Neural Codes and Distributed Representations: Foundations of Neural Computation / Edition 1

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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.

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Product Details

  • ISBN-13: 9780262511001
  • Publisher: MIT Press
  • Publication date: 7/25/1996
  • Series: Computational Neuroscience
  • Edition description: First Edition
  • Edition number: 1
  • Pages: 369
  • Product dimensions: 6.00 (w) x 8.90 (h) x 0.90 (d)

Meet the Author

Larry Abbott is Professor of Neuroscience and Co-Director of the Center for Theoretical Neuroscience at Columbia University.

Terrence J. Sejnowski is Francis Crick Professor, Director of the Computational Neurobiology Laboratory, and a Howard Hughes Medical Institute Investigator at the Salk Institute for Biological Studies and Professor of Biology at the University of California, San Diego.

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Table of Contents

1 Deciphering the Brain's Codes 1
2 A Neural Network for Coding of Trajectories by Time Series of Neuronal Population Vectors 19
3 Self-Organization of Firing Activities in Monkey's Motor Cortex: Trajectory Computation from Spike Signals 29
4 Theoretical Considerations for the Analysis of Population Coding in Motor Cortex 45
5 Statistically Efficient Estimation Using Population Coding 55
6 Parameter Extraction from Population Codes: A Critical Assessment 85
7 Energy Efficient Neural Codes 105
8 Seeing Beyond the Nyquist Limit 119
9 A Model of Spatial Map Formation in the Hippocampus of the Rat 129
10 Probabilistic Interpretation of Population Codes 139
11 Cortical Cells Should Fire Regularly, But Do Not 167
12 Role of Temporal Integration and Fluctuation Detection in the Highly Irregular Firing of a Leaky Integrator Neuron Model with Partial Reset 171
13 Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell 187
14 Coding of Time-Varying Signals in Spike Trains of Integrate-and-Fire Neurons with Random Threshold 201
15 Temporal Precision of Spike Trains in Extrastriate Cortex of the Behaving Macaque Monkey 225
16 Conversion of Temporal Correlations Between Stimuli to Spatial Correlations Between Attractors 243
17 Neural Network Model of the Cerebellum: Temporal Discrimination and the Timing of Motor Responses 261
18 Gamma Oscillation Model Predicts Intensity Coding by Phase Rather than Frequency 279
19 Effects of Input Synchrony on the Firing Rate of a Three-Conductance Cortical Neuron Model 293
20 NMDA-Based Pattern Discrimination in a Modeled Cortical Neuron 309
21 The Impact of Parallel Fiber Background Activity on the Cable Properties of Cerebellar Purkinje Cells 325
Index 341
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