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Dynamic Neuroscience

Statistics, Modeling, and Control

Editors: Chen, Zhe, Sarma, Sridevi V. (Eds.)

  • 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.
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Buy this book

eBook $139.00
price for USA in USD (gross)
  • ISBN 978-3-319-71976-4
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $179.00
price for USA in USD
  • ISBN 978-3-319-71975-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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.

About the authors

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).

Reviews

“This is a short monograph on the computational neurosciences of single and populations of neurons. … This serves as a reference for advanced engineers and mathematical neurobiologists primarily. Dayan's Theoretical Neuroscience, and Neural Engineering by MIT Press are useful for more background material. Brain Machine and Brain-Computer interfacing is also considered here.” (Joseph Grenier, Amazon.com, June, 2018)

Table of contents (13 chapters)

  • Introduction

    Chen, Zhe (et al.)

    Pages 1-25

  • Characterizing Complex, Multi-Scale Neural Phenomena Using State-Space Models

    Eden, Uri T. (et al.)

    Pages 29-52

  • Latent Variable Modeling of Neural Population Dynamics

    Chen, Zhe

    Pages 53-82

  • What Can Trial-to-Trial Variability Tell Us? A Distribution-Based Approach to Spike Train Decoding in the Rat Hippocampus and Entorhinal Cortex

    Prerau, Michael J. (et al.)

    Pages 83-109

  • Sparsity Meets Dynamics: Robust Solutions to Neuronal Identification and Inverse Problems

    Babadi, Behtash

    Pages 111-140

Buy this book

eBook $139.00
price for USA in USD (gross)
  • ISBN 978-3-319-71976-4
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $179.00
price for USA in USD
  • ISBN 978-3-319-71975-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Dynamic Neuroscience
Book Subtitle
Statistics, Modeling, and Control
Editors
  • Zhe Chen
  • Sridevi V. Sarma
Copyright
2018
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG
eBook ISBN
978-3-319-71976-4
DOI
10.1007/978-3-319-71976-4
Hardcover ISBN
978-3-319-71975-7
Edition Number
1
Number of Pages
XXI, 327
Number of Illustrations
80 b/w illustrations
Topics