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arXiv 1504.03063q-bio.NCphysics.bio-ph
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无尺度神经网络中快速稀疏同步的脑节律

Fast Sparsely Synchronized Brain Rhythms in A Scale-Free Neural Network

Sang-Yoon Kim, Woochang Lim

摘要:

我们考虑了一个有向Barabási-Albert无标度网络模型,该模型具有相同的入度和出度的对称优先连接,并研究了在快速尖峰Izhikevich中间神经元的抑制性种群中固定连接度的稀疏同步节律的出现。对于快速稀疏同步节律的研究,我们将$J$(突触抑制强度)固定在足够大的值,并通过增加$D$(噪声强度)来研究群体状态。对于小的$D$,与个体神经元的相同群体节律频率$f_p$和平均放电率(MFR)$f_i$的完全同步发生,而对于足够大的$D$,由于个体神经元的间歇性放电,与$f_p > {\langle f_i \rangle}$($\langle f_i \rangle$:整体平均MFR)的部分同步出现;特别是,$f_p > 4 {\langle f_i \rangle}$的情况被称为稀疏同步。仅对于部分和稀疏同步,MFRs和单个神经元动力学对群体同步的贡献取决于它们的程度而改变,与完全同步的情况不同。因此,单个神经元的动力学揭示了部分和稀疏同步情况下的非均匀网络结构,这与统计均匀随机图和小世界网络的情况相反。最后,我们研究了以下三种情况下网络结构对稀疏同步的影响:(1)对称连接度的变化(2)具有不同入度和出度的新节点的非对称优先连接(3)预先存在的节点之间的优先连接(没有添加新节点)。在这三种情况下,讨论了网络拓扑结构与稀疏同步的关系以及个体动力学对稀疏同步的贡献。

英文摘要:

We consider a directed Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees, and study emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast spiking Izhikevich interneurons. For a study on the fast sparsely synchronized rhythms, we fix $J$ (synaptic inhibition strength) at a sufficiently large value, and investigate the population states by increasing $D$ (noise intensity). For small $D$, full synchronization with the same population-rhythm frequency $f_p$ and mean firing rate (MFR) $f_i$ of individual neurons occurs, while for sufficiently large $D$ partial synchronization with $f_p > {\langle f_i \rangle}$ ($\langle f_i \rangle$: ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; particularly, the case of $f_p > 4 {\langle f_i \rangle}$ is referred to as sparse synchronization. Only for the partial and sparse synchronization, MFRs and contributions of individual neuronal dynamics to population synchronization change depending on their degrees, unlike the case of full synchronization. Consequently, dynamics of individual neurons reveal the inhomogeneous network structure for the case of partial and sparse synchronization, which is in contrast to the case of statistically homogeneous random graphs and small-world networks. Finally, we investigate the effect of network architecture on sparse synchronization in the following three cases: (1) variation in the degree of symmetric attachment (2) asymmetric preferential attachment of new nodes with different in- and out-degrees (3) preferential attachment between pre-existing nodes (without addition of new nodes). In these three cases, both relation between network topology and sparse synchronization and contributions of individual dynamics to the sparse synchronization are discussed.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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