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Statistical Signal Processing for Neuroscience and Neurotechnology [Kõva köide]

4.00/5 (7 hinnangut Goodreads-ist)
Edited by (Associate Professor, Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USA)
  • Formaat: Hardback, 433 pages, kõrgus x laius: 235x191 mm, kaal: 920 g
  • Ilmumisaeg: 22-Sep-2010
  • Kirjastus: Academic Press Inc
  • ISBN-10: 012375027X
  • ISBN-13: 9780123750273
Teised raamatud teemal:
Statistical Signal Processing for Neuroscience and NeurotechnologySuurem pilt
  • Formaat: Hardback, 433 pages, kõrgus x laius: 235x191 mm, kaal: 920 g
  • Ilmumisaeg: 22-Sep-2010
  • Kirjastus: Academic Press Inc
  • ISBN-10: 012375027X
  • ISBN-13: 9780123750273
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780123750273.html
Märksõnad:
This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving emerging problems in neuroscience, and which clearly presents new theory, algorithms, software and hardware tools that are specifically tailored to the nature of the neurobiological environment. It gives a broad overview of the basic principles, theories and methods in statistical signal processing for basic and applied neuroscience problems.

Written by experts in the field, the book is an ideal reference for researchers working in the field of neural engineering, neural interface, computational neuroscience, neuroinformatics, neuropsychology and neural physiology. By giving a broad overview of the basic principles, theories and methods, it is also an ideal introduction to statistical signal processing in neuroscience.
  • A comprehensive overview of the specific problems in neuroscience that require application of existing and development of new theory, techniques, and technology by the signal processing community
  • Contains state-of-the-art signal processing, information theory, and machine learning algorithms and techniques for neuroscience research
  • Presents quantitative and information-driven science that has been, or can be, applied to basic and translational neuroscience problems

Arvustused

"Large-scale recording of multiple single neurons has become an indispensable tool in system neuroscience. The chapters of this edited volume will take the reader from spike detection and processing through analyses to modeling and interpretation. Both experimentalists and theorists will benefit from the well-condensed and organized content."

György Buzsáki, M.D., Ph.D.

Center for Molecular and Behavioral Neuroscience

Rutgers University

Muu info

All the theory, algorithms, software and hardware tools for applying statistical signal processing to neuroscience
Preface xi
About the Editor xiii
About the Contributors xv
Chapter 1 Introduction
1(14)
1.1 Background
1(2)
1.2 Motivation
3(2)
1.3 Overview and Roadmap
5(7)
References
12(3)
Chapter 2 Detection and Classification of Extracellular Action Potential Recordings
15(60)
2.1 Introduction
15(1)
2.2 Spike Detection
16(16)
2.2.1 Known Spike
19(5)
2.2.2 Unknown Spike
24(2)
2.2.3 Practical Limitations
26(2)
2.2.4 Transform-Based Methods
28(4)
2.3 Spike Sorting
32(26)
2.3.1 Pattern Recognition Approach
32(5)
2.3.2 Blind Source Separation Approach
37(21)
2.4 Practical Implementation
58(7)
2.5 Discussion and Future Directions
65(4)
Acknowledgment
69(1)
References
69(6)
Chapter 3 Information-Theoretic Analysis of Neural Data
75(28)
3.1 Introduction
75(2)
3.2 The Encoder
77(6)
3.3 The Channel
83(7)
3.4 Rate-Distortion Theory
90(4)
3.5 Post-Shannon Information Theory
94(5)
3.6 Discussion
99(1)
References
100(3)
Chapter 4 Identification of Nonlinear Dynamics in Neural Population Activity
103(26)
4.1 Introduction
103(1)
4.2 Problem Statement
104(1)
4.3 Nonlinear Model of Neural Population Dynamics
105(10)
4.3.1 Model Configuration
106(3)
4.3.2 Model Estimation
109(2)
4.3.3 Model Selection
111(1)
4.3.4 Kernel Reconstruction and Interpretation
112(1)
4.3.5 Model Validation and Prediction
113(2)
4.4 Results: Application to Hippocampal CA3-CA1 Population Activity
115(6)
4.4.1 Behavioral Task
115(1)
4.4.2 Data Preprocessing
116(2)
4.4.3 CA3-CA1 MIMO Model
118(3)
4.5 Discussion
121(4)
4.6 Future Directions
125(1)
Acknowledgments
126(1)
References
126(3)
Chapter 5 Graphical Models of Functional and Effective Neuronal Connectivity
129(46)
5.1 Introduction
129(2)
5.2 Background and Overview
131(7)
5.2.1 The Crosscorrelogram
131(2)
5.2.2 Information-Theoretic Methods
133(2)
5.2.3 Granger Causality
135(1)
5.2.4 Generalized Linear Models
135(3)
5.3 Graphical Models
138(8)
5.3.1 Effective Connectivity
138(4)
5.3.2 Graph-Based Functional Connectivity Inference
142(4)
5.4 Results
146(18)
5.4.1 Spiking Neural Model
146(3)
5.4.2 Inferring Effective Connectivity
149(8)
5.4.3 Identifying Functional Connectivity
157(7)
5.5 Discussion and Future Directions
164(2)
Acknowledgments
166(1)
References
167(8)
Chapter 6 State Space Modeling of Neural Spike Train and Behavioral Data
175(44)
6.1 Introduction
175(2)
6.2 State Space Modeling Paradigm
177(3)
6.2.1 Notation
177(1)
6.2.2 Recursive Form of Bayes' Rule
178(1)
6.2.3 Classes of Filtering and Smoothing Problems
179(1)
6.3 Applications of the State Space Paradigm in Neuroscience Data Analysis
180(31)
6.3.1 Neural Spike Train Decoding and Point Process Filter Algorithms
180(6)
6.3.2 Neural Receptive Field Plasticity and Instantaneous Steepest Descent Filtering
186(4)
6.3.3 Tracking Spatial Receptive Field and Particle Filtering
190(6)
6.3.4 Dynamic Analysis of Behavioral Learning Experiments and the Expectation-Maximization Algorithm
196(6)
6.3.5 Markov Chain Monte Carlo Methods and the Analysis of Cortical UP/DOWN States
202(3)
6.3.6 State Space Smoothing, Dynamic Parameter Estimation, and Analysis of Population Learning
205(6)
6.4 Discussion
211(2)
Acknowledgments
213(1)
References
213(6)
Chapter 7 Neural Decoding for Motor and Communication Prostheses
219(46)
7.1 Introduction
219(3)
7.2 Plan and Movement Neural Activity
222(3)
7.3 Continuous Decoding for Motor Prostheses
225(15)
7.3.1 Recursive Bayesian Decoders
227(3)
7.3.2 Mixture of Trajectory Models
230(5)
7.3.3 Results
235(5)
7.4 Discrete Decoding for Communication Prostheses
240(12)
7.4.1 Independent Gaussian and Poisson Models
241(1)
7.4.2 Factor Analysis Methods
242(8)
7.4.3 Results
250(2)
7.5 Discussion
252(3)
7.6 Future Directions
255(2)
Acknowledgments
257(1)
References
258(7)
Chapter 8 Inner Products for Representation and Learning in the Spike Train Domain
265(46)
8.1 Introduction
265(3)
8.2 Functional Representations of Spike Trains
268(6)
8.2.1 Synaptic Models
268(4)
8.2.2 Intensity Estimation
272(2)
8.3 Inner Products for Spike Trains
274(11)
8.3.1 Defining Inner Products for Spike Trains
276(5)
8.3.2 Properties of the Defined Inner Products
281(2)
8.3.3 Distances Induced by Inner Products
283(2)
8.4 Applications
285(15)
8.4.1 Unsupervised Learning: Principal Component Analysis
285(9)
8.4.2 Supervised Learning: Fisher's Linear Discriminant
294(6)
8.5 Discussion
300(1)
Appendix A Higher-Order Spike Interactions through Nonlinearity
301(1)
Appendix B Proofs
302(2)
Appendix C Brief Introduction to RKHS Theory
304(1)
Acknowledgments
305(1)
References
305(6)
Chapter 9 Signal Processing and Machine Learning for Single-Trial Analysis of Simultaneously Acquired EEG and fMRI
311(24)
9.1 Introduction
311(2)
9.2 Hardware Design and Setup: Challenges in EEG/fMRI Acquisition
313(4)
9.2.1 EEG Cap Design
315(1)
9.2.2 EEG Amplifier
315(2)
9.2.3 Synchronized Sampling
317(1)
9.3 Signal Processing and Removal of BCG Artifacts
317(6)
9.3.1 The Kirchhoffian Account
319(4)
9.4 Linking Single-Trial Variations of Task-Relevant EEG Components to the Bold Signal
323(4)
9.4.1 Identifying EEG Components Using Linear Discrimination
323(1)
9.4.2 Constructing fMRI Regressors from Single-Trial Variability in EEG Components
324(3)
9.5 Results
327(3)
9.6 Future Directions
330(1)
Acknowledgments
331(1)
References
331(4)
Chapter 10 Statistical Pattern Recognition and Machine Learning in Brain-Computer Interfaces
335(34)
10.1 Introduction
335(2)
10.2 Signal Processing and Pattern Recognition in BCI Systems
337(13)
10.2.1 Signal Acquisition and Major Signal Types
337(2)
10.2.2 Pattern Recognition and Machine Learning
339(11)
10.3 Applications
350(8)
10.3.1 P300-Based Control of a Humanoid Robot
351(3)
10.3.2 Motor Imagery-Based Control of Virtual Environments
354(4)
10.4 Discussion
358(1)
Acknowledgments
359(1)
References
360(9)
Chapter 11 Prediction of Muscle Activity from Cortical Signals to Restore Hand Grasp in Subjects with Spinal Cord Injury
369(38)
11.1 Introduction
369(2)
11.2 Background
371(6)
11.2.1 BMIs as a Potential Control Solution
374(2)
11.2.2 BMIs for Control of Dynamics
376(1)
11.3 Methods
377(7)
11.3.1 Isometric Wrist Torque Tasks
378(1)
11.3.2 Hand Grasp Tasks
379(1)
11.3.3 Surgical Methods
379(2)
11.3.4 Data Collection
381(1)
11.3.5 Linear Systems Identification
381(2)
11.3.6 Nerve Block Effectiveness
383(1)
11.3.7 Stimulation
383(1)
11.3.8 Real-Time Control
384(1)
11.4 Results
384(10)
11.4.1 Offline Signal Prediction
384(5)
11.4.2 Real-Time FES Control
389(5)
11.5 Discussion
394(5)
11.5.1 Successful Proof of Concept
394(1)
11.5.2 Limitations in the Control of Complex Motor Tasks
395(2)
11.5.3 Limitations Related to the Use of FES for Control
397(2)
11.6 Future Directions
399(2)
11.6.1 Control of Higher-Dimensional Movement Using Natural Muscle Synergies
399(1)
11.6.2 Adaptation
400(1)
Acknowledgments
401(1)
References
401(6)
Index 407
Karim G. Oweiss received his B.S. (1993) and M.S. (1996) degrees with honors in electrical engineering from the University of Alexandria, Egypt, and his Ph.D. (2002) in electrical engineering and computer science from the University of Michigan, Ann Arbor. In that year he also completed postdoctoral training with the Department of Biomedical Engineering at the University of Michigan. In 2003, he joined the Department of Electrical and Computer Engineering and the Neuroscience Program at Michigan State University, where he is currently an associate professor and director of the Neural Systems Engineering Laboratory. His research interests are in statistical signal processing, information theory, machine learning, and control theory, with direct applications to studies of neuroplasticity, neural integration and coordination in sensorimotor systems, neurostimulation and neuromodulation in brain-machine interfaces, and computational neuroscience.Professor Oweiss is a member of the IEEE and the Society for Neuroscience. He served as a member of the board of directors of the IEEE Signal Processing Society on Brain-Machine Interfaces and is currently an active member of the technical and editorial committees of the IEEE Biomedical Circuits and Systems Society, the IEEE Life Sciences Society, and the IEEE Engineering in Medicine and Biology Society. He is also associate editor of IEEE Signal Processing Letters, Journal of Computational Intelligence and Neuroscience, and EURASIP Journal on Advances in Signal Processing. He currently serves on an NIH Federal Advisory Committee for the Emerging Technologies and Training in Neurosciences. In 2001, Professor Oweiss received the Excellence in Neural Engineering Award from the National Science Foundation.