适应性块化改善了前额叶皮质和基底质回路中的有效工作记忆容量
Aneri V Soni1, Michael J Frank1
1Brown University.
bioRxiv : the preprint server for biology
|November 28, 2024
概括
工作记忆容量的限制来自神经网络对多个项目重复使用大脑区域,从而在数量和精度之间进行权衡. 这种适应性"切割"策略会影响认知表现和神经条件.
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.
背景情况:
- 工作记忆 (WM) 限制的传统理论侧重于固定项目或共享资源池.
- 了解WM能力限制的神经基础仍然是认知神经科学的一个关键挑战.
研究的目的:
- 研究神经网络模型如何解释工作记忆容量的局限性.
- 探索前额叶皮层和基底腺节在适应性WM策略中的作用.
- 为神经系统疾病中WM缺陷提供计算解释.
主要方法:
- 开发一种神经网络模型,模拟前额叶皮质和基底质相互作用.
- 实施强化学习,以适应神经群体的重用,以完成WM任务.
- 在不同的WM负载下分析信息数量和精度之间的权衡.
主要成果:
- 神经网络展示了"分割"策略,为多个项目重复使用神经群体,模仿基于插槽的系统中的类似资源的约束.
- 适应性策略是通过强化学习和任务需求来调整的,与人类的表现保持一致.
- 多巴胺能信号动态对于适应性条状门关关是至关重要的,影响了WM的性能.
结论:
- 工作记忆容量的限制可能源于诸如自适应块等计算策略,而不是纯粹的解剖学约束.
- 该模型为帕金森病,多动症和精神分裂症等疾病中WM损伤提供了新的解释.
- 神经网络模拟表明工作内存容量的动态计算限制.
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