Totalistic cellular automata model of a neuronal network on a spherical surface
Totalistic cellular automata model of a neuronal network on a spherical surface
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球面上神经元网络的总体元胞自动机模型
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Johnrob Y. Bantang
中科院分区：
文献类型：
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作者：
R. Ramos;Johnrob Y. Bantang
我们对大脑如何工作的理解仍然是一个开放的挑战。当前的神经元模型（例如积分和激发，Hodgkin-Huxley）能够使用常微分方程来模拟神经元中的电压模式。耦合这些模型将很难手动和数值求解。在此之前，我们提出了一种细胞自动机神经元模型，可以有效地模拟大量神经元的动力学。我们选择了一个线性激活函数，它模拟了整合和激发神经元的神经元反应。在本文中，我们扩展了我们的分析，比较非极权主义与极权主义模式。我们还研究了球形晶格拓扑结构如何影响神经元网络动力学，而不是以前的环形拓扑结构。最后，通过考虑激活函数的长度和初始活跃神经元的比例，我们能够找到我们之前观察到的三个动力学类别。
Our understanding of how the brain works is still an open challenge. Current neuronal models (e.g. integrate-and-fire, Hodgkin-Huxley) are able to mimic voltage patterns in neurons using ordinary differential equations. Coupling these models would be difficult to solve manually and numerically. Previously, we proposed a cellular automata neuronal model that would efficiently simulate the dynamics of a large number of neurons. We chose a linear activation function that mimics the neuronal response of an integrate-and-fire neuron. In this paper, we extended our analysis by comparing nontotalistic vs totalistic modes. We also investigated how a spherical lattice topology affects the neuronal network dynamics as opposed to the previous toroidal topology. Finally, we were able to find three dynamical categories that we previously observed, by considering the length of the activation function and the fraction of initially active neurons.