长埃文斯的行为数据集及其类似精神分裂症的亚株通过几代人的行为数据集
Gábor Kőrösi1, Oliver Czimbalmos1, Gabriella Kekesi2
1Department of Computer Algorithms and Artificial Intelligence, Institute of Informatics, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary.
Scientific data
|February 9, 2026
概括
我们使用自动化系统创建了一大批大鼠行为数据集,捕捉运动,探索和认知功能. 该资源有助于研究遗传和菌株行为差异,可用开放数据和脚本.
科学领域:
- 神经科学是一个神经科学.
- 行为科学 行为科学
- 遗传学 遗传学 是一个
背景情况:
- 高通量行为分析对于理解复杂的神经疾病至关重要.
- 动物模型为人类大脑疾病提供了宝贵的见解.
- 综合,标准化的行为数据集的有限可用性阻碍了研究.
研究的目的:
- 为动物模型提供高通量行为数据集.
- 为了使行为表型的纵向和多变量研究.
- 为基于机器学习的标记器发现提供一个基准.
主要方法:
- 使用Ambitus,一个基于奖励的自动化走廊系统,用于数据采集.
- 记录了长埃文斯大鼠的运动,探索活动和认知功能.
- 在四个连续试验中收集了91个原始和衍生变量,对1342只老鼠.
主要成果:
- 从大批老鼠中获得了全面的行为数据集,其中包括一种类似精神分裂症的亚株.
- 确保 Zenodo 上所有数据文件,元数据和分析脚本的开放可用性.
- 建立了比较行为研究和计算神经科学的宝贵资源.
结论:
- 阿姆比图斯系统提供了一种强大的方法,用于高通量行为表型化.
- 开放式数据集有助于研究跨代效应和行为压力差异.
- 这一资源是开发和验证行为神经科学中的机器学习方法的基准.
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