How early eye circuits process and present visual features

从人类到果蝇，通过特征分辨单个物体并将这些特征与对世界的连贯概念联系起来的能力对于视觉行为和能看见动物的适应性至关重要。但是，眼睛神经细胞网络中的信息处理如何使物体识别成为可能仍然是一个谜。其中一些模式是复杂的，允许大脑从不同的角度识别物体。目前还不清楚眼睛/大脑如何将视觉信息表示为模式，识别这些模式，然后解决问题。然而，潜在的过程很可能发生在电路层面，神经元及其连接动态地相互作用。这些重要的问题对我们如何理解对象/模式识别的神经机制有直接的影响，与人工视觉系统，机器学习和机器人技术有明显的联系。然而，值得注意的是，它们可以在果蝇的简单眼睛和大脑中得到很好的研究。虽然苍蝇和人类的眼睛有着非常不同的结构，但两只眼睛都必须以某种方式从视觉场景中提取物体特征，并将这些神经表征与某种形式的内部活动“地图”联系起来，以执行目标导向的行为。重要的是，果蝇具有已知布局的硬连线电路，用于修改连接的遗传工具箱，并允许在视觉刺激/行为期间以可扩展的分辨率监测神经活动。这在人类的眼睛/大脑中是不可能的。我们现在希望利用果蝇可用的新接线图、遗传、电生理和光学成像工具以及最先进的数学分析来研究眼睛微电路水平上物体表征的神经机制。具体来说，我们感兴趣的是揭示什么样的处理策略早期视觉电路使用提取对象的功能;如何以及为什么眼睛电路分离和整合对象的颜色和形状的表示（“什么”信息）从它的位置和运动（“哪里”信息），以及如何适应这些表示时，同一个对象被认为是在不同的照明条件/背景。这项研究计划旨在开始识别和量化物体感知的基本早期神经机制，这些机制可能用于动物王国中看到动物的神经系统。我们从这些研究中获得的知识不仅将促进我们对动物如何看待的理解，而且因为基本的神经连接和突触机制在其他感觉系统和我们的大脑中广泛存在，将为神经系统中许多其他临床重要过程提供新的见解。因此，我们的研究结果应该引起学术界和工业界的极大兴趣，寻求了解机器传感的生物启发设计;这些原理通常比传统的工程概念更强大，更便宜，更小，更节能。从长远来看，我们的实验和建模的新知识甚至可能有助于制造新的自适应生物芯片，并测试它们作为感觉植入物的性能。

From humans to fruit flies, the ability of resolve individual objects by their features and to link these features to a coherent precept of the world is crucial for visual behaviours and fitness of seeing animals. But it remains a mystery how information processing within the networks of nerve cells in the eyes make object recognition possible.Eye circuits process and represent visual information as patterns. Some of these patterns are complex and allow the brain, for example, to recognize objects from different perspectives. It is not understood how the eyes/brains represent visual information as patterns, recognises those patterns, and then solves problems. However, it is likely that the underlying processes occur at the level of circuits, where neurons and their connections interact dynamically. These important questions have direct implications for how we understand neural mechanisms for object/pattern recognition, with obvious links to artificial visual systems, machine-learning and robotics. Yet remarkably, they can be particularly well studied in the simple eyes and brain of fruit fly, Drosophila. While fly and human eyes have a very different architecture, both eyes must somehow extract object features from visual scenes, and to link these neural representations to some form of internal activity "maps" to execute goal-oriented behaviours. Importantly, Drosophila has a hard-wired circuitry of known layout, genetic toolboxes for modifying connectivity, and allows monitoring of neural activity with scalable resolution during visual stimulation/behaviour. This would not be possible in human eyes/brain. We now wish to utilise new wiring diagrams, genetic, electrophysiological and optical imaging tools available for Drosophila and state of the art mathematical analysis to study neural mechanisms of object representation at the level of its eye microcircuits. Specifically, we are interested in uncovering what kind of processing strategies early visual circuits use to extract object features; how and why eye circuits separate and integrate the representations of object colour and shape ('what' information) from that of its location and motion ('where' information), and how these representations adapt when the same object is seen in different lighting conditions/backgrounds. This research plan aims to start identifying and quantifying the fundamental early neural mechanisms for object perception that are probably used in the nervous systems of seeing animals across the animal kingdom. The knowledge we gain from these studies will not only advance our understanding of how animals see but, because the basic underlying neural connectivity and synaptic mechanisms are so widely found in other sensory systems and in our brain, will provide new insight into many other, often clinically important processes in the nervous system. Thus, our results should be off great interest to academics and industry, seeking to understand biologically-inspired design for machine sensing; principles which are usually more robust, cheaper, smaller and more energy-efficient than conventional engineering concepts. In long term, the new knowledge from our experiments and modelling may even help to manufacture novel adaptive biochips and test their performance as sensory implants.