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arXiv 0910.0627math.PRcond-mat.dis-nnq-bio.NC
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活神经网络中的Bootstrap渗流

On Bootstrap Percolation in Living Neural Networks

Hamed Amini

摘要:

最近的活神经网络的实验研究表明,它们的整体激活诱导的电刺激可以解释使用引导渗流的概念在一个有向随机网络。该实验包括在外部激活一个初始随机部分的神经元,并观察放电的过程,直到其平衡。活跃的神经元的最终部分以非线性方式取决于初始分数。本文的主要结果是一个定理,它使我们能够找到的情况下,随机有向图与给定的节点度的相互作用的网络模型的最终比例的渐进的被解雇的神经元。这为物理学家在神经网络中观察到的现象提供了严格的数学证明。

英文摘要:

Recent experimental studies of living neural networks reveal that their global activation induced by electrical stimulation can be explained using the concept of bootstrap percolation on a directed random network. The experiment consists in activating externally an initial random fraction of the neurons and observe the process of firing until its equilibrium. The final portion of neurons that are active depends in a non linear way on the initial fraction. The main result of this paper is a theorem which enables us to find the asymptotic of final proportion of the fired neurons in the case of random directed graphs with given node degrees as the model for interacting network. This gives a rigorous mathematical proof of a phenomena observed by physicists in neural networks.

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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