2025/11/11 by Satpreet H. Singh, Singh, Satpreet H., Sonja Johnson-Yu +17
Biochemistry, Genetics and Molecular Biology · Computer Science · Earth and Planetary Sciences · Engineering · Environmental Science · #Artificial neural network #Context (archaeology) #Electric fish #Fish biology, ecology, and behavior #Foraging #Ichthyology and Marine Biology #Neuroethology #Reinforcement learning #Social learning #Subterranean biodiversity and taxonomy #cs.AI #cs.MA #cs.NE #cs.SY #eess.SY #q-bio.NC
paper · pdf · open access · doi:10.48550/arxiv.2511.08436
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/11/11 · openalex created_date 2025/11/13 · openalex updated_date 2026/07/28
How complex collective behavior emerges from individual interactions is a fundamental scientific question, but experimental cost and difficulty of simultaneous multi-brain recordings limit direct study in animals. Here we introduce a novel computational framework modeling weakly electric fish-like agents with biophysically inspired electrosensing and actuation, trained to forage collectively via multi-agent reinforcement learning (MARL). Trained agents reproduce hallmarks of real fish, including curvilinear homing trajectories and heavy-tailed electric organ discharge (EOD) interval statistics, while exhibiting emergent active sensing, social foraging, dominance-like asymmetries, and aggression. We perform in silico interventions including sensor ablations, EOD silencing, and food distribution changes to identify causal drivers of social foraging. Analyses of recurrent neural dynamics further show robust encoding of task-relevant variables and social context. Our work has broad implications for the neuroethology of weakly electric fish and other social animals where extensive multi-individual neural recordings, and thus traditional data-driven modeling, remain challenging.