共享-AE:在高维神经和行为活动中自动识别共享子空间
Daiyao Yi1, Hao Dong2, Michael James Higley2
1Department of Electrical & Computer Engineering, Yale University, New Haven, CT 06520, USA.
概括
本研究介绍了 Shared-AE,这是一种新的计算框架,可以共同分析神经活动和复杂的行为. 它有效地识别了共同和独特的特征,进步了我们对任务和社交互动期间大脑功能的理解.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 了解大脑功能需要将神经活动与行为联系起来.
- 分析像社交互动这样的复杂行为需要从神经和行为数据中提取联合特征.
- 现有的方法难以联合分析复杂的行为和神经信号.
研究的目的:
- 开发一种新的自编码器 (AE) 框架,共享AE,用于识别神经活动和复杂行为之间的共享和私有特征.
- 为了增强对神经表示和行为输出之间的对齐的理解.
- 为分析各种行为范式中的大规模神经记录提供计算工具.
主要方法:
- 实施一个名为 Shared-AE.的自动编码器 (AE) 框架.
- 包括一个新的规范化术语来识别跨模式的共享特征.
- 同时捕捉特定于神经活动和行为的独特私人特征.
- 应用到大规模的神经活动数据从小鼠在决策和社会行为过程中.
主要成果:
- 共享AE成功地识别了共享特征 (跨神经和行为活动) 和私有特征 (每个模式都独特).
- 该模型在分析来自不同行为背景 (决策和社会互动) 的神经数据方面表现出有效性.
- 在理解神经行为对齐方面取得了显著的改进.
结论:
- 共享-AE框架提供了一种强大的方法来剖析神经动态和复杂行为之间的关系.
- 这种方法促进了多式联络神经和行为数据的联合分析.
- 公开可用的代码有助于进一步研究计算神经科学和脑-计算机接口.
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