Neuronal connectivity at the cellular level in the cerebral cortex is far from random, with characteristics that point to a hierarchical design with intricately connected neuronal clusters. Here we investigate computationally the effects of varying neuronal cluster connectivity on network synchronization for two different spatial distributions of clusters: one where clusters are arranged in columns in a grid and the other where neurons from different clusters are spatially intermixed. We characterize each case by measuring the degree of neuronal spiking synchrony as a function of the number of connections per neuron and the degree of intercluster connectivity. We find that in both cases as the number of connections per neuron increases, there is an asynchronous to synchronous transition dependent only on intrinsic parameters of the biophysical model. We also observe in both cases that with very low intercluster connectivity clusters have independent firing dynamics yielding a low degree of synchrony. More importantly, we find that for a high number of connections per neuron but intermediate intercluster connectivity, the two spatial distributions of clusters differ in their response where the clusters in a grid have a higher degree of synchrony than the clusters that are intermixed.

中文翻译：

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柱状分组保留了神经元网络中的同步，并具有与距离相关的时间延迟

大脑皮层细胞水平的神经元连接远非随机，其特征指向具有复杂连接的神经元簇的分层设计。在这里，我们针对两个不同的群集空间分布，通过计算研究了变化的神经元群集连接性对网络同步的影响：一个群集将群集排列在网格中的列中，另一个群集将来自不同群集的神经元在空间上混合在一起。我们通过测量神经元突波同步程度作为每个神经元连接数和簇间连通度的函数来表征每种情况。我们发现，在两种情况下，随着每个神经元连接数量的增加，异步到同步的过渡仅取决于生物物理模型的固有参数。我们还观察到，在这两种情况下，群集间连接性非常低的群集都具有独立的触发动态性，从而产生较低的同步度。更重要的是，我们发现，对于每个神经元而言，连接数量众多但中间群集间连接性较高，群集的两个空间分布在响应方面有所不同，其中网格中的群集比混合在一起的群集具有更高的同步度。