Department of Electrical Engineering and Information Technology, Laboratory of Lighting Technology, Technical University of Darmstadt, 64289, Darmstadt, Germany.
Sci Rep. 2021 Jan 12;11(1):841. doi: 10.1038/s41598-020-79908-5.
Although research has made significant findings in the neurophysiological process behind the pupillary light reflex, the temporal prediction of the pupil diameter triggered by polychromatic or chromatic stimulus spectra is still not possible. State of the art pupil models rested in estimating a static diameter at the equilibrium-state for spectra along the Planckian locus. Neither the temporal receptor-weighting nor the spectral-dependent adaptation behaviour of the afferent pupil control path is mapped in such functions. Here we propose a deep learning-driven concept of a pupil model, which reconstructs the pupil's time course either from photometric and colourimetric or receptor-based stimulus quantities. By merging feed-forward neural networks with a biomechanical differential equation, we predict the temporal pupil light response with a mean absolute error below 0.1 mm from polychromatic (2007 [Formula: see text] 1 K, 4983 [Formula: see text] 3 K, 10,138 [Formula: see text] 22 K) and chromatic spectra (450 nm, 530 nm, 610 nm, 660 nm) at 100.01 ± 0.25 cd/m. This non-parametric and self-learning concept could open the door to a generalized description of the pupil behaviour.
尽管研究已经在瞳孔光反射的神经生理过程方面取得了重大发现，但对于多色或彩色刺激光谱引发的瞳孔直径的时间预测仍然不可能。最先进的瞳孔模型是基于泊松分布轨迹上的光谱来估计平衡状态下的静态瞳孔直径。这些模型没有映射出传入瞳孔控制通路的时间受体加权或光谱相关适应行为。在这里，我们提出了一种基于深度学习的瞳孔模型概念，可以根据光度和色度或基于受体的刺激量来重建瞳孔的时间过程。我们通过将前馈神经网络与生物力学微分方程相结合，从多色（2007 [公式：见文本] 1 K、4983 [公式：见文本] 3 K、10138 [公式：见文本] 22 K）和彩色光谱（450nm、530nm、610nm、660nm）预测时间瞳孔光反应，平均绝对误差低于 0.1mm，在 100.01 ± 0.25 cd/m 处。这种非参数和自学习的概念可能为瞳孔行为的广义描述打开大门。
