对于具有同步驱动自适应合的网络的平均场近似值.
N Fennelly1, A Neff2, R Lambiotte3
1School of Mathematics and Statistics, University College Dublin, Dublin 4 D04 V1W8, Ireland.
Chaos (Woodbury, N.Y.)
|January 27, 2025
概括
这项研究为神经元模型引入了自适应性可塑性,揭示了像 bistability 和 chaos 这样的新动态. 这些复杂的行为源于相差依赖的可塑性规则在合的 θ-神经元振荡器中.
科学领域:
- 计算神经科学是一种计算神经科学.
- 理论神经科学理论神经科学
- 复杂的系统复杂的系统.
背景情况:
- 突触可塑性对于神经元动力学和学习至关重要.
- 现有的模型经常使用复杂的尖峰时间依赖可塑性 (STDP).
- 适应性可塑性为模型神经网络演变提供了一种更易于处理的方法.
研究的目的:
- 将适应性可塑性纳入 θ-神经元振荡器的网络模型.
- 研究相差依赖可塑性对神经元同步和动态的影响.
- 分析复杂行为的出现,如 bistability 和混乱.
主要方法:
- 使用了 θ-神经元振荡器的网络模型.
- 实现了相差依赖可塑性的对联和全局更新.
- 导出和验证了对模拟的平均场近似值.
- 采用了分叉分析和利亚普诺夫指数来描述系统动态.
主要成果:
- 适应性可塑性模型表现出双稳定性和混乱动态.
- 观察到周期翻倍和边界危机分叉.
- 这些现象在缺乏自适应合的系统中是不存在的.
- 平均场近似准确地反映了跨稳定性制度的模拟结果.
结论:
- 适应性相差依赖的可塑性显著改变神经元网络的动态.
- 该模型为神经系统中复杂行为的出现提供了洞察力.
- 这种方法为研究突触可塑性提供了一个简单但强大的框架.
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