Li Bin, Todo Yuki, Tang Zheng
Division of Electrical Engineering and Computer Science, Kanazawa University, Kanazawa-shi 920-1192, Japan.
Faculty of Electrical, Information and Communication Engineering, Kanazawa University, Kanazawa-shi 920-1192, Japan.
Biomimetics (Basel). 2025 Jan 8;10(1):38. doi: 10.3390/biomimetics10010038.
Stereo-orientation selectivity is a fundamental neural mechanism in the brain that plays a crucial role in perception. However, due to the recognition process of high-dimensional spatial information commonly occurring in high-order cortex, we still know little about the mechanisms underlying stereo-orientation selectivity and lack a modeling strategy. A classical explanation for the mechanism of two-dimensional orientation selectivity within the primary visual cortex is based on the Hubel-Wiesel model, a cascading neural connection structure. The local-to-global information aggregation thought within the Hubel-Wiesel model not only contributed to neurophysiology but also inspired the development of computer vision fields. In this paper, we provide a clear and efficient conceptual understanding of stereo-orientation selectivity and propose a quantitative explanation for its generation based on the thought of local-to-global information aggregation within the Hubel-Wiesel model and develop an artificial visual system (AVS) for stereo-orientation recognition. Our approach involves modeling depth selective cells to receive depth information, simple stereo-orientation selective cells for combining distinct depth information inputs to generate various local stereo-orientation selectivity, and complex stereo-orientation selective cells responsible for integrating the same local information to generate global stereo-orientation selectivity. Simulation results demonstrate that our AVS is effective in stereo-orientation recognition and robust against spatial noise jitters. AVS achieved an overall over 90% accuracy on noise data in orientation recognition tasks, significantly outperforming deep models. In addition, the AVS contributes to enhancing deep models' performance, robustness, and stability in 3D object recognition tasks. Notably, AVS enhanced the TransNeXt model in improving its overall performance from 73.1% to 97.2% on the 3D-MNIST dataset and from 56.1% to 86.4% on the 3D-Fashion-MNIST dataset. Our explanation for the generation of stereo-orientation selectivity offers a reliable, explainable, and robust approach for extracting spatial features and provides a straightforward modeling method for neural computation research.
立体方向选择性是大脑中的一种基本神经机制，在感知中起着至关重要的作用。然而，由于高阶皮层中普遍存在的高维空间信息的识别过程，我们对立体方向选择性的潜在机制仍然知之甚少，并且缺乏一种建模策略。对于初级视觉皮层内二维方向选择性机制的经典解释基于Hubel-Wiesel模型，这是一种级联神经连接结构。Hubel-Wiesel模型中的局部到全局信息聚合思想不仅推动了神经生理学的发展，也启发了计算机视觉领域的进步。在本文中，我们对立体方向选择性提供了清晰有效的概念理解，并基于Hubel-Wiesel模型中的局部到全局信息聚合思想对其产生提出了定量解释，同时开发了一种用于立体方向识别的人工视觉系统（AVS）。我们的方法包括对深度选择性细胞进行建模以接收深度信息，对简单立体方向选择性细胞进行建模以组合不同的深度信息输入以产生各种局部立体方向选择性，以及对复杂立体方向选择性细胞进行建模以负责整合相同的局部信息以产生全局立体方向选择性。仿真结果表明，我们的AVS在立体方向识别中是有效的，并且对空间噪声抖动具有鲁棒性。在方向识别任务中，AVS在噪声数据上的总体准确率超过90%，显著优于深度模型。此外，AVS有助于提高深度模型在3D物体识别任务中的性能、鲁棒性和稳定性。值得注意的是，在3D-MNIST数据集上，AVS将TransNeXt模型的整体性能从73.1%提高到了97.2%，在3D-Fashion-MNIST数据集上从56.1%提高到了86.4%。我们对立体方向选择性产生的解释为提取空间特征提供了一种可靠、可解释且鲁棒的方法，并为神经计算研究提供了一种直接的建模方法。
