Biology subjects

Narula, G.

Publications and source records attributed to Narula, G..

3 recordsLinked to original sources

bioRxiv2026

BirdCODE: Detecting bird communication at scale

Deep learning-based animal sound identification is regularly applied to large audio datasets for ecological monitoring and citizen science, but existing methods lack the fine temporal resolution required to extract insights into animal communication from these same datasets. Here we introduce Bird Communication Detector (BirdCODE), a deep learning model that detects and classifies the vocalizations of over 9000 bird species with precise temporal boundaries, a several hundredfold increase the number of species over previous bioacoustic sound event detection models. In extensive benchmarking, BirdCODE achieves state-of-the-art performance in detection and classification of bird sounds. Applying BirdCODE to 1.3M citizen-science recordings, we present four case studies of how BirdCODE-computed sound event boundaries can be used to carry out phylogenetic analyses, to describe geographic and temporal variation in acoustic communication, and to characterize cross-species interactions. Together, these demonstrate how BirdCODE can enable large-scale, data-driven studies of bird communication. Model code, weights, and predictions are publicly available.

bioinformatics↗

bioRxiv2017

Learning to perform auditory discriminations from observation is efficient but less robust than learning from experience

Social learning enables complex societies. However, it is largely unknown how insights obtained from observation compare with insights gained from trial-and-error, in particular in terms of their robustness. We use aversive reinforcement to train \"experimenter\" zebra finches to discriminate between auditory stimuli in the presence of an \"observer\" finch. We find that experimenters are slow to successfully discriminate the stimuli but immediately generalize their ability to a new set of similar stimuli. By contrast, observers subjected to the same task instantly discriminate the initial stimulus set, but require more time for successful generalization. Drawing upon machine learning insights, we suggest that observer learning has evolved to rapidly absorb sensory statistics without pressure to minimize neural resources, whereas learning from experience is endowed with a form of regularization that enables robust inference.

animal behavior and cognition↗

bioRxiv2017

Welfare of zebra finches used in research

Over the past 50 years, songbirds have become a valuable model organism for scientists studying vocal communication from its behavioral, hormonal, neuronal, and genetic perspectives. Many advances in our understanding of vocal learning result from research using the zebra finch, a close-ended vocal learner. We review some of the manipulations used in zebra finch research, such as isolate housing, transient/irreversible impairment of hearing/vocal organs, implantation of small devices for chronic electrophysiology, head fixation for imaging, aversive song conditioning using sound playback, and mounting of miniature backpacks for behavioral monitoring. We highlight the use of these manipulations in scientific research, and estimate their impact on animal welfare, based on the literature and on data from our past and ongoing work. The assessment of harm-benefits tradeoffs is a legal prerequisite for animal research in Switzerland. We conclude that a diverse set of known stressors reliably lead to suppressed singing rate, and that by contraposition, increased singing rate may be a useful indicator of welfare. We hope that our study can contribute to answering some of the most burning questions about zebra finch welfare in research on vocal behaviors.

animal behavior and cognition↗