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Stytra: An open-source, integrated system for stimulation, tracking and closed-loop behavioral experiments.

Authors :
Vilim Štih
Luigi Petrucco
Andreas M Kist
Ruben Portugues
Source :
PLoS Computational Biology, Vol 15, Iss 4, p e1006699 (2019)
Publication Year :
2019
Publisher :
Public Library of Science (PLoS), 2019.

Abstract

We present Stytra, a flexible, open-source software package, written in Python and designed to cover all the general requirements involved in larval zebrafish behavioral experiments. It provides timed stimulus presentation, interfacing with external devices and simultaneous real-time tracking of behavioral parameters such as position, orientation, tail and eye motion in both freely-swimming and head-restrained preparations. Stytra logs all recorded quantities, metadata, and code version in standardized formats to allow full provenance tracking, from data acquisition through analysis to publication. The package is modular and expandable for different experimental protocols and setups. Current releases can be found at https://github.com/portugueslab/stytra. We also provide complete documentation with examples for extending the package to new stimuli and hardware, as well as a schema and parts list for behavioral setups. We showcase Stytra by reproducing previously published behavioral protocols in both head-restrained and freely-swimming larvae. We also demonstrate the use of the software in the context of a calcium imaging experiment, where it interfaces with other acquisition devices. Our aims are to enable more laboratories to easily implement behavioral experiments, as well as to provide a platform for sharing stimulus protocols that permits easy reproduction of experiments and straightforward validation. Finally, we demonstrate how Stytra can serve as a platform to design behavioral experiments involving tracking or visual stimulation with other animals and provide an example integration with the DeepLabCut neural network-based tracking method.

Subjects

Subjects :
Biology (General)
QH301-705.5

Details

Language :
English
ISSN :
1553734X and 15537358
Volume :
15
Issue :
4
Database :
Directory of Open Access Journals
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.3f80cab93a4f4d97909e153732b86a05
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pcbi.1006699