多头绒泡菌空间活动的电相关性：我们能在黑暗中看到绒泡菌机器吗？
On electrical correlates of Physarum polycephalum spatial activity: Can we see Physarum Machine in the dark?
摘要：
多头绒泡菌的疟原虫是一个肉眼可见的单细胞，它的原生质网络跨越营养源。在一个非常简单的实验装置中，我们记录了正在繁殖的疟原虫的电势。我们发现了一个复杂的相互作用的短程振荡行为相结合的远程，低频振荡，用于不同部分的疟原虫之间的信息交流。疟原虫对变化的环境条件的反应形成了电活动的基本模式，这些模式是以下事件的独特指标：疟原虫占据一个地点，疟原虫正常发挥功能，疟原虫由于基质干燥而变得“激动”，疟原虫离开一个地点，疟原虫形成菌核。使用集体粒子近似多头绒泡菌，我们发现匹配相关的电位在计算模拟中通过测量当地人口流量的节点位置，产生列车的高，低频率的振荡行为。在这些测量中存在的图案匹配的反应“语法”的疟原虫时，遇到新的节点，模拟消耗的营养物质，暴露于模拟的有害照明和菌核形成。粒子群的分布式计算能够通过移动模拟原质团的活跃生长区来计算有益的网络结构和菌核位置。结果表明，未来的承诺，非侵入性的研究复杂的动力学行为内-和健康状况-生活系统。
英文摘要：
Plasmodium of Physarum polycephalum is a single cell visible by unaided eye, which spans sources of nutrients with its protoplasmic network. In a very simple experimental setup we recorded electric potential of the propagating plasmodium. We discovered a complex interplay of short range oscillatory behaviour combined with long range, low frequency oscillations which serve to communicate information between different parts of the plasmodium. The plasmodium's response to changing environmental conditions forms basis patterns of electric activity, which are unique indicators of the following events: plasmodium occupies a site, plasmodium functions normally, plasmodium becomes `agitated' due to drying substrate, plasmodium departs a site, and plasmodium forms sclerotium. Using a collective particle approximation of Physarum polycephalum we found matching correlates of electrical potential in computational simulations by measuring local population flux at the node positions, generating trains of high and low frequency oscillatory behaviour. Motifs present in these measurements matched the response `grammar' of the plasmodium when encountering new nodes, simulated consumption of nutrients, exposure to simulated hazardous illumination and sclerotium formation. The distributed computation of the particle collective was able to calculate beneficial network structures and sclerotium position by shifting the active growth zone of the simulated plasmodium. The results show future promise for the non-invasive study of the complex dynamical behaviour within --- and health status of --- living systems.
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