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A Novel Artificial Visual System for Motion Direction Detection with Completely Modeled Retinal Direction-Selective Pathway

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Listed:
  • Sichen Tao

    (Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan)

  • Xiliang Zhang

    (Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan)

  • Yuxiao Hua

    (Faculty of Electrical and Computer Engineering, Kanazawa University Kakuma-Machi, Kanazawa 920-1192, Japan)

  • Zheng Tang

    (Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan)

  • Yuki Todo

    (Faculty of Electrical and Computer Engineering, Kanazawa University Kakuma-Machi, Kanazawa 920-1192, Japan)

Abstract

Some fundamental visual features have been found to be fully extracted before reaching the cerebral cortex. We focus on direction-selective ganglion cells (DSGCs), which exist at the terminal end of the retinal pathway, at the forefront of the visual system. By utilizing a layered pathway composed of various relevant cells in the early stage of the retina, DSGCs can extract multiple motion directions occurring in the visual field. However, despite a considerable amount of comprehensive research (from cells to structures), a definitive conclusion explaining the specific details of the underlying mechanisms has not been reached. In this paper, leveraging some important conclusions from neuroscience research, we propose a complete quantified model for the retinal motion direction selection pathway and elucidate the global motion direction information acquisition mechanism from DSGCs to the cortex using a simple spiking neural mechanism. This mechanism is referred to as the artificial visual system (AVS). We conduct extensive testing, including one million sets of two-dimensional eight-directional binary object motion instances with 10 different object sizes and random object shapes. We also evaluate AVS’s noise resistance and generalization performance by introducing random static and dynamic noises. Furthermore, to thoroughly validate AVS’s efficiency, we compare its performance with two state-of-the-art deep learning algorithms (LeNet-5 and EfficientNetB0) in all tests. The experimental results demonstrate that due to its highly biomimetic design and characteristics, AVS exhibits outstanding performance in motion direction detection. Additionally, AVS possesses biomimetic computing advantages in terms of hardware implementation, learning difficulty, and parameter quantity.

Suggested Citation

  • Sichen Tao & Xiliang Zhang & Yuxiao Hua & Zheng Tang & Yuki Todo, 2023. "A Novel Artificial Visual System for Motion Direction Detection with Completely Modeled Retinal Direction-Selective Pathway," Mathematics, MDPI, vol. 11(17), pages 1-18, August.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3732-:d:1229179
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    References listed on IDEAS

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    1. Alberto Cruz-Martín & Rana N. El-Danaf & Fumitaka Osakada & Balaji Sriram & Onkar S. Dhande & Phong L. Nguyen & Edward M. Callaway & Anirvan Ghosh & Andrew D. Huberman, 2014. "A dedicated circuit links direction-selective retinal ganglion cells to the primary visual cortex," Nature, Nature, vol. 507(7492), pages 358-361, March.
    2. Sichen Tao & Yuki Todo & Zheng Tang & Bin Li & Zhiming Zhang & Riku Inoue, 2022. "A Novel Artificial Visual System for Motion Direction Detection in Grayscale Images," Mathematics, MDPI, vol. 10(16), pages 1-32, August.
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