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.

---

Conda installation

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

---

Code setup:

1. Clone or download this repo.
   Use this command to clone the repo along with all its submodules, ensuring you get the full project, including any nested dependencies:
   git clone --recurse-submodules https://github.com/catniplab/latent_dynamics_workshop.git

2. 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

3. Activate the conda environment using conda activate lvmworkshop

4. cd to the project main directory (cd latent_dynamics_workshop), after cloning the repo, and run the following command in the terminal to install XFADS (eXponential FAmily Dynamical Systems), Dowling, Zhao, Park. 2024, and its dependencies
   pip install -e xfads/
   (xfads/ is the submodule folder that contains the pyproject.toml file and the xfads package folder)

Datasets

We will be focusing on two datasets – a toy dataset of spiking data with low dimensional dynamics governed by a simulated system and electrophysiological recordings from the motor cortex (M1) and dorsal premotor cortex (PMd) of a monkey during a delayed reaching task. The simulated system is a continuous attractor system with a ring topology in 2D - i.e., an abstract ring attractor system.

---

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
• Mahmoud Elmakki

Lecture history

• Cajal course on Neuro-AI (2025)
• Neural Latent State and Dynamics Inference Workshop (2022)