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https://github.com/neurogym/neurogym
A curated collection of neuroscience tasks with a common interface.
https://github.com/neurogym/neurogym
Last synced: about 1 month ago
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A curated collection of neuroscience tasks with a common interface.
- Host: GitHub
- URL: https://github.com/neurogym/neurogym
- Owner: neurogym
- License: mit
- Created: 2019-02-21T15:54:59.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-04-06T13:11:06.000Z (5 months ago)
- Last Synced: 2024-04-24T02:48:17.212Z (5 months ago)
- Language: Python
- Homepage:
- Size: 7.98 MB
- Stars: 232
- Watchers: 14
- Forks: 46
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-computational-neuroscience - NeuroGym - NeuroGym is a curated collection of neuroscience tasks with a common interface. The goal is to facilitate training of neural network models on neuroscience tasks. (Package / Computational Neuroscience)
README
**Status:** In Development. All tasks are subject to changes right now.
# NeuroGym
NeuroGym is a curated collection of neuroscience tasks with a common interface.
The goal is to facilitate training of neural network models on neuroscience tasks.
Documentation: https://neurogym.github.io/
- [Installation](#Installation)
- [Tasks](#Tasks)
- [Wrappers](#Wrappers)
- [Examples](#Examples)
- [Contributing](#Contributing)
- [Authors](#Authors)
NeuroGym inherits from the machine learning toolkit [Gym](https://github.com/openai/gym) by OpenAI,
and thus 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.
### Installation
You can perform a minimal install of ``neurogym`` with:
git clone https://github.com/neurogym/neurogym.git
cd neurogym
pip install -e .
Or a full install by replacing the last command with ``pip install -e '.[all]'``
### Tasks
Currently implemented tasks can be found [here](https://github.com/gyyang/neurogym/blob/master/docs/envs.md).
### Wrappers
Wrappers (see [list](https://github.com/gyyang/neurogym/blob/master/docs/wrappers.md))
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 OpenAI gym.
In this [example](https://github.com/gyyang/neurogym/blob/master/examples/example_neurogym_rl.ipynb) jupyter notebook we show how to train
a neural network with reinforcement learning algorithms using the
[Stable Baselines](https://github.com/hill-a/stable-baselines) toolbox.
### Contributing
Contributing new tasks should be easy. You can contribute tasks using the regular OpenAI gym format. If your task has a trial/period structure,
this [template](https://github.com/gyyang/neurogym/blob/master/examples/template.py) provides the basic structure that we recommend a task to have:
```
from gym 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 end, done
a dictionary with extra information:
ground truth correct response, info['gt']
boolean indicating the end of the trial, info['new_trial']
"""
return obs, reward, done, {'new_trial': new_trial, 'gt': gt}
```
### Authors
* Contact
[Manuel Molano](https://github.com/manuelmolano) ([email protected]).
[Guangyu Robert Yang](https://github.com/gyyang) ([email protected]).
* Contributors (listed in chronological order)
[Marta Fradera](https://github.com/martafradera),
[Jordi Pastor](https://github.com/pastorjordi),
[Jeremy Forest](https://github.com/jeremyforest),
[Ru-Yuan Zhang](https://github.com/ruyuanzhang)