Closed Loop Neuroscience

__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. * Presents the first volume to offer researchers a comprehensive overview of the technical realities of employing closed loop techniques in their work * Offers application to in-vitro, in-vivo, and hybrid systems * Contains an emphasis on the actual techniques used rather than on specific results obtained * Includes exhaustive protocols and descriptions of software and hardware, making it easy for readers to implement the proposed methodologies * Encompasses the clinical/neuroprosthetic aspect and how these systems can also be used to contribute to our understanding of basic neurophysiology * Edited work with chapters authored by leaders in the field from around the globe – the broadest, most expert coverage available 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 Ofneurophysiologicaldata; 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. Ahmed El Hady. Includes Index. Mode Of Access: World Wide Web. Content: Front Matter,Copyright,Dedication,List of Contributors,Foreword and Introduction,AcknowledgmentsEntitled to full textPart I: Theoretical AxisChapter 1 - Adaptive Bayesian Methods for Closed-Loop Neurophysiology, Pages 3-18 Chapter 2 - Information Geometric Analysis of Neurophysiological Data, Pages 19-34 Chapter 3 - Control Theory for Closed-Loop Neurophysiology, Pages 35-52 Chapter 4 - Testing the Theory of Practopoiesis Using Closed Loops, Pages 53-65 Chapter 5 - Local Field Potential Analysis for Closed-Loop Neuromodulation, Pages 67-80 Chapter 6 - Online Event Detection Requirements in Closed-Loop Neuroscience, Pages 81-91 Chapter 7 - Closing Dewey's Circuit, Pages 93-100 Chapter 8 - Stochastic Optimal Control of Spike Times in Single Neurons, Pages 101-111 Chapter 9 - Hybrid Systems Neuroscience, Pages 113-129 Chapter 10 - Computational Complexity and the Function-Structure-Environment Loop of the Brain, Pages 131-144 Chapter 11 - Subjective Physics, Pages 145-169 Chapter 12 - Contextual Emergence in Neuroscience, Pages 171-184 Chapter 13 - Closed-Loop Methodologies for Cellular Electrophysiology, Pages 187-199 Chapter 14 - Bidirectional Brain–Machine Interfaces, Pages 201-212 Chapter 15 - Adaptive Brain Stimulation for Parkinson's Disease, Pages 213-222 Chapter 16 - Closed-Loop Neuroprosthetics☆, Pages 223-227 Chapter 17 - Closed-Loop Stimulation in Emotional Circuits for Neuro-Psychiatric Disorders, Pages 229-239 Chapter 18 - Conscious Brain-to-Brain Communication Using Noninvasive Technologies☆, Pages 241-256 Chapter 19 - Philosophical Aspects of Closed-Loop Neuroscience, Pages 259-270 Chapter 20 - Closed Loops in Neuroscience and Computation: What It Means and Why It Matters, Pages 271-277 Subject Index, Pages 279-284

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.

• Presents the first volume to offer researchers a comprehensive overview of the technical realities of employing closed loop techniques in their work
• Offers application to in-vitro, in-vivo, and hybrid systems
• Contains an emphasis on the actual techniques used rather than on specific results obtained
• Includes exhaustive protocols and descriptions of software and hardware, making it easy for readers to implement the proposed methodologies
• Encompasses the clinical/neuroprosthetic aspect and how these systems can also be used to contribute to our understanding of basic neurophysiology
• Edited work with chapters authored by leaders in the field from around the globe – the broadest, most expert coverage available

"Closed Loop Neuroscience is a reference work that aims to introduce a new framework through which the brain is regarded as a closed loop system. The book is built on three axes: theoretical, experimental and philosophical, providing a multidisciplinary approach to the problem of how the brain can be realistically modeled. In the past five years, there has been a growing interest in closed loop approaches in experimentation. This book not only introduces closed loop experimental techniques, it also lays the foundation for a new framework through which the brain is viewed as inseparable from its environment. In the era of large-scale neuroscience, there is an increasing need to ask the foundational questions of how best to model the brain and how to probe it experimentally. Closed Loop Neuroscience will contribute significantly in this direction by questioning the very foundations of feedforward experimentation that have dominated neuroscientific investigations up to now."--Page 4 of cover