Computational modeling of color perception with biologically plausible spiking neural networks.

Hadar Cohen-Duwek; Hamutal Slovin; Elishai Ezra Tsur

doi:10.1371/journal.pcbi.1010648

Computational modeling of color perception with biologically plausible spiking neural networks.

Hadar Cohen-Duwek, Hamutal Slovin, Elishai Ezra Tsur

Computer Science discipline

Research output: Contribution to journal › Article › peer-review

Abstract

Biologically plausible computational modeling of visual perception has the potential to link high-level visual experiences to their underlying neurons' spiking dynamic. In this work, we propose a neuromorphic (brain-inspired) Spiking Neural Network (SNN)-driven model for the reconstruction of colorful images from retinal inputs. We compared our results to experimentally obtained V1 neuronal activity maps in a macaque monkey using voltage-sensitive dye imaging and used the model to demonstrate and critically explore color constancy, color assimilation, and ambiguous color perception. Our parametric implementation allows critical evaluation of visual phenomena in a single biologically plausible computational framework. It uses a parametrized combination of high and low pass image filtering and SNN-based filling-in Poisson processes to provide adequate color image perception while accounting for differences in individual perception.

Original language	American English
Article number	10
Pages (from-to)	e1010648
Number of pages	1
Journal	PLoS Computational Biology
Volume	18
Issue number	10
DOIs	https://doi.org/10.1371/journal.pcbi.1010648 https://doi.org/10.1371/journal.pcbi.1010648
State	Published - 1 Oct 2022

Bibliographical note

Publisher Copyright:
Copyright: © 2022 Cohen-Duwek et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Copyright: © 2022 Cohen-Duwek et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

Keywords

Color Perception/physiology
Action Potentials/physiology
Neural Networks, Computer
Computer Simulation
Neurons/physiology

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https://dblp.org/db/journals/ploscb/ploscb18.html#DuwekST22

Cite this

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title = "Computational modeling of color perception with biologically plausible spiking neural networks.",

abstract = "Biologically plausible computational modeling of visual perception has the potential to link high-level visual experiences to their underlying neurons' spiking dynamic. In this work, we propose a neuromorphic (brain-inspired) Spiking Neural Network (SNN)-driven model for the reconstruction of colorful images from retinal inputs. We compared our results to experimentally obtained V1 neuronal activity maps in a macaque monkey using voltage-sensitive dye imaging and used the model to demonstrate and critically explore color constancy, color assimilation, and ambiguous color perception. Our parametric implementation allows critical evaluation of visual phenomena in a single biologically plausible computational framework. It uses a parametrized combination of high and low pass image filtering and SNN-based filling-in Poisson processes to provide adequate color image perception while accounting for differences in individual perception.",

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Computational modeling of color perception with biologically plausible spiking neural networks.

Abstract

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Keywords

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