激发-抑制平衡网络中的关键雪崩使响应可靠性与最佳神经表征的灵敏度相协调
Zhuda Yang1, Junhao Liang2, Changsong Zhou1,3
1Hong Kong Baptist University, Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Kowloon Tong, Hong Kong.
Physical review letters
|February 6, 2025
概括
在平衡网络中的神经关键性通过最大限度地提高灵敏度和可靠性来增强信号处理. 抑制性连接的异质性驱动这些最佳表示,澄清了关键性的功能作用.
科学领域:
- 计算神经科学是一种神经科学.
- 系统神经科学 系统神经科学
- 理论神经科学 理论神经科学
背景情况:
- 神经关键性统一了多样化的神经元动态,但其功能意义尚不清楚.
- 像关键分支模型这样的现有模型不能完全解释可靠性和灵敏性.
- 激发-抑制平衡网络对于理解复杂的神经活动至关重要.
研究的目的:
- 调查神经动力学和信号表示在平衡网络中的关键性.
- 澄清神经元关键性的功能作用,特别是可靠性和灵敏性之间的平衡.
- 确定关键神经状态中最优信息处理的基础机制.
主要方法:
- 激发-抑制平衡网络的模拟.
- 在外部信号下分析神经动力学和雪崩特性.
- 研究抑制性连接异质性的影响.
主要成果:
- 关键平衡网络实现对外部信号的最大响应灵敏度和可靠性.
- 由于可靠的,信号诱导的雪崩,可以观察到外部信号的最佳表示.
- 抑制连接的异质性被确定为可靠的关键雪崩和最佳表示的关键机制.
结论:
- 在平衡网络中,神经元的关键性为同时实现最大灵敏度和可靠性提供了框架.
- 信号诱导的可靠的雪崩对于关键大脑状态中的最佳信息处理至关重要.
- 抑制性连接异质性是支持功能关键性的基本机制.
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