The Noisy Brain: Stochastic Dynamics as a Principle of Brain Function

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Overview


The activity of neurons in the brain is noisy in that their firing times are random when they are firing at a given rate. This introduces a random or stochastic property into brain processing which we show in this book is fundamental to understanding many aspects of brain function, including probabilistic decision making, perception, memory recall, short-term memory, attention, and even creativity. We show that in many of these processes, the noise caused by the random neuronal firing times is useful. However, this stochastic dynamics can be unstable or overstable, and we show that the stability of attractor networks in the brain in the face of noise may help to understand some important dysfunctions that occur in schizophrenia, normal aging, and obsessive-compulsive disorder.

The book provides a unifying computational approach to brain function which links synaptic and biophysical properties of neurons through the firing of single neurons to the properties of the noise in large connected networks of noisy neurons to the levels of functional neuroimaging and behaviour. Integrate-and-fire neuronal attractor networks with noise, complementary mean-field analyses using approaches from theoretical physics, and how they can be used to understand neuronal, functional neuroimaging, and behavioural mechanisms data on decision making, perception, memory recall, short-term memory, attention and brain dysfunctions that occur in schizophrenia, normal aging, and obsessive-compulsive disorder, are described.

'The Noisy Brain' will be valuable for those in the fields of neuroscience, psychology, cognitive neuroscience, and biology from advanced undergraduate level upwards. It will also be of interest to those interested in neuroeconomics, animal behaviour, zoology, psychiatry, medicine, physics, and philosophy. The book has been written with modular chapters and sections, making it possible to select particular Chapters for course work. Advanced material on the physics of stochastic dynamics in the brain is contained in the Appendix.

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Product Details

  • ISBN-13: 9780199587865
  • Publisher: Oxford University Press, USA
  • Publication date: 3/26/2010
  • Pages: 304
  • Product dimensions: 9.80 (w) x 6.60 (h) x 0.80 (d)

Meet the Author

Gustavo Deco (born in 1961 in Argentina) is Research Professor from the Institucio Catalana de Recerca i Estudis Avancats at the Pompeu Fabra University (UPF) where he is leading the Computational Neuroscience group at the Department of Technology and he is also director of the doctoral program in Computer Science and Digital Communication. His research interest includes computational neuroscience, neuropsycholgy, psycholinguistics, biological networks, statistical formulation of neural networks, and chaos theory. He has published three books, more than 140 papers in International Journals, 233 papers in International Conferences and 23 book chapters. He has also 48 patents in Europe, USA, Canada and Japan. Dr. Deco is reviewer for several international Journals, Conferences and European Research Councils, member of a few professional organizations and he is often invited as Lecturer in conferences, workshops and seminars as a guest speaker. Edmund T. Rolls is a neuroscientist at The Oxford Centre for Computational Neuroscience, Oxford, http://www.oxcns.org (http://www.oxcns.org), and was Professor of Experimental Psychology at the University of Oxford, and a Fellow and Tutor of Corpus Christi College, Oxford. He read preclinical medicine at the University of Cambridge, and now performs research linking computational neuroscience approaches to neurophysiological, human functional neuroimaging and neuropsychological studies in order to provide a fundamental basis for understanding human brain function and its disorders.

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Table of Contents

1. Introduction: neuronal, cortical, and network foundations
2. Stochastic neurodynamics
3. Short-term memory and stochastic dynamics
4. Attention and stochastic dynamics
5. Probabilistic decision-making
6. Confidence and decision-making
7. Perceptual detection and stochastic dynamics
8. Applications of this stochastic dynamical theory to brain function
A. Mean-field analyses, and stochastic dynamics
References
Index
B. Colour Plates

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