Mock Gardner Lab Resources

Department of Bioengineering

The Gardner Lab

Learning and complex behaviors in biological neural networks

Resources from the Gardner Lab

TweetyBERT

TweetyBERT combines a convolutional front-end with a transformer architecture to learn representations of bird vocalizations.

Full source, installation instructions and usage examples are on Github.

View on Github →

Relevant publications and Webinars
Validation

TweetyBERT's validation, and relevant data.

Read the publication →

Webinar

Deciphering Canary Song:A Deep Dive into Self-Supervised Learning with TweetyBERT with George Vengrovski

Watch the Webinar →

Webinar

TweetyBERT:Automated parsing of birdsong through self-supervised machine learning with Tim Gardner.

Watch the Webinar →

how to cite

If you use any version of TweetyBERT or its source code, please cite this preprint: 

Vengrovski G, Hulsey-Vincent M, Bemrose M, Gardner T. TweetyBERT: Automated parsing of birdsong through self-supervised machine learning. Patterns 2026. https://doi.org/10.1016/j.patter.2025.101491

ADDITIONAL Contact & support
Lab contact

George Vengrovski, georgev@uoregon.edu

Bug reports

Open an issue on the Github repository.

The Gardner Lab 

In 2019, the Gardner Lab relocated to the University of Oregon's Phil and Penny Knight Campus for Accelerating Scientific Impact. Based in the Department of Bioengineering in Eugene, Oregon, the Gardner Lab explores the principles underlying learning and production of complex behaviors in biological neural networks.