Coarse-graining

…it to arousal pathways, and uses coarse-graining to investigate both scale-dependent coding and shared dynamical properties across neural populations.

Studying and understanding the code of large neural populations hinges on learning accurate models of population activity. While statistical models based on pairwise interactions are highly accurate in describing the code of tens of neurons, accurate models of >100 neurons require higher order dependencies. Statistical models based on sparse nonlinear Random Projections (RP) of the population proved to be highly accurate and efficient for 100-200 neurons. The scaling of these models to larger populations is challenging yet critical for mapping the semantic organization of population codes, the

Physics offers countless examples for which theoretical predictions are astonishingly powerful. But it’s hard to imagine a similar precision in complex systems where the number and interdependencies between components simply prohibits a first-principles approach, look no further than the challenge of the billions of neurons and trillions of connections within our own brains. In such settings how do we even identify the important theoretical questions? We describe a systems-scale perspective in which we integrate information theory, dynamical systems and statistical physics to extract understan