Dynamic Neuroscience Statistics, Modeling, and Control - Chen, Zhe.; Sarma, Sridevi V.; SpringerLink (Online service) (Cham : Springer International Publishing : Imprint: Springer, 2018)
This book shows how to develop efficient quantitative methods to characterize neural data and extra information that reveals underlying dynamics and neurophysiological mechanisms. Written by active experts in the field, it contains an exchange of innovative ideas among researchers at both computational and experimental ends, as well as those at the interface. Authors discuss research challenges and new directions in emerging areas with two goals in mind: to collect recent advances in statistics, signal processing, modeling, and control methods in neuroscience; and to welcome and foster innovative or cross-disciplinary ideas along this line of research and discuss important research issues in neural data analysis. Making use of both tutorial and review materials, this book is written for neural, electrical, and biomedical engineers; computational neuroscientists; statisticians; computer scientists; and clinical engineers. Presents innovative methodological and algorithmic development in statistics, modeling, control, and signal processing for neural data analysis; Includes a coherent framework for a broad class of neural signal processing and control problems in neuroscience; Covers a wide range of representative case studies in neuroscience applications.
Introduction -- Part I Statistics & Signal Processing -- Characterizing Complex, Multi-scale Neural Phenomena Using State-Space Models -- Latent Variable Modeling of Neural Population Dynamics -- What Can Trial-to-Trial Variability Tell Us? A Distribution-Based Approach to Spike Train Decoding in the Rat Hippocampus and Entorhinal Cortex -- Sparsity Meets Dynamics: Robust Solutions to Neuronal Identification and Inverse Problems -- Artifact Rejection for Concurrent TMS-EEG Data -- Part II Modeling & Control Theory -- Characterizing Complex Human Behaviors and Neural Responses Using Dynamic Models -- Brain-Machine Interfaces -- Control-theoretic Approaches for Modeling, Analyzing and Manipulating Neuronal (In)activity -- From Physiological Signals to Pulsatile Dynamics: A Sparse System Identification Approach -- Neural Engine Hypothesis -- Inferring Neuronal Network Mechanisms Underlying Anesthesia induced Oscillations Using Mathematical Models -- Epilogue.
Keywords: Biomedical engineering; Signal processing; Bioinformatics; Neurosciences; Statistics .; Neural networks (Computer science) .; Biomedical Engineering and Bioengineering; Signal, Speech and Image Processing .; Computational and Systems Biology; Neuroscience; Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Mathematical Models of Cognitive Processes and Neural Networks