Closed Loop Neuroscience addresses the technical aspects of closed loop neurophysiology, presenting the implementation of these approaches spanning several domains of neuroscience, from cellular and network neurophysiology, through sensory and motor systems, and then clinical therapeutic devices.
Although closed-loop approaches have long been a part of the neuroscientific toolbox, these techniques are only now gaining popularity in research and clinical applications. As there is not yet a comprehensive methods book addressing the topic as a whole, this volume fills that gap, presenting state-of-the-art approaches and the technical advancements that enable their application to different scientific problems in neuroscience.
1. Foreword and Introduction - p. xv 2. Acknowledgments - p. xvii 3. Part I: Theoretical Axis - p. xvii 4. Chapter 1: Adaptive Bayesian Methods for Closed-Loop Neurophysiology - p. xvii 5. Chapter 2: Information Geometric Analysis of Neurophysiological Data - p. 18 6. Abstract - p. 19 7. 1 Introduction - p. 19 8. 2 Introduction of the IG Method - p. 20 9. 3 IG Analysis of Neurophysiological Data - p. 22 10. 4 IG Measures and the Underlying Network Parameters - p. 26 11. 5 Extension of the IG Method - p. 28 12. 6 Information Geometry and Closed-Loop Neuroscience - p. 32 13. 7 Conclusion - p. 32 14. Chapter 3: Control Theory for Closed-Loop Neurophysiology - p. 34 15. Chapter 4: Testing the Theory of Practopoiesis Using Closed Loops - p. 52 16. Chapter 5: Local Field Potential Analysis for Closed-Loop Neuromodulation - p. 65 17. Chapter 6: Online Event Detection Requirements in Closed-Loop Neuroscience - p. 80 18. Chapter 7: Closing Dewey's Circuit - p. 91 19. Chapter 8: Stochastic Optimal Control of Spike Times in Single Neurons - p. 100 20. Chapter 9: Hybrid Systems Neuroscience - p. 111 21. Chapter 10: Computational Complexity and the Function-Structure-Environment Loop of the Brain - p. 129 22. Chapter 11: Subjective Physics - p. 144 23. Chapter 12: Contextual Emergence in Neuroscience - p. 169 24. Part II: Experimental Axis - p. 184 25. Chapter 13: Closed-Loop Methodologies for Cellular Electrophysiology - p. 184 26. Chapter 14: Bidirectional Brain–Machine Interfaces - p. 199 27. Chapter 15: Adaptive Brain Stimulation for Parkinson's Disease - p. 212 28. Chapter 16: Closed-Loop Neuroprosthetics - p. 222 29. Chapter 17: Closed-Loop Stimulation in Emotional Circuits for Neuro-Psychiatric Disorders - p. 227 30. Chapter 18: Conscious Brain-to-Brain Communication Using Noninvasive Technologies - p. 239 31. Part III: Philosophical Axis - p. 256 32. Chapter 19: Philosophical Aspects of Closed-Loop Neuroscience - p. 256 33. Chapter 20: Closed Loops in Neuroscience and Computation: What It Means and Why It Matters - p. 270 34. Subject Index - p. 279
Closed Loop Neuroscience addresses the technical aspects of closed loop neurophysiology, presenting the implementation of these approaches spanning several domains of neuroscience, from cellular and network neurophysiology, through sensory and motor systems, and then clinical therapeutic devices.
Although closed-loop approaches have long been a part of the neuroscientific toolbox, these techniques are only now gaining popularity in research and clinical applications. As there is not yet a comprehensive methods book addressing the topic as a whole, this volume fills that gap, presenting state-of-the-art approaches and the technical advancements that enable their application to different scientific problems in neuroscience.
1. Foreword and Introduction - p. xv 2. Acknowledgments - p. xvii 3. Part I: Theoretical Axis - p. xvii 4. Chapter 1: Adaptive Bayesian Methods for Closed-Loop Neurophysiology - p. xvii 5. Chapter 2: Information Geometric Analysis of Neurophysiological Data - p. 18 6. Abstract - p. 19 7. 1 Introduction - p. 19 8. 2 Introduction of the IG Method - p. 20 9. 3 IG Analysis of Neurophysiological Data - p. 22 10. 4 IG Measures and the Underlying Network Parameters - p. 26 11. 5 Extension of the IG Method - p. 28 12. 6 Information Geometry and Closed-Loop Neuroscience - p. 32 13. 7 Conclusion - p. 32 14. Chapter 3: Control Theory for Closed-Loop Neurophysiology - p. 34 15. Chapter 4: Testing the Theory of Practopoiesis Using Closed Loops - p. 52 16. Chapter 5: Local Field Potential Analysis for Closed-Loop Neuromodulation - p. 65 17. Chapter 6: Online Event Detection Requirements in Closed-Loop Neuroscience - p. 80 18. Chapter 7: Closing Dewey's Circuit - p. 91 19. Chapter 8: Stochastic Optimal Control of Spike Times in Single Neurons - p. 100 20. Chapter 9: Hybrid Systems Neuroscience - p. 111 21. Chapter 10: Computational Complexity and the Function-Structure-Environment Loop of the Brain - p. 129 22. Chapter 11: Subjective Physics - p. 144 23. Chapter 12: Contextual Emergence in Neuroscience - p. 169 24. Part II: Experimental Axis - p. 184 25. Chapter 13: Closed-Loop Methodologies for Cellular Electrophysiology - p. 184 26. Chapter 14: Bidirectional Brain–Machine Interfaces - p. 199 27. Chapter 15: Adaptive Brain Stimulation for Parkinson's Disease - p. 212 28. Chapter 16: Closed-Loop Neuroprosthetics - p. 222 29. Chapter 17: Closed-Loop Stimulation in Emotional Circuits for Neuro-Psychiatric Disorders - p. 227 30. Chapter 18: Conscious Brain-to-Brain Communication Using Noninvasive Technologies - p. 239 31. Part III: Philosophical Axis - p. 256 32. Chapter 19: Philosophical Aspects of Closed-Loop Neuroscience - p. 256 33. Chapter 20: Closed Loops in Neuroscience and Computation: What It Means and Why It Matters - p. 270 34. Subject Index - p. 279
