%0 Journal Article %T Design and Development of a High-Throughput System for Learning and Memory Research on Zebrafish %A Hamed Hanafi Alamdari %A Nancy Kilcup %A Zachary Ford %A Florentin Wilfart %A David C. Roach %A Michael Schmidt %J Journal of Behavioral and Brain Science %P 351-368 %@ 2160-5874 %D 2018 %I Scientific Research Publishing %R 10.4236/jbbs.2018.86023 %X Background: Since 2004, zebrafish have become the state-of-the-art, in vivo model for biomedical research due to their genetic and physiological homology with humans, inexpensive high-quantity breeding, and quick development in a highly-controlled environment suitable for longitudinal studies. New Method: To fully utilize the zebrafish model, a novel, automated, high-throughput system was designed. Shoals of five zebrafish were placed in 16 tanks and automatically fed over two days for a total of 16 training sessions. Color LED lights were used as the stimulus for each shoal coinciding with the release of food for a duration of 20 seconds. This system was tested on two age groups: 6- and 11-month-old. Results: After three training sessions, the median height of the school in the tank during stimulus was significantly higher than that of the naïve fish during the first training session. All subsequent training sessions demonstrated similar behaviour. A decline in memory retention, as defined by a reduction in the median height during light stimulus (i.e. no simultaneous food delivery), was observed 8 days post training. Comparison with existing methods: The high-throughput nature of this system allows for simultaneous training of 16 tanks of fish under identical conditions without human interaction and provides a means to rapidly assess their learning and memory behaviours. Conclusion: Results provide a baseline for understanding the normal cognitive processes of learning and memory retention in zebrafish. This work paves the way for future studies on the impacts of therapeutic agents on these cognitive processes. %K Learning %K Behavior %K High Throughput System %K Automation %K Cognition %U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=85205