From fish out of water to new insights on navigation mechanisms in animals
Section snippets
Animals
All the experiments on the goldfish were approved by the Ben-Gurion University of the Negev Institutional Animal Care and Use Committee and were in accordance with the government regulations of the State of Israel. Goldfish (Carassius auratus), 15–18 cm in body length, and 80–120 g body weight were used in this study. A total of six fish were used for the study, one female, three males and two undetermined. Specifically: fish 2 was female, fish 3, 4, 5 were males and fish 1, 6 could not be
Fish operated vehicle
The FOV was composed of a chassis measuring 40×40×19 cm that housed the platform on which the water tank was placed. Underneath the platform four engines (Brushed DC motors) connected to four omni wheels (4″ OMNI, 595671, Actobotics) were mounted on 4 sides of the metal skeleton (Fig. 1A). A Perspex water tank was placed (35×35×28 cm) on the platform so that the water level reached 15 cm. A relatively shallow water level of 15 cm was selected to reduce surface waves while the FOV was moving.
Vehicle motor control system
The fish's control of the vehicle was enabled by streaming the video signal from the camera to the computer which performed segmentation and detection to find the fish's location and orientation in the water tank (Fig. 1B). If the fish was located near a boundary (i.e., wall) of the water tank while facing outward (Fig. 1D), the vehicle moved in that direction. If, however, it was facing inward (Fig. 1E), no motion occurred. If at any point, based on measurements from the LIDAR, the vehicle
Vehicle motor response characteristics
To characterize the FOV response dynamics and precision, we measured the vehicle’s performance by recording the vehicle location over time after receiving a step command to move forward. Fig. 2A shows the distance traversed as a function of the time from the onset of the motion command. The results are pooled from 28 trials, with seven trials for each main axis of the vehicle. Fig. 2B presents the speed of the vehicle after a command to move forward. In addition, we evaluated the accuracy of
Behavioral arena
The behavioral arena was a three by four-meter enclosure with an indent on the top right corner. Three sides consisted of the room walls painted white, one with a window, and the fourth side had an adjustable white curtain. Depending on the task, one or more colored corrugated polypropylene boards (60 cm × 40 cm) were placed on the walls to constitute the target or distractors. A bird’s eye view of the arena can be seen in Fig. 1C.
Behavioral experiments
Each session started by placing the fish in the water tank of the FOV as seen in Fig. 1A. The vehicle started out in the middle of the arena or otherwise as stated (Fig. 1C). We tested whether the fish could drive the vehicle towards a target in return for a food pellet reward which was identical to the fish regular food. Every time the fish reached the target, which was defined as the moment the vehicle touched the pink corrugated board, a single 0.002 g food pellet was dropped by the
Statistical analysis
For all fish we have performed t-text between success rate between the first and last sessions. For fish 1 and 2 which participated in different start location control trials, two one-tailed t-tests were conducted between different session results to determine whether they were equivalent. The comparisons were between each fish's success rates throughout the manipulations versus its last days of training.
The goal of this study was to test the ability of goldfish to control the FOV and navigate
Fish can overcome environmental manipulation
To further explore fish navigational skills, we challenged the fish with several control sessions in which we manipulated the environmental settings to explore different skills or strategies.
CRediT authorship contribution statement
Shachar Givon: Formal analysis, Investigation, Methodology. Matan Samina: Investigation, Methodology, Software. Ohad Ben-Shahar: Conceptualization, Investigation. Ronen Segev: Conceptualization, Investigation.
Acknowledgments
We thank Gustavo Glusman for technical assistance. We gratefully acknowledge financial support from the Israel Science Foundation – FIRST Program (grant no. 281/15), The Israel Science Foundation – First Program (grant no. 555/19), the Israel Science Foundation (grant no. 211/15), a Human Frontiers Science Foundation grant RGP0016/2019, the Frankel Center at the Computer Science Department, and the Helmsley Charitable Trust through the Agricultural, Biological and Cognitive Robotics Initiative
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