Computational Neuroscience of Vision

The human visual system is so incredibly complex that any attempt to understand how the brain processes visual information necessitates a range of approaches, on a number of different levels. Neurophysiological studies, at the single neuron level are required. We need to bring in neuropsychological studies of brain damaged patients in order to understand what different parts of the visual system do, and what each part is needed for. Neuroimaging work can provide valuable information on the locations in the brain where these processes are taking place. Additionally required is an understanding of the biophysical and synaptic properties of neurons to see how the computing elements of the brain work. A knowledge of the anatomical and functional architecture of the cortex further enhances our understanding. Finally, neural computation methods can bring together the evidence to understand how the visual system actually works. Most of the books looking at the topic of vision tend to take a particular approach and exclude the work and data being obtained from studies adopting other approaches This important new book from the eminent neuroscientist, Edmund Rolls (in collaboration with Gustavo Deco), is unique in combining all these approaches within a single volume to further our understanding of vision. This original approach enables a far more complete understanding of a very complex subject. This is a book which will be of great value to psychologists interested in vision and attentional processes, neuroscientists, and vision scientists

Computers & Technology

Rolls, Edmund T.

Deco, Gustavo

Computer Science

Oxford University Press

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.

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

Science & Math

Behavioral Sciences

A detailed formulation of neural networks from the information-theoretic viewpoint. The authors show how this perspective provides new insights into the design theory of neural networks. In particular they demonstrate how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from varied scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this an extremely valuable introduction to this topic.

An Information-Theoretic Approach to Neural Computing (Perspectives in Neural Computing)

Obradovic, Dragan

Springer

This book offers a new, theoretical approach to information dynamics, i.e., information processing in complex dynamical systems. The presentation establishes a consistent theoretical framework for the problem of discovering knowledge behind empirical, dynamical data and addresses applications in information processing and coding in dynamical systems. This will be an essential reference for those in neural computing, information theory, nonlinear dynamics and complex systems modeling.

Information Dynamics: Foundations and Applications

Schürmann, Bernd

Reference

A detailed formulation of neural networks from the information-theoretic viewpoint. The authors show how this perspective provides new insights into the design theory of neural networks. In particular they demonstrate how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from varied scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this an extremely valuable introduction to this topic.

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Books by Deco, Gustavo

See the best price to sell, buy, or rent books by Deco, Gustavo. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Computational Neuroscience of Vision

Computational Neuroscience of Vision

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

An Information-Theoretic Approach to Neural Computing (Perspectives in Neural Computing)

Information Dynamics: Foundations and Applications

Information Dynamics: Foundations and Applications

An Information-Theoretic Approach to Neural Computing (Perspectives in Neural Computing)