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Determination of the Number Ratio Between Excitation Neurons and Inhibitory Neurons in Activities of Neural Networks

机译：神经网络活动中兴奋性神经元与抑制性神经元的数量比的确定

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In this paper we found parameter regions to exhibit those different population states, called dividing zones including flat fading zone, rapid fading zone and critical zone. Based on the dividing zones we can choose the number ratio between inhibitory neurons and excitatory neurons and establish the couplings in a neural network. Our researches also show that the balance value, enabling the firing density to reach the dynamic balance, does not depend on initial conditions. In additions, the critical value is only related to the number ratio under the same initial conditions.

机译：在本文中，我们发现参数区域表现出那些不同的种群状态，称为划分区域，包括平坦衰落区，快速衰落区和临界区。根据划分区域，我们可以选择抑制性神经元和兴奋性神经元之间的数量比，并在神经网络中建立耦合。我们的研究还表明，使燃烧密度达到动态平衡的平衡值不依赖于初始条件。另外，临界值仅与相同初始条件下的数量比有关。

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来源
《Advances in cognitive neurodynamics (II)》|2011年|239-246|共8页
会议地点 Hangzhou(CN)
作者
Yan Liu; Rubin Wang; Zhikang Zhang; Xianfa Jiao; Jianting Cao;
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作者单位

Institute for Cognitive Neurodynamics, East China University of Science and Technology,Shanghai 200237, China;

Institute for Cognitive Neurodynamics, East China University of Science and Technology,Shanghai 200237, China;

Institute for Cognitive Neurodynamics, East China University of Science and Technology,Shanghai 200237, China;

Institute for Cognitive Neurodynamics, East China University of Science and Technology,Shanghai 200237, China;

Institute for Cognitive Neurodynamics, East China University of Science and Technology,Shanghai 200237, China;

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原文格式 PDF
正文语种 eng
中图分类神经系;
关键词
Inhibitory neural population; Excitatory neural population; Average number density; Critical state;

机译：抑制性神经群体;兴奋性神经群体;平均数密度;临界状态;

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4. Determination of the Number Ratio Between Excitation Neurons and Inhibitory Neurons in Activities of Neural Networks [C] . Yan Liu, Rubin Wang, Zhikang Zhang, International conference on cognitive neurodynamics . 2011

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Determination of the Number Ratio Between Excitation Neurons and Inhibitory Neurons in Activities of Neural Networks

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