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Computation and Neural Systems

  • Book
  • © 1993

Overview

Editors:
  1. Frank H. Eeckman

    1. Lawrence Livermore National Laboratory, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. James M. Bower

    1. California Institute of Technology, USA

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    You can also search for this editor in PubMed   Google Scholar

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Table of contents (80 chapters)

  1. Front Matter

    Pages i-xii
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  2. Analysis and Modeling Tools and Techniques

    1. Section 1 Analysis

      1. Self-Organising Ion Channel Densities: The Rationale for ‘Anti-Hebb’
        • Anthony J. Bell
        Pages 3-7
      2. A Modified Hodgkin-Huxley Spiking Model with Continuous Spiking Output
        • Frédéric E. Theunissen, Frank H. Eeckman, John P. Miller
        Pages 9-17
      3. The Neurone as a Nonlinear System: A Single Compartment Study
        • P. G. Hearne, J. Wray, D. J. Sanders, E. Agar, G. G. R. Green
        Pages 19-23
      4. Unsupervised Hebbian Learning and the Shape of the Neuron Activation Function
        • Jonathan L. Shapiro, Adam Prügel-Bennett
        Pages 25-29
      5. A Method for Estimating the Neural Input to a Neuron using the Ionic Current Model
        • Yoshimi Kamiyama, Hiroyuki Ishii, Shiro Usui
        Pages 31-35
      6. A Model of a Thalamic Relay Cell Incorporating Voltage-Clamp Data on Ionic Conductances
        • Diana K. Smetters
        Pages 37-41
      7. An Entropy Measure for Revealing Deterministic Structure in Spike Train Data
        • Garrett T. Kenyon, David C. Tam
        Pages 43-47
      8. A Multi-Neuronal Vectorial Phase-Space Analysis for Detecting Dynamical Interactions in Firing Patterns of Biological Neural Networks
        • David C. Tam
        Pages 49-53
      9. The ‘Ideal Homunculus’: Statistical Inference from Neural Population Responses
        • Peter Földiák
        Pages 55-60
      10. The Effect of Slow Synaptic Coupling on Populations of Spiking Neurons
        • C. A. van Vreeswijk, L. F. Abbott, A. Treves
        Pages 61-66
      11. Neural Geometry Revealed by Neurocomputer Analysis of Multi-Unit Recordings
        • Andras J. Pellionisz, David L. Tomko, James R. Bloedel
        Pages 67-71
      12. Signal Delay in Passive Dendritic Trees
        • Hagai Agmon-Snir, Idan Segev
        Pages 73-77

    2. Section 2 Modeling

      1. Matching Neural Models to Experiment
        • William R. Foster, Julian F. R. Paton, John J. Hopfield, Lyle H. Ungar, James S. Schwaber
        Pages 81-88
      2. Neuronal Model Predicts Reponses of the Rat Baroreflex
        • James S. Schwaber, Julian F. R. Paton, Robert F. Rogers, Eliza B. Graves
        Pages 89-96
      3. Simulations of Synaptic Integration in Neocortical Pyramidal Cells
        • Paul C. Bush, Terrence J. Sejnowski
        Pages 97-101
      4. Modeling Stimulus Specific Habituation: The Role of the Primordial Hippocampus
        • DeLiang Wang
        Pages 103-107
      5. Effects of Single Channel Kinetics upon Transmembrane Voltage Dynamics
        • Adam F. Strassberg, Louis J. DeFelice
        Pages 109-113
      6. An Object-Oriented Paradigm for the Design of Realistic Neural Simulators
        • David C. Tam, R. Kent Hutson
        Pages 115-119
      7. Neural Network Simulation in a CSP Language for Multicomputers
        • F. Bini, M. Mastroianni, S. Russo, G. Ventre
        Pages 121-126
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Keywords

About this book

Computational neuroscience is best defined by its focus on understanding the nervous systems as a computational device rather than by a particular experimental technique. Accordinlgy, while the majority of the papers in this book describe analysis and modeling efforts, other papers describe the results of new biological experiments explicitly placed in the context of computational issues. The distribution of subjects in Computation and Neural Systems reflects the current state of the field. In addition to the scientific results presented here, numerous papers also describe the ongoing technical developments that are critical for the continued growth of computational neuroscience.
Computation and Neural Systems includes papers presented at the First Annual Computation and Neural Systems meeting held in San Francisco, CA, July 26--29, 1992.

Editors and Affiliations

  • Lawrence Livermore National Laboratory, USA

    Frank H. Eeckman

  • California Institute of Technology, USA

    James M. Bower

Bibliographic Information

  • Book Title: Computation and Neural Systems

  • Editors: Frank H. Eeckman, James M. Bower

  • DOI: https://doi.org/10.1007/978-1-4615-3254-5

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 1993

  • Hardcover ISBN: 978-0-7923-9349-8Published: 31 July 1993

  • Softcover ISBN: 978-1-4613-6431-3Published: 13 September 2013

  • eBook ISBN: 978-1-4615-3254-5Published: 06 December 2012

  • Edition Number: 1

  • Number of Pages: XIII, 539

  • Topics: Artificial Intelligence, Complex Systems, Neurosciences, Statistical Physics and Dynamical Systems

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