Cover Title Page Copyright Contents Notes on Contributors Acknowledgment Introduction Computational Models of Brain and Behavior Part 1 Models of Brain Disorders Models of psychiatric disorders Models of neurological disorders Part 2 Neural Models of Behavioral Processes Part 3 Models of Brain Regions and Neurotransmitters Models of brain areas Models of neurotransmitters Part 4 Neural Modeling Approaches Higher-level models Lower-level models Part I: Models of Brain Disorders. Chapter 1: A Computational Model of Dyslexics' Perceptual Difficulties as Impaired Inference of Sound StatisticsIntroduction-Contraction Bias in Simple Discrimination Tasks Contraction Bias-a Simple Experimental Measure of Context Effects Dyslexia The Magnitude of Contraction Bias is Smaller in Dyslexics than in Controls The Implicit Memory Model (IMM) Account for the Contraction Bias Dyslexics Underweight Previous Trials Given Their Internal Noise Level General Discussion References Chapter 2: Computational Approximations to Intellectual Disability in Down Syndrome Introduction. Theories of Intellectual Disability and Atypical DevelopmentDown Syndrome Computational Approximations for Understanding Intellectual Disability in Down Syndrome Future Directions Concluding Remarks Acknowledgments References Chapter 3: Computational Psychiatry Introduction Computational Modeling of Mood Disorders The Function of Mood and its Relation to Behavior Bayesian Inference and Hierarchical Models Schizophrenia, Precision, and Inference Aberrant Salience and Psychosis Computational Phenotyping Using Social Games Summary References. Chapter 4: Computational Models of Post-traumatic Stress Disorder (PTSD)Introduction Models of Fear Conditioning Limitations and Future Directions Models of Changes in Arousal and Reactivity Limitations and Future Directions Models of Avoidance Limitations and Future Directions Models of Changes in Cognition and Mood Limitations and Future Directions Models of Intrusive Recollection Limitations and Future Directions Conclusions References Chapter 5: Reward Processing in Depression Introduction The Computational Approach and its Merits Depression and Reinforcement Learning. Depression and LikingDepression and Wanting Depression and Model-based RL Related Findings in Neuroeconomics and Quantum Decision Theory Implications and Future Directions References Chapter 6: Neurocomputational Models of Schizophrenia Introduction Models of Cognition in Schizophrenia Models of Schizophrenia Symptoms Models of Pharmacological and Nonpharmacological Treatment of Schizophrenia Conclusions References Chapter 7: Oscillatory Dynamics of Brain Microcircuits Introduction Oscillatory Brain Microcircuits-Modeling Perspectives.
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A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and differentA comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and differentA comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different",,,,,"A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer's disease, Parkinson's disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more.-Covers computational approximations to intellectual disability in down syndrome -Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease -Examines neural circuit models of serotonergic system (from microcircuits to cognition) -Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students-as well as researchers involved in computational neuroscience modeling research.
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