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Models of Neural Networks

Temporal Aspects of Coding and Information Processing in Biological Systems

  • Eytan Domany
  • J. Leo van Hemmen
  • Klaus Schulten

Part of the Physics of Neural Networks book series (NEURAL NETWORKS)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Wulfram Gerstner, J. Leo van Hemmen
    Pages 1-93
  3. Christoph von der Malsburg
    Pages 95-119
  4. Raphael Ritz, Wulfram Gerstner, J. Leo van Hemmen
    Pages 175-219
  5. Klaus Pawelzik
    Pages 253-285
  6. Thomas H. Brown, Sumantra Chattarji
    Pages 287-314
  7. Olaf Sporns, Giulio Tononi, Gerald M. Edelman
    Pages 315-341
  8. Back Matter
    Pages 343-347

About this book

Introduction

Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree of freedom. This capacity of a neural network rests on the fact that the neuronal action potential is a short, say 1 ms, spike, localized in space and time. Spatial as well as temporal correlations of activity may represent different states of a network. In particular, temporal correlations of activity may express that neurons process the same "object" of, for example, a visual scene by spiking at the very same time. The traditional description of a neural network through a firing rate, the famous S-shaped curve, presupposes a wide time window of, say, at least 100 ms. It thus fails to exploit the capacity to "bind" sets of coherently firing neurons for the purpose of both scene segmentation and figure-ground segregation. Feedback is a dominant feature of the structural organization of the brain. Recurrent neural networks have been studied extensively in the physical literature, starting with the ground breaking work of John Hop­ field (1982).

Keywords

Synapse artificial intelligence cortex figure-ground segregation neural networks spiking neurons

Editors and affiliations

  • Eytan Domany
    • 1
  • J. Leo van Hemmen
    • 2
  • Klaus Schulten
    • 3
  1. 1.Department of ElectronicsWeizmann Institute of ScienceRehovotIsrael
  2. 2.Institut für Theoretische PhysikTechnische Universität MünchenGarching bei MünchenGermany
  3. 3.Department of Physics and BeckmanInstitute University of IllinoisUrbanaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-4320-5
  • Copyright Information Springer-Verlag New York 1994
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-8736-0
  • Online ISBN 978-1-4612-4320-5
  • Series Print ISSN 0939-3145
  • Buy this book on publisher's site