Details

Subject(s)

• Computational neuroscience [Browse]
• Neurobiology—Mathematical models [Browse]

Editor

• Moustafa, Ahmed, 1977- [Browse]

Series

THEi Wiley ebooks. [More in this series]

Bibliographic references

Includes bibliographical references at the end of each chapters and index.

System details

Access using campus network via VPN at home (THEi Users Only).

Source of description

Description based on print version record.

Contents

• 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
• Theories of Intellectual Disability and Atypical Development
• Down Syndrome
• Computational Approximations for Understanding Intellectual Disability in Down Syndrome
• Future Directions
• Concluding Remarks
• Acknowledgments
• Chapter 3: Computational Psychiatry
• 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
• Chapter 4: Computational Models of Post-traumatic Stress Disorder (PTSD)
• Models of Fear Conditioning.
• Limitations and Future Directions
• Models of Changes in Arousal and Reactivity
• Models of Avoidance
• Models of Changes in Cognition and Mood
• Models of Intrusive Recollection
• Conclusions
• Chapter 5: Reward Processing in Depression
• 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
• Chapter 6: Neurocomputational Models of Schizophrenia
• Models of Cognition in Schizophrenia
• Models of Schizophrenia Symptoms
• Models of Pharmacological and Nonpharmacological Treatment of Schizophrenia
• Chapter 7: Oscillatory Dynamics of Brain Microcircuits
• Oscillatory Brain Microcircuits-Modeling Perspectives
• Theta/Gamma Oscillations in the Hippocampus and Alzheimer's Disease
• 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
• Chapter 9: Modeling Deep Brain Stimulation for Parkinson's Disease
• Volume Conductor Models of DBS
• Network Models of DBS
• Mean-field Models of DBS
• Simulation of Closed-loop Control of DBS
• 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
• Chapter 11: Multiscale Computer Modeling of Epilepsy
• Multiscale Modeling
• Dynamics
• Some Specific Models
• Conclusion
• Part II: Neural Models of Behavioral Processes
• Chapter 12: Simple Models of Sensory Information Processing
• Simple Models for Single Neurons
• Simple Models of Small Circuit Motifs
• Appendix
• Chapter 13: Motion Detection
• Artificial Neural Networks
• Motion Detection
• Artificial Recurrent Network-based Motion Model
• Discussion
• Chapter 14: Computation in the Olfactory System
• Olfactory Networks
• Computational Models of Olfactory Function
• 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
• Chapter 16: Multisensory Integration
• 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
• Chapter 17: Computational Models in Social Neuroscience
• Reinforcement Learning Models
• Social Learning
• Observational/Vicarious Learning
• Social Norm Learning and Conformity
• Learning About Others
• Mentalizing and Strategic Reasoning
• Chapter 18: Sleep is For the Brain
• Contemporary Computational Models of Sleep and Cognition
• The Synaptic Homeostasis Hypothesis
• Going Beyond Modeling of Simple Sleep Effects on Memory
• Chapter 19: Models of Neural Homeostasis
• History of Homeostasis
• Modeling Neural Homeostasis
• The Functional Roles of Neural Homeostasis
• Part III: Models of Brain Regions and Neurotransmitters
• Chapter 20: Striatum
• Architecture of the Striatal Network
• Task-related Neuronal Activity in the Striatum
• Computational Models of the Striatum
• Chapter 21: Amygdala Models
• Computational Models of Amygdala
• How to Develop a Biologically Based Computational Model
• Next Steps in Modeling the Amygdala and the Fear Circuit
• Chapter 22: Cerebellum and its Disorders.
• 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
• Chapter 23: Models of Dynamical Synapses and Cortical Development
• 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
• 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
• Chapter 25: Episodic Memory and the Hippocampus
• 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
• Chapter 26: How Do We Navigate Our Way to Places?
• Modeling Place Field Formation in Rodent Hippocampus
• Conclusion and Discussion
• Chapter 27: Models of Neuromodulation
• Dopaminergic System
• Serotonergic System
• Dopamine and Serotonin Opponency
• Cholinergic System
• Noradrenergic System.
• Universal Models of Neuromodulation.

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ISBN

• 1-119-15918-0
• 1-119-15907-5
• 1-119-15919-9

OCLC

979994420

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