Cerebral cortex : principles of operation
Author(s)
Bibliographic Information
Cerebral cortex : principles of operation
Oxford University Press, 2016
1st ed
- : [hardcover]
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Note
Includes bibliographical references (p. [890]-949) and index
Description and Table of Contents
Description
"A book remarkable in its ambition, and even more remarkable in its content. A truly landmark achievement by a neuroscientist who has brought together his lifetime of research knowledge and experience into this outstanding volume. Edmund Rolls is to be congratulated on this impressive synthesis of decades of neuroscience data."
David Nutt, Professor of Neuropsychopharmacology at Imperial College London and President of the European Brain Council
The aim of this book is to provide insight into the principles of operation of the cerebral cortex. These principles are key to understanding how we, as humans, function.
There have been few previous attempts to set out some of the important principles of operation of the cortex, and this book is pioneering. The book goes beyond separate connectional neuroanatomical, neurophysiological, neuroimaging, neuropsychiatric, and computational neuroscience approaches, by combining evidence from all these areas to formulate hypotheses about how and what the cerebral cortex computes. As clear hypotheses are needed in this most important area of 21st century science, how
our brains work, I have formulated a set of hypotheses about the principles of cortical operation to guide thinking and future research.
The book focusses on the principles of operation of the cerebral cortex, because at this time it is possible to propose and describe many principles, and many are likely to stand the test of time, and provide a foundation for further developments, even if some need to be changed. In this context, I have not attempted to produce an overall theory of operation of the cerebral cortex, because at this stage of our understanding, such a theory would be incorrect or incomplete. However, many of the
principles described will provide the foundations for more complete theories of the operation of the cerebral cortex. This book is intended to provide a foundation for future understanding, and it is hoped that future work will develop and add to these principles of operation of the cerebral cortex.
The book includes Appendices on the operation of many of the neuronal networks described in the book, together with simulation software written in Matlab.
Professor Edmund T. Rolls performs full-time research at the Oxford Centre for Computational Neuroscience, and is professor of Computational Neuroscience at the University of Warwick, and has acted as Professor of Experimental Psychology at the University of Oxford, and as Fellow and Tutor of Corpus Christi College, Oxford. His research links neurophysiological and computational neuroscience approaches to human functional neuroimaging and neuropsychological studies in order to provide a
fundamental basis for understanding human brain function and its disorders.
Table of Contents
1: Introduction
2: Hierarchical Organization
3: Localization of Function
4: Recurrent Collateral Connections and Attractor Networks
5: The Noisy Cortex: Stochastic dynamics, decisions, and memory
6: Attention, Short-term Memory, and Biased Competition
7: Diluted Connectivity
8: Coding Principles
9: Synaptic Modification for Learning
10: Synaptic and Neuronal Adaptation and Facilitation
11: Backprojections in the Neocortex
12: Memory and the Hippocampus
13: Limited Neurogenesis in the Adult Cortex
14: Invariance Learning and Vision
15: Emotion, Motivation, Reward Value, Pleasure, and their Mechanisms
16: Noise in the Cortex, Stability, Psychiatric Disease, and Aging
17: Syntax and Language
18: Evolutionary Trends in Cortical Design and Principles of Operation
19: Genetics and Self-Organization Build the Cortex
20: Cortex versus Basal Ganglia Design for Selection
21: Sleep and Dreaming
22: Which Cortical Computations Underlie Consciousness?
23: Cerebellar Cortex
24: The Hippocampus and Memory
25: Invariant Visual Object Recognition Learning
26: Synthesis
Introduction to Linear Algebra for Neural Networks
Neural Network Models
Information Theory and Neuronal Encoding
Simulation Software for Neuronal Network Models
References
Index
by "Nielsen BookData"