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Poster E in Poster Session E: Thursday, August 6, 10:30 am – 12:15 pm, Kimmel Center, Shorin & Rosenthal Rooms

Collision Avoidance and Food Distributions Shape Collective Fish Behavior

Nathan Wu¹, Satpreet H. Singh¹, Sonja Johnson-Yu¹, Roy Harpaz¹, Florian Engert¹, Kanaka Rajan¹; ¹Harvard University

Presenter: Nathan Wu

Collective behaviors in biology are thought to emerge from simple local rules, but the ecological constraints shaping these rules are difficult to test experimentally. Here, we use multi-agent reinforcement learning to train recurrent neural network agents modeled after larval zebrafish in a collective foraging task. We show that agents trained in environments with patchy food distributions spontaneously aggregate, even in the absence of food, because conspecifics serve as informative cues about likely food locations. Furthermore, penalties associated with collisions drive avoidance: stronger penalties increase preferred nearest-neighbor distances. These results provide a normative account of how learned ecological and evolutionary priors shape collective behavior in fish-like agents, establishing a virtual laboratory for testing hypotheses about the origins of collective behavior.

Topic Area: Methods, Tools, Theory & Neural Coding