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Closed loop neuroscience /
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London, United Kingdom :
Academic Press,
�2016.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Closed Loop Neuroscience; Copyright; Dedication; Contents; List of Contributors; Foreword and Introduction; Acknowledgments; Part I: Theoretical Axis; Chapter 1: Adaptive Bayesian Methods for Closed-Loop Neurophysiology; 1. Introduction; 2. Bayesian Active Learning; 2.1. Posterior and Predictive Distributions; 2.2. Utility Functions and Optimal Stimulus Selection; 2.2.1. Maximum Mutual Information (Infomax); 2.2.2. Minimum Mean Squared Error (MMSE); 2.2.3. Other Utility Functions; 2.2.4. Uncertainty Sampling; 3. Application: Tuning Curve Estimation; 3.1. Poisson Encoding Model.
- 3.2. Parametric Tuning Curves3.3. Nonparametric Tuning Curves With Gaussian Process Priors; 3.3.1. Gaussian Processes; 3.3.2. Transformed Gaussian Processes; 3.3.3. Posterior Updating; 3.3.4. Infomax Learning; 3.3.5. Simulations; 4. Application: Linear Receptive Field Estimation; 4.1. Generalized Linear Model; 4.2. Infomax Stimulus Selection for Poisson GLM; 4.3. Infomax for Hierarchical RF Models; 4.3.1. Posterior Distribution; 4.3.2. Localized RF Prior; 4.3.3. Active Learning With Localized Priors; 4.4. Comparison of Methods for RF Estimation; 4.4.1. Simulated Data.
- 4.4.2. Application to Neural Data5. Discussion; 5.1. Adaptation; 5.2. Greediness; 5.3. Model Specification; 5.4. Future Directions; References; Chapter 2: Information Geometric Analysis of NeurophysiologicalData; 1. Introduction; 2. Introduction of the IGMethod; 3. IG Analysis of NeurophysiologicalData; 4. IG Measures and the Underlying Network Parameters; 5. Extension of the IGMethod; 5.1. IG Measures Under Correlated Inputs; 5.2. Correlated Inputs and Higher-Order Interactions; 5.3. Relationship Between Higher-Order IG Measures and Network Parameters.
- 5.4. IG Measures and Oscillatory Brain States5.5. State-Space Analysis of Time-Varying IGMeasures; 5.6. Estimation of Spiking Irregularities forNonstationary FiringRate; 6. Information Geometry and Closed-Loop Neuroscience; 7. Conclusion; References; Chapter 3: Control Theory for Closed-Loop Neurophysiology; 1. Introduction; 2. Dynamics of Neural Physiology and Modeling Paradigms; 2.1. Fundamentals of Neural Function; 2.2. Dynamical Systems Models: Neuron-Level Models; 2.2.1. Voltage-Gated Conductance Equations; 2.3. Statistical Models; 2.4. Mean Field Models.
- 3. From Neurostimulation to Neurocontrol3.1. Actuating the Brain: Technologies for Neurostimulation; 3.2. Actuating the Brain: Characterization of Control Inputs; 3.3. Probing Brain Circuits With Principled Objective Functions; 3.4. Control of Single Neurons; 3.5. Control of Neuronal Oscillator Networks: Synchronization; 3.6. Control of Neuronal Oscillator Networks: Desynchronization; 3.7. Control of Bursting and Seizure Activity; 4. Identification and Estimation of Neuronal Dynamics; 4.1. Inference of Neuronal Network Structure and Connectivity; 4.2. State Estimation and Kalman Filtering.