AgResearch Ltd, Lincoln Research Centre, Christchurch 8140, New Zealand.
J Theor Biol. 2010 Mar 21;263(2):242-61. doi: 10.1016/j.jtbi.2009.11.021. Epub 2009 Dec 3.
Path integration is a navigation strategy widely observed in nature where an animal maintains a running estimate, called the home vector, of its location during an excursion. Evidence suggests it is both ancient and ubiquitous in nature, and has been studied for over a century. In that time, canonical and neural network models have flourished, based on a wide range of assumptions, justifications and supporting data. Despite the importance of the phenomenon, consensus and unifying principles appear lacking. A fundamental issue is the neural representation of space needed for biological path integration. This paper presents a scheme to classify path integration systems on the basis of the way the home vector records and updates the spatial relationship between the animal and its home location. Four extended classes of coordinate systems are used to unify and review both canonical and neural network models of path integration, from the arthropod and mammalian literature. This scheme demonstrates analytical equivalence between models which may otherwise appear unrelated, and distinguishes between models which may superficially appear similar. A thorough analysis is carried out of the equational forms of important facets of path integration including updating, steering, searching and systematic errors, using each of the four coordinate systems. The type of available directional cue, namely allothetic or idiothetic, is also considered. It is shown that on balance, the class of home vectors which includes the geocentric Cartesian coordinate system, appears to be the most robust for biological systems. A key conclusion is that deducing computational structure from behavioural data alone will be difficult or impossible, at least in the absence of an analysis of random errors. Consequently it is likely that further theoretical insights into path integration will require an in-depth study of the effect of noise on the four classes of home vectors.
运动中路径整合是一种在自然界中广泛存在的导航策略，动物在外出时会持续估计自己的位置，这个估计值被称为“家矢量”。有证据表明，它在自然界中既古老又普遍，并且已经被研究了一个多世纪。在这段时间里，基于广泛的假设、理由和支持数据，经典模型和神经网络模型都得到了蓬勃发展。尽管这种现象非常重要，但似乎缺乏共识和统一的原则。一个基本问题是生物路径整合所需的空间神经表示。本文提出了一种基于动物与其家位置之间的空间关系记录和更新方式对路径整合系统进行分类的方案。四个扩展的坐标系用于统一和回顾来自节肢动物和哺乳动物文献的经典和神经网络模型的路径整合。该方案证明了原本看似不相关的模型之间存在分析等效性，并区分了可能表面上相似的模型。使用这四个坐标系对路径整合的重要方面的等式形式进行了彻底分析，包括更新、转向、搜索和系统误差。还考虑了可用定向线索的类型，即他感或本体感觉。结果表明，总的来说，包括地心笛卡尔坐标系在内的家矢量类似乎是生物系统最稳健的。一个关键结论是，仅从行为数据推断计算结构将是困难的或不可能的，至少在没有对随机误差进行分析的情况下是如此。因此，很可能需要深入研究四类家矢量对噪声的影响，才能进一步深入了解路径整合。
