Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?
Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?
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DOI：
10.1371/journal.pcbi.1005735
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发表时间：
2017-10
影响因子：
4.3
通讯作者：
Graham P
中科院分区：
文献类型：
--
作者：
Dewar ADM;Wystrach A;Philippides A;Graham P
所有希望生存和繁殖的有机体都必须能够适应复杂的、不断变化的世界。然而，可用的计算能力受到生物学和进化的限制，有利于节俭但强大的机制。在这里，我们调查的信息进行了小种群的视觉反应神经元在果蝇。这些所谓的“环形神经元”，投射到中央复合体的椭圆体，据报道是复杂的视觉任务，如模式识别和视觉导航所必需的。最近，这些神经元的感受野已经被绘制出来，使我们能够研究它们能在多大程度上支持这种行为。例如，在模拟经典的模式识别实验中，我们表明，从环神经元的输出模式匹配观察到的苍蝇行为。然而，神经元的表现（与苍蝇一样）并不完美，并且可以通过添加额外的神经元来轻松改善，这表明神经元的感受野并没有优化用于识别抽象形状，这一结论对模式识别试验中苍蝇行为的认知解释提出了质疑。使用人工神经网络，我们然后评估它是多么容易解码更一般的信息刺激形状从环神经元种群代码。我们表明，这些神经元非常适合编码信息的大小，位置和方向，这是更相关的行为参数的苍蝇比抽象的图案属性。这使我们认为，为了理解神经系统的特性，我们必须考虑感知回路如何将信息服务于行为。神经科学中的一个普遍问题是理解感觉系统如何组织信息以服务于行为。计算方法可以用于此类研究，因为它们允许人们模拟行为动物的感官体验，同时考虑感官信息应该如何编码。在果蝇中，已知可识别神经元的小亚群对于特定的视觉任务是必要的，并且现在已经详细描述了这些群体的响应特性。令人惊讶的是，这些群体很小，每个只有14或28个神经元，这表明感觉瓶颈。在本文中，我们考虑如何从这些神经元的群体代码与控制特定行为所需的信息。我们的结论是，尽管以前的索赔，苍蝇不太可能拥有一个通用的模式学习能力。然而，关于物体形状和大小的隐含信息，这是许多生态重要的视觉引导行为所必需的，确实通过了感官瓶颈。这些发现表明，当特定的细胞群与特定的视觉行为配对时，神经系统可能特别经济。这是计算机视觉和仿生学以及感觉神经科学的一个普遍感兴趣的发现。
All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. Here we investigate the information carried in small populations of visually responsive neurons in Drosophila melanogaster. These so-called ‘ring neurons’, projecting to the ellipsoid body of the central complex, are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation. Recently the receptive fields of these neurons have been mapped, allowing us to investigate how well they can support such behaviours. For instance, in a simulation of classic pattern discrimination experiments, we show that the pattern of output from the ring neurons matches observed fly behaviour. However, performance of the neurons (as with flies) is not perfect and can be easily improved with the addition of extra neurons, suggesting the neurons’ receptive fields are not optimised for recognising abstract shapes, a conclusion which casts doubt on cognitive explanations of fly behaviour in pattern recognition assays. Using artificial neural networks, we then assess how easy it is to decode more general information about stimulus shape from the ring neuron population codes. We show that these neurons are well suited for encoding information about size, position and orientation, which are more relevant behavioural parameters for a fly than abstract pattern properties. This leads us to suggest that in order to understand the properties of neural systems, one must consider how perceptual circuits put information at the service of behaviour. A general problem in neuroscience is understanding how sensory systems organise information to be at the service of behaviour. Computational approaches can be useful for such studies as they allow one to simulate the sensory experience of a behaving animal whilst considering how sensory information should be encoded. In flies, small subpopulations of identifiable neurons are known to be necessary for particular visual tasks, and the response properties of these populations have now been described in detail. Surprisingly, these populations are small, with only 14 or 28 neurons each, which suggests something of a sensory bottleneck. In this paper, we consider how the population code from these neurons relates to the information required to control specific behaviours. We conclude that, despite previous claims, flies are unlikely to possess a general-purpose pattern-learning ability. However, implicit information about the shape and size of objects, which is necessary for many ecologically important visually guided behaviours, does pass through the sensory bottleneck. These findings show that nervous systems can be particularly economical when specific populations of cells are paired with specific visual behaviours. This is a general-interest finding for computer vision and biomimetics, as well as sensory neuroscience.
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影响因子：
1.6
作者：
Dewar, Alex D. M.;Wystrach, Antoine;Philippides, Andrew
通讯作者：
Philippides, Andrew
影响因子：
9.2
作者：
Maimon, Gaby;Straw, Andrew D.;Dickinson, Michael H.
通讯作者：
Dickinson, Michael H.
影响因子：
2.5
作者：
Ehmer, B;Gronenberg, W
通讯作者：
Gronenberg, W
影响因子：
64.8
作者：
Neuser, Kirsa;Triphan, Tilman;Strauss, Roland
通讯作者：
Strauss, Roland
影响因子：
3.7
作者：
Cope AJ;Sabo C;Vasilaki E;Barron AB;Marshall JA
通讯作者：
Marshall JA