Neural Control Engineering : The Emerging Intersection Between Control Theory and Neuroscience.

Intro -- Contents -- Series Foreword -- Preface -- Chapter 1. Introduction -- 1.1 Overview -- 1.2 A Motivational Example -- 1.3 Least Squares -- 1.4 Expectation and Covariance -- 1.5 Recursive Least Squares -- 1.6 It's a Bayesian World -- Exercises -- Chapter 2. Kalman Filtering -- 2.1 Linear Kalman Filtering -- 2.2 Nonlinear Kalman Filtering -- 2.3 Why Not Neuroscience? -- Exercises -- Chapter 3. The Hodgkin-Huxley Equations -- 3.1 Pre-Hodgkin and Huxley -- 3.2 Hodgkin and Huxley and Colleagues -- 3.3 Hodgkin and Huxley -- Exercises -- Chapter 4. Simplified Neuronal Models -- 4.1 The Van der Pol Equations -- 4.2 Frequency Demultiplication -- 4.3 Bonhoeffer and the Passivation of Iron -- 4.4 Fitzhugh and Neural Dynamics -- 4.5 Nagumo's Electrical Circuit -- 4.6 Rinzel's Reduction -- 4.7 Simplified Models and Control -- Exercises -- Chapter 5. Bridging from Kalman to Neuron -- 5.1 Introduction -- 5.2 Variables and Parameters -- 5.3 Tracking the Lorenz System -- 5.4 Parameter Tracking -- 5.5 The Fitzhugh-Nagumo Equations -- Exercises -- Chapter 6. Spatiotemporal Cortical Dynamics-The Wilson Cowan Equations -- 6.1 BeforeWilson and Cowan -- 6.2 Wilson and Cowan before 1973 -- 6.3 Wilson and Cowan during 1973 -- 6.4 Wilson and Cowan after 1973 -- 6.5 Spirals, Rings, and ChaoticWaves in Brain -- 6.6 Wilson-Cowan in a Control Framework -- Exercises -- Chapter 7. Empirical Models -- 7.1 Overview -- 7.2 The Second Rehnquist Court -- 7.3 The Geometry of Singular Value Decomposition -- 7.4 Static Image Decomposition -- 7.5 Dynamic Spatiotemporal Image Analysis -- 7.6 Spatiotemporal Brain Dynamics -- Exercises -- Chapter 8. Model Inadequacy -- 8.1 Introduction -- 8.2 The Philosophy of Model Inadequacy -- 8.3 The Mapping Paradigm-Initial Conditions -- 8.4 The Transformation Paradigm -- 8.5 Generalized Synchrony.

8.6 Data Assimilation as Synchronization of Truth and Model -- 8.7 The Consensus Set -- Exercises -- Chapter 9. Brain-Machine Interfaces -- 9.1 Overview -- 9.2 The Brain -- 9.3 In the Beginning -- 9.4 After the Beginning -- 9.5 Beyond Bins-Moving from Rates to Points in Time -- 9.6 Back from the Future -- 9.7 When Bad Models Happen to Good Monkeys -- 9.8 Toward the Future -- Chapter 10. Parkinson's Disease -- 10.1 Overview -- 10.2 The Networks of Parkinson's Disease -- 10.3 The Thalamus-It's Not a Simple Relay Anymore -- 10.4 The Contribution of China White -- 10.5 Dynamics of Parkinson's Networks -- 10.6 The Deep Brain Stimulation Paradox -- 10.7 Reductionist Cracking the Deep Brain Stimulation Paradox -- 10.8 A Cost Function for Deep Brain Stimulation -- 10.9 Fusing Experimental GPi Recordings with DBS Models -- 10.10 Toward a Control Framework for Parkinson's Disease -- 10.11 Looking Foward -- Chapter 11. Control Systems with Electrical Fields -- 11.1 Introduction -- 11.2 A Brief History of the Science of Electrical Fields and Neurons -- 11.3 Applications of Electrical Fields in Vitro -- 11.4 A Brief Affair with Chaos -- 11.5 And a Fling with Ice Ages -- 11.6 Feedback Control with Electrical Fields -- 11.7 Controlling Propagation-Speed Bumps for the Brain -- 11.8 Neurons in the Resistive Brain -- 11.9 How Small an Electrical FieldWill Modulate Neuronal Activity? -- 11.10 Transcranial Low-Frequency Fields -- 11.11 Electrical Fields for ControlWithin the Intact Brain -- 11.12 To Sleep Perchance to Dream -- 11.13 Toward an Implantable Field Controller -- Chapter 12. Assimilating Seizures -- 12.1 Introduction -- 12.2 Hodgkin-Huxley Revisited -- 12.3 The Dynamics of Potassium -- 12.4 Control of Single Cells with Hodgkin-Huxley and Potassium Dynamics -- 12.5 Assimilating Seizures -- 12.6 Assimilation in the Intact Brain -- 12.7 Perspective.

Chapter 13. Assimilating Minds -- 13.1 We Are All State Estimation Machines -- 13.2 Of Mind and Matter -- 13.3 Robot Beliefs versus Cogito Ergo MRI -- 13.4 Black versus Gray Swans -- 13.5 Mirror, Mirror, within My Mind -- 13.6 Carl Jung's Synchronicity -- Bibliography -- Index.

How powerful new methods in nonlinear control engineering can be applied to neuroscience, from fundamental model formulation to advanced medical applications.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.