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Neural Control Engineering: The Emerging Intersection between Control Theory and Neuroscience [Hardback]

4.33/5 (12 ratings by Goodreads)
(Pennsylvania State University)
  • Formāts: Hardback, 384 pages, height x width x depth: 229x178x16 mm, weight: 839 g, 203 b&w illus., 2 tables, 31 color plates
  • Sērija : Computational Neuroscience Series
  • Izdošanas datums: 10-Nov-2011
  • Izdevniecība: MIT Press
  • ISBN-10: 0262015374
  • ISBN-13: 9780262015370
Citas grāmatas par šo tēmu:
  • Formāts: Hardback, 384 pages, height x width x depth: 229x178x16 mm, weight: 839 g, 203 b&w illus., 2 tables, 31 color plates
  • Sērija : Computational Neuroscience Series
  • Izdošanas datums: 10-Nov-2011
  • Izdevniecība: MIT Press
  • ISBN-10: 0262015374
  • ISBN-13: 9780262015370
Citas grāmatas par šo tēmu:
How powerful new methods in nonlinear control engineering can be applied to neuroscience, from fundamental model formulation to advanced medical applications.

Over the past sixty years, powerful methods of model-based control engineering have been responsible for such dramatic advances in engineering systems as autolanding aircraft, autonomous vehicles, and even weather forecasting. Over those same decades, our models of the nervous system have evolved from single-cell membranes to neuronal networks to large-scale models of the human brain. Yet until recently control theory was completely inapplicable to the types of nonlinear models being developed in neuroscience. The revolution in nonlinear control engineering in the late 1990s has made the intersection of control theory and neuroscience possible. In Neural Control Engineering, Steven Schiff seeks to bridge the two fields, examining the application of new methods in nonlinear control engineering to neuroscience.

After presenting extensive material on formulating computational neuroscience models in a control environment--including some fundamentals of the algorithms helpful in crossing the divide from intuition to effective application--Schiff examines a range of applications, including brain-machine interfaces and neural simulation. He reports on research that he and his colleagues have undertaken showing that nonlinear control theory methods can be applied to models of single cells, small neuronal networks, and large-scale networks in disease states of Parkinson’s disease and epilepsy.

With Neural Control Engineering the reader acquires a working knowledge of the fundamentals of control theory and computational neuroscience sufficient not only to understand the literature in this trandisciplinary area but also to begin working to advance the field. The book will serve as an essential guide for scientists in either biology or engineering and for physicians who wish to gain expertise in these areas.
Series Foreword xi
Preface xiii
1 Introduction
1(24)
1.1 Overview
1(2)
1.2 A Motivational Example
3(7)
1.3 Least Squares
10(4)
1.4 Expectation and Covariance
14(3)
1.5 Recursive Least Squares
17(2)
1.6 It's a Bayesian World
19(6)
2 Kalman Filtering
25(16)
2.1 Linear Kalman Filtering
25(4)
2.2 Nonlinear Kalman Filtering
29(8)
2.3 Why Not Neuroscience?
37(4)
3 The Hodgkin-Huxley Equations
41(38)
3.1 Pre-Hodgkin and Huxley
41(2)
3.2 Hodgkin and Huxley and Colleagues
43(6)
3.3 Hodgkin and Huxley
49(30)
4 Simplified Neuronal Models
79(18)
4.1 The Van der Pol Equations
79(3)
4.2 Frequency Demultiplication
82(1)
4.3 Bonhoeffer and the Passivation of Iron
82(4)
4.4 Fitzhugh and Neural Dynamics
86(5)
4.5 Nagumo's Electrical Circuit
91(1)
4.6 Rinzel's Reduction
91(3)
4.7 Simplified Models and Control
94(3)
5 Bridging from Kalman to Neuron
97(18)
5.1 Introduction
97(2)
5.2 Variables and Parameters
99(2)
5.3 Tracking the Lorenz System
101(3)
5.4 Parameter Tracking
104(2)
5.5 The Fitzhugh-Nagumo Equations
106(9)
6 Spatiotemporal Cortical Dynamics---The Wilson Cowan Equations
115(38)
6.1 Before Wilson and Cowan
115(1)
6.2 Wilson and Cowan before 1973
116(10)
6.3 Wilson and Cowan during 1973
126(5)
6.4 Wilson and Cowan after 1973
131(2)
6.5 Spirals, Rings, and Chaotic Waves in Brain
133(6)
6.6 Wilson-Cowan in a Control Framework
139(14)
7 Empirical Models
153(30)
7.1 Overview
153(3)
7.2 The Second Rehnquist Court
156(5)
7.3 The Geometry of Singular Value Decomposition
161(3)
7.4 Static Image Decomposition
164(2)
7.5 Dynamic Spatiotemporal Image Analysis
166(2)
7.6 Spatiotemporal Brain Dynamics
168(15)
8 Model Inadequacy
183(32)
8.1 Introduction
183(2)
8.2 The Philosophy of Model Inadequacy
185(1)
8.3 The Mapping Paradigm---Initial Conditions
186(4)
8.4 The Transformation Paradigm
190(5)
8.5 Generalized Synchrony
195(4)
8.6 Data Assimilation as Synchronization of Truth and Model
199(9)
8.7 The Consensus Set
208(7)
9 Brain-Machine Interfaces
215(22)
9.1 Overview
215(1)
9.2 The Brain
215(1)
9.3 In the Beginning
216(3)
9.4 After the Beginning
219(5)
9.5 Beyond Bins---Moving from Rates to Points in Time
224(3)
9.6 Back from the Future
227(5)
9.7 When Bad Models Happen to Good Monkeys
232(2)
9.8 Toward the Future
234(3)
10 Parkinson's Disease
237(36)
10.1 Overview
237(2)
10.2 The Networks of Parkinson's Disease
239(1)
10.3 The Thalamus---It's Not a Simple Relay Anymore
240(1)
10.4 The Contribution of China White
241(1)
10.5 Dynamics of Parkinson's Networks
242(4)
10.6 The Deep Brain Stimulation Paradox
246(2)
10.7 Reductionist Cracking the Deep Brain Stimulation Paradox
248(8)
10.8 A Cost Function for Deep Brain Stimulation
256(3)
10.9 Fusing Experimental GPi Recordings with DBS Models
259(1)
10.10 Toward a Control Framework for Parkinson's Disease
259(10)
10.11 Looking Foward
269(4)
11 Control Systems with Electrical Fields
273(28)
11.1 Introduction
273(1)
11.2 A Brief History of the Science of Electrical Fields and Neurons
273(4)
11.3 Applications of Electrical Fields in Vitro
277(2)
11.4 A Brief Affair with Chaos
279(4)
11.5 And a Fling with Ice Ages
283(1)
11.6 Feedback Control with Electrical Fields
284(3)
11.7 Controlling Propagation---Speed Bumps for the Brain
287(2)
11.8 Neurons in the Resistive Brain
289(1)
11.9 How Small an Electrical Field Will Modulate Neuronal Activity?
290(3)
11.10 Transcranial Low-Frequency Fields
293(1)
11.11 Electrical Fields for Control Within the Intact Brain
293(4)
11.12 To Sleep Perchance to Dream
297(1)
11.13 Toward an Implantable Field Controller
298(3)
12 Assimilating Seizures
301(26)
12.1 Introduction
301(1)
12.2 Hodgkin-Huxley Revisited
302(4)
12.3 The Dynamics of Potassium
306(10)
12.4 Control of Single Cells with Hodgkin-Huxley and Potassium Dynamics
316(3)
12.5 Assimilating Seizures
319(1)
12.6 Assimilation in the Intact Brain
320(3)
12.7 Perspective
323(4)
13 Assimilating Minds
327(10)
13.1 We Are All State Estimation Machines
327(1)
13.2 Of Mind and Matter
327(1)
13.3 Robot Beliefs versus Cogito Ergo MRI
328(2)
13.4 Black versus Gray Swans
330(2)
13.5 Mirror, Mirror, within My Mind
332(1)
13.6 Carl Jung's Synchronicity
333(4)
Bibliography 337(20)
Index 357