Finding the way with a noisy brain.
Finding the way with a noisy brain.
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DOI：
10.1371/journal.pcbi.1000992
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发表时间：
2010-11-11
影响因子：
4.3
通讯作者：
Vickerstaff R
中科院分区：
文献类型：
--
作者：
Cheung A;Vickerstaff R
成功的导航是地球上几乎所有动物生存的基础，并且通过大小和特征截然不同的神经系统来实现。然而令人惊讶的是，我们对任何物种能够准确表示导航空间的详细神经回路知之甚少。路径整合是动物王国中最古老、最普遍的导航策略之一。尽管有大量的计算模型，从方程到神经网络形式，但目前对于这一重要现象如何在神经中发生，甚至在原则上也没有达成共识。最近，所有路径集成模型都根据一种新颖的、统一的分类系统进行了检查。在这里，我们将这一理论框架与定向行走理论的最新见解相结合，并提出了一种直观但数学上严格的证明，证明只有一类空间的神经表示可以容忍路径积分期间的噪声。这一结果表明，许多现有的路径整合模型由于不耐受噪声而在生物学上不合理。这一令人惊讶的结果对所有成功导航的动物（无论物种如何）的神经生物学空间表示施加了显着的计算限制。事实上，噪音耐受性可能是动物界神经结构规划进化的一个重要功能限制。导航能力使动物能够大大增加寻找资源、配偶和躲避捕食者的行动空间。好处有很多，人们普遍认为，现代大脑功能是从祖先的形式中产生的，是为了有效导航而进化的。自查尔斯·达尔文时代以来，人们已经认识到路径整合是许多物种与生俱来的导航策略。路径积分涉及在迂回旅程中添加逐步位移以计算净回家方向。在过去的一个世纪中，从鸟类到哺乳动物再到节肢动物都描述了这种现象，并且已经提出了一长串数学、算法和神经网络模型来解释必要的计算。这项工作展示了不同类型的模型在存在噪声的情况下如何表现。事实证明，只有一类模型可以在存在噪声的情况下正常运行。由于噪声似乎存在于大脑生理学的各个层面，因此我们得出了令人惊讶的结论：路径积分的一般计算原理在所有物种中必须相同。路径整合模型的两种子类型具有相同的关键计算原理，并与已知的神经解剖学和生理学进行比较。
Successful navigation is fundamental to the survival of nearly every animal on earth, and achieved by nervous systems of vastly different sizes and characteristics. Yet surprisingly little is known of the detailed neural circuitry from any species which can accurately represent space for navigation. Path integration is one of the oldest and most ubiquitous navigation strategies in the animal kingdom. Despite a plethora of computational models, from equational to neural network form, there is currently no consensus, even in principle, of how this important phenomenon occurs neurally. Recently, all path integration models were examined according to a novel, unifying classification system. Here we combine this theoretical framework with recent insights from directed walk theory, and develop an intuitive yet mathematically rigorous proof that only one class of neural representation of space can tolerate noise during path integration. This result suggests many existing models of path integration are not biologically plausible due to their intolerance to noise. This surprising result imposes significant computational limitations on the neurobiological spatial representation of all successfully navigating animals, irrespective of species. Indeed, noise-tolerance may be an important functional constraint on the evolution of neuroarchitectural plans in the animal kingdom. The ability to navigate allows animals to vastly increase the action space for finding resources, mates, and to avoid predators. The benefits are many and it is commonly believed that modern brain functions have emerged from ancestral forms evolved for effective navigation. Since the time of Charles Darwin, it has been recognized that path integration is a navigation strategy innate to many species. Path integration involves adding the stepwise displacements during a circuitous journey to compute a net homeward direction. Over the past century, this phenomenon has been described for birds to mammals to arthropods, and a long list of mathematical, algorithmic, and neural network models have been proposed to explain the necessary computations. This work shows how the different types of models behave in the presence of noise. It turns out that only one class of models can function properly in the presence of noise. Since noise appears to be present at all levels of brain physiology, we arrive at the surprising conclusion that the general computational principles for path integration must be the same across all species. Two subtypes of path integration models share the same critical computational principles, and are compared to known neuroanatomy and physiology.
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影响因子：
2.9
作者：
Gramann, K.;Mueller, H. J.;Debus, G.
通讯作者：
Debus, G.
影响因子：
56.9
作者：
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DOI：
10.1109/tnsre.2005.847368
发表时间：
2005-06-01
影响因子：
4.9
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2.4
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通讯作者：
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影响因子：
1.9
作者：
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通讯作者：
Taube, JS