Algorithmic dissection of optic flow memory in larval zebrafish

Abstract

The visual stabilization behavior in the larval zebrafish reflects the history of optic flow experienced in the recent past. This integrative process has gained traction in recent years as a simplified, tractable model of working memory and decision-making. Yet, its algorithmic bases are poorly understood. In the present study, we first demonstrate that fish integrate externally generated optic flow over time, disregarding self-generated optic flow. This observation suggests that the fish integrates optic flow to better estimate the state of the environment, rather than to keep track of their position. Second, through reverse correlation and delay-based paradigms, we reveal multiple timescales involved in the optic flow integration. With the help of quantitative modeling, we show that the fish becomes more forgetful about the past optic flow in a more dynamic visual environment. Next, with whole-brain, light-sheet calcium imaging, we find that optic flow selective neurons throughout the brain exhibit neural signatures of motor efference copies, mirroring the behavioral findings. Lastly, with two photon calcium imaging, we show that inferior olive neurons integrate forward and backward flow separately, giving clues to how the multiple timescales of the optic flow integration is implemented. Overall, the results here refine our algorithmic and functional understanding of the history dependence of the visual stabilization behaviors in the larval zebrafish, paving the way for deciphering its circuit implementations.