Neural Networks: Artificial Intelligence and Industrial Applications

Neural Networks and Artificial Intelligence

1. Front Matter

   Pages 1-1

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2. Neurobiology — Orals

   1. Segmentation Coding by the Visual System

      • Reinhard Eckhorn

      Pages 5-12

   2. Synchrony and Fast Plasticity in the Visual Cortex

      • Bob W. van Dijk

      Pages 13-20

   3. Rapid Neural Synchronization: From Spiking Cells to Synfire Webs

      • Andreas V. M. Herz

      Pages 21-28

   4. Dynamic Representations Provide the Gradual Specification of Movement Parameters

      • Klaus Kopecz, Wolfram Erlhagen, Gregor Schöner

      Pages 29-32

   5. Recording from Foveal Striate Cortex while the Monkey is Looking at the Stimulus

      • J. Krüger, I. Bondar, H. Haan

      Pages 33-36

   6. Propagation of Synfire Activity in Cortical Networks: a Statistical Approach

      • Marc-Oliver Gewaltig, Markus Diesmann, Ad Aertsen

      Pages 37-40

   7. Propagation of Synfire Activity in Cortical Networks — a Dynamical Systems Approach

      • Martin Arndt, Wolfram Erlhagen, Ad Aertsen

      Pages 41-44

3. Neurobiology — Posters

   1. Aspects of Spatiotemporal Learning in Artificial Neural Networks: Modeling Synaptic Membrane Currents Using SPICE Simulations

      • Jaap Hoekstra, Petra Mantel

      Pages 47-50

   2. Possible Functional Roles of the Bipartite Dendrites of Pyramidal Cells

      • Ralf Möller, Horst-Michael Groß

      Pages 51-54

   3. The Role of Cerebellum in Motor Control

      • Franz Mechsner

      Pages 55-58

   4. A Point Process Approach to Cortical Networks

      • Stefan Rotter, Ad Aertsen

      Pages 59-62

   5. Stochastic Resonance and Multi-modal Firing Patterns in Single-neuron Models

      • Danny Linders, Bert Kappen

      Pages 63-66

4. Cognitive Modelling and Rule Extraction — Orals

   1. The BB Neural Network Rule Extraction Method

      • F. R. Wiersma, M. Poel, A. M. Oudshoff

      Pages 69-72

   2. Implications of Hadley’s Definition of Systematicity

      • Nico Bakker

      Pages 73-76

   3. The Binding Problem in Distributed Systems

      • P. H. de Vries, G. J. Dalenoort

      Pages 77-80

   4. Integrating Symbolic and Subsymbolic Architectures for Parsing Arithmetic Expressions and Natural Language Sentences

      • Jonathan A. Tepper, Heather Powell, Dominic Palmer-Brown

      Pages 81-84

   5. Bayesian Strategies for Machine Learning: Rule Extraction and Concept Detection

      • Laura Martignon, Kathryn Blackmond Laskey

      Pages 85-88

5. Cognitive Modelling and Rule Extraction — Posters

   1. Learning at subsymbolic and symbolic levels

      • Alain Grumbach

      Pages 91-94