通过了解他人来了解自己:从人口背景中学习神经元身份
Vinam Arora1, Divyansha Lachi1, Ian J Knight1
1University of Pennsylvania.
ArXiv
|December 11, 2025
概括
一个新的自我监督框架NuCLR从神经活动中学习神经元表征,以识别单个神经元. 这种方法实现了对细胞类型和大脑区域的最先进解码,在动物中实现了泛化.
科学领域:
- 计算神经科学是一种神经科学.
- 机器学习用于神经科学
背景情况:
- 从神经活动中推断神经元身份 (细胞类型,连接性,大脑区域) 是一个挑战.
- 需要通用表示来解码神经元特定的信息.
研究的目的:
- 介绍NuCLR,这是一个自我监督的框架,用于学习神经元表示.
- 能够使单个神经元与其活动模式的差异化.
主要方法:
- NuCLR使用不同时间/刺激对神经元活动的对比学习.
- 一个时空变压器整合了人口上下文的等同变换.
- 根据电生理学和成像数据集进行评估.
主要成果:
- 实现了最先进的细胞类型和大脑区域解码.
- 向未见的动物展示了强大的零射击概括.
- 显示的性能随着更多的预训练动物而提高,并且具有标签效率.
结论:
- 庞大而多样化的神经数据集使模型能够学习可概括的神经元身份表示.
- NuCLR推进了神经元级别的表示学习和解码能力.
- 该框架为神经科学数据提供了标签有效的学习.
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