neurogym 1.0.4

NeuroGym: Gymnasium-style Cognitive Neuroscience Tasks

NeuroGym

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NeuroGym is a curated collection of neuroscience tasks with a common interface. The goal is to facilitate the training of neural network models on neuroscience tasks.

• NeuroGym
  • Installation
    • Psychopy installation
  • Tasks
  • Wrappers
  • Examples
  • Custom tasks
  • Acknowledgements

NeuroGym inherits from the machine learning toolkit Gymnasium, a maintained fork of OpenAI’s Gym library. It allows a wide range of well established machine learning algorithms to be easily trained on behavioral paradigms relevant for the neuroscience community. NeuroGym also incorporates several properties and functions (e.g. continuous-time and trial-based tasks) that are important for neuroscience applications. The toolkit also includes various modifier functions that allow easy configuration of new tasks.

Please see our extended project documentation for additional details.

Installation

Step 1: Create a virtual environment

Create and activate a virtual environment to install the current package, e.g. using conda (please refer to their site for questions about creating the environment):

conda activate # ensures you are in the base environment
conda create -n neurogym python=3.11 -y
conda activate neurogym

Step 2: Install neurogym

Then install the latest version of neurogym as follows:

pip install neurogym

Step 2b: Install editable package

Alternatively, get the latest updates by cloning the repo and installing the editable version of neurogym, by replacing step 2 above by:

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

Step 3 (Optional): Psychopy installation

NOTE: psycohopy installation is currently not working

If you need psychopy for your project, additionally run

pip install psychopy

Tasks

Currently implemented tasks can be found here.

Wrappers

Wrappers (see list) are short scripts that allow introducing modifications the original tasks. For instance, the Random Dots Motion task can be transformed into a reaction time task by passing it through the reaction_time wrapper. Alternatively, the combine wrapper allows training an agent in two different tasks simultaneously.

Examples

NeuroGym is compatible with most packages that use gymnasium. In this example jupyter notebook we show how to train a neural network with reinforcement learning algorithms using the Stable-Baselines3 toolbox.

Custom tasks

Creating custom new tasks should be easy. You can contribute tasks using the regular gymnasium format. If your task has a trial/period structure, this template provides the basic structure that we recommend a task to have:

from gymnasium import spaces
import neurogym as ngym

class YourTask(ngym.PeriodEnv):
    metadata = {}

    def __init__(self, dt=100, timing=None, extra_input_param=None):
        super().__init__(dt=dt)


    def new_trial(self, **kwargs):
        """
        new_trial() is called when a trial ends to generate the next trial.
        Here you have to set:
        The trial periods: fixation, stimulus...
        Optionally, you can set:
        The ground truth: the correct answer for the created trial.
        """

    def _step(self, action):
        """
        _step receives an action and returns:
            a new observation, obs
            reward associated with the action, reward
            a boolean variable indicating whether the experiment has terminated, terminated
                See more at https://gymnasium.farama.org/tutorials/gymnasium_basics/handling_time_limits/#termination
            a boolean variable indicating whether the experiment has been truncated, truncated
                See more at https://gymnasium.farama.org/tutorials/gymnasium_basics/handling_time_limits/#truncation
            a dictionary with extra information:
                ground truth correct response, info['gt']
                boolean indicating the end of the trial, info['new_trial']
        """

        return obs, reward, terminated, truncated, {'new_trial': new_trial, 'gt': gt}

Acknowledgements

For the authors of the package, please refer to the zenodo DOI at the top of the page.

Other contributors (listed in chronological order)

• Marta Fradera
• Jordi Pastor
• Jeremy Forest
• Ru-Yuan Zhang