Experimentally testable whole brain manifolds that recapitulate behavior

no code implementations • 20 Jun 2021 • Gerald M Pao, Cameron Smith, Joseph Park, Keichi Takahashi, Wassapon Watanakeesuntorn, Hiroaki Natsukawa, Sreekanth H Chalasani, Tom Lorimer, Ryousei Takano, Nuttida Rungratsameetaweemana, George Sugihara

Thus, as a final validation of how well GMN captures essential dynamic information, we show that the artificially generated time series can be used as a training set to predict out-of-sample observed fly locomotion, as well as brain activity in out of sample withheld data not used in model building.

Causal Inference Time Series +1

Empirical Mode Modeling: A data-driven approach to recover and forecast nonlinear dynamics from noisy data

no code implementations • 10 Mar 2021 • Joseph Park, Gerald M Pao, Erik Stabenau, George Sugihara, Thomas Lorimer

Data-driven, model-free analytics are natural choices for discovery and forecasting of complex, nonlinear systems.