Optimal searching behaviour generated intrinsically by the central pattern generator for locomotion

Sims, David W, Humphries, Nicolas E, Hu, Nan, Medan, Violeta and Berni, Jimena (2019) Optimal searching behaviour generated intrinsically by the central pattern generator for locomotion. eLife, 8 (e50316). pp. 1-31. ISSN 2050-084X

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Efficient searching for resources such as food by animals is key to their survival. It has been proposed that diverse animals from insects to sharks and humans adopt searching patterns that resemble a simple Lévy random walk, which is theoretically optimal for ‘blind foragers’ to locate sparse, patchy resources. To test if such patterns are generated intrinsically, or arise via environmental interactions, we tracked free-moving Drosophila larvae with (and without) blocked synaptic activity in the brain, suboesophageal ganglion (SOG) and sensory neurons. In brain-blocked larvae we found that extended substrate exploration emerges as multi-scale movement paths similar to truncated Lévy walks. Strikingly, power-law exponents of brain/SOG/sensory-blocked larvae averaged 1.96, close to a theoretical optimum (µ ~ 2.0) for locating sparse resources. Thus, efficient spatial exploration can emerge from autonomous patterns in neural activity. Our results provide the strongest evidence so far for the intrinsic generation of Lévy-like movement patterns.

Item Type: Article
Additional Information: All data generated and analysed in this study are available in Dryad (http://doi.org/10.5061/dryad.7m0cfxpq0).
Keywords: Lévy walk, Exploration, Central pattern generator, Locomotion, Drosophila,
Schools and Departments: Brighton and Sussex Medical School > Brighton and Sussex Medical School
Subjects: Q Science
Q Science > Q Science (General) > Q0179.9 Research
Q Science > QL Zoology > QL0750 Animal behaviour
Depositing User: Jimena Berni
Date Deposited: 06 Jan 2020 13:49
Last Modified: 06 Jan 2020 14:00
URI: http://sro.sussex.ac.uk/id/eprint/89165

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Project NameSussex Project NumberFunderFunder Ref
Hox Genes and the Diversification of Neuronal NetworksUnsetWellcome Trust and Royal SocietyWT105568AIA