nlb-tools 0.0.4

Python tools for participating in Neural Latents Benchmark '21

NLB Codepack (nlb_tools)

Python tools for participating in Neural Latents Benchmark '21.

Overview

Neural Latents Benchmark '21 (NLB'21) is a benchmark suite for unsupervised modeling of neural population activity. The suite includes four datasets spanning a variety of brain areas and experiments. The primary task in the benchmark is co-smoothing, or inference of firing rates of unseen neurons in the population.

This repo contains code to facilitate participation in NLB'21:

• nlb_tools/ has code to load and preprocess our dataset files, format data for modeling, and locally evaluate results
• examples/tutorials/ contains tutorial notebooks demonstrating basic usage of nlb_tools
• examples/baselines/ holds the code we used to run our baseline methods. They may serve as helpful references on more extensive usage of nlb_tools

Installation

The package can be installed with the following command:

pip install nlb-tools

However, to run the tutorial notebooks locally or make any modifications to the code, you should clone the repo. The package can then be installed with the following commands:

git clone https://github.com/neurallatents/nlb_tools.git
cd nlb_tools
pip install -e .

This package requires Python 3.7+ and was developed in Python 3.7, which is the Python version we recommend you use.

Getting started

We recommend reading/running through examples/tutorials/basic_example.ipynb to learn how to use nlb_tools to load and format data for our benchmark. You can also find Jupyter notebooks demonstrating running GPFA and SLDS for the benchmark in examples/tutorials/.

Other resources

For more information on the benchmark:

• our main webpage contains general information on our benchmark pipeline and introduces the datasets
• our EvalAI challenge is where submissions are evaluated and displayed on the leaderboard
• our datasets are available on DANDI: MC_Maze, MC_RTT, Area2_Bump, DMFC_RSG, MC_Maze_Large, MC_Maze_Medium, MC_Maze_Small
• our paper describes our motivations behind this benchmarking effort as well as various technical details and explanations of design choices made in preparing NLB'21
• our Slack workspace lets you interact directly with the developers and other participants. Please email fpei6 [at] gatech [dot] edu for an invite link