NEURAL CODES AND DISTRIBUTED REPRESENTATIONS. Foundations of Neural Computation (Broché)

Terrence-J Sejnowski, Laurence Abbott

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  • The MIT Press

  • Paru le : 01/10/1999
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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 include how neurons encode information through action potential firing patterns, how populations of 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.
    • Deciphering the Brain's Codes
    • A Neural Network for Coding of Trajectories by Time Series of Neuronal Population Vectors
    • Self-Organization of Firing Activities in Monkey's Motor Cortex: Trajectory Computation from Spike Signals
    • Theoretical Considerations for the Analysis of Population Coding in Motor Cortex
    • Statistically Efficient Estimation Using Population Coding
    • Parameter Extraction from Population Codes: A Critical Assessment
    • Energy Efficient Neural Codes
    • Seeing Beyond the Nyquist Limit
    • A Model of Spatial Map Formation in the Hippocampus of the Rat
    • Probabilistic Interpretation of Population Codes
    • Cortical Cells Should Fire Regularly, But Do Not
    • Role of Temporal Integration and Fluctuation Detection in the Highly Irregular Firing of a Leaky Integrator Neuron Model with Partial Reset
    • Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell
    • Coding of Time-Varying Signals in Spike Trains of Integrate-and-Fire Neurons with Random Threshold
    • Temporal Precision of Spike Trains in Extrastriate Cortex of the Behaving Macaque Monkey
    • Conversion of Temporal Correlations Between Stimuli to Spatial Correlations Between Attractors
    • Neural Network Model of the Cerebellum: Temporal Discrimination and the Timing of Motor Responses
    • Gamma Oscillation Model Predicts Intensity Coding by Phase Rather than Frequency
    • Effects of Input Synchrony on the Firing Rate of a Three-Conductance Cortical Neuron Model
    • NMDA-Based Pattern Discrimination in a Modeled Cortical Neuron
    • The Impact of Parallel Fiber Background Activity on the Cable Properties of Cerebellar Purkinje Cells.
  • Date de parution : 01/10/1999
  • Editeur : The MIT Press
  • Collection : computational neuroscience
  • ISBN : 0-262-51100-2
  • EAN : 9780262511001
  • Présentation : Broché
  • Nb. de pages : 345 pages
  • Poids : 0.6 Kg
  • Dimensions : 15,2 cm × 22,8 cm × 2,2 cm
Laurence Abbott is Professor of Biology and Director of the Volen Center for Complex Systems, Brandeis University. Terrence J. Sejnowski is a Howard Hughes Medical Investigator and Head of the Department of Computational Neurobiology at the Salk Institute for Biological Studies, and a professor at the University of California, San Diego.

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