LEADER 07199nam 2200709Ia 450 001 9910462011603321 005 20211025235308.0 010 $a1-283-83468-5 010 $a0-262-31208-5 035 $a(CKB)2670000000277345 035 $a(EBL)3339541 035 $a(SSID)ssj0000759772 035 $a(PQKBManifestationID)12378785 035 $a(PQKBTitleCode)TC0000759772 035 $a(PQKBWorkID)10801498 035 $a(PQKB)11622495 035 $a(MiAaPQ)EBC3339541 035 $a(OCoLC)822566746$z(OCoLC)818727347$z(OCoLC)961564329$z(OCoLC)962674938$z(OCoLC)964588565$z(OCoLC)965143100$z(OCoLC)966214607$z(OCoLC)988451370$z(OCoLC)991962695$z(OCoLC)1037906098$z(OCoLC)1038667406$z(OCoLC)1045506108$z(OCoLC)1055389275$z(OCoLC)1066454359$z(OCoLC)1081240464 035 $a(OCoLC-P)822566746 035 $a(MaCbMITP)8436 035 $a(Au-PeEL)EBL3339541 035 $a(CaPaEBR)ebr10629186 035 $a(CaONFJC)MIL414718 035 $a(OCoLC)822566746 035 $a(EXLCZ)992670000000277345 100 $a20100830d2012 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aNeural control engineering $ethe emerging intersection between control theory and neuroscience /$fSteven J. Schiff 210 $aCambridge, MA $cMIT Press$dc2012 215 $a1 online resource (403 p.) 225 1 $aComputational neuroscience series 300 $aDescription based upon print version of record. 311 1 $a0-262-01537-4 320 $aIncludes bibliographical references (p. [337]-356) and index. 327 $aContents; 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 327 $a4.3 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 Before Wilson and Cowan; 6.2 Wilson and Cowan before 1973; 6.3 Wilson and Cowan during 1973; 6.4 Wilson and Cowan after 1973 327 $a6.5 Spirals, Rings, and Chaotic Waves 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 327 $a8.7 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 327 $a10.7 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 Forward; 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 327 $a11.7 Controlling Propagation-Speed Bumps for the Brain 330 $aHow powerful new methods in nonlinear control engineering can be applied to neuroscience, from fundamental model formulation to advanced medical applications. Over the past sixty years, powerful methods of model-based control engineering have been responsible for such dramatic advances in engineering systems as autolanding aircraft, autonomous vehicles, and even weather forecasting. Over those same decades, our models of the nervous system have evolved from single-cell membranes to neuronal networks to large-scale models of the human brain. Yet until recently control theory was completely inapplicable to the types of nonlinear models being developed in neuroscience. The revolution in nonlinear control engineering in the late 1990's has made the intersection of control theory and neuroscience possible. In Neural Control Engineering, Steven Schiff seeks to bridge the two fields, examining the application of new methods in nonlinear control engineering to neuroscience. After presenting extensive material on formulating computational neuroscience models in a control environment--including some fundamentals of the algorithms helpful in crossing the divide from intuition to effective application--Schiff examines a range of applications, including brain-machine interfaces and neural stimulation. He reports on research that he and his colleagues have undertaken showing that nonlinear control theory methods can be applied to models of single cells, small neuronal networks, and large-scale networks in disease states of Parkinson's disease and epilepsy. With Neural Control Engineering the reader acquires a working knowledge of the fundamentals of control theory and computational neuroscience sufficient not only to understand the literature in this trandisciplinary area but also to begin working to advance the field. The book will serve as an essential guide for scientists in either biology or engineering and for physicians who wish to gain expertise in these areas. 410 0$aComputational neuroscience. 606 $aComputational neuroscience 606 $aNonlinear control theory 606 $aNeural Networks (Computer) 608 $aElectronic books. 615 0$aComputational neuroscience. 615 0$aNonlinear control theory. 615 12$aNeural Networks (Computer) 676 $a612.8 700 $aSchiff$b Steven J$01047224 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910462011603321 996 $aNeural control engineering$92474671 997 $aUNINA