PUBLICATION
Elements of a stochastic 3D prediction engine in larval zebrafish prey capture
- Authors
- Bolton, A.D., Haesemeyer, M., Jordi, J., Schaechtle, U., Saad, F.A., Mansinghka, V.K., Tenenbaum, J.B., Engert, F.
- ID
- ZDB-PUB-191127-10
- Date
- 2019
- Source
- eLIFE 8: (Journal)
- Registered Authors
- Engert, Florian
- Keywords
- neuroscience, physics of living systems, zebrafish
- MeSH Terms
-
- Animals
- Larva/physiology
- Models, Neurological*
- Predatory Behavior*
- Sensorimotor Cortex/physiology
- Visual Perception
- Zebrafish/physiology*
- PubMed
- 31769753 Full text @ Elife
Citation
Bolton, A.D., Haesemeyer, M., Jordi, J., Schaechtle, U., Saad, F.A., Mansinghka, V.K., Tenenbaum, J.B., Engert, F. (2019) Elements of a stochastic 3D prediction engine in larval zebrafish prey capture. eLIFE. 8:.
Abstract
The computational principles underlying predictive capabilities in animals are poorly understood. Here, we wondered whether predictive models mediating prey capture could be reduced to a simple set of sensorimotor rules performed by a primitive organism. For this task, we chose the larval zebrafish, a tractable vertebrate that pursues and captures swimming microbes. Using a novel naturalistic 3D setup, we show that the zebrafish combines position and velocity perception to construct a future positional estimate of its prey, indicating an ability to project trajectories forward in time. Importantly, the stochasticity in the fish's sensorimotor transformations provides a considerable advantage over equivalent noise-free strategies. This surprising result coalesces with recent findings that illustrate the benefits of biological stochasticity to adaptive behavior. In sum, our study reveals that zebrafish are equipped with a recursive prey capture algorithm, built up from simple stochastic rules, that embodies an implicit predictive model of the world.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping