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Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

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A Biomimetic Visual Sensing Framework: Unsupervised Orientation Topographic Mapping via Self-Organizing Neural

Tianqi Chen¹, Zhiyu Qiu², Yuki Todo³

¹Graduate School of Information Science and Technology, The University of Osaka, Suita 565-0871, Osaka, Japan.

Biomimetics (Basel, Switzerland)

|June 25, 2026

Summary

This summary is machine-generated.

This study introduces a biologically inspired Artificial Visual System (SOM-AVS) for unsupervised orientation detection. The model learns feature representations from static images without labeled data, showing robustness and adaptability.

Keywords:

artificial visual system bio-inspired model image classification machine learning noise robustness orientation detection unsupervised learning

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Last Updated: Jun 26, 2026

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Published on: October 28, 2018

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Topographical Estimation of Visual Population Receptive Fields by fMRI

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Area of Science:

Computational Neuroscience
Artificial Intelligence
Computer Vision

Background:

Early visual processing involves localized, orientation-sensitive neurons.
Unsupervised learning is crucial for understanding biological perception and developing AI systems.
Artificial Visual Systems (AVS) aim to mimic biological visual pathways.

Purpose of the Study:

To propose a biologically inspired Self-Organizing Map-based Artificial Visual System (SOM-AVS).
To achieve unsupervised orientation detection in static images.
To model early-stage visual processing characteristics and self-organization mechanisms.

Main Methods:

Combining a biologically motivated front-end visual processing module with an unsupervised Self-Organizing Map (SOM) layer.
Utilizing localized, orientation-sensitive responses inspired by biological systems.
Training the model on static images without requiring labeled data.

Main Results:

The SOM-AVS successfully formed distinct orientation-related representations.
The system demonstrated robustness against noise, limited experience, and small training sets.
The model exhibited adaptive behavior, adjusting representations based on new stimuli.

Conclusions:

SOM-AVS provides a framework for exploring self-organization in artificial vision.
The model successfully mimics key aspects of early biological visual processing.
Findings suggest potential for developing more adaptive, biologically inspired perception models.