A Bio-Inspired Visual Neural Model for Robustly and Steadily Detecting Motion Directions of Translating Objects Against Variable Contrast in the Figure-Ground and Noise Interference
(1) Background: At present, the bio-inspired visual neural models have made significant achievements in detecting the motion direction of the translating object. Variable contrast in the figure-ground and environmental noise interference, however, have a strong influence on the existing model. The r...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Biomimetics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-7673/10/1/51 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589010926567424 |
|---|---|
| author | Sheng Zhang Ke Li Zhonghua Luo Mengxi Xu Shengnan Zheng |
| author_facet | Sheng Zhang Ke Li Zhonghua Luo Mengxi Xu Shengnan Zheng |
| author_sort | Sheng Zhang |
| collection | DOAJ |
| description | (1) Background: At present, the bio-inspired visual neural models have made significant achievements in detecting the motion direction of the translating object. Variable contrast in the figure-ground and environmental noise interference, however, have a strong influence on the existing model. The responses of the lobula plate tangential cell (LPTC) neurons of Drosophila are robust and stable in the face of variable contrast in the figure-ground and environmental noise interference, which provides an excellent paradigm for addressing these challenges. (2) Methods: To resolve these challenges, we propose a bio-inspired visual neural model, which consists of four stages. Firstly, the photoreceptors (R1–R6) are utilized to perceive the change in luminance. Secondly, the change in luminance is divided into parallel ON and OFF pathways based on the lamina monopolar cell (LMC), and the spatial denoising and the spatio-temporal lateral inhibition (LI) mechanisms can suppress environmental noise and improve motion boundaries, respectively. Thirdly, the non-linear instantaneous feedback mechanism in divisive contrast normalization is adopted to reduce local contrast sensitivity; further, the parallel ON and OFF contrast pathways are activated. Finally, the parallel motion and contrast pathways converge on the LPTC in the lobula complex. (3) Results: By comparing numerous experimental simulations with state-of-the-art (SotA) bio-inspired models, we can draw four conclusions. Firstly, the effectiveness of the contrast neural computation and the spatial denoising mechanism is verified by the ablation study. Secondly, this model can robustly detect the motion direction of the translating object against variable contrast in the figure-ground and environmental noise interference. Specifically, the average detection success rate of the proposed bio-inspired model under the pure and real-world complex noise datasets was increased by 5.38% and 5.30%. Thirdly, this model can effectively reduce the fluctuation in this model response against variable contrast in the figure-ground and environmental noise interference, which shows the stability of this model; specifically, the average inter-quartile range of the coefficient of variation in the proposed bio-inspired model under the pure and real-world complex noise datasets was reduced by 38.77% and 47.84%, respectively. The average decline ratio of the sum of the coefficient of variation in the proposed bio-inspired model under the pure and real-world complex noise datasets was 57.03% and 67.47%, respectively. Finally, the robustness and stability of this model are further verified by comparing other early visual pre-processing mechanisms and engineering denoising methods. (4) Conclusions: This model can robustly and steadily detect the motion direction of the translating object under variable contrast in the figure-ground and environmental noise interference. |
| format | Article |
| id | doaj-art-d375282b12d941e8989b92e3857530c5 |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-d375282b12d941e8989b92e3857530c52025-01-24T13:24:44ZengMDPI AGBiomimetics2313-76732025-01-011015110.3390/biomimetics10010051A Bio-Inspired Visual Neural Model for Robustly and Steadily Detecting Motion Directions of Translating Objects Against Variable Contrast in the Figure-Ground and Noise InterferenceSheng Zhang0Ke Li1Zhonghua Luo2Mengxi Xu3Shengnan Zheng4College of Information Science and Engineering, Hohai University, Nanjing 211100, ChinaSchool of Mechanical and Electrical Engineering, Nanchang Institute of Technology, Nanchang 330044, ChinaSchool of Mechanical and Electrical Engineering, Nanchang Institute of Technology, Nanchang 330044, ChinaSchool of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaCollege of Information Science and Engineering, Hohai University, Nanjing 211100, China(1) Background: At present, the bio-inspired visual neural models have made significant achievements in detecting the motion direction of the translating object. Variable contrast in the figure-ground and environmental noise interference, however, have a strong influence on the existing model. The responses of the lobula plate tangential cell (LPTC) neurons of Drosophila are robust and stable in the face of variable contrast in the figure-ground and environmental noise interference, which provides an excellent paradigm for addressing these challenges. (2) Methods: To resolve these challenges, we propose a bio-inspired visual neural model, which consists of four stages. Firstly, the photoreceptors (R1–R6) are utilized to perceive the change in luminance. Secondly, the change in luminance is divided into parallel ON and OFF pathways based on the lamina monopolar cell (LMC), and the spatial denoising and the spatio-temporal lateral inhibition (LI) mechanisms can suppress environmental noise and improve motion boundaries, respectively. Thirdly, the non-linear instantaneous feedback mechanism in divisive contrast normalization is adopted to reduce local contrast sensitivity; further, the parallel ON and OFF contrast pathways are activated. Finally, the parallel motion and contrast pathways converge on the LPTC in the lobula complex. (3) Results: By comparing numerous experimental simulations with state-of-the-art (SotA) bio-inspired models, we can draw four conclusions. Firstly, the effectiveness of the contrast neural computation and the spatial denoising mechanism is verified by the ablation study. Secondly, this model can robustly detect the motion direction of the translating object against variable contrast in the figure-ground and environmental noise interference. Specifically, the average detection success rate of the proposed bio-inspired model under the pure and real-world complex noise datasets was increased by 5.38% and 5.30%. Thirdly, this model can effectively reduce the fluctuation in this model response against variable contrast in the figure-ground and environmental noise interference, which shows the stability of this model; specifically, the average inter-quartile range of the coefficient of variation in the proposed bio-inspired model under the pure and real-world complex noise datasets was reduced by 38.77% and 47.84%, respectively. The average decline ratio of the sum of the coefficient of variation in the proposed bio-inspired model under the pure and real-world complex noise datasets was 57.03% and 67.47%, respectively. Finally, the robustness and stability of this model are further verified by comparing other early visual pre-processing mechanisms and engineering denoising methods. (4) Conclusions: This model can robustly and steadily detect the motion direction of the translating object under variable contrast in the figure-ground and environmental noise interference.https://www.mdpi.com/2313-7673/10/1/51DrosophilaLPTC neuronsmotion directionstranslating objectsvariable contrast in figure-groundenvironmental noise interference |
| spellingShingle | Sheng Zhang Ke Li Zhonghua Luo Mengxi Xu Shengnan Zheng A Bio-Inspired Visual Neural Model for Robustly and Steadily Detecting Motion Directions of Translating Objects Against Variable Contrast in the Figure-Ground and Noise Interference Biomimetics Drosophila LPTC neurons motion directions translating objects variable contrast in figure-ground environmental noise interference |
| title | A Bio-Inspired Visual Neural Model for Robustly and Steadily Detecting Motion Directions of Translating Objects Against Variable Contrast in the Figure-Ground and Noise Interference |
| title_full | A Bio-Inspired Visual Neural Model for Robustly and Steadily Detecting Motion Directions of Translating Objects Against Variable Contrast in the Figure-Ground and Noise Interference |
| title_fullStr | A Bio-Inspired Visual Neural Model for Robustly and Steadily Detecting Motion Directions of Translating Objects Against Variable Contrast in the Figure-Ground and Noise Interference |
| title_full_unstemmed | A Bio-Inspired Visual Neural Model for Robustly and Steadily Detecting Motion Directions of Translating Objects Against Variable Contrast in the Figure-Ground and Noise Interference |
| title_short | A Bio-Inspired Visual Neural Model for Robustly and Steadily Detecting Motion Directions of Translating Objects Against Variable Contrast in the Figure-Ground and Noise Interference |
| title_sort | bio inspired visual neural model for robustly and steadily detecting motion directions of translating objects against variable contrast in the figure ground and noise interference |
| topic | Drosophila LPTC neurons motion directions translating objects variable contrast in figure-ground environmental noise interference |
| url | https://www.mdpi.com/2313-7673/10/1/51 |
| work_keys_str_mv | AT shengzhang abioinspiredvisualneuralmodelforrobustlyandsteadilydetectingmotiondirectionsoftranslatingobjectsagainstvariablecontrastinthefiguregroundandnoiseinterference AT keli abioinspiredvisualneuralmodelforrobustlyandsteadilydetectingmotiondirectionsoftranslatingobjectsagainstvariablecontrastinthefiguregroundandnoiseinterference AT zhonghualuo abioinspiredvisualneuralmodelforrobustlyandsteadilydetectingmotiondirectionsoftranslatingobjectsagainstvariablecontrastinthefiguregroundandnoiseinterference AT mengxixu abioinspiredvisualneuralmodelforrobustlyandsteadilydetectingmotiondirectionsoftranslatingobjectsagainstvariablecontrastinthefiguregroundandnoiseinterference AT shengnanzheng abioinspiredvisualneuralmodelforrobustlyandsteadilydetectingmotiondirectionsoftranslatingobjectsagainstvariablecontrastinthefiguregroundandnoiseinterference AT shengzhang bioinspiredvisualneuralmodelforrobustlyandsteadilydetectingmotiondirectionsoftranslatingobjectsagainstvariablecontrastinthefiguregroundandnoiseinterference AT keli bioinspiredvisualneuralmodelforrobustlyandsteadilydetectingmotiondirectionsoftranslatingobjectsagainstvariablecontrastinthefiguregroundandnoiseinterference AT zhonghualuo bioinspiredvisualneuralmodelforrobustlyandsteadilydetectingmotiondirectionsoftranslatingobjectsagainstvariablecontrastinthefiguregroundandnoiseinterference AT mengxixu bioinspiredvisualneuralmodelforrobustlyandsteadilydetectingmotiondirectionsoftranslatingobjectsagainstvariablecontrastinthefiguregroundandnoiseinterference AT shengnanzheng bioinspiredvisualneuralmodelforrobustlyandsteadilydetectingmotiondirectionsoftranslatingobjectsagainstvariablecontrastinthefiguregroundandnoiseinterference |