在工作记忆任务期间,前额叶大脑活动的循环神经网络模型
Emilia P Piwek1, Mark G Stokes1, Christopher Summerfield1
1Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom.
PLoS computational biology
|October 18, 2023
概括
经过记忆任务训练的反复神经网络自然学会了重组神经表征,反映了追溯提示后在中观察到的大脑活动. 这表明了优先级记忆如何更新回忆的计算基础.
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:
- 复古线索通过优先考虑特定项目来增强短期记忆回忆.
- 背后回应效应背后的神经机制和计算原理仍然不清楚.
- 之前的研究观察到,在回应后,的侧面前额皮质 (LPFC) 呈现几何变化.
研究的目的:
- 为了研究LPFC中在反时观察到的表示几何变换如何可以通过误差最小化来学习.
- 通过使用循环神经网络 (RNN) 来计算模拟反线索对短期记忆的影响.
- 探索表示几何变化对记忆维护和回忆的功能意义.
主要方法:
- 训练有素的监督RNN执行一个用连接刺激和反向提示的cued-recall任务.
- 在RNNs中分析了神经群体活动几何 (直角与平行子空间) 在反射提示呈现之前和之后.
- 研究了受过训练的RNN的学习动态,连接模式和概率线索的影响.
主要成果:
- 在LPFC中观察到的直角到平行几何变换在训练后的RNN中自发出现.
- 平行几何形状,标志着一个共同的读数,专门开发的信息需要延长维护时,建议强度.
- RNN展示了学习动态和连接模式,与代表转换的错误驱动学习相一致.
结论:
- 循环神经网络可以学会在反复提示任务期间在大脑中观察到的表示几何变化.
- 在RNN中平行几何学的出现表明,它可能会在延长维护期间稳定内存表示.
- 研究结果支持理论模型,这些理论模型认为,回溯线索通过将记忆表征转换为以行动为导向的格式来促进回忆.
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