Behavioral algorithms of ontogenetic switching in larval and juvenile zebrafish phototaxis

Abstract

Animals undergo major behavioral adjustments during ontogeny, but the cognitive and neural computations controlling these processes remain elusive. Here, we describe that zebrafish transition from light-seeking to dark-seeking, as they grow from larval to juvenile stage, within the first few weeks of their life. We apply a combination of complementary phototaxis assays in virtual reality and modeling to dissect the algorithmic basis of this transition. We identify three parallel pathways, one analyzing whole-field luminance levels, one comparing spatial light levels across eyes, and one computing eye-specific temporal derivatives. Larvae mostly use the latter two spatio-temporal computations for navigation, while juveniles largely employ the first one. We build a library of agent-based models to predict animal behavior across stimulation conditions and in more complex environments. Model-based extraction of latent cognitive variables points towards potential neural correlates of the observed behavioral inversion and illustrates a novel way to explore the mechanisms of vertebrate ontogeny. We suggest that zebrafish phototaxis is regulated via parallel processing streams, which could be a universal implementation to change strategies depending on developmental stage, context, or internal state, making behavior flexible and goal-oriented.