Models of Neural Networks

1. Front Matter

   Pages i-xv

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2. Coding and Information Processing in Neural Networks

   • Wulfram Gerstner, J. Leo van Hemmen

   Pages 1-93

3. The Correlation Theory of Brain Function

   • Christoph von der Malsburg

   Pages 95-119

4. Firing Rates and Weil-Timed Events in the Cerebral Cortex

   • M. Abeles

   Pages 121-140

5. The Role of Synchrony in Neocortical Processing and Synaptic Plasticity

   • Wolf Singer

   Pages 141-173

6. Associative Binding and Segregation in a Network of Spiking Neurons

   • Raphael Ritz, Wulfram Gerstner, J. Leo van Hemmen

   Pages 175-219

7. Modeling the Sensory Computations of the Olfactory Bulb

   • Zhaoping Li

   Pages 221-251

8. Detecting Coherence in Neuronal Data

   • Klaus Pawelzik

   Pages 253-285

9. Hebbian Synaptic Plasticity: Evolution of the Contemporary Concept

   • Thomas H. Brown, Sumantra Chattarji

   Pages 287-314

10. Reentry and Dynamical Interactions of Cortical Networks

    • Olaf Sporns, Giulio Tononi, Gerald M. Edelman

    Pages 315-341

11. Back Matter

    Pages 343-347

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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).

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

• Department of Electronics, Weizmann Institute of Science, Rehovot, Israel

  Eytan Domany

• Institut für Theoretische Physik, Technische Universität München, Garching bei München, Germany

  J. Leo Hemmen

• Department of Physics and Beckman, Institute University of Illinois, Urbana, USA

  Klaus Schulten

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