Finding food in the dark: how trajectories of a gymnotiform fish change with spatial learning

Author:

Mirmiran Camille1,Fraser Maia12,Maler Leonard32ORCID

Affiliation:

1. University of Ottawa 1 Department of Mathematics and Statistics , , Ottawa, ON , Canada , K1N 6N5

2. Centre for Neural Dynamics, University of Ottawa 3 , Ottawa, ON , Canada , K1N 6N5

3. University of Ottawa 2 Department of Cellular and Molecular Medicine , , Ottawa, ON , Canada , K1H 8M5

Abstract

ABSTRACT We analyzed the trajectories of freely foraging Gymnotus sp., a pulse-type gymnotiform weakly electric fish, swimming in a dark arena. For each fish, we compared the its initial behavior as it learned the relative location of landmarks and food with its behavior after learning was complete, i.e. after time/distance to locate food had reached a minimal asymptotic level. During initial exploration when the fish did not know the arena layout, trajectories included many sharp angle head turns that occurred at nearly completely random intervals. After spatial learning was complete, head turns became far smoother. Interestingly, the fish still did not take a stereotyped direct route to the food but instead took smooth but variable curved trajectories. We also measured the fish's heading angle error (heading angle – heading angle towards food). After spatial learning, the fish's initial heading angle errors were strongly biased to zero, i.e. the fish mostly turned towards the food. As the fish approached closer to the food, they switched to a random search strategy with a more uniform distribution of heading angle errors.

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

The Company of Biologists

Subject

Insect Science,Molecular Biology,Animal Science and Zoology,Aquatic Science,Physiology,Ecology, Evolution, Behavior and Systematics

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