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This repository contains code to reproduce all figures of the behavior paper by the International Brain Laboratory. If reusing any part of this code please cite the bioRxiv paper in which these figures appear.


These instructions require anaconda ( for Python 3 and git (

In an Anaconda prompt window:

  1. Clone or download the paper-bahavior repository
  2. Install the other dependencies by running pip install -r requirements.txt

To call the functions in this repo, either run python from within the paper-bahavior folder or add the folder to your python path:

import sys

Obtain a DataJoint account through IBL JupyterHub

IBL Jupyterhub provides an online environment to explore the IBL behavior data pipeline.

  1. Use your GitHub account to log in and go to the resource folder.
  2. Navigate to public_notebooks/Explore IBL pipeline. The Notebook 05-Access the database locally provides the instruction to obtain the credentials to access the database. Copy the value of dj.config
  3. In your local python IDE, do the following: a. import datajoint as dj b. set your local config variable dj.config with the values copied from JupyterHub c. dj.config.save_local()

You'll be able to run the code after the settings above.

How to run the code

All the scripts start with the name of the figure they produce. The figure panels will appear in the exported_figs subfolder.

Load figures without DataJoint

To load the figures from data saved in local CSV files, edit line 21 of so that QUERY = False. When running the scripts for the first time the required data will be downloaded to ./data

Download data without any code

To download data locally without running any code, simply load in an internet browser the link provided as the URL in


If you have any problems running this code, please open an issue in the iblenv repository where we support users in using the IBL software tools.

You can read more about the IBL dataset types and additional computations on the behavioral data, such as training status and psychometric functions.

Known issues

The data used in this paper have a number of issues. The authors are confident that these issues do not affect the results of the paper but nevertheless users should be aware of these shortcomings.

  1. NaN values may be found throughout the data. These resulted from failures to either produce an event (for example the go cue tone wasn't played) or to record an event (e.g. the stimulus was produced but the photodiode failed to detect it).
  2. Some events violated the task structure outlined in the paper. For example during some sessions the go cue tone happened much later than the stimulus onset. Although this conceivably affected the reaction time on some trials, it did not occur frequently enough to significantly affect the median reaction times.


The International Brain Laboratory et al. (2020) A standardized and reproducible method to measure decision-making in mice. bioRxiv, 909838







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