Neural Systems: Analysis and Modeling

1. Front Matter

   Pages i-ix

   PDF

2. Analysis and Modeling Tools and Techniques

   1. Front Matter

      Pages 1-1

      PDF

   2. Analysis

      1. Optimal Real-Time Signal Processing in the Nervous System

         • William Bialek

         Pages 5-28

      2. Measuring the coding efficiency of sensory neurons

         • Fred Rieke, David Warland, William Bialek

         Pages 29-38

      3. Non-linear Analysis of Models for Biological Pattern Formation: Application to Ocular Dominance Stripes

         • M. J. Lyons, L. G. Harrison

         Pages 39-46

      4. A Hierarchical Sensory-Motor Architecture of Oscillating Cortical Area Subnetworks

         • Bill Baird, Frank Eeckman

         Pages 47-66

      5. A Computationally Efficient Spike Initiator Model that Produces a Wide Variety of Neural Responses

         • B. R. Parnas, E. R. Lewis

         Pages 67-75

      6. Linearization by Noise and/or Additional Shunting Current of a Modified FitzHugh Nagumo Spiking Model

         • Frédéric E. Theunissen, Frank H. Eeckman, John P. Miller

         Pages 77-91

   3. Modeling

      1. Genesis: a neuronal simulation system

         • Upinder S. Bhalla, James M. Bower

         Pages 95-102

      2. CAJAL - 91: A Biological Neural Network Simulator

         • Edward K. Blum, Peyvand M. Khademi, Kevin Chau, Patrick Leung, Xin Wang

         Pages 103-111

      3. Nodus: A User Friendly Neuron Simulator for Macintosh Computers

         • Erik De Schutter

         Pages 113-119

      4. NeMoSys: An Approach to Realistic Neural Simulation

         • Frank H. Eeckman, Frédéric E. Theunissen, John P. Miller

         Pages 121-126

      5. NEURON — A Program for Simulation of Nerve Equations

         • Michael Hines

         Pages 127-136

3. Sensory Systems

   1. Front Matter

      Pages 137-137

      PDF

   2. Visual System

      1. Models of Activity-Dependent Neural Development

         • Kenneth D. Miller

         Pages 141-165

      2. Visual Inputs and Information Processing in Sensory Cortex: An <i>in vivo</i> Developmental Study

         • Sarah L. Pallas, Laurie S. Carman, Mriganka Sur

         Pages 167-178

      3. Motion Detection and Directional Selectivity in the Crayfish Visual System

         • Raymon M. Glantz, Annemarie Bartels

         Pages 179-191

      4. Neither DoG nor LoG fits the receptive field of the vertebrate cone

         • Josef Skrzypek, George Wu

         Pages 193-209

      5. Cellular and Network Determinants of Visual Motion Properties in Cortical Neurons: Studies with an<i> In Vitro</i> Preparation of Visual Cortex

         • Philip S. Ulinski, Linda Larson-Prior, N. Traverse Slater

         Pages 211-218

   3. Auditory System

      1. Reconstruction of Target Images in the Sonar of Bats

         • James A. Simmons, Prestor A. Saillant

         Pages 221-253

In recent years there has been tremendous activity in computational neuroscience resulting from two parallel developments. On the one hand, our knowledge of real nervous systems has increased dramatically over the years; on the other, there is now enough computing power available to perform realistic simulations of actual neural circuits. This is leading to a revolution in quantitative neuroscience, which is attracting a growing number of scientists from non-biological disciplines. These scientists bring with them expertise in signal processing, information theory, and dynamical systems theory that has helped transform our ways of approaching neural systems. New developments in experimental techniques have enabled biologists to gather the data necessary to test these new theories. While we do not yet understand how the brain sees, hears or smells, we do have testable models of specific components of visual, auditory, and olfactory processing. Some of these models have been applied to help construct artificial vision and hearing systems. Similarly, our understanding of motor control has grown to the point where it has become a useful guide in the development of artificial robots. Many neuroscientists believe that we have only scratched the surface, and that a more complete understanding of biological information processing is likely to lead to technologies whose impact will propel another industrial revolution.
Neural Systems: Analysis and Modeling contains the collected papers of the 1991 Conference on Analysis and Modeling of Neural Systems (AMNS), and the papers presented at the satellite symposium on compartmental modeling, held July 23-26, 1992, in San Francisco, California. The papers included, present an update of the most recent developments in quantitative analysis and modeling techniques for the study of neural systems.

• Motor
• Sensor
• information theory
• integrated circuit
• metal-oxide-semiconductor transistor
• robot
• systems theory

• Lawrence Livermore National Laboratory, USA

  Frank H. Eeckman

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