Computational Models of Brain and Behavior

 
 
Wiley-Blackwell (Verlag)
  • erschienen am 11. September 2017
  • |
  • 584 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-119-15907-0 (ISBN)
 
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.
1. Auflage
  • Englisch
  • Newark
  • |
  • Großbritannien
John Wiley & Sons Inc
  • Für Beruf und Forschung
  • 19,07 MB
978-1-119-15907-0 (9781119159070)
1119159075 (1119159075)
weitere Ausgaben werden ermittelt
DR. AHMED A. MOUSTAFA, PhD is a Senior Lecturer in Cognitive and Behavioral Neuroscience at the MARCS Institute for Brain, Behavior, and Development, School of Social Sciences and Psychology, Western Sydney University. He has published more than 100 papers in high-ranking journals including Science, Proceedings of the National Academy of Science, Journal of Neuroscience, and Brain, Neuroscience and Biobehavioral Reviews.
  • 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 Statistics
  • Introduction-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 Development
  • Down 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 Liking
  • Depression 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
  • Theta/Gamma Oscillations in the Hippocampus and Alzheimer's Disease
  • Concluding Remarks
  • References
  • Chapter 8: Computational Models of Pharmacological and Immunological Treatment in Alzheimer's Disease
  • What is Alzheimer's Disease?
  • AD Stages
  • AD Treatment and Mechanism of Action
  • Computational Models of AD Therapy and Drug Discovery
  • Future Directions and Conclusions
  • References
  • Chapter 9: Modeling Deep Brain Stimulation for Parkinson's Disease
  • Introduction
  • Volume Conductor Models of DBS
  • Network Models of DBS
  • Mean-field Models of DBS
  • Simulation of Closed-loop Control of DBS
  • Conclusions
  • References
  • Chapter 10: The Development of Medications for Parkinson's Disease Using Computational Modeling
  • Introduction: How Computational Models Can Be Used to Provide Treatments for Parkinson's Disease
  • Providing a Computational Model of Treatments of Parkinson's Disease
  • Parkinson's Disease
  • The Striatum and Neurotransmitters
  • Striatal Neuron Receptors and Ion Channels
  • Subthalamic Nucleus Structure and Receptors
  • Striatum and Subthalamic Nucleus Structure Oscillations as Biomarkers for PD
  • Local Field Potentials and the Subthalamic Nucleus Structure
  • Computational Models for Parkinson's Disease and Dopaminergic Medications
  • Modeling the Effects of PD Treatment
  • Future Directions
  • References
  • Chapter 11: Multiscale Computer Modeling of Epilepsy
  • Introduction
  • Multiscale Modeling
  • Dynamics
  • Some Specific Models
  • Conclusion
  • References
  • Part II: Neural Models of Behavioral Processes
  • Chapter 12: Simple Models of Sensory Information Processing
  • Introduction
  • Simple Models for Single Neurons
  • Simple Models of Small Circuit Motifs
  • Appendix
  • References
  • Chapter 13: Motion Detection
  • Introduction
  • Artificial Neural Networks
  • Motion Detection
  • Artificial Recurrent Network-based Motion Model
  • Discussion
  • Conclusion
  • References
  • Chapter 14: Computation in the Olfactory System
  • Introduction
  • Olfactory Networks
  • Computational Models of Olfactory Function
  • Conclusions
  • References
  • Chapter 15: Computational Models of Olfaction in Fruit Flies
  • Introduction: Anatomy and Physiology of Olfactory System in Fruit Flies
  • The Structure and Function of the Drosophila Olfactory System
  • Prior Models of Olfaction in Fruit Flies
  • Models of Olfactory Associative Learning
  • Memory (Brea, Urbanczik, & Senn, 2014)
  • Decorrelation and Integration Dynamics of the Antennal Lobe (AL) (Muezzinoglu, Huerta, Abarbanel, Ryan, & Rabinovich, 2009)
  • Mushroom Body as Classifier (Muezzinoglu et al., 2009b)
  • Self-organization in the Olfactory System (Nowotny, Huerta, Abarbanel, & Rabinovich, 2005)
  • Compound Odor Discrimination (Wessnitzer, Young, Armstrong, & Webb, 2012)
  • Conclusions and Future Work
  • References
  • Chapter 16: Multisensory Integration
  • Introduction
  • SC Multisensory Integration
  • Models of SC Multisensory Integration
  • Evaluation of the Multisensory Transform
  • The CTMM
  • Estimating Unisensory Inputs
  • CTMM Evaluations
  • Using the CTMM to Predict New Relationships
  • Discussion
  • Acknowledgments
  • References
  • Chapter 17: Computational Models in Social Neuroscience
  • Introduction
  • Reinforcement Learning Models
  • Social Learning
  • Observational/Vicarious Learning
  • Social Norm Learning and Conformity
  • Learning About Others
  • Mentalizing and Strategic Reasoning
  • Conclusion
  • References
  • Chapter 18: Sleep is For the Brain
  • Introduction
  • Contemporary Computational Models of Sleep and Cognition
  • The Synaptic Homeostasis Hypothesis
  • Going Beyond Modeling of Simple Sleep Effects on Memory
  • Conclusion
  • References
  • Chapter 19: Models of Neural Homeostasis
  • Introduction
  • History of Homeostasis
  • Modeling Neural Homeostasis
  • The Functional Roles of Neural Homeostasis
  • Summary
  • References
  • Part III: Models of Brain Regions and Neurotransmitters
  • Chapter 20: Striatum
  • Introduction
  • Architecture of the Striatal Network
  • Task-related Neuronal Activity in the Striatum
  • Computational Models of the Striatum
  • Summary
  • References
  • Chapter 21: Amygdala Models
  • Introduction
  • Computational Models of Amygdala
  • How to Develop a Biologically Based Computational Model
  • Next Steps in Modeling the Amygdala and the Fear Circuit
  • Acknowledgments
  • References
  • Chapter 22: Cerebellum and its Disorders
  • Introduction
  • Cerebellar Functions and Models
  • Basal Ganglia, Thalamocortical Circuitry and Cerebellum
  • Modeling Cerebellum and the Interconnected Circuits
  • Modeling Population Responses to Understand Circuit Function
  • Modeling Brain Disorders
  • Conclusion and Perspectives
  • Acknowledgments
  • References
  • Chapter 23: Models of Dynamical Synapses and Cortical Development
  • Introduction
  • Dynamical Synapses and Modulation of Neural Network Activity in Two Conditions of GABAA Reversal Potential
  • Dynamical Synapses and the Profiles of Structural Connectivity in the Two Conditions of GABAA Signaling
  • The Influence of Dynamical Synapses and Shaping the Firing Rate Activity (Hz)
  • Impact of Dynamical Synapses on the Profiles of Connectivity
  • Open Questions and Future Directions
  • Acknowledgments
  • References
  • Chapter 24: Computational Models of Memory Formation in Healthy and Diseased Microcircuits of the Hippocampus
  • What is Associative Memory?
  • Early Views of Associative Memory in Hippocampus
  • Neuronal Diversity, Microcircuits and Rhythms in the Hippocampus
  • Conclusions
  • References
  • Chapter 25: Episodic Memory and the Hippocampus
  • Introduction
  • Understanding of Episodic Memory in Marr's Three Levels
  • Computational Models of Episodic Memory
  • Associative Network Models with Symmetric Connections
  • Sequence Memory Models with Asymmetric Connections
  • Combined Approach Using Computational Model and Experiment
  • Summary
  • References
  • Chapter 26: How Do We Navigate Our Way to Places?
  • Introduction
  • Modeling Place Field Formation in Rodent Hippocampus
  • Conclusion and Discussion
  • References
  • Appendix
  • Chapter 27: Models of Neuromodulation
  • Introduction
  • Dopaminergic System
  • Serotonergic System
  • Dopamine and Serotonin Opponency
  • Cholinergic System
  • Noradrenergic System
  • Universal Models of Neuromodulation
  • Conclusions
  • References
  • Chapter 28: Neural Circuit Models of the Serotonergic System
  • Introduction
  • The Serotonergic System
  • Serotonergic effects on Neural Microcircuits
  • Serotonergic Effects on Decision Making
  • Conclusions
  • References
  • Part IV: Neural Modeling Approaches
  • Chapter 29: A Behavioral Framework for Information Representation in the Brain
  • Introduction
  • Information Processing by the Intelligent Agent
  • A Reasoned Framework of Information Representation in the Brain
  • Extension to Some Cognitive Mechanisms
  • Discussion
  • References
  • Chapter 30: Probing Human Brain Function with Artificial Neural Networks
  • Introduction
  • Modeling Brain Responses
  • Conclusion
  • References
  • Chapter 31: Large-scale Computational Models of Ongoing Brain Activity
  • Introduction-Intrinsic Dynamics and Their Origin
  • Brain Connectivity-Structure, Function and Graphs
  • Computational Models of Ongoing Spontaneous Activity
  • Discussion
  • Conclusions
  • References
  • Chapter 32: Optimizing Electrical Stimulation for Closed-loop Control of Neural Ensembles
  • Introduction
  • Electrical Stimulation Treatment of Epilepsy
  • Deep Brain Stimulation for Parkinson's Disease Treatment
  • Optimizing Electrical Stimulation for Visual Prostheses
  • Conclusion
  • References
  • Chapter 33: Complex Probabilistic Inference
  • Introduction
  • Tractable Algorithmic Approaches to Complex Inference
  • Psychological Mechanisms
  • Neural Implementations of Probabilistic Inference
  • Conclusions and Open Questions
  • Acknowledgments
  • References
  • Chapter 34: A Flexible and Efficient Hierarchical Bayesian Approach to the Exploration of Individual Differences in Cognitive-model-based Neuroscience
  • Introduction
  • Bayesian Hierarchical Modeling
  • Plausible Values
  • Discussion
  • References
  • Chapter 35: Information Theory, Memory, Prediction, and Timing in Associative Learning
  • Introduction
  • The Analytic Theory of Associative Learning
  • Outline of the Theory
  • A Theoretical About-Face: Conditioning as a Special Case of Cue Competition
  • Finding Principles for Determining the On-deck Models
  • Discussion
  • Acknowledgments
  • References
  • Chapter 36: The Utility of Phase Models in Studying Neural Synchronization
  • Introduction
  • Derivation of the Phase Model
  • Phase Response Curve
  • Two Weakly Coupled Oscillators
  • Summary of Reciprocal Coupling
  • Conclusion
  • Morris-Lecar Model
  • References
  • Chapter 37: Phase Oscillator Network Models of Brain Dynamics
  • Introduction
  • Theta Neurons
  • Winfree Oscillators
  • Conclusion and Discussion
  • Acknowledgments
  • References
  • Chapter 38: The Neuronal Signal and Its Models
  • The Action Potential
  • Propagation of the Action Potential
  • The Experimental Acquisition of the Neuronal Signal and the Quantitative Models
  • Continuous-time Models
  • Discrete-time Models
  • An Approach Based on Signal Waveform Modeling
  • Conclusion and Remarks
  • Acknowledgments
  • References
  • Chapter 39: History Dependent Neuronal Activity Modeled with Fractional Order Dynamics
  • Modeling of History Dependent Neuronal Activity
  • The Emergent Dynamics from History Dependent Processes
  • Using Fractional Order Differential Equations to Model History Dependent Power Law Behavior
  • Numerical Integration of Fractional Order Differential Equations
  • Fractional Order Models of Neuronal Activity
  • Biophysical and Computational Interpretations
  • Software
  • Acknowledgments
  • References
  • Index
  • EULA

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