School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi China.
Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA USA.
Cogn Neurodyn. 2014 Feb;8(1):55-69. doi: 10.1007/s11571-013-9263-z. Epub 2013 Jul 23.
The synchronization frequency of neural networks and its dynamics have important roles in deciphering the working mechanisms of the brain. It has been widely recognized that the properties of functional network synchronization and its dynamics are jointly determined by network topology, network connection strength, i.e., the connection strength of different edges in the network, and external input signals, among other factors. However, mathematical and computational characterization of the relationships between network synchronization frequency and these three important factors are still lacking. This paper presents a novel computational simulation framework to quantitatively characterize the relationships between neural network synchronization frequency and network attributes and input signals. Specifically, we constructed a series of neural networks including simulated small-world networks, real functional working memory network derived from functional magnetic resonance imaging, and real large-scale structural brain networks derived from diffusion tensor imaging, and performed synchronization simulations on these networks via the Izhikevich neuron spiking model. Our experiments demonstrate that both of the network synchronization strength and synchronization frequency change according to the combination of input signal frequency and network self-synchronization frequency. In particular, our extensive experiments show that the network synchronization frequency can be represented via a linear combination of the network self-synchronization frequency and the input signal frequency. This finding could be attributed to an intrinsically-preserved principle in different types of neural systems, offering novel insights into the working mechanism of neural systems.
神经网络的同步频率及其动力学在破译大脑工作机制方面具有重要作用。人们已经广泛认识到，功能网络同步及其动力学的特性是由网络拓扑结构、网络连接强度（即网络中不同边的连接强度）以及外部输入信号等因素共同决定的。然而，网络同步频率与这三个重要因素之间的关系在数学和计算方面的特征化仍然缺乏。本文提出了一种新的计算模拟框架，用于定量刻画神经网络同步频率与网络属性和输入信号之间的关系。具体来说，我们构建了一系列神经网络，包括模拟的小世界网络、来自功能磁共振成像的真实工作记忆功能网络以及来自扩散张量成像的真实大规模结构脑网络，并通过 Izhikevich 神经元尖峰模型对这些网络进行了同步模拟。我们的实验表明，网络同步强度和同步频率都根据输入信号频率和网络自同步频率的组合而变化。特别是，我们广泛的实验表明，网络同步频率可以通过网络自同步频率和输入信号频率的线性组合来表示。这一发现可能归因于不同类型的神经系统中内在保留的原则，为神经系统的工作机制提供了新的见解。
