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Hulsey-Vincent, M. R.

Publications and source records attributed to Hulsey-Vincent, M. R..

2 recordsLinked to original sources

Lesions Involving Medial Anterior Forebrain Pathway Circuitry Destabilize Phrase Timing in Adult Canary Song

Basal ganglia-thalamocortical circuits are essential for learning complex motor sequences, yet how they control flexible motor behavior remains poorly understood. The homologous songbird Anterior Forebrain Pathway (AFP) drives song motor learning and was previously thought not to play a role in song performance, as early lesions showed no effect. This perspective has recently been revised by evidence that the AFP influences song syntax in some species. We revisit this question in adult canaries by performing bilateral excitotoxic lesions targeting the lateral and medial subdivisions of the AFP. To measure behavioral changes, we developed a high-throughput song-annotation pipeline that incorporates a supervised classifier into the self-supervised TweetyBERT model. This removed the memory bottleneck of UMAP clustering, enabling phrase-level analysis across thousands of songs per bird. We find that lesions involving the medial AFP produce a stuttering-like behavior, defined here as prolonged, variable syllable repetition before transitions, resulting in a significant increase in phrase-duration variability. This effect was strongest in birds with medial and lateral AFP lesions, and was not observed in birds with lateral-only AFP lesions. The increased variability persisted throughout the post-lesion recording period and was accompanied by small changes in the acoustic structure of syllables. Our results implicate the medial AFP in the ongoing control of phrase duration in adult canary song, challenging the view that the AFP is dispensable once song is learned. These findings position the medial AFP as a tractable model for understanding how basal ganglia and cortical dynamics maintain complex learned motor sequences.

neuroscience↗

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

Deep neural networks can be trained to parse animal vocalizations - serving to identify the units of communication, and annotating sequences of vocalizations for subsequent statistical analysis. However, current methods rely on human labelled data for training. The challenge of parsing animal vocalizations in a fully unsupervised manner remains an open problem. Addressing this challenge, we introduce TweetyBERT, a self-supervised transformer neural network developed for analysis of birdsong. The model is trained to predict masked or hidden fragments of audio, but is not exposed to human supervision or labels. Applied to canary song, TweetyBERT autonomously learns the behavioral units of song such as notes, syllables, and phrases - capturing intricate acoustic and temporal patterns. This approach of developing self-supervised models specifically tailored to animal communication will significantly accelerate the analysis of unlabeled vocal data.

animal behavior and cognition↗