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Poster A16 in Poster Session A - Tuesday, August 6, 2024, 4:15 – 6:15 pm, Johnson Ice Rink
Emergent active sensing behaviors in artificial electric fish agents
Sonja Johnson-Yu1, Satpreet H. Singh1, Federico Pedraja2, Denis Turcu2, Pratyusha Sharma3, Naomi Saphra1, Nathaniel B. Sawtell2, Kanaka Rajan1; 1Harvard University, 2Columbia University, 3Massachusetts Institute of Technology
Weakly electric fish, such as Gnathonemus petersii, generate pulsatile electric organ discharges (EODs) that enable them to sense their environment through active electrolocation. This plays a crucial role in several key behaviors, such as navigation, foraging, and avoiding predators. While the anatomical and physiological organization of the active electrosensory system has been extensively studied, the contribution of active electrolocation to adaptive behavior in naturalistic settings remains relatively underexplored. Here we present a preliminary in silico model of active sensing in electric fish, using a neural network-based artificial agent trained by deep reinforcement learning to perform an analogous active sensing task in a 2D environment. The trained agent recapitulates key features of natural EOD statistics, shows emergent behavioral modularity, and provides intuitions about the representation of key latent variables governing agent behavior, such as energy levels (satiety).
Keywords: active sensing deep reinforcement learning artificial agents weakly electric fish