论文标题
通过表示人工神经网络中的视觉空间的双缝干扰的出现
Emergence of Double-slit Interference by Representing Visual Space in Artificial Neural Networks
论文作者
论文摘要
人工神经网络在图像识别方面取得了令人难以置信的成功，但是视觉空间表示的基本机制仍然是一个巨大的谜。内嗅皮层中的网格细胞（2014年诺贝尔奖）支持定期表示作为编码空间的度量。在这里，我们开发了一个自我监督的卷积神经网络，以执行视觉空间位置，从而导致波浪的单缝衍射和双缝干扰模式的出现。我们的发现揭示了CNN在一定程度上编码视觉空间的性质。就视觉空间编码而言，CNN不再是黑匣子，它是可以解释的。我们的发现表明，波的周期性特性提供了空间度量，这表明空间坐标框架在人工神经网络中的一般作用。
Artificial neural networks have realized incredible successes at image recognition, but the underlying mechanism of visual space representation remains a huge mystery. Grid cells (2014 Nobel Prize) in the entorhinal cortex support a periodic representation as a metric for coding space. Here, we develop a self-supervised convolutional neural network to perform visual space location, leading to the emergence of single-slit diffraction and double-slit interference patterns of waves. Our discoveries reveal the nature of CNN encoding visual space to a certain extent. CNN is no longer a black box in terms of visual spatial encoding, it is interpretable. Our findings indicate that the periodicity property of waves provides a space metric, suggesting a general role of spatial coordinate frame in artificial neural networks.
