Anisotropic retinal circuits for processing of colour and space in nature

在视觉中，在空间，时间和颜色上变化的恒定光模式流驱动我们视网膜中数百万感光神经元的电活动。根据光的颜色和形状，不同的光感受器被激活，形成类似相机的图像。然而，要将这些信息发送到大脑，它需要通过视神经进行传输。就像普通的视频电缆一样，这种神经可以传输的信息量是有限的。在人类中，视神经具有与以视频速率驱动逐像素UHD电视画面所需的信息速率容量大致相同的信息速率容量。然而，在整个视野中，人类视网膜的分辨率要高出100倍，这意味着只有1%的像素可以发送到大脑。这就是为什么我们需要视网膜。视网膜不是将每个光感受器直接连接到大脑，而是在一系列预处理步骤中比较相邻光感受器组之间的信号，以压缩传输的图像。例如，如果1000个相邻的光感受器发出清晰的蓝色天空的图像部分的信号，则不需要将该信息的1000个版本发送到大脑- 1个就可以了。视网膜如何实现这一点以及许多其他类型的计算是一个活跃的研究领域，可以潜在地使广泛的应用受益，从医学到计算机视觉和“智能”相机系统的设计。像人类一样，所有脊椎动物（如小鼠、鸟类或鱼类）的眼睛都有一个视神经，视网膜作为其输入。然而，根据动物的不同，以及在视觉空间中的位置不同，需要发送到大脑的信息类型会有很大的不同。例如，一只老鼠需要擅长发现天空中的黑点，比如捕食性鸟类的轮廓。由于掠食性鸟类从不从下方攻击，这种特殊的计算只需要一半的眼睛。相比之下，对于深海鱼类来说，它可能是必不可少的，因此在漆黑的海洋背景下，在任何方向上都可以检测到其他动物发出的微弱发光信号。对不同类型视网膜计算的需求推动了不同动物视网膜组织方式的专业化。总之，这些为我们理解我们的感官如何工作，大脑如何进化以及如何有效地检测图像中的重要信息提供了巨大的资源。我们将使用高度视觉化的斑马鱼来研究位于这种动物眼睛不同部位的视网膜回路如何彼此不同，以最好地提取斑马鱼视觉世界中的关键信息。斑马鱼栖息在印度次大陆的浅水区。在这个水下世界中，动物前方和下方的视野往往包含大量的颜色，我们最近发现，斑马鱼为计算颜色的电路投入了更多的神经元，以调查它们的下视野。相比之下，上视野由明暗对比主导，因此斑马鱼投入更多的神经元来检测明亮和黑暗的边缘。然而，不仅颜色，而且空间细节可用于视觉检测形状在上视野和下视野之间变化。在浅水中，你永远不会远离地面，这是大多数空间细节都需要使用视觉来探索的地方。因此，我们现在将研究，如果像颜色一样，计算空间细节的视网膜电路也主要用于测量地面-如果是这样，它们如何与计算颜色的电路重叠。毕竟，在小小的斑马鱼眼睛里，神经元的空间就这么多，有些功能可能不得不让位给其他功能。研究在斑马鱼眼睛的不同位置实现了哪些颜色和空间计算，将为如何优化感官系统以优先传输对用户重要的信息提供新的思路。

In vision, a constant stream of light patterns that vary in space, time and colour drive electrical activity in millions of photoreceptor neurons in our retinas. Depending on the colour and shape of this light, different sets of photoreceptors are activated to form a camera-like image. However, to send this information to the brain, it needs to be transmitted by the optic nerve. Much like a regular video cable, the amount of information that can be transmitted by this nerve is limited. In humans, the optic nerve has about the same information rate capacity as required to drive a pixel-by-pixel UHD TV picture at video rates. However, across its entire visual field the human retina is 100x more finely resolved still, meaning that only 1% of all pixels could be send to the brain. This is why we need a retina. Instead of wiring each photoreceptor directly to the brain, the retina compares the signals across groups of neighbouring photoreceptors in a series of pre-processing steps to compress the transmitted image. For example, if 1000s of neighbouring photoreceptors signal an image part of a clear-blue sky, there is no need to send 1000 versions of this information to the brain - 1 will do. How the retina achieves this, and many other types of computations is an area of active research that can potentially benefit a wide range of applications, ranging from medicine to computer vision and the design of "intelligent" camera systems. Like in humans, the eyes of all vertebrates such as mice, birds or fish have an optic nerve with a retina as its input. However, depending on the animal, and depending on the position in visual space, the types of information that needs to be sent to the brain varies dramatically. For example, a mouse needs to excel at spotting dark spots in the sky such as the silhouette of a predatory bird. As predatory birds never attack from below, this special computation is only required in half of the eye. In contrast, for a deep-sea fish it may be essential so detect faint luminescence signals emanating from other animals on the backdrop of the pitch-black ocean in any direction. The need for different types of retinal computations has driven specialisations in the way that the retinas of different animals are organised. Together, these present a vast resource for driving our understanding of how our senses work, how brains evolve, and how important information in images can be efficiently detected. We will use the highly visual zebrafish to study how retinal circuits that are positioned in different parts of this animal's eye differ from one another to best extract key information in the zebrafish's visual world. Zebrafish inhabit shallow freshwaters of the Indian subcontinent. In this underwater world, the visual field in front of and below the animal tends to contain a lot of colour, and we recently found that zebrafish invest more neurons for circuits computing colour to survey their lower visual field. In contrast, the upper visual field is dominated by light-dark contrasts, and so zebrafish invest more neurons into detecting bright and dark edges. However, not only colour, but also spatial detail available for vision to detect shapes varies between the upper and lower visual field. In shallow water, you are never far from the ground, and this is where most spatial detail is to be explored using vision. Accordingly, we will now study if like for colour, retinal circuits computing spatial detail are also predominately set-up to survey the ground - and if so, how they overlap with circuits computing colour. After all, there is only so much space for neurons in the tiny zebrafish's eye, and some functions may have to give way to allow the space for others. Studying which colour- and space-computations are implemented in different positions of the zebrafish's eye will shed new light onto how sensory systems can be optimised to preferentially transmit information that matters to the user.