BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

472 related articles for article (PubMed ID: 11802915)

  • 1. 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; 14(2):325-46. PubMed ID: 11802915
    [TBL] [Abstract][Full Text] [Related]  

  • 2. An adjustment to the time-rescaling method for application to short-trial spike train data.
    Wiener MC
    Neural Comput; 2003 Nov; 15(11):2565-76. PubMed ID: 14577854
    [TBL] [Abstract][Full Text] [Related]  

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

  • 4. Estimating a state-space model from point process observations.
    Smith AC; Brown EN
    Neural Comput; 2003 May; 15(5):965-91. PubMed ID: 12803953
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A spike-train probability model.
    Kass RE; Ventura V
    Neural Comput; 2001 Aug; 13(8):1713-20. PubMed ID: 11506667
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Direct estimation of inhomogeneous Markov interval models of spike trains.
    Wójcik DK; Mochol G; Jakuczun W; Wypych M; Waleszczyk WJ
    Neural Comput; 2009 Aug; 21(8):2105-13. PubMed ID: 19538090
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Construction and analysis of non-Poisson stimulus-response models of neural spiking activity.
    Barbieri R; Quirk MC; Frank LM; Wilson MA; Brown EN
    J Neurosci Methods; 2001 Jan; 105(1):25-37. PubMed ID: 11166363
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A comparison of descriptive models of a single spike train by information-geometric measure.
    Nakahara H; Amari S; Richmond BJ
    Neural Comput; 2006 Mar; 18(3):545-68. PubMed ID: 16483407
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Discrete time rescaling theorem: determining goodness of fit for discrete time statistical models of neural spiking.
    Haslinger R; Pipa G; Brown E
    Neural Comput; 2010 Oct; 22(10):2477-506. PubMed ID: 20608868
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Computing confidence intervals for point process models.
    Sarma SV; Nguyen DP; Czanner G; Wirth S; Wilson MA; Suzuki W; Brown EN
    Neural Comput; 2011 Nov; 23(11):2731-45. PubMed ID: 21851280
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A common goodness-of-fit framework for neural population models using marked point process time-rescaling.
    Tao L; Weber KE; Arai K; Eden UT
    J Comput Neurosci; 2018 Oct; 45(2):147-162. PubMed ID: 30298220
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.
    Truccolo W; Eden UT; Fellows MR; Donoghue JP; Brown EN
    J Neurophysiol; 2005 Feb; 93(2):1074-89. PubMed ID: 15356183
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Unbiased estimation of precise temporal correlations between spike trains.
    Stark E; Abeles M
    J Neurosci Methods; 2009 Apr; 179(1):90-100. PubMed ID: 19167428
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Estimating the temporal interval entropy of neuronal discharge.
    Reeke GN; Coop AD
    Neural Comput; 2004 May; 16(5):941-70. PubMed ID: 15070505
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Change-point analysis of neuron spike train data.
    Bélisle P; Joseph L; MacGibbon B; Wolfson DB; du Berger R
    Biometrics; 1998 Mar; 54(1):113-23. PubMed ID: 9544510
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A reproducing kernel Hilbert space framework for spike train signal processing.
    Paiva AR; Park I; Príncipe JC
    Neural Comput; 2009 Feb; 21(2):424-49. PubMed ID: 19431265
    [TBL] [Abstract][Full Text] [Related]  

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

  • 18. A continuous entropy rate estimator for spike trains using a K-means-based context tree.
    Lin TW; Reeke GN
    Neural Comput; 2010 Apr; 22(4):998-1024. PubMed ID: 19922298
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Statistical models for neural encoding, decoding, and optimal stimulus design.
    Paninski L; Pillow J; Lewi J
    Prog Brain Res; 2007; 165():493-507. PubMed ID: 17925266
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Gaussian process approach to spiking neurons for inhomogeneous Poisson inputs.
    Amemori KI; Ishii S
    Neural Comput; 2001 Dec; 13(12):2763-97. PubMed ID: 11705410
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 24.