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An authoritative and in-depth treatment of state space methods, with a range of applications in neural and clinical data.
List 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.
About the author
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.
Summary
This authoritative work provides an in-depth treatment of state space methods, with a range of applications in neural and clinical data. Including real-world case studies, and covering the state of the art in research and practice, this is an ideal resource for anyone working in the fields of neuroscience and physiological data analysis.
