Matthews Tristan, Osorio Daniel, Cavallaro Andrea, Chittka Lars
Centre for Intelligent Sensing, Queen Mary University of London, London, United Kingdom.
School of Life Sciences, University of Sussex, Brighton, United Kingdom.
Front Comput Neurosci. 2018 Mar 27;12:15. doi: 10.3389/fncom.2018.00015. eCollection 2018.
Computational models that predict the spectral sensitivities of primate cone photoreceptors have focussed only on the spectral, not spatial, dimensions. On the ecologically valid task of foraging for fruit, such models predict the M-cone ("green") peak spectral sensitivity 10-20 nm further from the L-cone ("red") sensitivity peak than it is in nature and assume their separation is limited by other visual constraints, such as the requirement of high-acuity spatial vision for closer M and L peak sensitivities. We explore the possibility that a spatio-chromatic analysis can better predict cone spectral tuning without appealing to other visual constraints. We build a computational model of the primate retina and simulate chromatic gratings of varying spatial frequencies using measured spectra. We then implement the case study of foveal processing in routinely trichromatic primates for the task of discriminating fruit and leaf spectra. We perform an exhaustive search for the configurations of M and L cone spectral sensitivities that optimally distinguish the colour patterns within these spectral images. Under such conditions, the model suggests that: (1) a long-wavelength limit is required to constrain the L cone spectral sensitivity to its natural position; (2) the optimal M cone peak spectral sensitivity occurs at 525 nm, close to the observed position in nature (535 nm); (3) spatial frequency has a small effect upon the spectral tuning of the cones; (4) a selective pressure toward less correlated M and L spectral sensitivities is provided by the need to reduce noise caused by the luminance variation that occurs in natural scenes.
预测灵长类视锥光感受器光谱敏感性的计算模型仅关注光谱维度,而非空间维度。在觅食水果这一具有生态效度的任务中,此类模型预测,M视锥(“绿色”)的峰值光谱敏感性与L视锥(“红色”)的敏感性峰值相比,其偏离程度比实际情况远10 - 20纳米,并且假定它们之间的分离受到其他视觉限制,比如近距离的M和L峰值敏感性对高敏锐度空间视觉的要求。我们探讨了一种可能性,即不借助其他视觉限制,通过空间 - 色度分析能更好地预测视锥光谱调谐。我们构建了一个灵长类视网膜的计算模型,并使用测量光谱模拟不同空间频率的彩色光栅。然后,我们针对区分水果和树叶光谱的任务,对常规三色灵长类动物的中央凹处理进行案例研究。我们对M和L视锥光谱敏感性的配置进行了详尽搜索,以最优地区分这些光谱图像中的颜色模式。在这种情况下,该模型表明:(1)需要一个长波长限制来将L视锥光谱敏感性限制在其自然位置;(2)最优的M视锥峰值光谱敏感性出现在约525纳米处,接近自然界中观察到的位置(约535纳米);(3)空间频率对视锥的光谱调谐影响较小;(4)减少自然场景中发生的亮度变化所引起的噪声的需求,为M和L光谱敏感性之间较低的相关性提供了一种选择压力。