Author

Gabrielle I Coste

Johns Hopkins University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Assay:imaging· 3 Organism:mouse· 3 Research Focus:cell type annotation· 2 Approach:deep learning· 2 Research Focus:trajectory inference· 2 Research Focus:differential expression· 1 Research Focus:spatial domain detection· 1 Approach:statistical model· 1

Frequent Co-Authors

Adam S. CharlesiD· 3 shared Austin R. GravesiD· 3 shared Richard L. HuganiriD· 3 shared Dasun Premathilaka· 1 shared Dwight E. BerglesiD· 1 shared Evan Li· 1 shared Jeremias SulamiD· 1 shared Michael I. MilleriD· 1 shared

Activity by Year

2023-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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3 shared papersNeuroscience

Adam S. CharlesORCID

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Author profile →

3 papers

Recent·Most cited

A Space-Time Hidden Markov Model for In Vivo NanoScale Synaptic Plasticity Tracking

Shashwat Kumar, Gabrielle I Coste, Dasun Premathilaka, Richard L. Huganir, Austin R. Graves, Adam S. Charles, Michael I. Miller

The paper introduces SynTrack, a novel algorithm for tracking synaptic dynamics in vivo, achieving high accuracy and scalability in longitudinal synapse tracking.

bioRxiv · Oct 10, 2025 · PreprintNeuroscience

Automatic detection of fluorescently labeled synapses in volumetric in vivo imaging data

Zhining Chen, Gabrielle I Coste, Evan Li, Richard L. Huganir, Austin R. Graves, Adam S. Charles

The paper develops a convolutional neural network to detect and segment fluorescently labeled synapses in vivo, enabling large-scale analysis of synapses in living mice.

bioRxiv · Jan 22, 2025 · PreprintNeuroscience

Cross-modality supervised image restoration enables nanoscale tracking of synaptic plasticity in living mice

Yu Kang T. Xu, Austin R. Graves, Gabrielle I Coste, Richard L. Huganir, Dwight E. Bergles, Adam S. Charles, Jeremias Sulam

The paper develops a deep learning image restoration method to enhance in vivo synaptic imaging resolution using ex vivo super-resolution techniques, enabling tracking of synaptic plasticity in living mice.

Nature Methods · May 11, 2023Peer-reviewedNeuroscience