|ZFIN ID: ZDB-PUB-191127-10|
Elements of a stochastic 3D prediction engine in larval zebrafish prey capture
Bolton, A.D., Haesemeyer, M., Jordi, J., Schaechtle, U., Saad, F.A., Mansinghka, V.K., Tenenbaum, J.B., Engert, F.
|Source:||eLIFE 8: (Journal)|
|Registered Authors:||Engert, Florian|
|Keywords:||neuroscience, physics of living systems, zebrafish|
|PubMed:||31769753 Full text @ Elife|
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:.
ABSTRACTThe 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.
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