Department of Biomedical Science, University of Sheffield, Sheffield, S10 T2N, UK.
National Key laboratory of Cognitive Neuroscience and Learning, Beijing, Beijing Normal University, Beijing, 100875, China.
J Physiol. 2017 Aug 15;595(16):5427-5437. doi: 10.1113/JP273645. Epub 2017 Apr 17.
A photoreceptor's information capture is constrained by the structure and function of its light-sensitive parts. Specifically, in a fly photoreceptor, this limit is set by the number of its photon sampling units (microvilli), constituting its light sensor (the rhabdomere), and the speed and recoverability of their phototransduction reactions. In this review, using an insightful constructionist viewpoint of a fly photoreceptor being an 'imperfect' photon counting machine, we explain how these constraints give rise to adaptive quantal information sampling in time, which maximises information in responses to salient light changes while antialiasing visual signals. Interestingly, such sampling innately determines also why photoreceptors extract more information, and more economically, from naturalistic light contrast changes than Gaussian white-noise stimuli, and we explicate why this is so. Our main message is that stochasticity in quantal information sampling is less noise and more processing, representing an 'evolutionary adaptation' to generate a reliable neural estimate of the variable world.
感光器的信息捕捉受到其光敏部分的结构和功能的限制。具体来说,在果蝇感光器中,这种限制由其光子采样单元(微绒毛)的数量决定,这些微绒毛构成了其光传感器(光感受器),以及它们的光转导反应的速度和可恢复性。在这篇综述中,我们使用了一种富有洞察力的观点,即将果蝇感光器视为一种“不完美”的光子计数机,解释了这些限制如何导致在时间上自适应的量子信息采样,从而在对显著光变化的反应中最大化信息,同时对视觉信号进行抗混叠。有趣的是,这种采样内在地决定了为什么感光器从自然对比度变化的光中提取更多信息,而且比高斯白噪声刺激更经济,我们解释了原因。我们的主要信息是,量子信息采样中的随机性是一种“进化适应”,可以生成对可变世界的可靠神经估计,它代表了更多的处理,而不是更多的噪声。