A lexical approach for identifying behavioural action sequences.
Animals display characteristic behavioural patterns when performing a task, such as the spiraling of a soaring bird or the surge-and-cast of a male moth searching for a female. Identifying such recurring sequences occurring rarely in noisy behavioural data is key to understanding the behavioural res...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1009672 |
| _version_ | 1819319653745295360 |
|---|---|
| author | Gautam Reddy Laura Desban Hidenori Tanaka Julian Roussel Olivier Mirat Claire Wyart |
| author_facet | Gautam Reddy Laura Desban Hidenori Tanaka Julian Roussel Olivier Mirat Claire Wyart |
| author_sort | Gautam Reddy |
| collection | DOAJ |
| description | Animals display characteristic behavioural patterns when performing a task, such as the spiraling of a soaring bird or the surge-and-cast of a male moth searching for a female. Identifying such recurring sequences occurring rarely in noisy behavioural data is key to understanding the behavioural response to a distributed stimulus in unrestrained animals. Existing models seek to describe the dynamics of behaviour or segment individual locomotor episodes rather than to identify the rare and transient sequences of locomotor episodes that make up the behavioural response. To fill this gap, we develop a lexical, hierarchical model of behaviour. We designed an unsupervised algorithm called "BASS" to efficiently identify and segment recurring behavioural action sequences transiently occurring in long behavioural recordings. When applied to navigating larval zebrafish, BASS extracts a dictionary of remarkably long, non-Markovian sequences consisting of repeats and mixtures of slow forward and turn bouts. Applied to a novel chemotaxis assay, BASS uncovers chemotactic strategies deployed by zebrafish to avoid aversive cues consisting of sequences of fast large-angle turns and burst swims. In a simulated dataset of soaring gliders climbing thermals, BASS finds the spiraling patterns characteristic of soaring behaviour. In both cases, BASS succeeds in identifying rare action sequences in the behaviour deployed by freely moving animals. BASS can be easily incorporated into the pipelines of existing behavioural analyses across diverse species, and even more broadly used as a generic algorithm for pattern recognition in low-dimensional sequential data. |
| first_indexed | 2024-12-24T11:07:07Z |
| format | Article |
| id | doaj.art-5b475a40fe2247df81c3f68e4431ce80 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-24T11:07:07Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-5b475a40fe2247df81c3f68e4431ce802022-12-21T16:58:37ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-01-01181e100967210.1371/journal.pcbi.1009672A lexical approach for identifying behavioural action sequences.Gautam ReddyLaura DesbanHidenori TanakaJulian RousselOlivier MiratClaire WyartAnimals display characteristic behavioural patterns when performing a task, such as the spiraling of a soaring bird or the surge-and-cast of a male moth searching for a female. Identifying such recurring sequences occurring rarely in noisy behavioural data is key to understanding the behavioural response to a distributed stimulus in unrestrained animals. Existing models seek to describe the dynamics of behaviour or segment individual locomotor episodes rather than to identify the rare and transient sequences of locomotor episodes that make up the behavioural response. To fill this gap, we develop a lexical, hierarchical model of behaviour. We designed an unsupervised algorithm called "BASS" to efficiently identify and segment recurring behavioural action sequences transiently occurring in long behavioural recordings. When applied to navigating larval zebrafish, BASS extracts a dictionary of remarkably long, non-Markovian sequences consisting of repeats and mixtures of slow forward and turn bouts. Applied to a novel chemotaxis assay, BASS uncovers chemotactic strategies deployed by zebrafish to avoid aversive cues consisting of sequences of fast large-angle turns and burst swims. In a simulated dataset of soaring gliders climbing thermals, BASS finds the spiraling patterns characteristic of soaring behaviour. In both cases, BASS succeeds in identifying rare action sequences in the behaviour deployed by freely moving animals. BASS can be easily incorporated into the pipelines of existing behavioural analyses across diverse species, and even more broadly used as a generic algorithm for pattern recognition in low-dimensional sequential data.https://doi.org/10.1371/journal.pcbi.1009672 |
| spellingShingle | Gautam Reddy Laura Desban Hidenori Tanaka Julian Roussel Olivier Mirat Claire Wyart A lexical approach for identifying behavioural action sequences. PLoS Computational Biology |
| title | A lexical approach for identifying behavioural action sequences. |
| title_full | A lexical approach for identifying behavioural action sequences. |
| title_fullStr | A lexical approach for identifying behavioural action sequences. |
| title_full_unstemmed | A lexical approach for identifying behavioural action sequences. |
| title_short | A lexical approach for identifying behavioural action sequences. |
| title_sort | lexical approach for identifying behavioural action sequences |
| url | https://doi.org/10.1371/journal.pcbi.1009672 |
| work_keys_str_mv | AT gautamreddy alexicalapproachforidentifyingbehaviouralactionsequences AT lauradesban alexicalapproachforidentifyingbehaviouralactionsequences AT hidenoritanaka alexicalapproachforidentifyingbehaviouralactionsequences AT julianroussel alexicalapproachforidentifyingbehaviouralactionsequences AT oliviermirat alexicalapproachforidentifyingbehaviouralactionsequences AT clairewyart alexicalapproachforidentifyingbehaviouralactionsequences AT gautamreddy lexicalapproachforidentifyingbehaviouralactionsequences AT lauradesban lexicalapproachforidentifyingbehaviouralactionsequences AT hidenoritanaka lexicalapproachforidentifyingbehaviouralactionsequences AT julianroussel lexicalapproachforidentifyingbehaviouralactionsequences AT oliviermirat lexicalapproachforidentifyingbehaviouralactionsequences AT clairewyart lexicalapproachforidentifyingbehaviouralactionsequences |