Advanced state space methods for neural and clinical data

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.

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