Author

S. C. Turaga

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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Animal Behavior & Cognition· 1 Biophysics· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Organism:Drosophila· 1 Assay:imaging· 1 Assay:proteomics· 1 Assay:spatial transcriptomics· 1 Research Focus:trajectory inference· 1

Frequent Co-Authors

J. H. Macke· 2 shared A. Kreshuk· 1 shared A. Nern· 1 shared A. Speiser· 1 shared B. J. Arthur· 1 shared C. J. Obara· 1 shared D. Stern· 1 shared E. Gruntman· 1 shared

Activity by Year

2020-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 15 mutual co-author links

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2 shared papersNeuroscience

J. H. Macke

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Author profile →

3 papers

Recent·Most cited

Connectome-constrained deep mechanistic networks predict neural responses across the fly visual system at single-neuron resolution

J. K. Lappalainen, F. D. Tschopp, S. Prakhya, M. McGill, A. Nern, K. Shinomiya, S.-y. Takemura, E. Gruntman, J. H. Macke, S. C. Turaga

The paper predicts neural responses in the fly visual system using a connectome-constrained deep mechanistic model, showing agreement with experimental data across 24 studies.

bioRxiv · Mar 11, 2023 · PreprintNeuroscience

SongExplorer: A deep learning workflow for discovery and segmentation of animal acoustic communication signals

B. J. Arthur, Y. Ding, M. Sosale, F. Khalif, E. Kim, P. Waddell, S. C. Turaga, D. Stern

SongExplorer uses deep learning to discover, annotate, and segment animal acoustic signals with higher accuracy than existing methods and comparable to human experts.

bioRxiv · Mar 26, 2021 · PreprintAnimal Behavior & Cognition

Deep learning enables fast and dense single-molecule localization with high accuracy

A. Speiser, L.-R. Müller, U. Matti, C. J. Obara, W. R. Legant, A. Kreshuk, J. H. Macke, J. Ries, S. C. Turaga

This paper introduces DECODE, a deep learning tool that improves single-molecule localization speed and density in SMLM with high accuracy across various imaging conditions.

bioRxiv · Oct 26, 2020Biophysics