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Bolton, A. D.

Publications and source records attributed to Bolton, A. D..

2 recordsLinked to original sources

Covert attention to obstacles biases escapes via the Mauthner cell

To study the evolutionary origins of object perception, we investigated whether a primitive vertebrate, the larval zebrafish, is sensitive to the presence of obstacles. The zebrafish, which has become a useful model to study brain-wide circuit dynamics, executes fast escape swims when in danger of predation. We posited that collisions with solid objects during escape would be maladaptive to the zebrafish, and therefore the direction of escape swims should be informed by the locations of barriers. To answer this question, we developed a novel closed-loop high-speed imaging rig outfitted with barriers of various qualities. Using this system, we show that when larval zebrafish escape in response to a non-directional vibrational stimulus, they use visual scene information to avoid collisions with obstacles. Our study demonstrates that fish compute absolute distance to obstacles, as distant barriers outside of collision range elicit less bias than nearby collidable barriers that occupy the same visual field. The computation of barrier features is covert, as the fishs reaction to barriers during routine swimming does not predict that they will avoid barriers when escaping. Finally, through two-photon laser ablations, we suggest the presence of an excitatory input from the visual system to Mauthner cells in the brainstem escape network that is responsible for escape direction bias. We propose that zebrafish construct "object solidity" via an integrative visual computation that is more complex than retinal occupancy alone, suggesting a primitive understanding of object features and possibly the origins of a structured model of the physical world.

animal behavior and cognition↗

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

Many predatory animals rely on accurate sensory perception, predictive models, and precise pursuits to catch moving prey. Larval zebrafish intercept paramecia during their hunting behavior, but the precise trajectories of their prey have never been recorded in relation to fish movements in three dimensions.\n\nAs a means of uncovering what a simple organism understands about its physical world, we have constructed a 3D-imaging setup to simultaneously record the behavior of larval zebrafish, as well as their moving prey, during hunting. We show that zebrafish robustly transform their 3D displacement and rotation according to the position of their prey while modulating both of these variables depending on prey velocity. This is true for both azimuth and altitude, but particulars of the hunting algorithm in the two planes are slightly different to accommodate an asymmetric strike zone. We show that the combination of position and velocity perception provides the fish with a preferred future positional estimate, indicating an ability to project trajectories forward in time. Using computational models, we show that this projection ability is critical for prey capture efficiency and success. Further, we demonstrate that fish use a graded stochasticity algorithm where the variance around the mean result of each swim scales with distance from the target. Notably, this strategy provides the animal with a considerable improvement over equivalent noise-free strategies.\n\nIn sum, our quantitative and probabilistic modeling shows that zebrafish are equipped with a stochastic recursive algorithm that embodies an implicit predictive model of the world. This algorithm, built by a simple set of behavioral rules, allows the fish to optimize their hunting strategy in a naturalistic three-dimensional environment.

animal behavior and cognition↗