A Computational Framework for Modeling Emergence of Color Vision in the Human Brain

• URL: http://arxiv.org/abs/2408.16916v1
• Date: Thu, 29 Aug 2024 21:27:06 GMT
• Title: A Computational Framework for Modeling Emergence of Color Vision in the Human Brain
• Authors: Atsunobu Kotani, Ren Ng,
• Abstract summary: It is a mystery how the brain decodes color vision purely from the optic nerve signals it receives. We introduce a computational framework for modeling this emergence of human color vision by simulating both the eye and the cortex.
• Score: 9.10623460958915
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: It is a mystery how the brain decodes color vision purely from the optic nerve signals it receives, with a core inferential challenge being how it disentangles internal perception with the correct color dimensionality from the unknown encoding properties of the eye. In this paper, we introduce a computational framework for modeling this emergence of human color vision by simulating both the eye and the cortex. Existing research often overlooks how the cortex develops color vision or represents color space internally, assuming that the color dimensionality is known a priori; however, we argue that the visual cortex has the capability and the challenge of inferring the color dimensionality purely from fluctuations in the optic nerve signals. To validate our theory, we introduce a simulation engine for biological eyes based on established vision science and generate optic nerve signals resulting from looking at natural images. Further, we propose a model of cortical learning based on self-supervised principle and show that this model naturally learns to generate color vision by disentangling retinal invariants from the sensory signals. When the retina contains N types of color photoreceptors, our simulation shows that N-dimensional color vision naturally emerges, verified through formal colorimetry. Using this framework, we also present the first simulation work that successfully boosts the color dimensionality, as observed in gene therapy on squirrel monkeys, and demonstrates the possibility of enhancing human color vision from 3D to 4D.

Related papers

• The relativity of color perception [0.4999814847776097]
  We present a rigorous mathematical model that permits to explain on a purely theoretical basis the relativistic color perception phenomena argued by Yilmaz. We base our theory on a quantum interpretation of color perception together with just one assumption, called trichromacy axiom. We show how this approach allows us to reconcile trichromacy with Hering's opponency and also to derive the relativistic properties of perceived colors without any additional mathematical or experimental assumption.
  arXiv  Detail & Related papers  (2025-04-18T14:46:29Z)
• Paleoinspired Vision: From Exploring Colour Vision Evolution to Inspiring Camera Design [44.353407126161514]
  We present a model of visual transduction in the retina, introducing a novel opsin layer. We quantify evolutionary pressures by measuring machine vision recognition accuracy on colour images shaped by specific opsins. We develop an evolutionary conservation optimisation algorithm to reconstruct the spectral sensitivity of opsins.
  arXiv  Detail & Related papers  (2024-12-27T04:07:52Z)
• The Art of Deception: Color Visual Illusions and Diffusion Models [55.830105086695]
  Recent studies have shown that artificial neural networks (ANNs) can also be deceived by visual illusions. We show how visual illusions are encoded in diffusion models. We also show how to generate new unseen visual illusions in realistic images using text-to-image diffusion models.
  arXiv  Detail & Related papers  (2024-12-13T13:07:08Z)
• Primary visual cortex contributes to color constancy by predicting rather than discounting the illuminant: evidence from a computational study [15.2781669109191]
  We build an electrophysiologically based V1 neural model to learn the color of the light source from a natural image dataset. We find that both the spatial structures and color weights of the receptive fields of the learned model neurons are quite similar to those of the simple and DO neurons recorded in V1.
  arXiv  Detail & Related papers  (2024-12-10T01:42:49Z)
• Neuro-3D: Towards 3D Visual Decoding from EEG Signals [49.502364730056044]
  We introduce a new neuroscience task: decoding 3D visual perception from EEG signals. We first present EEG-3D, a dataset featuring multimodal analysis data and EEG recordings from 12 subjects viewing 72 categories of 3D objects rendered in both videos and images. We propose Neuro-3D, a 3D visual decoding framework based on EEG signals.
  arXiv  Detail & Related papers  (2024-11-19T05:52:17Z)
• Investigating Color Illusions from the Perspective of Computational Color Constancy [2.608935407927351]
  We argue that any model that can reproduce our sensation on color illusions should also be able to provide pixel-wise estimates of the light source. In this study, we take several color constancy methods and modify them to reproduce the behavior of the human visual system on color illusions.
  arXiv  Detail & Related papers  (2023-12-20T15:34:15Z)
• Event-Driven Imaging in Turbid Media: A Confluence of Optoelectronics and Neuromorphic Computation [9.53078750806038]
  A new optical-computational method is introduced to unveil images of targets whose visibility is severely obscured by light scattering in dense, turbid media. The scheme is human vision inspired whereby diffuse photons collected from the turbid medium are first transformed to spike trains by a dynamic vision sensor as in the retina. Image reconstruction is achieved under conditions of turbidity where an original image is unintelligible to the human eye or a digital video camera.
  arXiv  Detail & Related papers  (2023-09-13T00:38:59Z)
• Divergences in Color Perception between Deep Neural Networks and Humans [3.0315685825606633]
  We develop experiments for evaluating the perceptual coherence of color embeddings in deep neural networks (DNNs) We assess how well these algorithms predict human color similarity judgments collected via an online survey. We compare DNN performance against an interpretable and cognitively plausible model of color perception based on wavelet decomposition.
  arXiv  Detail & Related papers  (2023-09-11T20:26:40Z)
• ColorSense: A Study on Color Vision in Machine Visual Recognition [57.916512479603064]
  We collect 110,000 non-trivial human annotations of foreground and background color labels from visual recognition benchmarks. We validate the use of our datasets by demonstrating that the level of color discrimination has a dominating effect on the performance of machine perception models. Our findings suggest that object recognition tasks such as classification and localization are susceptible to color vision bias.
  arXiv  Detail & Related papers  (2022-12-16T18:51:41Z)
• Adapting Brain-Like Neural Networks for Modeling Cortical Visual Prostheses [68.96380145211093]
  Cortical prostheses are devices implanted in the visual cortex that attempt to restore lost vision by electrically stimulating neurons. Currently, the vision provided by these devices is limited, and accurately predicting the visual percepts resulting from stimulation is an open challenge. We propose to address this challenge by utilizing 'brain-like' convolutional neural networks (CNNs), which have emerged as promising models of the visual system.
  arXiv  Detail & Related papers  (2022-09-27T17:33:19Z)
• Adversarially trained neural representations may already be as robust as corresponding biological neural representations [66.73634912993006]
  We develop a method for performing adversarial visual attacks directly on primate brain activity. We report that the biological neurons that make up visual systems of primates exhibit susceptibility to adversarial perturbations that is comparable in magnitude to existing (robustly trained) artificial neural networks.
  arXiv  Detail & Related papers  (2022-06-19T04:15:29Z)
• Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises [7.689542442882423]
  We designed a dual-stream vision model inspired by the human brain. This model features retina-like input layers and includes two streams: one determining the next point of focus (the fixation), while the other interprets the visuals surrounding the fixation. We evaluated this model against various benchmarks in terms of object recognition, gaze behavior and adversarial robustness.
  arXiv  Detail & Related papers  (2022-06-15T03:44:42Z)
• Peripheral Vision Transformer [52.55309200601883]
  We take a biologically inspired approach and explore to model peripheral vision in deep neural networks for visual recognition. We propose to incorporate peripheral position encoding to the multi-head self-attention layers to let the network learn to partition the visual field into diverse peripheral regions given training data. We evaluate the proposed network, dubbed PerViT, on the large-scale ImageNet dataset and systematically investigate the inner workings of the model for machine perception.
  arXiv  Detail & Related papers  (2022-06-14T12:47:47Z)
• Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks [61.60177890353585]
  Deep convolutional neural networks (CNNs) have been shown to provide excellent models for its functional analogue in the brain, the ventral stream in visual cortex. Here we consider some prominent statistical patterns that are known to exist in the internal representations of either CNNs or the visual cortex. We show that CNNs and visual cortex share a similarly tight relationship between dimensionality expansion/reduction of object representations and reformatting of image information.
  arXiv  Detail & Related papers  (2022-05-27T08:06:40Z)
• Nonparametric Data Analysis on the Space of Perceived Colors [0.0]
  This article is concerned with perceived colors regarded as random objects on a Resnikoff 3D homogeneous space model. Two applications to color differentiation in machine vision are illustrated for the proposed statistical methodology.
  arXiv  Detail & Related papers  (2020-04-05T17:43:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.