Ye Zeyuan, Wessel Ralf, Franken Tom P
Department of Physics, Washington University in St. Louis, St. Louis, MO 63130, USA.
Department of Neuroscience, Washington University in St. Louis, St. Louis, MO 63110, USA.
iScience. 2025 Mar 11;28(4):112199. doi: 10.1016/j.isci.2025.112199. eCollection 2025 Apr 18.
To make sense of visual scenes, the brain must segment foreground from background. This is thought to be facilitated by neurons that signal border ownership (BOS), which indicate which side of a border in their receptive field is owned by an object. How these signals emerge without a teaching signal of what is foreground remains unclear. Here we find that many units in PredNet, a self-supervised deep neural network trained to predict future frames in natural videos, are selective for BOS. They share key properties with BOS neurons in the brain, including robustness to object transformations and hysteresis. Ablation revealed that BOS units contribute more to prediction than other units for videos with moving objects. Our findings suggest that BOS neurons might emerge due to an evolutionary or developmental pressure to predict future input in natural, complex dynamic environments, even without an explicit requirement to segment foreground from background.
为了理解视觉场景，大脑必须将前景与背景区分开来。人们认为，发出边界所有权（BOS）信号的神经元有助于实现这一点，这些神经元会指示其感受野中边界的哪一侧属于某个物体。在没有关于什么是前景的教学信号的情况下，这些信号是如何出现的仍不清楚。在这里，我们发现，PredNet（一种经过训练以预测自然视频中未来帧的自监督深度神经网络）中的许多单元对BOS具有选择性。它们与大脑中的BOS神经元具有关键特性，包括对物体变换的鲁棒性和滞后现象。消融实验表明，对于有移动物体的视频，BOS单元对预测的贡献比其他单元更大。我们的研究结果表明，即使没有明确要求将前景与背景区分开来，BOS神经元也可能是由于在自然、复杂动态环境中预测未来输入的进化或发育压力而出现的。
