Toolkit for computer classification of complex social behaviors in experimental animals

pip install Simba-UW-no-tf==1.2.21


SimBA (Simple Behavioral Analysis)

License: LGPL v3 Gitter chat Download: Weights SimBA: listserv DOI

SimBAxTF Downloads
SimBA w/o TF Downloads
SimBAxTF-development wheel Downloads

Pre-print: Simple Behavioral Analysis (SimBA) – an open source toolkit for computer classification of complex social behaviors in experimental animals


June-12-2020: SimBA version 1.2 release

New Features

  • Multi-animal DLC support - Documentation
  • Multi-animal SLEAP support - Documentation
  • SimBA 'pseudo-labelling' module - Documentation
  • Easy install of SimBA via pip - Documentation
  • Plenty of new quality-of-life features (e.g., time-bin analyzes / improved visualizations options) - Documentation
  • Many, many, many, many bug-fixes

Please join our Gitter chat if you have any questions, or even if you would simply like to discuss potential applications for SimBA in your work. Please come by, stay inside, wash your hands, and check on your lab mates reguarly!

April-25-2020: SimBA pre-print manuscript release

A pre-print SimBA manuscript on bioRxiv! The manuscript details the use of SimBA for generation of social predictive classifiers in rat and mouse resident-intruder protocols - please check it out using the link above. All data, pose-estimation models, and the final classifiers generated in the manuscript, can be accessed through our OSF repository and through the Resource menu further down this page.

March-05-2020: SimBA version 1.1 release

New Features

What is SimBA?

Several excellent computational frameworks exist that enable high-throughput and consistent tracking of freely moving unmarked animals. Here we introduce and distribute a plug-and play pipeline that enabled users to use these pose-estimation approaches in combination with behavioral annotatation and generation of supervised machine-learning behavioral predictive classifiers. We have developed this pipeline for the analysis of complex social behaviors, but have included the flexibility for users to generate predictive classifiers across other behavioral modalities with minimal effort and no specialized computational background.

SimBA does not require computer science and programing experience, and SimBA is optimized for wide-ranging video acquisition parameters and quality. SimBA is written for Microsoft Windows. We may be able to provide support and advice for specific use instances, especially if it benefits multiple users and advances the scope of SimBA. Feel free to post issues and bugs here or contact us directly and we'll work on squashing them as they appear. We hope that users will contribute to the community!

  • The SimBA pipeline requires no programing knowledge
  • Specialized commercial or custom-made equipment is not required
  • Extensive annotations are not required
  • The pipeline is flexible and can be used to create and validate classifiers for different behaviors and environments
  • Currently included behavioral classifiers have been validated in mice and rats
  • SimBA is written for Windows

SimBA provides several validated classifer libraries using videos filmed from above at 90Β° angle with pose-estimation data from 8 body parts per animal; please see our OSF repository for access to all files. SimBA now accepts any user-defined pose-estimation annotation schemes with the inclusion of the Flexible Annotation Module in v1.1. SimBA now supports maDLC and SLEAP for similar looking animals with the release of maDLC/SLEAP module in v1.2.

Installation note: SimBA can be installed either with TensorFlow compatability (for generating DeepLabCut, DeepPoseKit and SLEAP pose-estimation models), or without TensorFlow (for stand-alone use with classifiers and other functions). Please choose the appropriate branch for your needs, using pip install. More details are found in the Installation Documentation.

Listserv for release information: If you would like to receive notification for new releases of SimBA, please fill out this form and you will be added to the listserv.



SimBA GUI workflow

Pipeline πŸ‘·

Documentation: General methods

Step 1: Pre-process videos

Step 2: Create tracking model and generate pose-estimation data

Step 3: Building classfier(s)

Step 4: Analysis/Visualization

Click here for the full generic tutorial on building classifiers in SimBA.

Scenario tutorials

To faciliate the initial use of SimBA, we provide several use scenarios. We have created these scenarios around a hypothetical experiment that take a user from initial use (completely new start) all the way through analyzing a complete experiment and then adding additional experimental datasets to an initial project.

Scenario 1: Building classifiers from scratch

Scenario 2: Using a classifier on new experimental data

Scenario 3: Updating a classifier with further annotated data

Scenario 4: Analyzing and adding new Experimental data to a previously started project

Installation βš™οΈ

Tutorial πŸ“š

Resource πŸ’Ύ

All data (classifiers etc.) is available on our Open Science Framework repository. For a schematic overview of the data respository folder structure (as of March-20-2020), click HERE.


Below is a link to download trained behavior classification models to apply it on your dataset

SimBA visualization examples

Labelled images

Tracking weights

Golden Lab webpage

License πŸ“ƒ

This project is licensed under the GNU Lesser General Public License v3.0. Note that the software is provided "as is", without warranty of any kind, express or implied. If you use the code or data, please cite us :)

References πŸ“œ

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Contributors 🀼