Explore cutting-edge theories in single-trial high dimensional neural data analysis through this Stanford University seminar. Delve into fundamental conceptual questions and a new definition of neural task complexity. Gain insights on neural dimensionality, random projections, and low-rank matrix perturbation theory. Examine static decoding techniques for recovering dimensionality and latent state recovery using both simulations and monkey data. Investigate the discovery of structure in subsampled neural dynamics and learn about modeling data using latent linear dynamical systems and tensor components analysis. Enhance your understanding of advanced neuroscience concepts and analytical approaches in this comprehensive 1-hour and 16-minute presentation.
Stanford Seminar - Towards Theories of Single-Trial High Dimensional Neural Data Analysis
Stanford University via YouTube
Overview
Syllabus
Introduction.
A major conceptual elephant in systems neuroscience.
Talk outline.
Fundamental conceptual questions.
New definition of neural task complexity.
Neural Dimensionality and Task Complexity: Intuition.
A larger context: random projections.
Conclusions.
Towards a single trial theory.
Low-rank Matrix Perturbation Theory.
Static Decoding - Recovering Dimensionality.
Latent State Recovery: Simulations.
Latent State Recovery: Monkey Data.
Discovering structure in subsampled neural dynamics.
Data often modeled using latent linear dynamical systems.
Tensor components analysis.
Taught by
Stanford Online
