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A learning artificial visual system for motion direction detection

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Abstract

Research on the visual system and its development is not only crucial for understanding how we see and interpret the environment from basic visual processing to complex perception but also aids in the development of technologies like virtual reality, augmented reality, and computer vision. In our previous paper, we have proposed a novel biological motion direction detection mechanism. Based on this mechanism, the authors of this paper proposed a learning artificial visual system (AVS) and use it to achieve motion direction detection. The learning AVS consists of two layers: the local visual feature detective neuron layer and the global feature detective neuron layer. For local motion direction detection, we use dendritic neurons to implement local motion direction-detective neurons and use them to extract local motion direction information. Global feature detective neurons are simply implemented by summing the local motion direction-detective neurons in each direction of possible motion. We trained the learning AVS by eliminating unnecessary branches or synapses and keeping only those that are strongly connected to the function of motion direction detection. Computer simulations demonstrate that AVS can learn the correct motion direction of objects with different shapes, size and positions. We compare the performance of AVS with support vector machine (SVM) and traditional convolutional neural networks (CNNs)—LeNet-5 and EfficientNet-B0 (EfN)—in motion direction detection and find that our learning AVS has better recognition accuracy, noise immunity and less demands on size of dataset and learning costs.

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Data availability

The data used in this study are fully available for public access. There are no restrictions on accessing or using the data. You can freely download the data from the following links or by contacting the corresponding authors for any additional information or specific requests. We are committed to open and transparent research practices. Dataset: Original Motion Direction Dataset. Program: Dendritic Neuron-Based Motion Direction Program. Experiments data: CNNs data, Dmodel data. Noise detecting result: Dmodel and pretrained EfN-b0 detection results, unpretrained EfN-b0 and LeNet-5 detection results by training and testing data ratio.

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Acknowledgements

This work was supported by JST SPRING, Grant Number JPMJSP2135 and JSPS KAKENHI, Grant Number 23K11261.

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Author notes

  1. Yuki Kobayashi, Yuki Todo and Zheng Tang have contributed equally.

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Kanazawa University, Kakuma, Kanazawa, Ishikawa, 9201162, Japan

    Tianqi Chen, Yuki Kobayashi & Yuki Todo

  2. Department of Intelligence Information Systems, University of Toyama, Toyama, 9308555, Japan

    Zheng Tang

Authors
  1. Tianqi Chen
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  2. Yuki Kobayashi
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  3. Yuki Todo
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  4. Zheng Tang
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Correspondence to Yuki Todo.

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Chen, T., Kobayashi, Y., Todo, Y. et al. A learning artificial visual system for motion direction detection. Neural Comput & Applic 36, 17181–17197 (2024). https://doi.org/10.1007/s00521-024-09921-6

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