基于衍生和基于关联的方法的比较,以估计神经网络中的有效连接性
Niklas Laasch1, Wilhelm Braun2, Lisa Knoff3
1Institute of Computational Neuroscience, Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany. niklas.laasch@posteo.de.
Scientific reports
|February 13, 2025
概括
一种结合滞后交叉相关性 (LCC) 和基于衍生品的协差分析的新方法可靠地估计神经网络连接. 这种方法准确地重建了稀疏,杂的系统中的有效连接,具有延迟,在模拟和生物数据中表现优于其他方法.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 系统生物学 系统生物学
背景情况:
- 从活动模式了解神经系统的连接性至关重要,但具有挑战性.
- 没有普遍接受的方法可以推断定向有效连接.
- 准确的连接推断对于理解神经网络功能至关重要.
研究的目的:
- 系统地比较用于估计神经网络中有效连接的不同方法.
- 从观察到的活动中确定最可靠的方法来重建连接.
- 为生物研究中有效的连接性估计提供实际指导.
主要方法:
- 使用了Hopf神经元模型与已知的基本真相连接.
- 使用各种算法重建连接矩阵,包括滞后交叉相关性 (LCC) 和基于导数的协差分析.
- 在纯刺激性,稀疏性,具有延迟和噪声的非线性网络以及线性网络中比较方法性能.
主要成果:
- 结合LCC和基于衍生品的协差分析方法显示,在稀疏的非线性网络中,具有延迟的地面真相连接性得到了最可靠的估计.
- 在线网络中,LCC的性能与线性网络中输入的性能相当,但计算成本显著降低.
- 在C. elegans*的经验数据上,LCC方法的表现优于基于水库计算的方法.
结论:
- 一种基于LCC的计算简单方法可靠地估计了稀疏的神经系统中的定向有效连接,具有时空延迟和噪音.
- 这些发现为有效的连接估计提供了具体的建议,当只知道神经元活动时.
- 这种方法对于在生物研究中分析复杂的神经系统是有价值的.
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