Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality.

ABSTRACT: Animals make decisions under the principle of reward value maximization and surprise minimization. It is still unclear how these principles are represented in the brain and are reflected in behavior. We addressed this question using a closed-loop virtual reality system to train adult zebrafish for active avoidance. Analysis of the neural activity of the dorsal pallium during training revealed neural ensembles assigning rules to the colors of the surrounding walls. Additionally, one third of fish generated another ensemble that becomes activated only when the real perceived scenery shows discrepancy from the predicted favorable scenery. The fish with the latter ensemble escape more efficiently than the fish with the former ensembles alone, even though both fish have successfully learned to

SUBMITTER: Torigoe M 

PROVIDER: S-EPMC8481257 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature