Biology subjects

Miron, M.

Publications and source records attributed to Miron, M..

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↗

bioRxiv2026

Zebra finch females flexibly communicate with each other and with AI-driven acoustic interaction models

Vocal interactions are fundamental for social functioning across animals, including humans. The diverse rules underlying these exchanges remain largely unknown, and emerging AI technologies offer promising avenues for investigation. We used computational tools to collect and analyze >1,000 hours of vocal interactions between female zebra finches and discovered that their interactions were characterized by correlated call production and structure, rapid acoustic modulation, and response selectivity. To test these interaction rules, we developed a generative audio large language model (ZF-AIM Acoustic Interaction Model) that engaged in real-time vocal exchanges with birds. When birds interacted with ZF-AIM, their vocal production and flexibility recapitulated key naturalistic features, which did not happen with non-interactive playbacks. Targeted ablations of ZF-AIM revealed that call timing and structure differentially contribute to natural vocal interactions. Using these AI-animal interactions, we demonstrate how AI can be leveraged to reveal fundamental rules underlying animal communication.

animal behavior and cognition↗

bioRxiv2019

A single-cell reference map for human blood and tissue T cell activation reveals functional states in health and disease

Human T cells coordinate adaptive immunity by localization in diverse tissue sites, though blood T cells are the most readily studied. Here, we used single-cell RNA-seq to define the functional responses of T cells isolated from human lungs, lymph nodes, bone marrow, and blood to TCR-stimulation. We reveal how human T cells in tissues relate to those in blood, and define activation states for CD4+ and CD8+T cells across all sites, including an interferon-response state for CD4+T cells and distinct effector states for CD8+T cells. We further show how profiles of individual tumor-associated T cells can be projected onto this healthy reference map, revealing their functional state.

immunology↗