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Advanced State Space Methods for Neural and Clinical Data
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  • Cited by 16
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Toutounji, Hazem Hertäg, Loreen and Durstewitz, Daniel 2018. Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience. p. 1.

    ur Rehman, Muhammad Javvad Dass, Sarat C and Asirvadam, Vijanth S 2018. A Bayesian parameter learning procedure for nonlinear dynamical systems via the ensemble Kalman filter. p. 161.

    Hu, Sile Zhang, Qiaosheng Wang, Jing and Chen, Zhe 2018. Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity. Journal of Neurophysiology, Vol. 119, Issue. 4, p. 1394.

    Chen, Zhe and Sarma, Sridevi V. 2018. Dynamic Neuroscience. p. 1.

    Chen, Zhe 2018. Dynamic Neuroscience. p. 53.

    Urien, Louise Xiao, Zhengdong Dale, Jahrane Bauer, Elizabeth P. Chen, Zhe and Wang, Jing 2018. Rate and Temporal Coding Mechanisms in the Anterior Cingulate Cortex for Pain Anticipation. Scientific Reports, Vol. 8, Issue. 1,

    Borisov, A. V. Bosov, A. V. Kibzun, A. I. Miller, G. B. and Semenikhin, K. V. 2018. The Conditionally Minimax Nonlinear Filtering Method and Modern Approaches to State Estimation in Nonlinear Stochastic Systems. Automation and Remote Control, Vol. 79, Issue. 1, p. 1.

    Marinescu, Ioana E. Lawlor, Patrick N. and Kording, Konrad P. 2018. Quasi-experimental causality in neuroscience and behavioural research. Nature Human Behaviour,

    Chen, Zhe Zhang, Qiaosheng Tong, Ai Phuong Sieu Manders, Toby R and Wang, Jing 2017. Deciphering neuronal population codes for acute thermal pain. Journal of Neural Engineering, Vol. 14, Issue. 3, p. 036023.

    Hu, Sile Zhang, Qiaosheng Wang, Jing and Chen, Zhe 2017. A real-time rodent neural interface for deciphering acute pain signals from neuronal ensemble spike activity. p. 93.

    Chen, Zhe 2017. Unfolding representations of trajectory coding in neuronal population spike activity. p. 1.

    Chen, Zhe Hu, Sile Zhang, Qiaosheng and Wang, Jing 2017. Quickest detection for abrupt changes in neuronal ensemble spiking activity using model-based and model-free approaches. p. 481.

    Chen, Zhe 2017. A Primer on Neural Signal Processing. IEEE Circuits and Systems Magazine, Vol. 17, Issue. 1, p. 33.

    Chen, Zhe Linderman, Scott W. and Wilson, Matthew A. 2016. Bayesian nonparametric methods for discovering latent structures of rat hippocampal ensemble spikes. p. 1.

    Agarwal, Rahul Chen, Zhe Kloosterman, Fabian Wilson, Matthew A. and Sarma, Sridevi V. 2016. A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields. Neural Computation, Vol. 28, Issue. 7, p. 1356.

    She, Qi Chen, Guanrong and Chan, Rosa H. M. 2016. Evaluating the Small-World-Ness of a Sampled Network: Functional Connectivity of Entorhinal-Hippocampal Circuitry. Scientific Reports, Vol. 6, Issue. 1,

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  • Edited by Zhe Chen, New York University

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    Advanced State Space Methods for Neural and Clinical Data
    • Online ISBN: 9781139941433
    • Book DOI: https://doi.org/10.1017/CBO9781139941433
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Book description

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.

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