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.
- 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
For installation of conda follow the instructions here: https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html#
Windows users would need to install and work in the Git BASH terminal. For installing git, see: https://git-scm.com/downloads
-
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 finallypip install git+https://github.com/yuanz271/vlgpax.git
- For Linux and MacOS use
-
Activate the conda environment using
conda activate lvmworkshop
We will be focusing on two datasets โ 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.
- To setup this dataset move to the code pack folder using
cd code_pack/
then runpython generate_vdp_data.py
- No action required. Included in the github.com repo.
Start Jupyter Notebook by typing jupyter notebook
or JupyterLab by typing jupyter lab
Contributors
- Matt Dowling
- Tushar Arora
- Ayesha Vermani
- Abel Sagodi