Challenges and opportunities in statistical neural data analysis
Liam Paninski, Ph.D.
Professor, Statistics Department
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; 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.