神经认知多层次因果建模:一个框架,弥合神经活动和认知之间的解释性差距
Moritz Grosse-Wentrup1,2,3, Akshey Kumar1,4, Anja Meunier1,4
1Research Group Neuroinformatics, Faculty of Computer Science, University of Vienna, Vienna, Austria.
PLoS computational biology
|December 16, 2024
概括
我们开发了神经认知多层次因果建模 (NC-MCM) 来将大脑活动与认知联系起来. 这个框架从神经元数据中学习因果认知状态,为理解复杂的生物系统提供了一种新方法.
科学领域:
- 认知神经科学 认知神经科学
- 计算神经科学是一种神经科学.
- 系统神经科学 系统神经科学
背景情况:
- 了解神经元活动与认知之间的联系是神经科学的一个重大挑战.
- 当前的模型往往难以弥合低级神经动力学和高级认知功能之间的差距.
- 需要一种物理主义的方法来解释认知,而不是诉诸于二元论.
研究的目的:
- 介绍神经认知多层次因果建模 (NC-MCM) 作为一个框架,以弥合神经活动和认知之间的解释性差距.
- 从神经元数据中开发一种学习认知水平因果模型的算法.
- 展示框架对认知状态及其与行为关系的表示和推理的能力.
主要方法:
- 神经认知多层次因果建模 (NC-MCM) 框架.
- 从神经元激活模式学习认知水平因果模型的算法.
- 在线虫C. elegans的成像数据的应用.
主要成果:
- 从神经元活动中成功学习了C. elegans的认知状态.
- 该NC-MCM框架提供了神经元多元体及其与行为关系的简洁图形表示.
- 学习认知水平模型支持因果推理,优于其他多重学习算法.
结论:
- NC-MCM提供了一种数据驱动的方法来学习神经元动态的因果解释性抽象.
- 这种框架对于理解复杂的生物系统至关重要,因为手工模型是不可行的.
- 在物理主义的框架内,NC-MCM促进了关于行为神经元和认知原因的可互换推理.
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