在等级决策中处理不确定性的神经基础.
Mien Brabeeba Wang1, Nancy Lynch1, Michael M Halassa2
1Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Nature communications
|October 16, 2025
概括
新的神经架构CogLinks通过整合强化学习和执行控制来模拟动物如何做出复杂的等级决策. 这个框架解释了灵活的行为,并提供了对精神分裂症等认知障碍的见解.
科学领域:
- 计算神经科学是一种计算神经科学.
- 认知科学是一种认知科学.
- 神经生物学 神经生物学 神经生物学
背景情况:
- 自然环境中的等级决策涉及处理多层次的不确定性.
- 现有的模型在不确定性下解释灵活,以目标为导向的行为时面临挑战.
研究的目的:
- 介绍CogLinks,一个生物接地的神经架构.
- 解释CogLinks如何集成皮质前皮质和前皮质前皮质网络.
- 展示CogLinks支持等级决策和模型认知功能障碍的能力.
主要方法:
- 开发了基于生物的神经架构 (CogLinks).
- 用于强化学习的综合皮质三层状回路和用于执行控制的前部甲状腺皮质网络.
- 利用数学分析和有针对性的病变研究.
- 将模型应用于精神分裂症的计算精神病学问题.
主要成果:
- 在CogLinks架构中,不同形式的不确定性表现出专业化.
- 神经系统之间的相互作用通过调节探索和战略切换来支持层次决策.
- 精神分裂症的神经功能障碍与非典型的决策推理模式有关.
结论:
- "CogLinks"弥合了神经基质与高级认知之间的差距.
- 该模型为理解层次决策及其神经支提供了一个框架.
- CogLinks为计算精神病学提供了一种新的方法,解释精神分裂症等疾病的认知缺陷.
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