离散的突触事件在平衡的神经网络中诱导全球振荡
Denis S Goldobin1,2, Matteo di Volo3, Alessandro Torcini4,5,6
1<a href="https://ror.org/03ymmms77">Institute of Continuous Media Mechanics</a>, Ural Branch of RAS, Academician Korolev street 1, 614013 Perm, Russia.
Physical review letters
|December 23, 2024
概括
这项研究引入了神经网络的新平均场模型,该模型解释了突触射击噪声. 该模型准确地预测了神经动力学中的全球振荡,超过了标准扩散近似.
科学领域:
- 计算神经科学是一种神经科学.
- 理论神经科学 理论神经科学
- 网络动态 网络动态
背景情况:
- 神经动态是由离散的突触事件驱动的.
- 当前的模型经常使用扩散近似,将突触输入视为高斯噪声.
- 这种简化可能无法捕捉所有新出现的网络行为.
研究的目的:
- 发展一个含义场形式主义,包括突触射击噪声.
- 分析稀疏平衡网络中的神经动态.
- 将新模型的预测与扩散近似进行比较.
主要方法:
- 平均场形式主义的导出,包括突触射击噪声.
- 稀疏平衡神经网络的分析.
- 在不同的刺激驱动和抑制反下调查网络过渡.
主要成果:
- 开发的形式主义准确地预测了通过连续或歇斯底里过渡出现的全球振荡.
- 在低刺激驱动和高抑制反条件下观察到这些振荡.
- 扩散近似无法预测这些新出现的振荡.
- 在低进度时,振荡性质从漂移驱动转变为集群激活.
结论:
- 突触射击噪声对于准确建模神经网络中新出现的全球振荡至关重要.
- 导出平均场方法提供了一个比扩散近似更准确的描述.
- 网络行为,包括振荡特征,对输入统计和网络拓敏感.
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