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具有活动依赖性神经突生长的神经网络模型中的复杂周期性行为。

Complex periodic behaviour in a neural network model with activity-dependent neurite outgrowth.

作者信息

van Ooyen A, van Pelt J

机构信息

Netherlands Institute for Brain Research, Amsterdam.

出版信息

J Theor Biol. 1996 Apr 7;179(3):229-42. doi: 10.1006/jtbi.1996.0063.

DOI:10.1006/jtbi.1996.0063
PMID:8762335
Abstract

Empirical studies have demonstrated that electrical activity of the neuron can directly affect neurite outgrowth. High levels of activity cause neurites to retract, whereas low levels allow further outgrowth. Previously we studied networks in which all the cells reacted in the same way on electrical activity. Since experiments have shown that neurons may in fact react differentially, we study in this paper networks in which the range of activity where outgrowth takes place varies among cells. We show that this can lead to complex periodic behaviour in electrical activity and connectivity of individual cells. The precise behaviour depends on the spatial distribution of the cells and the distribution of the outgrowth properties over the cells. Any other cellular property that adapts slowly to electrical activity such that neuronal activity is attempted to be maintained at a given level, can lead to similar results.

摘要

实证研究表明,神经元的电活动可直接影响神经突的生长。高水平的活动会导致神经突回缩,而低水平的活动则允许神经突进一步生长。此前我们研究的网络中,所有细胞对电活动的反应方式相同。由于实验表明神经元实际上可能有不同的反应,因此我们在本文中研究的网络中,神经突生长发生的活动范围在不同细胞之间有所不同。我们表明,这可能导致电活动和单个细胞连接性的复杂周期性行为。确切的行为取决于细胞的空间分布以及细胞上生长特性的分布。任何其他缓慢适应电活动从而试图将神经元活动维持在给定水平的细胞特性,都可能导致类似的结果。

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