A collection of tools for the discrete classification of animal behaviors using low-dimensional representations of videos (such as skeletons provided by tracking algorithms). Our approach combines strong supervision, weak supervision, and self-supervision to improve model performance. See the preprint here for more details.
This repo currently supports fitting the following types of base models on behavioral time series data:
- Dense MLP network with initial 1D convolutional layer
- RNNs - both LSTMs and GRUs
- Temporal Convolutional Networks (TCNs)
See the documentation to get started!
If you use daart in your analysis of behavioral data, please cite our preprint!
@inproceedings{whiteway2021semi,
title={Semi-supervised sequence modeling for improved behavioral segmentation},
author={Whiteway, Matthew R and Schaffer, Evan S and Wu, Anqi and Buchanan, E Kelly and Onder, Omer F and Mishra, Neeli and Paninski, Liam},
journal={bioRxiv},
year={2021},
publisher={Cold Spring Harbor Laboratory}
}