Data from: Using an insect mushroom body circuit to encode route memory in complex natural environments

Ants, like many other animals, use visual memory to follow extended routes through complex environments, but it is unknown how their small brains implement this capability. The mushroom body neuropils have been identified as a crucial memory circuit in the insect brain, but their function has mostly been explored for simple olfactory association tasks. We show that a spiking neural model of this circuit originally developed to describe fruitfly (Drosophila melanogaster) olfactory association, can also account for the ability of desert ants (Cataglyphis velox) to rapidly learn visual routes through complex natural environments. We further demonstrate that abstracting the key computational principles of this circuit, which include one-shot learning of sparse codes, enables the theoretical storage capacity of the ant mushroom body to be estimated at hundreds of independent images.

与诸多其他动物类似，蚂蚁依托视觉记忆在复杂环境中沿延伸路径行进，但目前学界尚未明确其微型大脑如何实现这一能力。蕈形体神经纤维丛（mushroom body neuropils）已被证实为昆虫脑中关键的记忆回路，但针对其功能的研究大多聚焦于简单的嗅觉联想任务。本研究表明，最初用于阐释果蝇（Drosophila melanogaster）嗅觉联想机制的该回路脉冲神经网络（spiking neural model），同样可以解释沙漠蚁（Cataglyphis velox）快速学习复杂自然环境中视觉路径的能力。本研究进一步揭示，提取该回路的核心计算原理（包括稀疏编码的单样本学习（one-shot learning）），可估算出蚂蚁蕈形体的理论存储容量可达数百幅独立图像。