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Neural Latent State and Dynamics Inference Workshop

Neural recordings are high-dimensional and complex. We aim to find spatiotemporal structure in data in order to "understand" it better, but what is the right kind of structure to look for? In this workshop, we will introduce the statistical problem of inferring latent state trajectories from high-dimensional neural time series and how it is related to dimensionality reduction methods such as principal component analysis (PCA). Subsequently, we will introduce the statistically more difficult, but theoretically more satisfying inference of the latent nonlinear dynamical system. There will be hands-on components to try some of the methods.

Schedule: 11 October 2022

  • 13:30-14:30 Lecture 1: Latent state trajectories and dimensionality reduction
  • 14:30-14:45 Break & conda/python/code installation
  • 14:45-16:00 Exercise session 1: dimensionality reduction
  • 16:00-17:00 Lecture 2: Latent state dynamics
  • 17:00-17:15 Break
  • 17:15-18:15 Exercise session 2: inferring latent dynamics
  • 18:15-18:30 Summary and discussions

Datasets:

  1. Vanderpol Oscillator

  2. Monkey reaching task

Code setup:

  1. Clone the repo using git clone --recursive

  2. If you missed the --recursive option, your nlb_tools folder will be empty. Use git submodule update --init --recursive to download the submodules

  3. Make a conda environment using the requirements.txt with conda env create -f env.yml

Contributors

  • Matt Dowling
  • Tushar Arora