Links for Additional Information

Emery Brown, Ph.D.

Department of Brain and Cognitive Sciences
Professor of Computational Neuroscience
Professor of Health Sciences and Technology

Room 32-212
617-324-1880 (phone)
617-324-1884 (fax)

Biosketch

B.A., Applied Mathematics, Harvard College, 1978
M.A., Statistics, Harvard University, 1984
M.D., Medicine, Harvard Medical School, 1987
Ph.D., Statistics, Harvard University, 1988
1988-1989 Research Fellow in Endocrinology, Harvard Medical School.
1989-1992 Clinical Fellow in Anaesthesia, Harvard Medical School.
1992-1993 Instructor in Anaesthesia, Harvard Medical School.
1993-2001 Assistant Professor of Anaesthesia, Harvard Medical School.
2001-2003 Associate Professor of Anaesthesia, Harvard Medical School.
2003- Associate Professor of Anaesthesia and Health Sciences and Technology, Harvard Medical School.
2005- Professor of Computational Neuroscience, MIT.
2005- Professor of Health Sciences and Technology, MIT.

Research Summary

Neural Signal Processing Algorithms
Recent technological and experimental advances in the capabilities to record signals from neural systems have led to an unprecedented increase in the types and volume of data collected in neuroscience experiments and hence, in the need for appropriate techniques to analyze them. Therefore, using combinations of likelihood, Bayesian, state-space, time-series and point process approaches, a primary focus of the research in my laboratory is the development of statistical methods and signal-processing algorithms for neuroscience data analysis. We have used our methods to:

  • characterize how hippocampal neurons represent spatial information in their ensemble firing patterns.
  • analyze formation of spatial receptive fields in the hippocampus during learning of novel environments.
  • relate changes in hippocampal neural activity to changes in performance during procedural learning.
  • improve signal extraction from fMR imaging time-series.
  • characterize the spiking properties of neurons in primary motor cortex.
  • localize dynamically sources of neural activity in the brain from EEG and MEG recordings made during cognitive, motor and somatosensory tasks.
  • measure the period of the circadian pacemaker (human biological clock) and its sensitivity to light.
  • characterize the dynamics of human heart beats in physiological and pathological states.

Selected Publications

  • Department of Brain and Cognitive Sciences, MIT http://web.mit.edu/bcs/
  • Harvard-MIT Division of Health Sciences and Technology http://hst.mit.edu/
  • Department of Anesthesia and Critical Care, Massachusetts General Hospital http://www.etherdome.org
  • Neuroscience Statistics Research Laboratory https://neurostat.mgh.harvard.edu/
  • Division of Sleep Medicine, Harvard Medical School http://sleep.med.harvard.edu/
  • Program in Neuroscience, Division of Medical Sciences, Harvard Medical School http://www.hms.harvard.edu/dms/neuroscience/
  • Brown EN, Choe Y, Shanahan TL, Czeisler CA. A mathematical model of diurnal variation in plasma melatonin levels. American Journal of Physiology, (Endocrinology and Metabolism 35) 1997, 272:E506-E16.
  • Brown EN, Frank LM, Tang D, Quirk MC, Wilson MA. A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells. Journal of Neuroscience, 1998, 18:7411-25.
  • Czeisler CA, Duffy JF, Shanahan TL, Brown EN, Mitchell JF, Rimmer DW, Ronda JM, Silva E, Allan JS, Emens JS, Dijk DJ, Kronauer RE. Age-independent stability, precision, and near 24 hour period of the human circadian pacemaker. Science, 1999, 284:2177-81.
  • Brown EN, Choe Y, Luithardt H, Czeisler CA. A statistical model of the human core-temperature circadian rhythm. American Journal of Physiology, 2000, 279:E669-E83.
  • Purdon PL, Solo V, Weisskoff RM, Brown EN. Locally regularized spatio-temporal modeling and model comparison for functional MRI. NeuroImage, 2001. Oct;14(4):912-23.
  • Brown EN, Meehan PM, Dempster AP. A stochastic differential equation model of diurnal cortisol patterns. American Journal of Physiology (Endocrinology and Metabolism), 2001, 280:E450-61.
  • Brown EN, Nguyen DP, Frank LM, Wilson MA, Solo V. An analysis of neural receptive field plasticity by point process adaptive filtering. Proceedings of the National Academy of Sciences, 2001, 98:12261-66.
  • Brown EN, Barbieri R, Ventura V, Kass RE, Frank LM. The time-rescaling theorem and its application to neural spike train data analysis. Neural Computation, 2002, 14(2):325-46.
  • Frank LM, Eden UT, Solo V, Wilson MA, Brown EN. Contrasting patterns of receptive field plasticity in the hippocampus and the entorhinal cortex: an adaptive filtering approach. Journal of Neuroscience, 2002, 22:3817-30.
  • Smith AC, Brown EN. Estimating a state-space model from point process observations. Neural Computation, 2003, 15: 965-91.
  • Wirth S, Yanike M. Frank LM, Smith AC, Brown EN, Suzuki WA. Single neurons in the monkey hippocampus and learning of new associations. Science, 2003, 300: 1578-81.
  • Klerman EB, Adler GK, Jin M, Maliszewski AM, Brown EN. A statistical model of diurnal variation in human growth hormone. American Journal of Physiology, 2003, E1118-26.
  • Barbieri R, Frank LM, Nguyen DP, Quirk MC, Solo V, Wilson MA, Brown EN. Dynamic analyses of information encoding by neural ensembles. Neural Computation, 2004, 16 (2): 277-307.
  • Eden UT, Frank LM, Barbieri R, Solo V, Brown EN, Dynamic analyses of neural encoding by point process adaptive filtering. Neural Computation, 2004, 16(5): 971-998.
  • Brown EN, Mitra PP, Kass RE. Multiple neural spike train data analysis: state-of-the-art and future challenges. Nature Neuroscience, 2004; 7(5): 456-61.
  • Smith AC, Frank LM, Wirth S, Yanike M, Hu D, Kubota Y, Graybiel AM, Suzuki WA, Brown EN. Dynamic analysis of learning in behavioral experiments. Journal of Neuroscience, 2004, 24(2): 447-461.
  • Frank LM, Stanley GB, Brown EN. Hippocampal plasticity across multiple days of exposure to novel environments. Journal of Neuroscience, 2004, 24 (35):7681-89.
  • Truccolo W, Eden UT, Fellow M, Donoghue JD, Brown EN. A point process framework for relating neural spiking activity to spiking history, neural ensemble and covariate effects. Journal of Neurophysiology (published online Sept. 8, 2004), 2005, 93:1074-1089.
  • Barbieri R, Matten EC, Alabi, A. Brown EN. A point process model of human heart beat intervals: new definitions of heart rate and heart rate variability. American Journal of Physiology: Heart and Circulatory Physiology (published on line Sept. 16, 2004), 2005, 288:H424-H435.
  • Okatan M, Wilson MA, Brown EN. Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity. Neural Computation, 2005, 9:1927-61.
  • Barbieri R, Brown EN. Analysis of heart beat dynamics by point process adaptive filtering. IEEE Transactions on Biomedical Engineering, 2006, 53(1): 4-12.
  • Srinivasan L, Eden UT, Willsky AS, Brown EN. A state-space analysis for reconstruction of goal-directed movements using neural signals. Neural Computation, 2006, In Press.
  • Ergun A, Barbieri R, Eden UT, Wilson MA, Brown EN. Construction of point process adaptive filter algorithms for neural systems using sequential Monte Carlo methods. IEEE Transactions on Biomedical Engineering, 2006, In Press.

Last Updated: April 15, 2008