基于网络理论的渗滤隧道网络雪崩动力学建模

Vivek Dey, Steffen Kampman, Rafael Gutierrez, Gianaurelio Cuniberti, Pavan Nukala
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引用次数: 0

摘要

类脑自组装网络能以最小的功耗从无组织的噪声信号中推断和分析信息。这些网络以时空雪崩和噼啪行为为特征,其物理模型有望预测和理解它们的计算能力。在此,我们采用基于网络理论的方法,为 Ag-hBNsystem 中的渗滤隧道网络提供了一个物理模型,该网络由插在 hBN vander Waals 层中的 Ag 节点(原子团)组成。通过对构成性电化学丝形成和焦耳加热湮灭的单边可塑性建模,我们确定了决定网络连通性的独立参数。我们构建了一个相图,并表明参数空间的一小部分区域包含长程时间相关的信号,其中只有一个子集包含噼啪雪崩动力学。物理系统会自发地在这一区域进行自我组织,从而最大限度地提高信息传递的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network-theory based modeling of avalanche dynamics in percolative tunnelling networks
Brain-like self-assembled networks can infer and analyze information out of unorganized noisy signals with minimal power consumption. These networks are characterized by spatiotemporal avalanches and their crackling behavior, and their physical models are expected to predict and understand their computational capabilities. Here, we use a network theory-based approach to provide a physical model for percolative tunnelling networks, found in Ag-hBN system, consisting of nodes (atomic clusters) of Ag intercalated in the hBN van der Waals layers. By modeling a single edge plasticity through constitutive electrochemical filament formation, and annihilation through Joule heating, we identify independent parameters that determine the network connectivity. We construct a phase diagram and show that a small region of the parameter space contains signals which are long-range temporally correlated, and only a subset of them contains crackling avalanche dynamics. Physical systems spontaneously selforganize to this region for possibly maximizing the efficiency of information transfer.
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