Information Theory and the Brain
Information Theory and the Brain
Peter Hancock, University of Stirling
Peter Földiák, University of St Andrews, Scotland
Information Theory and the Brain deals with an expanding area of neuroscience which provides a framework for understanding neuronal processing. It is derived from a conference held in Newquay, UK, where a select group of scientists from around the world met to discuss the topic. This book begins with an introduction to the basic concepts of information theory and then illustrates these concepts with examples from research over 40 years. Throughout the book, the contributors highlight current research from four different areas: 1) biological networks, 2) information theory and artificial networks, 3) information theory and psychology, 4) formal analysis. Each section includes an introduction and glossary covering basic concepts. This book will appeal to graduate students and researchers in neuroscience as well as computer scientists and cognitive scientists. Neuroscientists interested in any aspect of neural networks or information processing will find this a very useful addition to the current literature in this rapidly growing field.
- Broad audience: neuroscience, computer science, vision
- Current research presented in an available form with brief introduction and glossary
- Exciting addition to rapidly growing area of computational neuroscience
Product details
April 2011Adobe eBook Reader
9780511825248
0 pages
0kg
123 b/w illus. 4 tables
This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
- List of contributors
- Preface
- 1. Introductory information theory and the brain Roland Baddeley
- Part I. Biological Networks:
- 2. Problems and solutions in early visual processing Brian G. Burton
- 3. Coding efficiency and the metabolic cost of sensory and neural information Simon B. Laughlin, John C. Anderson, David O'Carroll and Rob de Ruyter van Stevenick
- 4. Coding third-order image structure Mitchell Thomson
- Part II. Information Theory and Artificial Networks:
- 5. Experiments with low entropy neural networks George Harpur and Richard Prager
- 6. The emergence of dominance stripes and orientation maps in a network of firing neurons Stephen P. Luttrell
- 7. Dynamic changes in receptive fields induced by cortical reorganization Germán Mato and Néstor Parga
- 8. Time to learn about objects Guy Wallis
- 9. Principles of cortical processing applied to and motivated by artificial object recognition Norbert Krüger, Michael Pötzsch and Gabriele Peters
- 10. Performance measurement based on usable information Martin Elliffee
- Part III. Information Theory and Psychology:
- 11. Modelling clarity change in spontaneous speech Matthew Aylett
- 12. Free gifts from connectionist modelling John A. Bullinaria
- 13. Information and resource allocation Janne Sinkkonen
- Part IV. Formal Analysis:
- 14. Quantitative analysis of a Schaffer collateral model Simon Schultz, Stefano Panzeri, Edmund Rolls and Alessandro Treves
- 15. A quantitative model of information processing in CA1 Carlo Fulvi Mari, Stefano Panzeri, Edmund Rolls and Alessandro Treves
- 16. Stochastic resonance and bursting in a binary-threshold neuron with intrinsic noise Paul C. Bressloff and Peter Roper
- 17. Information density and cortical magnification factors M. D. Plumbley
- References
- Index.
