Weakly electric fish, like Gnathonemus petersii, use a remarkable electrical modality for active sensing and communication, but studying their rich electrosensing and electrocommunication behavior and associated neural activity in naturalistic settings remains experimentally challenging. Here, we present a novel biologically-inspired computational framework to study these behaviors, where recurrent neural network (RNN) based artificial agents trained via multi-agent reinforcement learning (MARL) learn to modulate their electric organ discharges (EODs) and movement patterns to collectively forage in virtual environments. Trained agents demonstrate several emergent features consistent with real fish collectives, including heavy tailed EOD interval distributions, environmental context dependent shifts in EOD interval distributions, and social interaction patterns like freeloading, where agents reduce their EOD rates while benefiting from neighboring agents' active sensing. A minimal two-fish assay further isolates the role of electro-communication, showing that access to conspecific EODs and relative dominance jointly shape foraging success. Notably, these behaviors emerge through evolution-inspired rewards for individual fitness and emergent inter-agent interactions, rather than through rewarding agents explicitly for social interactions. Our work has broad implications for the neuroethology of weakly electric fish, as well as other social, communicating animals in which extensive recordings from multiple individuals, and thus traditional data-driven modeling, are infeasible.

翻译：弱电鱼（如彼得氏长颌鱼）利用独特的电感受模式进行主动感知与通信，但在自然环境中研究其丰富的电场感知与电场通信行为及相关神经活动仍面临实验挑战。本文提出一种新颖的仿生计算框架来研究这些行为：基于循环神经网络（RNN）的人工智能体通过多智能体强化学习（MARL）训练，学习调节其发电器官放电（EOD）模式与运动轨迹，以在虚拟环境中实现集体觅食。训练后的智能体展现出与真实鱼群一致的多种涌现特征，包括EOD间隔的重尾分布、环境上下文依赖的EOD间隔分布偏移，以及如“搭便车”等社会交互模式（智能体在受益于邻近个体主动感知时降低自身EOD频率）。一个简化的双鱼实验进一步分离了电场通信的作用，表明获取同种EOD信息与相对支配地位共同影响觅食成功率。值得注意的是，这些行为是通过受进化启发的个体适应度奖励与智能体间涌现的交互作用产生的，而非通过显式奖励社会交互实现。本研究对弱电鱼神经行为学及其他社会性通信动物研究具有广泛意义——这些领域常因难以获取多个体的连续记录而无法采用传统数据驱动建模方法。