IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i5p1169-d1081733.html
   My bibliography  Save this article

A Moving Target Detection Model Inspired by Spatio-Temporal Information Accumulation of Avian Tectal Neurons

Author

Listed:
  • Shuman Huang

    (Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Xiaoke Niu

    (Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Zhizhong Wang

    (Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Gang Liu

    (Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Li Shi

    (Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
    Department of Automation, Tsinghua University, Beijing 100084, China)

Abstract

Moving target detection in cluttered backgrounds is always considered a challenging problem for artificial visual systems, but it is an innate instinct of many animal species, especially the avian. It has been reported that spatio-temporal information accumulation computation may contribute to the high efficiency and sensitivity of avian tectal neurons in detecting moving targets. However, its functional roles for moving target detection are not clear. Here we established a novel computational model for detecting moving targets. The proposed model mainly consists of three layers: retina layer, superficial layers of optic tectum, and intermediate-deep layers of optic tectum; in the last of which motion information would be enhanced by the accumulation process. The validity and reliability of this model were tested on synthetic videos and natural scenes. Compared to EMD, without the process of information accumulation, this model satisfactorily reproduces the characteristics of tectal response. Furthermore, experimental results showed the proposed model has significant improvements over existing models (EMD, DSTMD, and STMD plus) on STNS and RIST datasets. These findings do not only contribute to the understanding of the complicated processing of visual motion in avians, but also further provide a potential solution for detecting moving targets against cluttered environments.

Suggested Citation

  • Shuman Huang & Xiaoke Niu & Zhizhong Wang & Gang Liu & Li Shi, 2023. "A Moving Target Detection Model Inspired by Spatio-Temporal Information Accumulation of Avian Tectal Neurons," Mathematics, MDPI, vol. 11(5), pages 1-18, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1169-:d:1081733
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/5/1169/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/5/1169/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Timm Lochmann & Timothy J Blanche & Daniel A Butts, 2013. "Construction of Direction Selectivity through Local Energy Computations in Primary Visual Cortex," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-13, March.
    2. Maximilian Joesch & Bettina Schnell & Shamprasad Varija Raghu & Dierk F. Reiff & Alexander Borst, 2010. "ON and OFF pathways in Drosophila motion vision," Nature, Nature, vol. 468(7321), pages 300-304, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Omer Mano & Damon A Clark, 2017. "Graphics Processing Unit-Accelerated Code for Computing Second-Order Wiener Kernels and Spike-Triggered Covariance," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-11, January.
    2. Andrea Censi & Andrew D Straw & Rosalyn W Sayaman & Richard M Murray & Michael H Dickinson, 2013. "Discriminating External and Internal Causes for Heading Changes in Freely Flying Drosophila," PLOS Computational Biology, Public Library of Science, vol. 9(2), pages 1-14, February.
    3. Kit D. Longden & Edward M. Rogers & Aljoscha Nern & Heather Dionne & Michael B. Reiser, 2023. "Different spectral sensitivities of ON- and OFF-motion pathways enhance the detection of approaching color objects in Drosophila," Nature Communications, Nature, vol. 14(1), pages 1-22, December.
    4. James M McFarland & Yuwei Cui & Daniel A Butts, 2013. "Inferring Nonlinear Neuronal Computation Based on Physiologically Plausible Inputs," PLOS Computational Biology, Public Library of Science, vol. 9(7), pages 1-18, July.
    5. Jian K Liu & Tim Gollisch, 2015. "Spike-Triggered Covariance Analysis Reveals Phenomenological Diversity of Contrast Adaptation in the Retina," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-30, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1169-:d:1081733. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.