arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 1405.4064nlin.AOq-bio.NC
收藏

随机小世界网络上简化积分激发神经模型的自组织临界性

Self-organized criticality of a simplified integrate-and-fire neural model on random and small-world network

Hyung Wooc Choi, Nam Jung, Jae Woo Lee

摘要:

我们考虑了一个简化的积分-点火神经模型在规则网络、小世界网络和随机网络上的点火结构的临界性。我们简化了Levina,Herrmann和Geisel(LHG)提出的积分-点火模型。在我们的模型中,我们将突触强度设置为恒定值。我们观察到雪崩大小和雪崩寿命的概率分布的幂律行为。小世界网络和随机网络中的临界指数与全连通网络中的临界指数相同。然而,在规则的一维环中,该模型没有表现出临界行为。在简化的LHG模型中,捷径在自组织临界性中起着至关重要的作用。全连通网络、小世界网络和随机网络三种网络中的简化LHG模型属于同一普适类。

英文摘要:

We consider the criticality for firing structures of a simplified integrate-and-fire neural model on the regular network, small-world network, and random networks. We simplify an integrate-and-fire model suggested by Levina, Herrmann and Geisel (LHG). In our model we set up the synaptic strength as a constant value. We observed the power law behaviors of the probability distribution of the avalanche size and the life time of the avalanche. The critical exponents in the small-world network and the random network were the same as those in the fully connected network. However, in the regular one-dimensional ring, the model does not show the critical behaviors. In the simplified LHG model, the short-cuts are crucial role in the self-organized criticality. The simplified LHG model in three types of networks such as the fully connected network, the small-world network, and random network belong to the same universality class.

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。

↑