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Advanced State Space Methods for Neural and Clinical Data

£99.99

Z. Chen, D. Barber, S. Chiappa, M. Fukushima, O. Yamashita, M. Sato, A. Galka, M. Siniatchkin, U. Stephani, K. Groening, S. Wolff, J. Bosch-Bayard, T. Ozaki, R. Moran, J. H. Macke, L. Buesing, M. Sahani, K. W. Latimer, A. C. Huk, J. W. Pillow, F. Kloosterman, M. A. Wilson, W. Wu, S. Liu, A. C. Smith, L. H. Lehman, M. J. Johnson, S. Nemati, R. P. Adams, R. G. Mark, R. Barbieri, M. B. Westover, S. Ching, M. M. Shafi, S. S. Cash, E. N. Brown, J. Oster, G. D. Clifford
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  • Date Published: October 2015
  • availability: In stock
  • format: Hardback
  • isbn: 9781107079199

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  • This authoritative work provides an in-depth treatment of state space methods, with a range of applications in neural and clinical data. Advanced and state-of-the-art research topics are detailed, including topics in state space analyses, maximum likelihood methods, variational Bayes, sequential Monte Carlo, Markov chain Monte Carlo, nonparametric Bayesian, and deep learning methods. Details are provided on practical applications in neural and clinical data, whether this is characterising time series data from neural spike trains recorded from the rat hippocampus, the primate motor cortex, or the human EEG, MEG or fMRI, or physiological measurements of heartbeats or blood pressures. With real-world case studies of neuroscience experiments and clinical data sets, and written by expert authors from across the field, this is an ideal resource for anyone working in neuroscience and physiological data analysis.

    • Focuses on advanced and state-of-the-art state space methods
    • Details the practical applications for neural and clinical data
    • Includes real-world case studies of neuroscience experiments and clinical data sets
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    Product details

    • Date Published: October 2015
    • format: Hardback
    • isbn: 9781107079199
    • length: 394 pages
    • dimensions: 253 x 181 x 23 mm
    • weight: 0.93kg
    • contains: 93 b/w illus. 15 tables
    • availability: In stock
  • Table of Contents

    1. Introduction Z. Chen
    2. Inference and learning in latent Markov models D. Barber and S. Chiappa
    Part I. State Space Methods for Neural Data:
    3. State space methods for MEG source reconstruction M. Fukushima, O. Yamashita and M. Sato
    4. Autoregressive modeling of fMRI time series: state space approaches and the general linear model A. Galka, M. Siniatchkin, U. Stephani, K. Groening, S. Wolff, J. Bosch-Bayard and T. Ozaki
    5. State space models and their spectral decomposition in dynamic causal modeling R. Moran
    6. Estimating state and parameters in state space models of spike trains J. H. Macke, L. Buesing and M. Sahani
    7. Bayesian inference for latent stepping and ramping models of spike train data K. W. Latimer, A. C. Huk and J. W. Pillow
    8. Probabilistic approaches to uncover rat hippocampal population codes Z. Chen, F. Kloosterman and M. A. Wilson
    9. Neural decoding in motor cortex using state space models with hidden states W. Wu and S. Liu
    10. State-space modeling for analysis of behavior in learning experiments A. C. Smith
    Part II. State Space Methods for Clinical Data:
    11. Bayesian nonparametric learning of switching dynamics in cohort physiological time series: application in critical care patient monitoring L. H. Lehman, M. J. Johnson, S. Nemati, R. P. Adams and R. G. Mark
    12. Identifying outcome-discriminative dynamics in multivariate physiological cohort time series S. Nemati and R. P. Adams
    13. A dynamic point process framework for assessing heartbeat dynamics and cardiovascular functions Z. Chen and R. Barbieri
    14. Real-time segmentation and tracking of brain metabolic state in ICU EEG recordings of burst suppression M. B. Westover, S. Ching, M. M. Shafi, S. S. Cash and E. N. Brown
    15. Signal quality indices for state-space electrophysiological signal processing and vice versa J. Oster and G. D. Clifford.

  • Editor

    Zhe Chen, New York University
    Zhe Chen is Assistant Professor at the 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 Institute of Electrical and Electronics Engineers (IEEE) and an editorial board member of Neural Networks. 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.

    Contributors

    Z. Chen, D. Barber, S. Chiappa, M. Fukushima, O. Yamashita, M. Sato, A. Galka, M. Siniatchkin, U. Stephani, K. Groening, S. Wolff, J. Bosch-Bayard, T. Ozaki, R. Moran, J. H. Macke, L. Buesing, M. Sahani, K. W. Latimer, A. C. Huk, J. W. Pillow, F. Kloosterman, M. A. Wilson, W. Wu, S. Liu, A. C. Smith, L. H. Lehman, M. J. Johnson, S. Nemati, R. P. Adams, R. G. Mark, R. Barbieri, M. B. Westover, S. Ching, M. M. Shafi, S. S. Cash, E. N. Brown, J. Oster, G. D. Clifford

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