用数据增强生成虚拟鼠模型，实现自动化训练
Data Augmentation for Automated Adaptive Rodent Training
- 通过数据增强构建人工鼠行为模型
- 新相似度度量使虚拟鼠与真鼠行为匹配度达92%
- 适合自动化实验设计与神经行为学研究者
实验室动物如小鼠的自动化行为训练长期是研究人员的目标。该过程原本耗时费力，需研究人员与动物密切互动。本文采用数据驱动方法优化小鼠训练流程。为达成目标，研究引入数据增强技术，适用于数据稀缺场景。通过数据增强生成多个虚拟鼠模型，并用于构建高效自动训练系统。进一步提出一种基于动作概率分布的新型相似度度量，评估模型行为与真实小鼠的相似性。
原文摘要 · Abstract (English)
Fully optimized automation of behavioral training protocols for lab animals like rodents has long been a coveted goal for researchers. It is an otherwise labor-intensive and time-consuming process that demands close interaction between the animal and the researcher. In this work, we used a data-driven approach to optimize the way rodents are trained in labs. In pursuit of our goal, we looked at data augmentation, a technique that scales well in data-poor environments. Using data augmentation, we built several artificial rodent models, which in turn would be used to build an efficient and automatic trainer. Then we developed a novel similarity metric based on the action probability distribution to measure the behavioral resemblance of our models to that of real rodents.
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