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Tracking changes in behavioural dynamics using prediction error
- Source :
- PloS one, vol 16, iss 5, PLoS ONE, PLoS ONE, Vol 16, Iss 5, p e0251053 (2021)
- Publication Year :
- 2021
- Publisher :
- eScholarship, University of California, 2021.
-
Abstract
- Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterize behavioural dynamics. For the model roundworm Caenorhabditis elegans, body pose can be accurately quantified from video as coordinates in a single low-dimensional space. We focus on this well-established case as an illustrative example and propose a method to reveal subtle variations in behaviour at high time resolution. Our data-driven method, based on empirical dynamic modeling, quantifies behavioural change as prediction error with respect to a time-delay-embedded ‘attractor’ of behavioural dynamics. Because this attractor is constructed from a user-specified reference data set, the approach can be tailored to specific behaviours of interest at the individual or group level. We validate the approach by detecting small changes in the movement dynamics of C. elegans at the initiation and completion of delta turns. We then examine an escape response initiated by an aversive stimulus and find that the method can track return to baseline behaviour in individual worms and reveal variations in the escape response between worms. We suggest that this general approach – defining dynamic behaviours using reference attractors and quantifying dynamic changes using prediction error – may be of broad interest and relevance to behavioural researchers working with video-derived time series.
- Subjects :
- Research Facilities
Nematoda
Computer science
Image Processing
Libraries
Velocity
Social Sciences
Information Centers
computer.software_genre
Systems Science
Motion (physics)
Computer-Assisted
Attractor
Image Processing, Computer-Assisted
Psychology
Foraging
Caenorhabditis elegans
Multidisciplinary
Animal Behavior
Behavior, Animal
biology
Physics
Classical Mechanics
Eukaryota
Animal Models
Dynamical Systems
Experimental Organism Systems
Physical Sciences
Medicine
Aversive Stimulus
Research Article
Computer and Information Sciences
General Science & Technology
Science
Movement
Escape response
Research and Analysis Methods
Machine learning
Motion
Model Organisms
Animals
Relevance (information retrieval)
Set (psychology)
Behavior
Animal
business.industry
Organisms
Biology and Life Sciences
biology.organism_classification
Invertebrates
System dynamics
Reference data
Animal Studies
Caenorhabditis
Generic health relevance
Artificial intelligence
Focus (optics)
business
Zoology
computer
Mathematics
Forecasting
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- PloS one, vol 16, iss 5, PLoS ONE, PLoS ONE, Vol 16, Iss 5, p e0251053 (2021)
- Accession number :
- edsair.doi.dedup.....c0798b07c7272f9bf58fc8a1f4e7b1be