Percolation in living neural networks
Percolation in living neural networks
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DOI：
10.1103/physrevlett.97.188102
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发表时间：
2006-11-03
影响因子：
8.6
通讯作者：
Tlusty, Tsvi
中科院分区：
文献类型：
--
作者：
Breskin, Ilan;Soriano, Jordi;Tlusty, Tsvi
我们通过测量神经元对全局电刺激的反应来研究活的神经网络。通过降低突触强度，化学阻断神经递质受体，神经连接性降低。我们使用一个图论的方法来表明，连通性经历了一个渗流过渡。这发生在巨行星的分裂过程中，其特征是指数β接近或等于0.65的幂律。β与兴奋性和抑制性神经元之间的平衡无关，表明度分布是高斯分布而不是无标度分布。
We study living neural networks by measuring the neurons' response to a global electrical stimulation. Neural connectivity is lowered by reducing the synaptic strength, chemically blocking neurotransmitter receptors. We use a graph-theoretic approach to show that the connectivity undergoes a percolation transition. This occurs as the giant component disintegrates, characterized by a power law with an exponent beta similar or equal to 0.65. beta is independent of the balance between excitatory and inhibitory neurons and indicates that the degree distribution is Gaussian rather than scale free.