Tuesday, February 16, 2016
4:00 pm
Broad 100

Liam Paninski - Special Seminar in Computational Neuroscience

Challenges and Opportunities in Statistical Neural Data Analysis
Liam Paninski, Professor, Department of Statistics and the Center for Theoretical Neuroscience, Columbia University

Systems and circuit-level neuroscience has entered a golden age: with modern fast computers, machine learning methods, and large-scale multineuronal recording and high-resolution imaging techniques, we can analyze neural activity at scales that were impossible even five years ago.  One can now argue that the major bottlenecks in systems neuroscience no longer lie just in collecting data from large neural populations, but rather in understanding this data.  I'll discuss several cases where basic neuroscience problems can be usefully recast in statistical language, enabling us to pursue new scientific questions; examples include deconvolution, demixing, and denoising of calcium imaging data, and inference of network connectivity and low-dimensional dynamical structure from the resulting multineuronal spiking data.

Refreshments will be served at 3:45 pm outside the Broad 100.

Host: John O'Doherty

Contact Mary Martin mmartin@caltech.edu at 626-395-5884
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