Probabilistic Models of the Brain: Perception and Neural Function (Neural Information Processing)

Neurophysiological, neuroanatomical, and brain imaging studies have helped to shed light on how the brain transforms raw sensory information into a form that is useful for goal-directed behavior. A fundamental question that is seldom addressed by these studies, however, is why the brain uses the types of representations it does and what evolutionary advantage, if any, these representations confer. It is difficult to address such questions directly via animal experiments. A promising alternative is to use probabilistic principles such as maximum likelihood and Bayesian inference to derive models of brain function.This book surveys some of the current probabilistic approaches to modeling and understanding brain function. Although most of the examples focus on vision, many of the models and techniques are applicable to other modalities as well. The book presents top-down computational models as well as bottom-up neurally motivated models of brain function. The topics covered include Bayesian and information-theoretic models of perception, probabilistic theories of neural coding and spike timing, computational models of lateral and cortico-cortical feedback connections, and the development of receptive field properties from natural signals. A survey of probabilistic approaches to modeling and understanding brain function.Neurophysiological, neuroanatomical, and brain imaging studies have helped to shed light on how the brain transforms raw sensory information into a form that is useful for goal-directed behavior. A fundamental question that is seldom addressed by these studies, however, is why the brain uses the types of representations it does and what evolutionary advantage, if any, these representations confer. It is difficult to address such questions directly via animal experiments. A promising alternative is to use probabilistic principles such as maximum likelihood and Bayesian inference to derive models of brain function.This book surveys some of the current probabilistic approaches to modeling and understanding brain function. Although most of the examples focus on vision, many of the models and techniques are applicable to other modalities as well. The book presents top-down computational models as well as bottom-up neurally motivated models of brain function. The topics covered include Bayesian and information-theoretic models of perception, probabilistic theories of neural coding and spike timing, computational models of lateral and cortico-cortical feedback connections, and the development of receptive field properties from natural signals. Cover 1 Neural Information Processing Series 3 Probabilistic Models of the Brain: Perception and Neural Function 4 Copyright 5 Contents 6 Series Foreword 8 Preface 10 Introduction 12 Part I: Perception 22 1 Bayesian Modelling of Visual Perception 24 2 Vision, Psychophysics and Bayes 48 3 Visual Cue Integration for Depth Perception 72 4 Velocity Likelihoods in Biological and Machine Vision 88 5 Learning Motion Analysis 108 6 Information Theoretic Approach to Neural Coding and Parameter Estimation: A Perspective 128 7 From Generic to Specific: An Information Theoretic Perspective on the Value o fHigh-Level Information 146 8 Sparse Correlation Kernel Reconstruction and Superresolution 166 Part II: Neural Function 190 9 Natural Image Statistics for CorticalOrientati on Map Development 192 10 Natural Image Statistics and Divisive Normalization 214 11 A Probabilistic Network Model of Population Responses 234 12 Efficient Coding of Time-Varying Signals Using a Spiking Population Code 254 13 Sparse Codes and Spikes 268 14 Distributed Synchrony: A Probabilistic Model of Neural Signaling 284 15 Learning to Use Spike Timing in a Restricted Boltzmann Machine 296 16 Predictive Coding, Cortical Feedback, and Spike-Timing Dependent Plasticity 308 Contributors 328 Index 332

Neurophysiological, neuroanatomical, and brain imaging studies have helped to shed light on how the brain transforms raw sensory information into a form that is useful for goal-directed behavior. A fundamental question that is seldom addressed by these studies, however,is why the brain uses the types of representations it does and what evolutionary advantage, if any,these representations confer. It is difficult to address such questions directly via animal experiments. A promising alternative is to use probabilistic principles such as maximum likelihood and Bayesian inference to derive models of brain function.This book surveys some of the current probabilistic approaches to modeling and understanding brain function. Although most of the examples focus on vision, many of the models and techniques are applicable to other modalities as well. The book presents top-down computational models as well as bottom-up neurally motivated models of brain function. The topics covered include Bayesian and information-theoretic models of perception,probabilistic theories of neural coding and spike timing, computational models of lateral and cortico-cortical feedback connections, and the development of receptive field properties from natural signals.

Neurophysiological, neuroanatomical, and brain imaging studies have helped to shed light on how the brain transforms raw sensory information into a form that is useful for goal-directed behavior. A fundamental question that is seldom addressed by these studies, however, is why the brain uses the types of representations it does and what evolutionary advantage, if any, these representations confer. It is difficult to address such questions directly via animal experiments. A promising alternative is to use probabilistic principles such as maximum likelihood and Bayesian inference to derive models of brain function. This book surveys some of the current probabilistic approaches to modeling and understanding brain function. Although most of the examples focus on vision, many of the models and techniques are applicable to other modalities as well. The book presents top-down computational models as well as bottom-up neurally motivated models of brain function. The topics covered include Bayesian and information-theoretic models of perception, probabilistic theories of neural coding and spike timing, computational models of lateral and cortico-cortical feedback connections, and the development of receptive field properties from natural signals. Each waking moment, our body's sensory receptors convey a vast amount of information about the surrounding environment to the brain.