905. PubMed ID: 24348527
    [TBL] [Abstract][Full Text] [Related]  

  • 13. The copula approach to characterizing dependence structure in neural populations.
    Jenison RL
    Chin J Physiol; 2010 Dec; 53(6):373-81. PubMed ID: 21793349
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Generation of spatiotemporally correlated spike trains and local field potentials using a multivariate autoregressive process.
    Gutnisky DA; Josić K
    J Neurophysiol; 2010 May; 103(5):2912-30. PubMed ID: 20032244
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A hidden Markov model for decoding and the analysis of replay in spike trains.
    Box M; Jones MW; Whiteley N
    J Comput Neurosci; 2016 Dec; 41(3):339-366. PubMed ID: 27624733
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Detection of bursts in extracellular spike trains using hidden semi-Markov point process models.
    Tokdar S; Xi P; Kelly RC; Kass RE
    J Comput Neurosci; 2010 Aug; 29(1-2):203-212. PubMed ID: 19697116
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Bayesian semiparametric copula estimation with application to psychiatric genetics.
    Rosen O; Thompson WK
    Biom J; 2015 May; 57(3):468-84. PubMed ID: 25664559
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Generating spike trains with specified correlation coefficients.
    Macke JH; Berens P; Ecker AS; Tolias AS; Bethge M
    Neural Comput; 2009 Feb; 21(2):397-423. PubMed ID: 19196233
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Bayesian variable selection for non-Gaussian responses: a marginally calibrated copula approach.
    Klein N; Smith MS
    Biometrics; 2021 Sep; 77(3):809-823. PubMed ID: 32818303
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Controlling spike timing and synchrony in oscillatory neurons.
    Stigen T; Danzl P; Moehlis J; Netoff T
    J Neurophysiol; 2011 May; 105(5):2074-82. PubMed ID: 21586672
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.