The neuronal basis of social affiliation in zebrafish

≪p≫It is a central question in biology how coordinated - or even cooperative interactions between heterogeneous conspecifics emerge from computations in nervous systems. While group behavior such as social affiliation is often characterized as a collective property emerging from individual-level perceptual and behavioral rules, the lacking experimental control over interacting animals has prevented a mechanistic analysis of the underlying neuronal circuits. I recently developed a novel virtual reality setup for zebrafish that enabled me for the first time to analyze naturally unfolding affiliation (shoaling) of single animals with defined sensory experience (Larsch & Baier, 2018). Using this unique system, I identified fish-like biological motion as a potent visual trigger of affiliation. I then traced this signal into the brain and discovered a specific visual pathway that drives shoaling (Kappel et al., Larsch, 2021). In this proposal, I suggest to leverage these sensory entry points to reveal the sensorimotor transformation of shoaling as a model for the neuronal control of reciprocal interaction. Specifically, I plan to (1) define the functional architecture of brain areas active during shoaling and their contribution to behavior by combining my established methods for activity mapping with cutting-edge tools for neuronal perturbation and precise behavior tracking; (2) reveal how a set of disease-associated genetic mutations with defects in shoaling affects the underlying neuronal circuits by tissue-specific restoration of gene function and comparative analysis of whole-brain activity and behavior; (3) Identify origins of social individuality by investigating genetic variants and associated circuit changes in socially diverging zebrafish lines that I created via artificial selection for extreme shoaling. My proposed research will reveal how individuals coordinate social affiliation at the interface of behavioral algorithms, neuronal circuits, and genetic factors.≪/p≫