Neural computation

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  Año 2003, Vol. 15, Número 8

  • Computation in a Single Neuron: Hodgkin and Huxley Revisited

    William Bialek, Blaise Agüera y Arcas, Adrienne L. Fairhall

    págs. 1715-1749

    

  • Learning the Nonlinearity of Neurons from Natural Visual Stimuli

    Peter Konig, Christoph Kayser, Konrad P. Körding

    págs. 1751-1759

    

  • Analytic Expressions for Rate and CV of a Type I Neuron Driven by White Gaussian Noise

    Adi Bulsara, André Longtin, Benjamin Lindner

    págs. 1760-1787

    

  • What Causes a Neuron to Spike?

    Adrienne L. Fairhall, Blaise Agüera y Arcas

    págs. 1789-1807

    

  • Rate Models for Conductance-Based Cortical Neuronal Networks

    Oren Shriki, Haim Sompolinsky, David Hansel

    págs. 1809-1841

    

  • Neural Representation of Probabilistic Information

    C. H. Anderson, J. W. Clark, M. J. Barber

    págs. 1843-1864

    

  • Learning the Gestalt Rule of Collinearity from Object Motion

    Carsten Prodöhl, Christoph von der Malsburg, Rolf P. Wurtz

    págs. 1865-1896

    

  • Recurrent Neural Networks with Small Weights Implement Definite Memory Machines

    Barbara Hammer, Peter Tino

    págs. 1897-1929

    

  • Architectural Bias in Recurrent Neural Networks: Fractal Analysis

    Peter Tino, Barbara Hammer

    págs. 1931-1957

    

  • An Effective Bayesian Neural Network Classifier with a Comparison Study to Support Vector Machine

    Faming Liang

    págs. 1959-1989

    

  • Variational Bayesian Learning of ICA with Missing Data

    Terrence J. Sejnowski, Te-Won Lee, Kwokleung Chan

    págs. 1991-2011