Neuroscience Statistics Research Unit

Summary Science Features

Current research includes a focus on neural signal processing algorithms. Recent technological and experimental advances have recorded signals from neural systems that have led to an increase in the types and volume of data collected in neuroscience experiments and the need for appropriate techniques to analyze them. Using combinations of likelihood, Bayesian, state-space, time-series and point process approaches, a focus of the unit has been to develop statistical methods and signal-processing algorithms for neuroscience data analysis.

The circadian and neuroendocrine rhythm research is a collaboration with Dr. Gail Adler and Dr. Charles Czeisler in the Circadian, Neuroendocrine and Sleep Disorders Section of the Division of Endocrinology in the Department of Medicine at Brigham and Women's Hospital. Areas of investigation include: computing accurate estimates of the period, phases and amplitude of the human circadian pacemaker from different marker rhythms; accurately decomposing neuroendocrine rhythms into their circadian pacemaker; and characterizing differences between normal physiology and disease states in terms of the components of neuroendocrine rhythms.

Other research has characterized how hippocampal neurons represent spatial information in their ensemble firing patterns and analyzed the formation of spatial receptive fields in the hippocampus during learning of novel environments. This research has led to a study of how to relate changes in hippocampal neural activity to changes in performance during procedural learning, to improve signal extraction from functional magnetic resource imaging (fMRI) time-series, and to characterize the spiking properties of neurons in primary motor cortex, among other discoveries. The study is being performed in collaboration with Matt Wilson at MIT, Wendy Suzuki at NYU, Victor Solo at the University of Michigan, and Loren Frank at UCSF.