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Title Computational Neuroscience [electronic resource] / edited by Kim T. Blackwell
Imprint Burlington : Elsevier Science, 2014
book jacket
Descript 1 online resource (436 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Series Progress in molecular biology and translational science ; 123
Progress in molecular biology and translational science ; 123
Note Front Cover; Computational Neuroscience; Copyright; Contents; Contributors; Preface; Chapter One: Markov Modeling of Ion Channels: Implications for Understanding Disease; 1. Why Do We Need Modeling?; 1.1. Ion channel diseases: Mutations focus attention on ion channels; 1.2. Structure and function of voltage-gated sodium channels; 1.3. Epilepsy and pain can be induced by sodium channel mutations; 1.4. How can Markov models help understand the pathophysiology of channelopathies?; 2. Markov Models Built Based on Whole-Cell Patch-Clamp Data; 3. Practical Considerations for Fitting Models to Data
3.1. Fitting experimental data to Markov models4. Conclusion and Outlook; Acknowledgments; References; Chapter Two: Ionic Mechanisms in Peripheral Pain; 1. Biological Background; 2. Modeling of Peripheral Pain; 2.1. Role of Na currents; 2.2. Role of Ih; 2.3. Subthreshold spontaneous oscillations; 2.4. Repetitive spiking; 2.5. Changes in the central terminal, windup; 2.6. Models of action potential propagation; 3. In Silico Pharmacology; 3.1. Pharmaceutical target approach; 3.2. Advantages of modeling approaches; 3.3. Constructing models from data; 3.4. Quality of solution and model
3.5. An example of applying computational search and addressing robustness4. Conclusion; References; Chapter Three: Implications of Cellular Models of Dopamine Neurons for Schizophrenia; 1. Dopamine Neuron Electrophysiology; 2. Depolarization Block Hypothesis of Antipsychotic Drug Action; 3. Computational Model of Pacemaking and Depolarization Block; 4. Availability of Sodium Current Controls Entry into DP Block; 5. The Ether-a-Go-Go-Related Gene Potassium Channel and Schizophrenia; 6. ERG Conductance Both Delays Entry into and Speeds Recovery from Depolarization Block
7. Computational Model of Bursting and DP Block8. Conclusions; Acknowledgment; Appendix: Full Model Equations and Parameters; References; Chapter Four: The Role of IP3 Receptor Channel Clustering in Ca2+ Wave Propagation During Oocyte Maturation; 1. Introduction; 2. Methods; 3. Results; 4. Discussion; Acknowledgment; References; Chapter Five: Modeling Mitochondrial Function and Its Role in Disease; 1. Introduction; 2. Energy Metabolism; 2.1. Tricarboxylic acid cycle; 2.2. Substrate entry into the TCA cycle; 2.3. Oxidative phosphorylation; 2.4. Substrate transport
2.5. Ionic and substrate homeostasis3. Mitochondrial Signaling; 4. Mitochondria in Disease; 4.1. Ischemic disease; 4.2. Neurodegenerative disease; 5. Models of Mitochondrial Energy Metabolism; 6. Models of Mitochondrial Signaling; 7. Concluding Remarks; Acknowledgments; References; Chapter Six: Mathematical Modeling of Neuronal Polarization During Development; 1. Biological Background; 2. Biophysical Model; 2.1. Model for active transport, diffusion, and degradation of factor X; 2.2. Model for regulation of neurite growth by factor Y; 3. Mathematical Analysis; 3.1. Factor X along neurite
3.2. Factor X in the soma
Progress in Molecular Biology and Translational Science provides a forum for discussion of new discoveries, approaches, and ideas in molecular biology. It contains contributions from leaders in their fields and abundant references. This volume brings together different aspects of, and approaches to, molecular and multi-scale modeling, with applications to a diverse range of neurological diseases. Mathematical and computational modeling offers a powerful approach for examining the interaction between molecular pathways and ionic channels in producing neuron electrical activity
Description based on print version record
Link Print version: Blackwell, Kim L. Computational Neuroscience. Burlington : Elsevier Science, ©2014 9780123978974
Subject Computational neuroscience
Models, Neurological
Neural networks (Neurobiology)
Neurobiology -- Mathematical models
Electronic books
Alt Author Blackwell, Kim L
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