在睡眠之前和之后,从人类神经元中读取特定的记忆
bioRxiv : the preprint server for biology
|July 9, 2025
概括
科学家们使用变压器神经网络解码了来自大脑活动的特定记忆. 这种方法显示了睡眠前后大脑区域之间的记忆信息如何转移,有助于未来的脑电脑界面对记忆障碍.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 认知科学 认知科学
背景情况:
- 情节性记忆,即回忆过去的单一事件的能力,对人类智力至关重要.
- 从人类大脑活动中解读特定的记忆是一个重大的科学挑战.
研究的目的:
- 开发一种能够从神经元活动中解码特定记忆的模型.
- 为了研究记忆表现如何在大脑区域和睡眠-觉醒周期之间发生变化.
主要方法:
- 开发了一个在内脑电图 (iEEG) 数据上训练的变压器神经网络模型.
- 利用跨道的注意力来整合跨大脑区域和时间尺度的神经尖峰.
- 训练参与者特定的模型来预测概念 (例如,人,地方) 的记忆检索.
主要成果:
- 该模型成功地预测了神经元活动中特定概念的记忆检索.
- 中间叶 (MTL) 活动对睡眠前解码记忆至关重要.
- 额叶皮层 (FC) 活动成为睡眠后解码记忆的关键.
- 通过唤醒/睡眠周期,在神经群体中证明了记忆信息的转换.
结论:
- 记忆的神经表现分布在大脑区域,并随着睡眠而动态变化.
- 这种解码能力为大脑-计算机接口提供了调节特定记忆的潜力.
- 建议在情节性记忆障碍中进行治疗干预的途径.
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