This book provides insights into the principles of operation of the cerebral cortex. These principles are key to understanding how we, as humans, function. The book includes Appendices on the operation of many of the neuronal networks described in the book, together with simulation software written in Matlab.
This book provides insights into the principles of operation of the cerebral cortex. These principles are key to understanding how we, as humans, function. 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.
Inhaltsangabe
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
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
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