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Spatiotemporal Behavior of Small-World Neuronal Networks Using a Map-Based Model

机译:使用基于地图的模型的小世界神经元网络的时空行为

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摘要

In this paper, the dynamics of a map-based model is proposed firstly. It is a simple model which can not only reproduce rich behaviors of biological neurons but also compute efficiently. Then, the dynamics of two coupled maps that model the behavior of two electrically coupled neurons is discussed. By tuning the coupling strength, synchronization of two spiking or bursting neurons are simulated. Furthermore, the spatiotemporal behavior of a small-world neuronal network using map-based model is studied. Detailed investigations reveal that the collective dynamics of neuronal activity will be affected by varying some key parameters, such as the coupling strength of neurons, connection probability and the number of nearest neighbor in small-world topology. Our study suggests that the map-based model will give us a new opportunity to reproduce the real biological network containing a large number of neurons.
机译:本文首先提出了一种基于地图的模型动力学。这是一个简单的模型,不仅可以重现生物神经元的丰富行为,而且可以高效地进行计算。然后,讨论了模拟两个电耦合神经元行为的两个耦合图的动力学。通过调整耦合强度,可以模拟两个尖峰或爆发神经元的同步。此外,使用基于地图的模型研究了一个小世界神经元网络的时空行为。详细的研究表明,神经元活动的集体动力学将受到变化的一些关键参数的影响,例如小世界拓扑中神经元的耦合强度,连接概率和最近邻居的数量。我们的研究表明,基于地图的模型将为我们提供一个重现包含大量神经元的真实生物网络的新机会。

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