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

13:30-13:45 Conda/python/code installation
13:45-14:30 Lecture 1: Latent state trajectories and dimensionality reduction
14:30-15:15 Hands-on 1: dimensionality reduction
15:15-15:45 Lecture 2: Latent neural dynamics theory and algorithms
15:45-16:15 Hands-on 2: inferring latent dynamics
16:15-16:30 Summary and discussions

Conda installation

For installation of conda follow the instructions here: https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html#

Git installation (for Windows)

Windows users would need to install and work in the Git BASH terminal. For installing git, see: https://git-scm.com/downloads

Code setup:

Clone or download this repo.
Make a conda environment using the requirements.txt with
- For Linux and MacOS use conda env create -f env.yml
- For Windows use conda env create -f env_windows.yml and then
  GPU: pip install jax==0.3.13 https://whls.blob.core.windows.net/unstable/cuda111/jaxlib-0.3.7+cuda11.cudnn82-cp39-none-win_amd64.whl
  or
  CPU: pip install "jax[cpu]===0.3.14" -f https://whls.blob.core.windows.net/unstable/index.html --use-deprecated legacy-resolver
  and finally pip install git+https://github.com/yuanz271/vlgpax.git
Activate the conda environment using conda activate lvmworkshop

Datasets

We will be focusing on two datasets – a toy dataset of spiking data with low dimensional dynamics governed by a Van der Pol Oscillator and electrophysiological recordings from the motor cortex (M1) and dorsal premotor cortex (PMd) of a monkey during a delayed reaching task.

Van der Pol Oscillator

To setup this dataset move to the code pack folder using cd code_pack/ then run python generate_vdp_data.py

Monkey reaching task

No action required. Included in the github.com repo.

Starting Jupyter Notebook or JupyterLab

Start Jupyter Notebook by typing jupyter notebook or JupyterLab by typing jupyter lab

Contributors

Matt Dowling
Tushar Arora
Ayesha Vermani
Abel Sagodi

catniplab / latent_dynamics_workshop