Neural Codes and Distributed Representations

Foundations of Neural Computation
Overview

Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years.

The present volume focuses on neural codes and representations, topics of broad interest to neuroscientists and modelers. The topics addressed are: how neurons encode information through action potential firing patterns, how populations of neurons represent information, and how individual neurons use dendritic processing and biophysical properties of synapses to decode spike trains. The papers encompass a wide range of levels of investigation, from dendrites and neurons to networks and systems.

Table of Contents

  1. Introduction
  2. 1. Deciphering the Brain's Codes

    Masakazu Konishi

  3. 2. A Neural Network for Coding of Trajectories by Time Series of Neuronal Population Vectors

    Alexander V. Lukashin and Apostolos P. Georgopoulos

  4. 3. Self-Organization of Firing Activities in Monkey's Motor Cortex: Trajectory Computation from Spike Signals

    Siming Lin, Jennie Si and A. B. Schwartz

  5. 4. Theoretical Considerations for the Analysis of Population Coding in Motor Cortex

    Terrence D. Sanger

  6. 5. Statistically Efficient Estimation Using Population Coding

    Alexandre Pouget, Kechen Zhang, Sophie Deneve and Peter E. Latham

  7. 6. Parameter Extraction from Population Codes: A Critical Assessment

    Herman P. Snippe

  8. 7. Energy Efficient Neural Codes

    William B. Levy and Robert A. Baxter

  9. 8. Seeing Beyond the Nyquist Limit

    Daniel L. Ruderman and William Bialek

  10. 9. A Model of Spatial Map Formation in the Hippocampus of the Rat

    Kenneth I. Blum and L. F. Abbott

  11. 10. Probabilistic Interpretation of Population Codes

    Richard S. Zemel, Peter Dayan and Alexandre Pouget

  12. 11. Cortical Cells Should Fire Regularly, But Do Not

    William R. Softky and Christof Koch

  13. 12. Role of Temporal Integration and Fluctuation Detection in the Highly Irregular Firing of a Leaky Integrator Neuron Model with Partial Reset

    Guido Bugmann, Chris Christodoulou and John G. Taylor

  14. 13. Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell

    Todd W. Troyer and Kenneth D. Miller

  15. 14. Coding of Time-Varying Signals in Spike Trains of Integrate-and-Fire Neurons with Random Threshold

    Fabrizio Gabbiani and Christof Koch

  16. 15. Temporal Precision of Spike Trains in Extrastriate Cortex of the Behaving Macaque Monkey

    Wyeth Bair and Christof Koch

  17. 16. Conversion of Temporal Correlations Between Stimuli to Spatial Correlations Between Attractors

    M. Griniasty, M. V. Tsodyks and Daniel J. Amit

  18. 17. Neural Network Model of the Cerebellum: Temporal Discrimination and the Timing of Motor Responses

    Dean V. Buonomano and Michael D. Mauk

  19. 18. Gamma Oscillation Model Predicts Intensity Coding by Phase Rather than Frequency

    Roger D. Traub, Miles A. Whittington and John G. R. Jefferys

  20. 19. Effects of Input Synchrony on the Firing Rate of a Three-Conductance Cortical Neuron Model

    Venkatesh N. Murthy and Eberhard E. Fetz

  21. 20. NMDA-Based Pattern Discrimination in a Modeled Cortical Neuron

    Bartlett W. Mel

  22. 21. The Impact of Parallel Fiber Background Activity on the Cable Properties of Cerebellar Purkinje Cells

    Moshe Rappe, Yosef Yarom and Idan Segev

  23. Index