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A Chemical Neural Network and Collective Behavior in Globally Coupled Oscillators.

机译:全球耦合振荡器中的化学神经网络和集体行为。

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摘要

The nervous system controls almost all actions in the body, and understanding its detailed structure and mechanism is one of the great challenges of science. Artificial neural networks have been modeled computationally to solve specific problems such as robot motion; however, few experimental studies have been designed to simulate biological neural networks because of the lack of experimental media with neural-like properties. An experimental network based on the photosensitive Belousov-Zhabotinsky reaction has been developed, in which the local excitability is controlled by light intensity. The spatiotemporal dynamics of these networks has been characterized, including sustained oscillations and collapse to the steady state. Here, we extend this work by incorporating the features of an actual network of neurons into the chemical system.;Many oscillatory systems exist in nature, and they can form collective behavior due to the interactions between them. The simplest collective behavior of oscillators is phase or frequency synchronization. Two distinct types of transitions, the quorum sensing transition and the Kuramoto transition to synchronization have been observed in a globally coupled oscillator system. For the Kuramoto transition to synchronization, the oscillators are gradually synchronized as the number density increases at low coupling strength. For the quorum sensing transition to synchronization, at high coupling strength, the oscillators are quiescent if the number density is lower than a critical value, and synchronized oscillations suddenly switch on as the number density reaches the critical value. We have studied populations of ferroin-coated catalytic particles and have characterized the two types of transitions to synchronization as a function of the population density and coupling strength of the oscillators with the surrounding solution.;Experimental studies of photochemical oscillators have shown more complex synchronization transitions compared to the ferroin-catalyzed oscillators. In this synchronization behavior, clusters of oscillators form in which frequency and phase are synchronized but with different phases for different phase clusters. Based on large populations of Ru(bpy &parr0;2+3 catalyzed oscillators, we experimentally study the formation of phase clusters and their stability as a function of the density of the oscillators. We also simulate the cluster behavior based on the three-variable ZBKE model and compare our results with experiment.
机译:神经系统控制着人体几乎所有的动作,而了解其详细的结构和机制是科学的巨大挑战之一。人工神经网络已经过计算建模,以解决特定问题,例如机器人运动;然而,由于缺乏具有神经样性质的实验介质,很少有实验研究可以模拟生物神经网络。已经建立了基于光敏Belousov-Zhabotinsky反应的实验网络,其中局部激发性受光强度控制。这些网络的时空动力学已经表征,包括持续的振荡和崩溃到稳态。在这里,我们通过将实际的神经元网络的特征合并到化学系统中来扩展这项工作。自然界中存在着许多振荡系统,由于它们之间的相互作用,它们可以形成集体行为。振荡器最简单的集体行为是相位或频率同步。在全局耦合的振荡器系统中,已经观察到两种不同类型的过渡,即群体感应过渡和Kuramoto过渡到同步。对于仓本向同步的过渡,随着数密度在低耦合强度下增加,振荡器逐渐同步。为了使群体感应过渡到同步,在高耦合强度下,如果数量密度低于临界值,则振荡器将处于静止状态,并且当数量密度达到临界值时,同步振荡会突然打开。我们研究了涂有铁氧体的催化颗粒的种群,并表征了两种类型的同步跃迁,这些跃迁是随着种群密度和振荡器与周围溶液耦合强度的函数而变化的;;对光化学振荡器的实验研究表明,更复杂的同步跃迁与铁蛋白催化的振荡器相比。在这种同步行为中,形成了振荡器簇,其中频率和相位被同步,但是对于不同的相位簇具有不同的相位。基于大量的Ru(bpy&parr0; 2 + 3催化的振荡器),我们通过实验研究了相簇的形成及其作为振荡器密度的函数的稳定性,并基于三变量ZBKE模拟了簇的行为。建模并将我们的结果与实验进行比较。

著录项

  • 作者

    Wang, Fang.;

  • 作者单位

    West Virginia University.;

  • 授予单位 West Virginia University.;
  • 学科 Chemistry Physical.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 139 p.
  • 总页数 139
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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