在不同的拓和异质性下,由尖峰时间依赖的可塑性调节的神经雪崩动态
Jiayi Yang1, Peihua Feng1, Ying Wu1
1State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an, 710049 Shanxi China.
Cognitive neurodynamics
|June 3, 2024
概括
峰值时间依赖的可塑性 (STDP) 增强了大脑中神经元雪崩同步. 这种学习规则影响网络同步和关键状态,为神经信息处理提供了洞察力.
科学领域:
- 计算神经科学是一种计算神经科学.
- 网络动态 网络动态
- 神经可塑性 神经可塑性
背景情况:
- 神经元雪崩代表了网络自我组织的关键状态,在大脑信号水平和尺度上观察到.
- 这些雪崩显著影响神经信息传输和处理.
研究的目的:
- 为了研究神经元发射的集体行为,使用泄漏的整合和燃烧模型.
- 探索如何影响尖端时间依赖的可塑性 (STDP) 影响网络同步和神经元雪崩.
主要方法:
- 利用泄漏的整合和火模式来模拟神经元的发射.
- 实施STDP以更新基于相邻神经元之间的竞争的连接重量.
- 分析了跨不同拓和异质性级别的网络行为.
主要成果:
- 发现STDP增强了网络同步,并增加了大规模神经元雪崩的可能性.
- 无论是STDP结构还是网络密度都会影响雪崩的关键状态;学习率与功率定律斜率正相关,而时间常数与负相关.
- STDP的同步促进只有在合适的异质性水平上才有效,而长期增强对时间常数和学习速度敏感.
结论:
- STDP显著影响神经网络中的神经元同步和雪崩动态.
- 这些发现更深入地了解了STDP学习规则如何在各种神经网络配置中促进同步.
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