High-dimensional signal and noise in 20,000 neuron recordings: Neyman Seminar

Seminar | February 19 | 4-5 p.m. | 1011 Evans Hall

 Carsen Stringer, HHMI Janelia Research

 Department of Statistics

Interpreting high-dimensional datasets requires new computational and analytical methods. I have developed such methods to extract and analyze neural activity from 20,000 neurons recorded simultaneously in awake, behaving mice. The neural activity was not low-dimensional as commonly thought, but instead was high-dimensional and obeyed a power-law scaling across its eigenvalues. We developed a theory that proposes that neural responses to external stimuli maximize information capacity while maintaining a smooth neural code. I then observed power-law eigenvalue scaling in many real-world datasets, and therefore developed a nonlinear manifold embedding algorithm called Rastermap that can capture such high-dimensional structure. In addition, I developed methods to relate neural activity to animal behavior via reduced-rank regression. Using these methods I found that variability in stimulus-driven neural responses was not "noise" but instead represented the mouse's behaviors.

 Berkeley, CA 94720, 5106422781