Brown - Plenary Lecture

Videos

dialup		broadband-low		broadband-medium		broadband-high
							wmv

Slides

PDF

Neural systems encode representations of biological signals in the firing patterns of their electrical activity, termed spike trains. The advent in the last 10 years of the capability to record with multiple electrode arrays the simultaneous spiking activity of many neurons (>100) has made it possible to study information encoding by ensembles rather than by simply single neurons. In particular, it makes it possible to study how groups of neurons act in concert to define the function of a given brain region. Spike trains are point process time-series and their codes are both dynamic and stochastic. Even though the signal is often continuous, its representation in the nervous systems is as a high-dimensional point process time-series. Because neural spike trains are point processes, standard signal processing techniques for continuous-valued data will have limited utility in the analysis of neural systems. Therefore, accurate analysis of neural signals requires the development of quantitative techniques to characterize correctly the point process nature of neural encoding. In this presentation, we discuss the application of the state-space paradigm in neural spike train signal processing. We use the Bayes’ rule, Chapman-Kolmogorov equations to derive algorithms useful for neural spike train decoding, dynamic analysis of neural encoding (neural plasticity) and adaptive-decoding. We show how this approach leads to a natural definition of signal-to-noise ratio for a point process representation of a neural system. We illustrate the methods in three examples: Decoding position from the ensemble activity hippocampal pyramidal neurons and tracking the temporal evolution in hippocampal place receptive fields, and decoding motor cortex representations of movement velocity.

Date

Friday, July 14

Biography

Emery N. Brown, M.D., Ph.D. is Professor of Computational Neuroscience and Professor of Health Sciences and Technology at Massachusetts Institute of Technology and Massachusetts General Hospital Professor of Anaesthesia at Harvard Medical School. He is an anesthesiologist and the Director of the Neuroscience Statistics Research Laboratory in the Department of Anesthesia and Critical Care at Massachusetts General Hospital.

Dr. Brown earned his BA degree in Applied Mathematics from Harvard College, his MA and Ph.D. degrees in statistics from Harvard University and his MD from Harvard Medical School. He served his internship in internal medicine at the Brigham and Women’s Hospital and his residency in anesthesia at Massachusetts General Hospital.

His methodology research focuses on use of dynamic estimation methods to analyze neurophysiological systems in three areas: signal processing algorithms to study how individuals and ensembles of neurons represent information; statistical methods for the analysis of functional neural imaging data; statistical models to characterize human circadian and neuroendocrine rhythms. His experimental research uses combined fMRI and EEG to study the neurophysiological changes in brain regions associated with the states of general anesthesia.

Dr. Brown is on the editorial boards of the Annals of Applied Statistics, the Journal of Neurophysiology, IEEE Transactions on Neural and Rehabilitation Engineering and Anesthesia and Analgesia.

Dr. Brown is a member of the Committee on Applied and Theoretical Statistics of the National Academies, an elected member of the Association of University Anesthesiologists, a fellow of the American Statistical Association, a fellow of the American Institute of Medical and Biological Engineering and he is the Co-Director of the Neuroinformatics Course at the Marine Biological Laboratory in Woods Hole, MA.