Multidimensional_dopamine_RL

Introduction

This code was used for studying the A multidimensional distributional map of future reward in dopamine neurons. Raw data is available in a Figshare public repository.

Usage

This code was developed in Python 3.7. All required packages are in the requirements.txt file.

To generate each figure:

1. Download the repository and save it in directory.
2. Update the directory and the type_neurons (DA or Putative_DA) in the corresponding script. For example, to replicate Figures 3A, B and C run script Fig3a_b_c.py with type_neurons="DA".

The script Parse_data_neurons.py processes raw data of the public repository (saved in directory) and outputs the estimated tuning parameters for reward magnitude and time, as well as the mean responses over the selected time windows aligned to cue (time_init_cue and time_end_cue) and reward (time_init_reward and time_end_reward) for all neurons of the chosen type (DA or Putative_DA) and saves it in directory_save.

All scripts except Extended_data_Fig10 take less than 10 seconds on a MacBook Pro M2 with 8GB of RAM (macOS 13.0). Extended_data_Fig10 scripts take 20 minutes to run.