Hierarchical Compression Reveals Sub-Second to Day-Long Structure in Larval Zebrafish Behavior
Animal behavior is dynamic, evolving over multiple timescales from milliseconds to days and even across a lifetime. To understand the mechanisms governing these dynamics, it is necessary to capture multi-timescale structure from behavioral data. Here, we develop computational tools and study the beh...
Gespeichert in:
| Veröffentlicht in: | eNeuro Jg. 7; H. 4; S. ENEURO.0408-19.2020 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
Society for Neuroscience
01.07.2020
|
| Schlagworte: | |
| ISSN: | 2373-2822, 2373-2822 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Zusammenfassung: | Animal behavior is dynamic, evolving over multiple timescales from milliseconds to days and even across a lifetime. To understand the mechanisms governing these dynamics, it is necessary to capture multi-timescale structure from behavioral data. Here, we develop computational tools and study the behavior of hundreds of larval zebrafish tracked continuously across multiple 24-h day/night cycles. We extracted millions of movements and pauses, termed bouts, and used unsupervised learning to reduce each larva’s behavior to an alternating sequence of active and inactive bout types, termed modules. Through hierarchical compression, we identified recurrent behavioral patterns, termed motifs. Module and motif usage varied across the day/night cycle, revealing structure at sub-second to day-long timescales. We further demonstrate that module and motif analysis can uncover novel pharmacological and genetic mutant phenotypes. Overall, our work reveals the organization of larval zebrafish behavior at multiple timescales and provides tools to identify structure from large-scale behavioral datasets. |
|---|---|
| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 This work was supported by a Medical Research Council Doctoral Training grant (M.G.), a UCL Excellence fellowship (J.R.), a Wellcome Trust Investigator Award (J.R.), and a European Research Council starting grant (J.R.). The authors declare no competing financial interests. Author contributions: M.G. and J.R. designed research; M.G. performed research; J.R. contributed unpublished reagents/analytic tools; M.G. and J.R. analyzed data; M.G. and J.R. wrote the paper. |
| ISSN: | 2373-2822 2373-2822 |
| DOI: | 10.1523/ENEURO.0408-19.2020 |