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Dynamic Neuroscience pp 239–265Cite as

From Physiological Signals to Pulsatile Dynamics: A Sparse System Identification Approach

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Abstract

Various brain signals including neuronal spiking activity have an impulsive profile that upon interactions with physiological or measurement processes lead to observed pulsatile experimental data. For example, neuroendocrine data and electrodermal activity data have a pulsatile nature. To characterize the pulsatile nature of such signals, we utilize physiological state-space models and optimization formulations that take advantage of the sparse nature of the underlying pulses. We illustrate that our methods are successful in characterizing pulsatile dynamics underlying pulsatile physiological data. In particular, we deconvolve cortisol and skin conductance data as well as concurrent cortisol and adrenocorticotropic data. Moreover, we design impulsive inputs for constant and circadian demands and holding costs on desired pulsatile profiles. Characterization of such pulsatile signals will aid our understanding of the pathological states related to these signals and has the potential to be used in designing bio-inspired pulsatile controllers; immediate applications include understanding normal and pathological neuroendocrine and affective states.

Keywords

  • Pulsatile Dynamics
  • Holding Cost
  • Pulsatile Profile
  • Skin Conductance Data
  • Momentum Profile

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Acknowledgements

This chapter summarizes and generalizes the concepts that appeared in my PhD thesis and related publications. I would like to thank my PhD advisors and all coauthors who have contributed to previously published work. Especially, I am greatly indebted to my PhD advisors Professor Emery N. Brown and Professor Munther A. Dahleh; this work would not have been possible without their guidance, depth of knowledge, and invaluable advice. I would also like to express my gratitude to Professor George Verghese, Dr. Elizabeth Klerman, and Dr. Gail Adler for very helpful conversations, and insightful feedback. I am also grateful to the National Science Foundation (NSF) for supporting this research through the NSF Graduate Research Fellowship. In honor of Professor Emery N. Brown’s 60th birthday, I would like to add that being Emery’s student and having him as a role model has always been a great honor and privilege for me; I am very lucky that I could benefit from the light of Emery’s wisdom and learn so much from him during my S.M. and Ph.D. studies as well as my postdoctoral training. He has been immensely encouraging and a great source of enthusiasm, inspiration, and positive energy.

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  1. University of Houston, Houston, TX, USA

    Rose T. Faghih

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Correspondence to Rose T. Faghih .

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  1. School of Medicine, New York University, New York, NY, USA

    Zhe Chen

  2. Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA

    Sridevi V. Sarma

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Faghih, R.T. (2018). From Physiological Signals to Pulsatile Dynamics: A Sparse System Identification Approach. In: Chen, Z., Sarma, S.V. (eds) Dynamic Neuroscience. Springer, Cham. https://doi.org/10.1007/978-3-319-71976-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-71976-4_10

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