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      Elements of a stochastic 3D prediction engine in larval zebrafish prey capture

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          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.

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          Most cited references36

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          Bayesian integration in sensorimotor learning.

          When we learn a new motor skill, such as playing an approaching tennis ball, both our sensors and the task possess variability. Our sensors provide imperfect information about the ball's velocity, so we can only estimate it. Combining information from multiple modalities can reduce the error in this estimate. On a longer time scale, not all velocities are a priori equally probable, and over the course of a match there will be a probability distribution of velocities. According to bayesian theory, an optimal estimate results from combining information about the distribution of velocities-the prior-with evidence from sensory feedback. As uncertainty increases, when playing in fog or at dusk, the system should increasingly rely on prior knowledge. To use a bayesian strategy, the brain would need to represent the prior distribution and the level of uncertainty in the sensory feedback. Here we control the statistical variations of a new sensorimotor task and manipulate the uncertainty of the sensory feedback. We show that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process. The central nervous system therefore employs probabilistic models during sensorimotor learning.
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            Intelligence without representation

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              Stochastic resonance and the benefits of noise: from ice ages to crayfish and SQUIDs.

              Noise in dynamical systems is usually considered a nuisance. But in certain nonlinear systems, including electronic circuits and biological sensory apparatus, the presence of noise can in fact enhance the detection of weak signals. This phenomenon, called stochastic resonance, may find useful application in physical, technological and biomedical contexts.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                26 November 2019
                2019
                : 8
                : e51975
                Affiliations
                [1 ]deptCenter for Brain Science Harvard University CambridgeUnited States
                [2 ]deptBrain and Cognitive Sciences Massachusetts Institute of Technology CambridgeUnited States
                Emory University United States
                Emory University United States
                Emory University United States
                University of Oregon United States
                University of California, Davis United States
                Author notes
                [†]

                Harvard University, Cambridge, United States.

                Author information
                https://orcid.org/0000-0003-3059-7226
                http://orcid.org/0000-0003-2704-3601
                Article
                51975
                10.7554/eLife.51975
                6930116
                31769753
                37dfdada-4433-4095-a765-26e62dfeb9a8
                © 2019, Bolton et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 18 September 2019
                : 25 November 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: U19NS104653
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Neuroscience
                Physics of Living Systems
                Custom metadata
                Zebrafish implement a stochastic recursive algorithm during prey capture that reflects an implicit physical model of the world.

                Life sciences
                prey capture,prediction,physical models,biological stochasticity,computation,animal cognition,zebrafish

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