ArticleScientific reports2021
Deep learning-based pupil model predicts time and spectral dependent light responses.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- Model-based comparison of latency estimation methods for the pupillary light reflex.Scientific reports · 2026Article
- Regulation of pupil size in natural vision across the human lifespan.Royal Society open science · 2024Article
- Pupil light reflex in young elite athletes: autonomic nervous system activity and viscoelastic properties.Frontiers in physiology · 2024Article
- Integrative Lighting Aimed at Patients with Psychiatric and Neurological Disorders.Clocks & sleep · 2023Article
- Optimising metameric spectra for integrative lighting to modulate the circadian system without affecting visual appearance.Scientific reports · 2021Article
- The Sternberg Paradigm: Correcting Encoding Latencies in Visual and Auditory Test Designs.Vision (Basel, Switzerland) · 2021Article
- PupilEXT: Flexible Open-Source Platform for High-Resolution Pupillometry in Vision Research.Frontiers in neuroscience · 2021Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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