CAREER: Using Advanced Statistical Techniques to Decipher the Neural Code

Project Summary The central problem in systems neuroscience is to understand the neural code, at both large and small physiological scales. Progress has been limited by a lack of sufficiently rich experimental data, a shortage of quantitative techniques to characterize and analyze the data, and an insufficient number of interdisciplinary researchers skilled in both systems neurophysiology and advanced statistical methods. Recent developments open new possibilities for collaborative efforts to tackle these basic problems. First, advances in multi-electrode recordings make it possible to study the simultaneous activity of large ensembles of neurons in a wide variety of experimental settings. Similarly, recent improvements in high-resolution voltage- and calcium-sensitive imaging technology now provide data capable of constraining highly-detailed biophysical models of information processing in single cells. A major bottleneck now is in analyzing and quantitatively understanding this data. Specific methodological advances in four fields are proposed: 1) encoding and decoding information in population spike trains; 2) single spike-train analysis and optimal stimulus design; 3) highly-detailed biophysical models and optimal processing of dendritic imaging data; and 4) information-theoretic analyses of sparse neural data. In each case, the investigator and his research group will develop novel mathematical models and tools for fitting these models directly to the observed data. Computer code implementing these novel techniques will be made publicly available to enhance the infrastructure for research and education. This work will have impact on the burgeoning field of neural prosthetics, which will require substantial improvements in our ability to design signaling interfaces between artificial and real neural tissue. Understanding encoding and decoding in populations of neurons and developing models that allow us to predict the effects of experimental perturbations to their behavior is key to this endeavor. This research on neural coding will also likely lead to mathematical results and statistical techniques which are of independent general interest and utility, with fundamental impacts on information theory, image processing, and optimal filtering and prediction of point processes (which in turn impact hundreds of other disciplines). In addition, the investigator is developing an advanced training course for graduate students and postdocs in statistical neuroscience (the first course of this kind in the world), as well as an introductory undergraduate course. Lecture notes will be made publicly available online and will shape a textbook in progress in advanced neural data analysis. Training opportunities will be pursued at Columbia University (strengthening already close ties with the Department of Statistics and Center for Theoretical Neuroscience) and with collaborators in the U.S. and internationally.