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Journal Abstract Search

293 related items for PubMed ID: 15003377

  • 1. Coding with noisy neurons: stability of tuning curve estimation strongly depends on the analysis method.
    Etzold A, Schwegler H, Eurich CW.
    J Neurosci Methods; 2004 Apr 30; 134(2):109-19. PubMed ID: 15003377
    [Abstract] [Full Text] [Related]

  • 2. Mean field theory for a balanced hypercolumn model of orientation selectivity in primary visual cortex.
    Lerchner A, Sterner G, Hertz J, Ahmadi M.
    Network; 2006 Jun 30; 17(2):131-50. PubMed ID: 16818394
    [Abstract] [Full Text] [Related]

  • 3. Effects of inhibitory gain and conductance fluctuations in a simple model for contrast-invariant orientation tuning in cat V1.
    Palmer SE, Miller KD.
    J Neurophysiol; 2007 Jul 30; 98(1):63-78. PubMed ID: 17507506
    [Abstract] [Full Text] [Related]

  • 4. Orientation tuning properties of simple cells in area V1 derived from an approximate analysis of nonlinear neural field models.
    Wennekers T.
    Neural Comput; 2001 Aug 30; 13(8):1721-47. PubMed ID: 11506668
    [Abstract] [Full Text] [Related]

  • 5. PSTH-based classification of sensory stimuli using ensembles of single neurons.
    Foffani G, Moxon KA.
    J Neurosci Methods; 2004 May 30; 135(1-2):107-20. PubMed ID: 15020095
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  • 7. Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations.
    Seriès P, Latham PE, Pouget A.
    Nat Neurosci; 2004 Oct 30; 7(10):1129-35. PubMed ID: 15452579
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  • 10. Relationship between contrast adaptation and orientation tuning in V1 and V2 of cat visual cortex.
    Crowder NA, Price NS, Hietanen MA, Dreher B, Clifford CW, Ibbotson MR.
    J Neurophysiol; 2006 Jan 30; 95(1):271-83. PubMed ID: 16192327
    [Abstract] [Full Text] [Related]

  • 11. Improved tracking of time-varying encoding properties of visual neurons by extended recursive least-squares.
    Lesica NA, Stanley GB.
    IEEE Trans Neural Syst Rehabil Eng; 2005 Jun 30; 13(2):194-200. PubMed ID: 16003899
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  • 13. Classification of stimuli based on stimulus-response curves and their variability.
    Lansky P, Pokora O, Rospars JP.
    Brain Res; 2008 Aug 15; 1225():57-66. PubMed ID: 18538308
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  • 14. Quantifying variability in neural responses and its application for the validation of model predictions.
    Hsu A, Borst A, Theunissen FE.
    Network; 2004 May 15; 15(2):91-109. PubMed ID: 15214701
    [Abstract] [Full Text] [Related]

  • 15. Tuning for shape dimensions in macaque inferior temporal cortex.
    Kayaert G, Biederman I, Op de Beeck HP, Vogels R.
    Eur J Neurosci; 2005 Jul 15; 22(1):212-24. PubMed ID: 16029211
    [Abstract] [Full Text] [Related]

  • 16. A simple cell model with dominating opponent inhibition for robust image processing.
    Hansen T, Neumann H.
    Neural Netw; 2004 Jul 15; 17(5-6):647-62. PubMed ID: 15288890
    [Abstract] [Full Text] [Related]

  • 17. Bottom-up and top-down dynamics in visual cortex.
    Schummers J, Sharma J, Sur M.
    Prog Brain Res; 2005 Jul 15; 149():65-81. PubMed ID: 16226577
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  • 18. Comparing acceleration and speed tuning in macaque MT: physiology and modeling.
    Price NS, Ono S, Mustari MJ, Ibbotson MR.
    J Neurophysiol; 2005 Nov 15; 94(5):3451-64. PubMed ID: 16079192
    [Abstract] [Full Text] [Related]

  • 19. Common-input models for multiple neural spike-train data.
    Kulkarni JE, Paninski L.
    Network; 2007 Dec 15; 18(4):375-407. PubMed ID: 17943613
    [Abstract] [Full Text] [Related]

  • 20. Statistical models for neural encoding, decoding, and optimal stimulus design.
    Paninski L, Pillow J, Lewi J.
    Prog Brain Res; 2007 Dec 15; 165():493-507. PubMed ID: 17925266
    [Abstract] [Full Text] [Related]

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