在模块化神经网络中,对称性破坏和雪崩形状
Antonio de Candia1,2, Davide Conte3, Hanieh A Golpayegan4
1Department of Physics "E. Pancini", Università di Napoli Federico II, Naples, Italy.
Frontiers in computational neuroscience
|February 16, 2026
概括
大脑网络的模块化影响大脑动态. 这项研究揭示了模块化网络结构和突触差异如何创建不同的大脑活动阶段和关键行为,解释观察到的神经雪崩形状.
科学领域:
- 计算神经科学是一种神经科学.
- 网络科学 网络科学
- 理论神经科学 理论神经科学
背景情况:
- 大脑网络在各种尺度上表现出模块化组织.
- 了解网络结构和大脑动态之间的关系至关重要.
研究的目的:
- 在具有不同突触强度的模块化网络上研究静态威尔逊-考恩模型.
- 探索模块化网络架构产生的相位图和关键行为.
主要方法:
- 在具有不同的模块内和模块间突触强度的网络上模拟了威尔逊-考恩模型.
- 分析了系统的相位图,识别了对称和失对称相位.
- 沿过渡线检查了关键点和雪崩动态.
主要成果:
- 识别了对称 (低/高活性) 和破坏对称 (活性子集) 的相位.
- 发现了两条关键线,展示了权力定律的雪崩分布.
- 沿着关键线观察到明显的雪崩形状 (对称,右倾斜,左倾斜).
结论:
- 模块化网络结构和突触差异产生复杂的大脑动态.
- 该模型解释了实验观察到的对称和左倾神经雪崩形状.
- 将模块化大脑组织与关键动态联系起来,并提供理论框架.
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