The goal of sensory processing is to represent the environment of an animal. All sensory systems share a similar constraint: they need to encode a wide range of stimulus magnitudes within their narrow neuronal response range. The most efficient way, exploited by even the simplest nervous systems, is to encode relative changes in stimulus magnitude rather than the absolute magnitudes. For instance, the retina encodes contrast, which are the variations of light intensity occurring in time and in space. From this perspective, it is easy to understand why the bright plumage of a moving bird gains a lot of attention, while an octopus remains motionless and mimics its surroundings for concealment. Stronger contrasts simply cause stronger visual signals. However, the gains in retinal performance associated with higher contrast are far more than what can be attributed to just a trivial linear increase in signal strength. Here we discuss how this improvement in performance is reflected throughout different parts of the neural circuitry, within its neural code and how high contrast activates many non-linear mechanisms to unlock several sophisticated retinal computations that are virtually impossible in low contrast conditions.

感觉加工的目的是代表动物的环境。所有的感觉系统都具有类似的约束条件：它们需要在狭窄的神经元反应范围内编码各种刺激幅度。即使是最简单的神经系统，最有效的方法是对刺激幅度而不是绝对幅度的相对变化进行编码。例如，视网膜编码对比度，对比度是时间和空间上发生的光强变化。从这个角度来看，很容易理解为什么运动中的鸟的明亮羽毛会吸引很多注意力，而章鱼却一动不动，并模仿其周围环境进行隐藏。较强的对比度只会引起较强的视觉信号。然而，与更高对比度相关的视网膜性能获得的增益远远超过仅因信号强度线性变化而带来的增益。在这里，我们讨论如何在神经电路的神经代码内的神经电路的不同部分反映出性能的提高，以及高对比度如何激活许多非线性机制来解锁几种复杂的视网膜计算，而这些计算在低对比度条件下实际上是不可能的。