Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying neural circuits and computational principles, we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning, and motor control. We focus on cast and surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a spike-based plasticity rule, the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. We show that, without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short time scales generates cast-and-surge motor commands. Our generic systems approach predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. Our work successfully combines biological computational principles with spike-based machine learning. It shows how knowledge transfer from static to arbitrary complex dynamic conditions can be achieved by foraging insects and may serve as inspiration for agent-based machine learning.

觅食是生物体的一项重要行为任务。不同物种的行为策略及其抽象数学模型已被详细描述。为了探索底层神经回路和计算原理之间的联系，我们提出了昆虫蘑菇体的生物学详细神经回路模型如何实现感觉处理、学习和运动控制。我们重点研究飞行昆虫在湍流气味羽流中觅食时所采用的投射和涌动策略。使用基于尖峰的可塑性规则，该模型可以快速学习将个体嗅觉感官线索与经典调节范式中的食物配对。我们证明，无需重新训练，系统就能动态回忆记忆以检测复杂感官场景中的相关线索。这种感官证据在短时间尺度上的积累会产生铸造和浪涌运动命令。我们的通用系统方法预测人口稀疏有利于学习，而时间稀疏是动态记忆回忆和精确行为控制所必需的。我们的工作成功地将生物计算原理与基于尖峰的机器学习结合起来。它展示了如何通过觅食昆虫来实现从静态到任意复杂动态条件的知识转移，并且可以为基于代理的机器学习提供灵感。