Neuroengineering · Behavior · Computation
We study how brains learn complex vocal behavior, build AI that reveals the structure of animal communication, and engineer new interfaces to the nervous system.
01 / Song learningUniversity of Oregon
Knight Campus
What we investigate
01We use songbirds to uncover how biological neural networks learn and produce complex, precisely timed behavior.
Our experiments span vocal learning, neural circuit dynamics, electrophysiology, and cellular-scale imaging in singing birds.
02We build self-supervised models at the natural timescale of birdsong, allowing machines to discover, parse, and compare vocal units with very little labeled data.
SongMAE combines 5 ms representations with spatial masking to resolve individual syllables while retaining strong species-classification and detection capabilities.
03We build miniature, long-lived interfaces for recording and stimulating the nervous system.
Projects include direct laser-write 3D printing, thin-film ultramicroelectrodes, miniature microscopes, and chronic peripheral nerve interfaces.
Featured research · bioRxiv preprint
Most bioacoustic encoders inherit design assumptions from human speech. SongMAE instead matches the faster temporal structure of birdsong, producing representations that organize naturally around syllables and generalize to species excluded from training.
Read the preprint Preprint; not yet peer reviewed.
SongMAE helps us see the structure of song. This work asks which neural circuits keep that structure moving.
Featured research · bioRxiv preprint
Canary song depends not only on producing the right syllables, but on knowing when to move to the next one. By pairing targeted neural-circuit lesions with high-throughput TweetyBERT analysis of thousands of songs, we found that disruptions involving medial Anterior Forebrain Pathway circuitry destabilized phrase timing. Affected birds developed sustained, prolonged, and variable repetition of particular syllables, revealing an ongoing role for basal-ganglia circuits in fluent learned behavior.
Read the preprint “Stuttering-like” describes prolonged syllable repetition before transition; it does not imply direct equivalence with human developmental stuttering. Preprint; not yet peer reviewed.
About the lab
The Gardner Lab studies birdsong vocal production to understand how biological neural networks learn and produce complex behavior. Our work ranges from machine-learning analysis of behavior and electrophysiology to cellular-scale imaging in singing birds.
Alongside this basic science, we develop new neural interfaces designed to form stable, long-term connections with the brain and peripheral nervous system.
A growing focus is designing AI around the natural temporal structure of animal communication, rather than importing assumptions from human speech.
Lab community
Principal Investigator
Associate professor of neuroengineering studying songbirds, neural circuit dynamics, and next-generation brain interfaces.
Postdoctoral Scholar
Taylor studies how inhibitory-circuit gene programs regulate critical-period plasticity. Her recent work identified Calbindin (Calb1) levels in visual-cortex inhibitory neurons as a determinant of plasticity, showing that reactivating critical-period-stage gene expression can restore juvenile-like plasticity in adult circuits. In the Gardner Lab, she will investigate mechanisms of plasticity regulation through chronic neural recordings in mice.
PhD Student · Institute of Neuroscience
George develops self-supervised AI for understanding the structure of birdsong. His work includes TweetyBERT, which learns to parse unlabeled canary song into notes, syllables, and phrases, and SongMAE, a large-scale bioacoustic encoder designed around the natural timescale of birdsong.
PhD Student · Institute of Neuroscience
Rose studies how basal ganglia circuits control the timing and sequencing of learned vocal behavior. Her work combines targeted experiments in adult canaries with high-throughput TweetyBERT analysis, revealing how disruption involving the medial anterior forebrain pathway destabilizes phrase timing and produces stuttering-like syllable repetition.
PhD Student · Knight Campus Bioengineering
Diana develops new devices for neuroscience applications. As an undergraduate at the University of Illinois Urbana-Champaign, she gained experience in microfabrication, materials science, and electrical engineering in a biosensors lab. She now applies those skills to sensor design and more effective interfaces for the peripheral and central nervous systems.
PhD Student · Knight Campus Bioengineering
Peter is working on self-supervised models of birdsong with an emphasis on JEPA architectures. Previously, he worked on non-invasive neural interfaces and low-cost microscopes.
Undergraduate
Cole applies the lab’s TweetyBERT machine-learning model to label birdsong and investigate the higher-order structure of song syntax.
Publications & press
Explore peer-reviewed research in neural computation, vocal learning, machine learning, and neural interface engineering.
View Google ScholarSoftware resources
Browse code and workflows developed in the lab for analyzing birdsong and connecting machine learning with behavior.
Join the conversation
We welcome conversations with prospective students, postdocs, collaborators, and people building new tools for neuroscience.
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