感知电气环境:通过电子传感器重建机械物体

IF 2 3区 物理与天体物理 Q2 PHYSICS, MATHEMATICAL
Ryan A Palmer, Isaac V Chenchiah, Daniel Robert
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引用次数: 0

摘要

越来越多的经验证据表明,许多陆生节肢动物,如蜜蜂、蜘蛛和毛虫,都能感知周围环境中的电场。这种相对较新发现的感官可能与视觉、听觉、嗅觉和气声感应等其他基本感官一样,在更广泛的感官生态学中发挥着独特的作用。可偏转的毛发是接收电刺激的主要候选器官。节肢动物可以通过单个神经支配的绒毛的偏转来传递环境和生态信息。然而,目前还不清楚动物能从毛发受体中获得哪些信息,以及这些信息与其所处环境的关系。本文探讨了节肢动物如何确定其所处环境的几何和电气信息。我们使用二维模型,探讨了通过多次观察和几根偏转毛发进行电感物体识别和重建的可能性。我们分析了毛的数量、观察到的形状和观察路径如何改变重建表征的准确性。本文的研究结果表明,通过电子机械测量和获取远距离环境的几何信息具有极大的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensing electrical environments: mechanical object reconstruction via electrosensors
Increasing empirical evidence suggests that many terrestrial arthropods, such as bees, spiders, and caterpillars, sense electric fields in their environments. This relatively newly discovered sense may play a unique role within their broader sensory ecology, alongside other fundamental senses such as vision, hearing, olfaction, and aero-acoustic sensing. Deflectable hairs are the primary candidate for the reception of electrical stimuli. From the deflections of individually innervated hairs, the arthropod can transduce environmental and ecological information. However, it is unclear what information an animal can elicit from hair receptors and how it relates to their environment. This paper explores how an arthropod may ascertain geometric and electrical information about its environment. Using two-dimensional models, we explore the possibility of electroreceptive object recognition and reconstruction via multiple observations and several deflecting hairs. We analyse how the number of hairs, the observed shape, and the observation path alter the accuracy of the reconstructed representations. The results herein indicate the formidable possibility that geometric information about the environment can be electro-mechanically measured and acquired at a distance.
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来源期刊
CiteScore
4.10
自引率
14.30%
发文量
542
审稿时长
1.9 months
期刊介绍: Publishing 50 issues a year, Journal of Physics A: Mathematical and Theoretical is a major journal of theoretical physics reporting research on the mathematical structures that describe fundamental processes of the physical world and on the analytical, computational and numerical methods for exploring these structures.
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