High-dimensional geometry of visual cortex
Janelia Research Campus
Recording
Abstract
Interpreting high-dimensional datasets requires new computational and analytical methods. We 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. We 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.
Topics
Related Job Opportunities
Postdoctoral Scientist - Sarvestani Lab
The Sarvestani Lab at Cornell University is recruiting a postdoctoral scientist in systems neuroscience to study how visual and motor systems across the brain and body support perception and…
PhD Studentship: Mitochondrial Metabolism and Novel Therapeutic Strategies for Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) (Fixed Term)
Supervisors: Professor Andrew Murray, Department of Physiology, Development and Neuroscience, University of Cambridge Dr Ross Lindsay, Novo Nordisk Funding: Fully funded PhD studentship (Home/UK…
Research Associate (Fixed Term)
We seek a highly motivated Postdoctoral Research Associate to join the laboratory of Professor Kathy Niakan. We are based in the Loke Centre for Trophoblast Research (LCTR), in the Department of…