Author

A. C. Costa

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· 3 Biophysics· 3 Ecology· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Organism:C. elegans· 7 Research Focus:cell type annotation· 7 Assay:imaging· 7 Approach:statistical model· 5 Approach:deep learning· 4 Research Focus:trajectory inference· 3 Research Focus:clustering· 1 Approach:experimental assay· 1

Frequent Co-Authors

G. J. Stephens· 5 shared T. Ahamed· 4 shared D. Jordan· 2 shared A. d. J. Navarrete· 1 shared A. M. Davidson· 1 shared C. Law· 1 shared E. Kirbizakis· 1 shared L. Avery· 1 shared

Activity by Year

2019-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 11 mutual co-author links

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5 shared papersAnimal Behavior & Cognition

G. J. Stephens

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Author profile →

8 papers

Recent·Most cited

A Markovian dynamics for C. elegans behavior across scales

A. C. Costa, T. Ahamed, D. Jordan, G. J. Stephens

This paper develops a Markov model of C. elegans foraging behavior that captures dynamics across scales, revealing new behavioral states and a trade-off between local and global search strategies.

bioRxiv · Oct 19, 2023 · PreprintAnimal Behavior & Cognition

Fluctuating landscapes and heavy tails in animal behavior

A. C. Costa, M. Vergassola

The paper shows that slow non-ergodic dynamics in C. elegans locomotion lead to heavy-tailed statistics, explained by time-dependent potential landscapes.

bioRxiv · Jan 3, 2023Biophysics

A deep learning approach to capture the essence of Candida albicans morphologies

V. Bettauer, A. C. Costa, R. P. Omran, S. Massahi, E. Kirbizakis, S. Simpson, V. Dumeaux, C. Law, M. Whiteway, M. T. Hallett

This paper develops a deep learning system called Candescence to detect and classify C. albicans morphologies from microscopy images using a curriculum-based learning strategy and GANs.

bioRxiv · Jun 10, 2021Microbiology

Maximally predictive ensemble dynamics from data

A. C. Costa, T. Ahamed, D. Jordan, G. J. Stephens

This paper introduces a method to infer macroscopic dynamics from data by maximizing predictive information, applied to both physical systems and the movement of C. elegans.

bioRxiv · May 26, 2021 · PreprintBiophysics

Free-living marine nematodes diversity at Ponta Delgada-Sao Miguel (Azores archipelago, North-East Atlantic Ocean): first results from shallow soft-bottom habitats

A. d. J. Navarrete, V. Aramayo, A. M. Davidson, A. C. Costa

The paper reports the first survey of free-living marine nematode diversity in shallow soft-bottom habitats of Sao Miguel Island, Azores, identifying 43 genera across multiple families and highlighting potential new species.

bioRxiv · Sep 9, 2020 · PreprintEcology

WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans

L. Hebert, T. Ahamed, A. C. Costa, L. O'Shaughnessy, G. J. Stephens

WormPose is an open-source tool for 2D pose estimation in C. elegans using synthetic data and convolutional networks, validated on both synthetic and real worms.

bioRxiv · Jul 9, 2020Animal Behavior & Cognition

Capturing the Continuous Complexity of Behavior in C. elegans

T. Ahamed, A. C. Costa, G. J. Stephens

This paper develops a method to analyze C. elegans behavior by capturing its continuous state space, revealing both stereotyped and chaotic dynamics.

bioRxiv · Nov 1, 2019 · PreprintAnimal Behavior & Cognition

Modeling the ballistic-to-diffusive transition in nematode motility reveals variation in exploratory behavior across species

S. J. Helms, W. M. Rozemuller, A. C. Costa, L. Avery, G. J. Stephens, T. S. Shimizu

The paper models nematode motility transitions and finds species-specific variations in exploratory behavior linked to trade-offs between exploration and exploitation.

bioRxiv · Mar 24, 2019Biophysics