Dynamic Neuroscience

TRAMA

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.

SOMMARIO

1. IntroductionPart I Statistics & Signal Processing2 Characterizing Complex, Multi-scale Neural Phenomena Using State-Space Models3 Latent Variable Modeling of Neural Population Dynamics4 What Can Trial-to-Trial Variability Tell Us? A Distribution-Based Approach to Spike Train Decoding in the Rat Hippocampus and Entorhinal Cortex5 Sparsity Meets Dynamics: Robust Solutions to Neuronal Identification and Inverse Problems6 Artifact Rejection for Concurrent TMS-EEG DataPart II Modeling & Control Theory7 Characterizing Complex Human Behaviors and Neural Responses Using Dynamic Models8 Brain-Machine Interfaces9 Control-theoretic Approaches for Modeling, Analyzing and Manipulating Neuronal (In)activity10 From Physiological Signals to Pulsatile Dynamics: A Sparse System Identification Approach11 Neural Engine Hypothesis12 Inferring Neuronal Network Mechanisms Underlying Anesthesia induced Oscillations Using Mathematical ModelsEpilogue

AUTORE

Zhe Chen is Assistant Professor in the Departments of Psychiatry and Neuroscience and Physiology at New York University School of Medicine, having previously worked at the RIKEN Brain Science Institute, Harvard Medical School, and Massachusetts Institute of Technology. He is a Senior Member of the IEEE, and an editorial board member of Neural Networks (Elsevier) and Journal of Neural Engineering (IOP). Professor Chen has received a number of awards including the Early Career Award from the Mathematical Biosciences Institute, and has had his work funded by the US National Science Foundation and the National Institutes of Health. He is the lead author of the book Correlative Learning: A Basis for Brain and Adaptive Systems (Johns & Wiley, 2007) and the editor of the book Advanced State Space Methods for Neural and Clinical Data (Cambridge University Press, 2015).Sridevi Sarma is Associate Professor in the Department of Biomedical Engineering at Johns Hopkins University (JHU), having previously worked at Massachusetts Institute of Technology and Harvard Medical School. She is the Associate Director of the Institute for Computational Medicine at JHU. Professor Sarma is a recipient of the GE faculty for the future scholarship, a L'Oreal For Women in Science fellow, the Burroughs Wellcome Fund Careers at the Scientific Interface Award, the Krishna Kumar New Investigator Award from the North American Neuromodulation Society (NANS), and the Presidential Early Career Award for Scientists and Engineers (PECASE).

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9783030101398
• Dimensioni: 235 x 155 mm Ø 539 gr
• Formato: Brossura
• Illustration Notes: XXI, 327 p. 80 illus., 62 illus. in color.
• Pagine Arabe: 327
• Pagine Romane: xxi