缓慢的升级源于神经网络的自发波动
Jake Gavenas1,2,3, Ueli Rutishauser4,5,6,7, Aaron Schurger8,9,10,11
1Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA. john.gavenas@cshs.org.
Nature communications
|August 23, 2024
概括
自发的行为依赖于中部额叶皮层中缓慢的大脑信号. 我们的模型显示这些信号来自网络动态,由人类神经元记录证实,解释了动作时间.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 认知神经科学 认知神经科学
背景情况:
- 内源性行动启动对于目标导向行为至关重要.
- 在自发的自愿行动之前,大约在运动前两秒钟,中侧额叶皮层 (MFC) 的缓慢放缓活动发生.
- 产生这些缓慢升信号的潜在神经机制仍然不清楚.
研究的目的:
- 调查MFC中自发缓慢升级活动背后的机制.
- 开发一个解释这些神经信号出现的计算模型.
- 用人类神经生理学数据验证模型预测.
主要方法:
- 开发一个尖端神经网络模型来模拟自发的升活动.
- 在模型中分析单个神经元和群体活动.
- 模型预测与MFC中人类单个神经元记录的比较.
主要成果:
- 尖端神经网络模型成功地在单个神经元和群体活动中产生了自发的缓慢升级活动,在值跨越之前启动了2秒.
- 在人类MFC记录中证实了一个关键的预测 - - 神经元中的相关发射模式,这些神经元在发作前聚集在一起.
- 该模型表明,缓慢增速信号源于集群网络中的有限自发波动.
结论:
- 在MFC中缓慢升级的信号可能反映出有界的自发波动.
- 这些波动源于集群神经网络中的准获胜者占据全部的动态.
- 暂时稳定缓慢作用的突触在观察到的神经动态中起着至关重要的作用.
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