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ORCIDInformation Theory and the Brain, 2000
- Roland Baddeley, Peter J. B. Hancock, Peter Földiák:
Information Theory and the Brain. Addison-Wesley 2000, ISBN 978-0-521-63197-6 - Roland Baddeley:
Preface. xiii-xiv - Roland Baddeley:
Introductory Information Theory and the Brain. 1-20 - Brian G. Burton:
Problems and Solutions in Early Visual Processing. 25-40 - Simon B. Laughlin
, John C. Anderson, David C. O'Carroll, Robert R. de Ruyter van Steveninck:
Coding Efficiency and the Metabolic Cost of Sensory and Neural Information. 41-61 - Mitchell Thompson:
Coding Third-Order Image Structure. 62-78 - George Harpur, Richard W. Prager:
Experiments with Low-Entropy Neural Networks. 84-100 - Stephen P. Luttrell:
The Emergence of Dominance Stripes and Orientation Maps in a Network of Firing Neurons. 101-121 - Germán Mato, Néstor Parga:
Dynamic Changes in Receptive Fields Induced by Cortical Reorganization. 122-138 - Guy M. Wallis:
Time to Learn About Objects. 139-163 - Norbert Krüger, Michael Pötzsch, Gabriele Peters:
Principles of Cortical Processing Applied to and Motivated by Artificial Object Recognition. 164-179 - Martin Elliffe:
Performance Measurement Based on Usable Information. 180-200 - Matthew P. Aylett:
Modelling Clarity Change in Spontaneous Speech. 204-220 - John A. Bullinaria:
Free Gifts from Connectionist Modelling. 221-240 - Janne Sinkkonen:
Information and Resource Allocation. 241-254 - Simon R. Schultz, Stefano Panzeri, Edmund T. Rolls, Alessandro Treves:
Quantitative Analysis of a Schaffer Collateral Model. 257-272 - Carlo Fulvi Mari, Stefano Panzeri, Edmund T. Rolls, Alessandro Treves:
A Quantitative Model of Information Processing in CA1. 273-289 - Paul C. Bressloff, Peter Roper:
Stochastic Resonance and Bursting in a Binary-Threshold Neuron with Intrinsic Noise. 290-304 - Mark D. Plumbley:
Information Density and Cortical Magnification Factors. 305-317
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