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 & 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 & 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.

中文翻译：

---

一种在飞行昆虫觅食期间用于感觉运动控制的尖刺神经程序。

觅食是对生物体至关重要的行为任务。已经针对各种物种详细描述了行为策略及其抽象数学模型。为了探索基本神经回路与计算原理之间的联系，我们介绍了昆虫蘑菇体的生物学上详细的神经回路模型如何实现感觉处理，学习和运动控制。当在湍急的气味羽流中觅食时，我们专注于飞行昆虫采用的投加策略。使用基于峰值的可塑性规则，该模型可以快速学习在经典条件范式中将与食品配对的单个嗅觉提示关联起来。我们表明，在不进行重新训练的情况下，系统会动态地调用记忆以检测复杂感官场景中的相关线索。在短时间范围内积累这些感官证据会产生铸模和喘振电动机命令。我们的通用系统方法可以预测人口稀疏性有助于学习，而动态记忆调用和精确的行为控制则需要时间稀疏性。我们的工作成功地将生物计算原理与基于峰值的机器学习相结合。它显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，以及如何为基于代理的机器学习提供灵感。我们的工作成功地将生物学计算原理与基于峰值的机器学习相结合。它显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，以及如何为基于代理的机器学习提供灵感。我们的工作成功地将生物计算原理与基于峰值的机器学习相结合。它显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，以及如何为基于代理的机器学习提供灵感。