Network Connections That Evolve to Circumvent the Inverse Optics Problem

A fundamental problem in vision science is how useful perceptions and behaviors arise in the absence of information about the physical sources of retinal stimuli (the inverse optics problem). Psychophysical studies show that human observers contend with this problem by using the frequency of occurrence of stimulus patterns in cumulative experience to generate percepts. To begin to understand the neural mechanisms underlying this strategy, we examined the connectivity of simple neural networks evolved to respond according to the cumulative rank of stimulus luminance values. Evolved similarities with the connectivity of early level visual neurons suggests that biological visual circuitry uses the same mechanisms as a means of creating useful perceptions and behaviors without information about the real world.视觉科学领域的一个核心问题是：在缺乏视网膜刺激物理来源信息的前提下，机体如何产生有效的感知与行为（即逆光学问题（inverse optics problem））。心理物理学研究表明，人类观察者通过利用累积经验中刺激模式的出现频率生成感知，以此应对该难题。为了阐明该策略背后的神经机制，我们对经过演化的简单神经网络的连接模式展开了分析，这类网络会依据刺激亮度值的累积秩做出响应。演化得到的网络连接模式与早期视觉神经元的连接模式存在相似性，这表明生物视觉环路采用了相同的机制，无需依赖真实世界的相关信息即可生成有效的感知与行为。