Quantitative analysis of miniature synaptic calcium transients using positive unlabeled deep learning
We developed a positive unlabeled deep learning scheme for detection and segmentation of miniature synaptic calcium transients. Combining deep learning and feature analysis, it measures the impact of cLTP on transient morphology and dynamics.
Authors
- Renée Hložek (ORCID: https://orcid.org/0000-0002-0965-7864)
- Theresa Wiesner (ORCID: https://orcid.org/0000-0001-5620-2945)
- Paul De Koninck (ORCID: https://orcid.org/0000-0002-6436-1062)
- Simon Labrecque
- Anthony Bilodeau (ORCID: https://orcid.org/0000-0002-7041-6402)
- Christian Gagné (ORCID: https://orcid.org/0000-0003-3697-4184)
- Flavie Lavoie‐Cardinal (ORCID: https://orcid.org/0000-0002-4496-5088)
- Mado Lemieux (ORCID: https://orcid.org/0009-0001-7581-9163)
- Bi Fan (ORCID: https://orcid.org/0000-0003-2193-6943)
- Gabriel Nadeau
- G. LECLERC
- Frederic Beaupré (ORCID: https://orcid.org/0009-0000-4412-5153)
- Katrine Castonguay
Institutions
- Canadian Institute for Advanced Research (CA)
- University of Toronto (CA)
- Canadian Institute for Theoretical Astrophysics (CA)
- Mila - Quebec Artificial Intelligence Institute (CA)
- Université Laval (CA)
Publication Details
- Journal
- Digital Discovery
- Published
- 2024-11-20
- DOI
- https://doi.org/10.1039/d4dd00197d
- Citations
- 2
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.42
Funders
- National Science Foundation
- Canadian Institute for Advanced Research
- Canada Research Coordinating Committee
- Canada Research Chairs
- Connaught Fund
- Canadian Institutes of Health Research
- Natural Sciences and Engineering Research Council of Canada
- Fonds de Recherche du Québec - Santé
- Fonds de recherche du Québec – Nature et technologies