The Neurobiology of Computation

1. Front Matter

   Pages i-xv

   PDF

2. Sub-cellular

   1. Front Matter

      Pages 1-1

      PDF

   2. A Mathematical Description of Quantal Analysis with Applications

      • Kenneth I. Blum, Marco A. P. Idiart

      Pages 3-7

   3. Fast Kinetic Models for Simulating AMPA, NMDA, GABA _A and GABA _B Receptors

      • Alain Destexhe, Zachary F. Mainen, Terrence J. Sejnowski

      Pages 9-14

   4. An Equilibrium Model of Biochemical Interactions Between Glutamatergic and Dopaminergic Signals in Striatal Neurons

      • Rolf Kötter

      Pages 15-20

   5. Simulation of the Molecular Mechanisms Regulating Neurotransmitter Release

      • Jim-Shih Liaw, Michel Baudry, Gilbert A. Chauvet, Theodore W. Berger

      Pages 21-26

   6. The Potassium A-Current and Low Firing Rates in Hodgkin-Huxley Models

      • Maureen E. Rush, John Rinzel

      Pages 27-32

   7. Apparent Diffusion Coefficient Estimation Errors from Using Ratio of Bound to Unbound Ca2+

      • James L. Winslow

      Pages 33-38

3. Cellular

   1. Front Matter

      Pages 39-39

      PDF

   2. Information Maintenance by Retinal Ganglion Cell Spikes

      • David A. August, William B. Levy

      Pages 41-46

   3. Modeling the Bursting Interneurons of the Lobster Cardiac Ganglion

      • Evyatar Av-Ron, Hanna Parnas, Lee A. Segel

      Pages 47-52

   4. Precision and Reliability of Neocortical Spike Trains in the Behaving Monkey

      • Wyeth Bair, Christof Koch

      Pages 53-58

   5. Summation and Division in V1 Simple Cells

      • Matteo Carandini, David J. Heeger

      Pages 59-65

   6. Qualitative Electrotonic Comparison of Three Classes of Hippocampal Neurons in the Rat

      • Nicholas T. Carnevale, Kenneth Y. Tsai, Brenda J. Claiborne, Thomas H. Brown

      Pages 67-72

   7. A New Spike Train Analysis Technique for Detecting Trends in the Firing Patterns of Neurons

      • Michelle A. Fitzurka, David C. Tam

      Pages 73-78

   8. A Mathematical Model of Retinal Photoreceptors

      • Ernest C. Hymel, Carey R. Murphey, Burgess N. Christensen

      Pages 79-85

   9. Short-Term Memory as a Single Cell Phenomenon

      • Marco A. P. Idiart, John E. Lisman

      Pages 87-92

   10. The Importance of Membrane Properties and Synaptic Location in Octopus Cells of the Mammalian Cochlear Nucleus: A Modeling Study

       • Kenneth L. Levy, Daryl R. Kipke

       Pages 93-98

   11. Hypertext Software for Modeling Neurons

       • Carey R. Murphey

       Pages 99-104

   12. The STEM cell: a computational model of biological neurons useful for spatio-tem-poral pattern recognition

       • Sean D. Murphy, Edward W. Kairiss

       Pages 105-110

This volume includes papers presented at the Third Annual Computation and Neural Systems meeting (CNS*94) held in Monterey California, July 21 - July 26, 1994. This collection includes 71 of the more than 100 papers presented at this year's meeting. Acceptance for meeting presentation was based on the peer review of preliminary papers by at least two referees. The papers in this volume were submitted in final form after the meeting. As represented by this volume, CNS meetings continue to expand in quality, size and breadth of focus as increasing numbers of neuroscientists are taking a computational approach to understanding nervous system function. The CNS meetings are intended to showcase the best of current research in computational neuroscience. As such the meeting is fundamentally focused on understanding the relationship between the structure of neIVOUS systems and their function. What is clear from the continued expansion of the CNS meetings is that computational approaches are increasingly being applied at all levels of neurobiological analysis. in an ever growing number of experimental preparations. and neural subsystems. Thus. experimental subjects range from crickets to primates; sensory systems range from vision to electroreception; experimental approaches range from realistic models of ion channels to the analysis of the information content of spike trains. For this reason, the eNS meetings represent an opportunity for computational neurobiologists to consider their research results in a much broader context than is usually possible.

• Chaos
• Neuromodulation
• artificial intelligence
• classification
• learning
• nonlinear dynamics
• optimization
• proving
• robot

• California Institute of Technology, USA

  James M. Bower

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