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Chi-Sang Poon, Ph.D.

Department of Health Sciences and Technology
Principal Research Scientist
Division of Health Sciences and Technology

Room 56-046
617-258-5405 (phone)

Biosketch

B.Sc. (Eng.) Electrical Engineering, University of Hong Kong 1975 M.Phil. Bioelectronics, The Chinese University of Hong Kong, 1977 Ph.D. Bioengineering and systems science, UCLA, 1981 Visiting Associate Professor, Harvard-MIT Division of Health Sciences & Technology, 1988 Visiting Scientist, Biologie Fonctionnelle du Neurone, C.N.R.S., France, 1994 Principal Research Scientist, Harvard-MIT Division of Health Sciences & Technology, 1989-present
Memberships

Fellow, Institute of Electrical and Electronics Engineers (IEEE)
Fellow, American Institute for Medical and Biological Engineering (AIMBE)

Research Summary

The Poon lab is located in MIT Building 56 and has 4 graduate students, 2 postdocs and 2 research staff members. The graduate students in the lab come from electrical engineering and computer science, chemical engineering, physics, and engineering & applied sciences at Harvard. Students from biology are welcome.

Research in the Poon lab takes a multidisciplinary approach that combines biology with engineering and computational science to investigate the mechanisms of neural control at both the cellular and systems levels. The biological research is currently focused on neuronal plasticity as a form of neural computation, with particular emphasis on the roles of various ligand-gated and voltage-gated ion channels in long-term potentiation and depression of neuronal excitability. The engineering research is aimed at developing advanced nonlinear control and signal processing algorithms and microelectronic devices that can be used to model and analyze complex biological systems that exhibit chaotic, self-organized and self-tuning behaviors. An overarching theme of this cross-cutting research is to elucidate the biological intelligence that underlies homeostatic regulation at the cellular and systems levels, and to evolve novel artificial intelligence and engineering paradigms based on such biologically-inspired principles. Ultimately, we want to develop intelligent robots based on engineering systems that use biologically inspired control mechanisms to achieve stability. Two model systems that are now under investigation are respiratory rhythm and cardiac rhythm, with the goal of understanding how the feedback and feedforward loops operate over time and space to achieve homeostasis.

Respiratory rhythm
Our work on respiration focuses on mechanisms of control in neural and neuronal plasticity, in which physiological changes during development and in response to environmental stimuli produce adaptive behavior. This process is sometimes called úbrain calculus? because it uses computing blocks that can perform integrated and differentiated processes on a neuronal level to perform high-order calculations. Different specialized populations of neurons in the brain work together to establish rhythms and make adaptive modifications to incoming stimuli. In this sense, they operate like electronic signal conditioning devices, in which high-level performance requires stability, speed, response to external signals and appropriate control loops. We are studying how neuronal networks are organized and integrated to produce and regulate respiratory rhythms.

Cardiovascular rhythm
Another project in the Poon lab studies control mechanisms that are used by the brain to regulate cardiovascular rhythms and maintain blood pressure. One critical problem in studying cardiac rhythm is distinguishing chaos (random fluctuations) from background noise so that we can identify subtle patterns in heart-rate variability that may be correlated with specific types of arrhythmia or sudden cardiac death. We have developed mathematical methods for separating chaos from additive noise, and we are using them to study the chaotic dynamics of the heartbeat. We hope to apply this approach to develop diagnostic and predictive tools for cardiac malfunction.

Selected Publications

  • Poon, C.-S. Quantitative social sciences. Nature 368:297-298, 1994.
  • Barahona, M. and C.-S. Poon. Detection of nonlinear dynamics in short, noisy time series. Nature, 381:215-217 1996.
  • Poon, C.-S. and C.K. Merrill. Decrease of cardiac chaos in congestive heart failure. Nature 389:492-495, 1997
  • Poon, C.-S., Z. Zhou, and J. Champagnat. NMDA receptor activity in utero averts respiratory depression and anomalous long-term depression in newborn mice. J. Neurosci. (Rapid Communication) 20:RC73 (1-6), 2000.
  • Zhou, Z. and C.-S. Poon. Field potential analysis of synaptic transmission in spiking neurons in a sparse and irregular neuronal structure in vitro. J. Neurosci. Methods 94(2):193-203, 2000.
  • Poon, C.-S. and M. Barahona . Titration of chaos with added noise. Proc. Natl. Acad. Sci. USA, 98(13): 7107-7112., 2001.
  • Fan, G., R.Z.H. Chen, G. Csankovszki, Y. Sun, M. Siniaia, C.-S. Poon, B. Bates, C. Beard, C. Wilson, R. Yaenisch. DNA methylation perturbs the survival and function of CNS neurons in postnatal animals. J. Neurosci. 21(3):788-797, 2001.
  • Young, D.L. and C.-S. Poon. A Hebbian covariance feedback learning paradigm for self-tuning optimal control. IEEE Trans. on Systems, Man, and Cybernetics, Part B, 31:173-186, 2001.
  • "Frontiers in Modeling and Control of Breathing: Integration at molecular, cellular and systems levels. Advances in Experimental Medicine and Biology," Vol. 499, C.-S. Poon and H. Kazemi, eds. (Kluwer Scientific/Plenum Publishers); November, 2001, 550pp.
  • Young, D.L. and C.-S. Poon. Soul searching and heart throbbing for biological modeling. Behavioral and Brain Sciences, 26(6): 1080-1081, 2002.
  • Rachmuth, G. and C.-S. Poon. Design of a neuromorphic Hebbian synapse using analog VLSI., pp. 1-4, 1st IEEE/EMBS International Conference on Neural Engineering, March 2003.
  • C. Tin and C.-S. Poon. Internal models of sensorimotor integration: Perspectives from adaptive control theory. J. Neural Engineering, 2:S147-S163, 2005.
  • C.-S. Poon and D.M. Merfeld. (Guest Editors.) Special Issue on: úSensory Integration, State Estimation, and Motor Control in the Brain: Role of Internal Models? J. Neural Engineering, 2005.
  • G. Rachmuth, Y.S. Yang and C.-S. Poon. 1.2V Sub-nanoampere A/D converter. Electronics Lett. 41:455-6, 2005.
  • Song G, Yu Y. and Poon C-S. Cytoarchitecture of pneumotaxic integration of respiratory and non-respiratory information in the rat. J. Neurosci. 26:300-310., 2006.

Last Updated: April 16, 2008