Automated visual choice discrimination learning in zebrafish

Training experimental animals to discriminate between different visual stimuli has been an important tool in cognitive neuroscience as well as in vision research for many decades.

Current methods used for visual choice discrimination training of zebrafish require human observers for response tracking, stimulus presentation and reward delivery and, consequently, are very labor intensive and possibly experimenter biased. By combining video tracking of fish positions, stimulus presentation on computer monitors and food delivery by computer-controlled electromagnetic valves, we developed a method that allows for a fully automated training of multiple adult zebrafish to arbitrary visual stimuli in parallel.

The standardized training procedure facilitates the comparison of results across different experiments and laboratories and contributes to the usability of zebrafish as vertebrate model organisms in behavioral brain research and vision research.

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involved person:

Dr. Kaspar Müller, a former Neuhauss-Team member

Prof. Stephan Neuhauss