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

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 escape, consistent with the hypothesis that the latter ensemble guides zebrafish to take action to minimize this prediction error. Our results suggest that zebrafish can use both principles of goal-directed behavior, but with different behavioral consequences depending on the repertoire of the adopted principles.

Divergent encoding of active avoidance behavior in corticostriatal and corticolimbic projections

Active avoidance behavior, in which an animal performs an action to avoid a stressor, is crucial for survival and may provide insight into avoidance behaviors seen in anxiety disorders. Active avoidance requires the dorsomedial prefrontal cortex (dmPFC), which is thought to regulate avoidance via downstream projections to the striatum and amygdala. However, the endogenous activity of projection-defined dmPFC subpopulations during active avoidance learning remains unexplored. Here we utilized fiber photometry to record from the dmPFC and its downstream projections to the dorsomedial striatum (DMS) and the basolateral amygdala (BLA) during active avoidance learning in mice. We examined neural activity during conditioned stimulus (CS) presentations, active avoidance, and cued freezing. Both prefrontal projections showed learning-related increases in activity during CS onset throughout active avoidance training. The dmPFC as a whole showed increased activity during avoidance and decreased activity during cued freezing. Finally, dmPFC-DMS and dmPFC-BLA projections showed divergent encoding of active avoidance behavior, with the dmPFC-DMS projection showing increased activity and the dmPFC-BLA showing decreased activity during active avoidance. Our results identify differential prefrontal encoding of active and passive coping behaviors in the same behavioral paradigm and demonstrate divergent encoding of active avoidance in projection-specific dmPFC subpopulations.

Future state prediction errors guide active avoidance behavior by adult zebrafish

SummaryHuman predicts future. To ask if fish also has this capacity, we established the virtual reality training system with live imaging of the telencephalic neurons of adult zebrafish in the active avoidance and found that, at the onset of the trial, learned fish conceives two future conditions as the favorable status on its way to the safe goal, i.e. one with the backwardly moving landscape and the other with the color of the safe goal. And the two different neural ensembles monitor the discrepancy between these predictions and the perceived real external status. Once fish reaches the goal, another ensemble is set to work to monitor whether fish keeps staying in the safe goal. The manipulation to artificially enhance these prediction errors elevated the activities of these ensembles and induced fish to behave to correct errors, revealing that fish sets behavioral strategy to actively realize these predictions.