American Statistical Association
Systems and circuit neuroscience have recently experienced something of a renaissance, driven by a remarkable explosion in the development of groundbreaking new experimental tools. These methods have in turn opened up a variety of challenging statistical problems, in which we must often perform some high-dimensional inference under strong computational constraints (for example, in some cases real-time processing is required). This talk will review some recent progress on three exemplary problems, each of fundamental neuroscientific importance: 1) Optimal filtering and smoothing of voltage signals on large, complex dendritic trees; 2) Optimal decoding of sensory information from the activity of large neural populations, and 3) Inference of connectivity in large neuronal networks given limited, noisy observations.
Dr. Liam Paninski is an Associate Professor in the Department of Statistics at Columbia University. He is also affiliated with the Center for Theoretical Neuroscience and Doctoral Program in Neurobiology and Behavior. He received his Ph.D. from NYU’s Center for Neural Science and was previously a senior research fellow at the Gatsby Computational Neuroscience Unit at University College London.
|Date:||Tuesday, December 14, 2010|
|Time:||3:00 - 4:00 P.M.|
New York State Psychiatric Institute
1051 Riverside Drive
6th Floor Multipurpose Room (6602)
New York, New York