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Research PaperResearchia:202603.26017

A Metric for Three-Dimensional Color Discrimination Derived from V1 Population Fisher Information

Michael Menke

Abstract

We derive a Riemannian metric on three-dimensional color space from the Fisher information of neural population codes in the visual pathway. Photoreceptor adaptation, retinal opponent channels, and cortical population encoding each map onto a geometric construction, producing a metric tensor whose components correspond to measurable neural quantities. The resulting 17-parameter model is fitted jointly to four independent threshold datasets: MacAdam's (1942) chromaticity ellipses, the Koenderink ...

Submitted: March 26, 2026Subjects: Neuroscience; Neuroscience

Description / Details

We derive a Riemannian metric on three-dimensional color space from the Fisher information of neural population codes in the visual pathway. Photoreceptor adaptation, retinal opponent channels, and cortical population encoding each map onto a geometric construction, producing a metric tensor whose components correspond to measurable neural quantities. The resulting 17-parameter model is fitted jointly to four independent threshold datasets: MacAdam's (1942) chromaticity ellipses, the Koenderink et al. (2026) three-dimensional ellipsoids, Wright's (1941) wavelength discrimination function, and the Huang et al. (2012) threshold color difference ellipses, covering 96 independently measured discrimination conditions across varied chromaticities and luminances. The joint fit achieves STRESS of 23.9 on MacAdam, 20.8 on Koenderink et al., 30.1 on Wright, and 30.8 on Huang et al.


Source: arXiv:2603.24356v1 - http://arxiv.org/abs/2603.24356v1 PDF: https://arxiv.org/pdf/2603.24356v1 Original Link: http://arxiv.org/abs/2603.24356v1

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Date:
Mar 26, 2026
Topic:
Neuroscience
Area:
Neuroscience
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