Neural computation

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  Año 1998, Vol. 10, Número 5

  • Dynamics of membrane excitability determine interspike interval variability: A link between

    Boris S. Gutkin, Bard Ermentrout

    págs. 1047-1065

    

  • Dynamics of Membrane Excitability Determine Interspike Interval Variability: A Link Between Spike Generation Mechanisms and Cortical Spike Train Statistics

    G. Bard Ermentrout, Boris S. Gutkin

    págs. 1047-1065

    

  • Dynamics of Membrane Excitability Determine Interspike Interval Variability: A Link Between Spike Generation Mechanisms and Cortical Spike Train Statistics

    pág. 1047

    

  • Correction to proof that recurrent neural networks can robustly recognize only regular languages

    Michael Casey

    págs. 1067-1069

    

  • Correction to Proof That Recurrent Neural Networks Can Robustly Recognize Only Regular Languages

    Michael Casey

    págs. 1067-1069

    

  • Correction to Proof That Recurrent Neural Networks Can Robustly Recognize Only Regular Languages

    pág. 1067

    

  • On the Effect of Analog Noise in Discrete-Time Analog Computations

    Wolfgang Maass, Pekka Orponen

    págs. 1071-1095

    

  • On the effect of analog noise in discrete-time analog computations

    Pekka Orponen, Wolfgang Maass

    págs. 1071-1095

    

  • On the Effect of Analog Noise in Discrete-Time Analog Computations

    Pekka Orponen, Wolfgang Maass

    pág. 1071

    

  • Category learning through multimodality sensing

    Dana H. Ballard, Virginia R. de Sa

    págs. 1097-1117

    

  • Category Learning Through Multimodality Sensing

    Dana H. Ballard, Virginia R. De Sa

    págs. 1097-1117

    

  • Category Learning Through Multimodality Sensing

    Virginia R. De Sa, Dana H. Ballard

    pág. 1097

    

  • A hierarchical model of binocular rivalry

    Peter Dayan

    págs. 1119-1135

    

  • A Hierarchical Model of Binocular Rivalry

    Peter Dayan

    págs. 1119-1135

    

  • A Hierarchical Model of Binocular Rivalry

    pág. 1119

    

  • Efficient learning in Boltzmann machines using linear response theory

    F.B. Rodriguez, Hilbert J. Kappen

    págs. 1137-1156

    

  • Efficient Learning in Boltzmann Machines Using Linear Response Theory

    F.B. Rodriguez, Hilbert J. Kappen

    págs. 1137-1156

    

  • Efficient Learning in Boltzmann Machines Using Linear Response Theory

    Hilbert J. Kappen, F.B. Rodriguez

    pág. 1137

    

  • A learning theorem for networks at detailed stochastic equilibrium

    Javier R. Movellan

    págs. 1157-1178

    

  • A Learning Theorem for Networks at Detailed Stochastic Equilibrium

    Javier R. Movellan

    págs. 1157-1178

    

  • A Learning Theorem for Networks at Detailed Stochastic Equilibrium

    pág. 1157

    

  • Asymmetric dynamics in optimal variance adaptation

    Anthony Zador, Michael DeWeese

    págs. 1179-1202

    

  • Asymmetric Dynamics in Optimal Variance Adaptation

    Michael DeWeese, Anthony Zador

    págs. 1179-1202

    

  • Asymmetric Dynamics in Optimal Variance Adaptation

    Michael DeWeese, Anthony Zador

    pág. 1179

    

  • Computation with infinite neural networks

    Christopher K. I. Williams

    págs. 1203-1216

    

  • Computation with Infinite Neural Networks

    Christopher K. I. Williams

    págs. 1203-1216

    

  • Computation with Infinite Neural Networks

    pág. 1203

    

  • Bayesian radial basis functions of variable dimension

    B. K. Mallick, Christopher C. Holmes

    págs. 1217-1233

    

  • Bayesian Radial Basis Functions of Variable Dimension

    B. K. Mallick, Christopher C. Holmes

    págs. 1217-1233

    

  • Bayesian Radial Basis Functions of Variable Dimension

    pág. 1217