![]() The M1-ipRGCs are part of the afferent control path, responsible for the sustained constriction mechanism through the olivary pretectal nucleus and the Edinger-Westphal nucleus 9, 20. ![]() Six different subtypes of ipRGCs (M1–M6) 11, 12, project to the olivary pretectal nucleus 13, 14, the dorsal lateral geniculate nucleus 15, 16, 17, and the suprachiasmatic nucleus of the hypotalamus 18, 19. Such an effect could only be explained after the discovery of intrinsically photosensitive ganglion cells in the retina 9, 10, which have a peak sensitivity of approximately 470–480 nm. The pupil light reflex's wavelength sensitivity has a temporal influence, exhibiting a shift of the peak sensitivity with increasing adaptation time from 510 nm to the short wavelength range of 470 nm 7, 8. Studies with monochromatic light stimuli confirmed Keeler’s hypothesis by showing that the pupil light response's wavelength sensitivity cannot be described by the photopic luminous efficiency function V(λ) alone 4, 5, 6. He indicated that a part of the afferent pupil path is controlled by a mechanism which might be independent of vision 3. Even before the ipRGC-turning-point, it is noticeable that the parameters of time and wavelength dependence, including the chromatic adaptation effect, were not considered in the development of pupil models.Ĭlyde Keeler showed in 1926 that blind and rod-less mice still exhibited a persistent pupillary light response 3. ![]() Since the discovery of the intrinsically photosensitive retinal ganglion cells (ipRGCs), research has mainly focused on the understanding of the neurophysiological process behind the pupil light reflex but less on summarizing this outcome in a combined model. Starting with the first pupil studies by Blanchard 1 and Reeves 2 in 1918, after more than 100 years of research, no valid model has been developed that summarizes the pupil control path's essential dependencies. The development of a generalized human pupil model, which is able to predict the pupil aperture depending on photometric or physical quantities, has not been finished. This non-parametric and self-learning concept could open the door to a generalized description of the pupil behaviour. 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 \(\pm\) 1 K, 4983 \(\pm\) 3 K, 10,138 \(\pm\) 22 K) and chromatic spectra (450 nm, 530 nm, 610 nm, 660 nm) at 100.01 ± 0.25 cd/m 2. 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. Neither the temporal receptor-weighting nor the spectral-dependent adaptation behaviour of the afferent pupil control path is mapped in such functions. State of the art pupil models rested in estimating a static diameter at the equilibrium-state for spectra along the Planckian locus. 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.
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