Recent technological and experimental advances in our ability to record signals from the brain have led to an unprecedented increase in the types and volume of data collected in neuroscience experiment—and hence, the need for appropriate techniques to analyze them. The Brown lab develops statistical methods and signal-processing algorithms for neuroscience data analysis, using a combinations of approaches such as likelihood, Bayesian, state-space, time-series and point process.

We have used our methods to address a vast range of questions, from characterizing how hippocampal neurons represent spatial information and improving signal extraction in functional magnetic resonance imaging to measuring the period of the human biological clock and mapping the dynamics of heartbeats.

In addition to these topics, our lab has a deep interest in general anesthesia. General anesthesia is a fascinating man-made, neurophysiological phenomenon that has been developed empirically to enable safe and humane performance of surgical and non-surgical procedures. The state consists of unconsciousness, amnesia, analgesia, and immobility along with maintenance of physiological stability. In the United States, more than 60,000 patients receive general anesthesia daily. Despite use of general anesthesia in this country for nearly 166 years, how these drugs act in the brain and central nervous system to create this state remains poorly understood. Working in partnership with clinicians, it is our goal to apply our statistical tools to probe the physiological basis of how general anesthesia works.