论文标题
网格细胞在神经网络中无处不在
Grid Cells Are Ubiquitous in Neural Networks
论文作者
论文摘要
据信网格细胞在空间和非空间认知任务中都起着重要作用。最近的一项研究观察到LSTM中网格细胞的出现以进行路径积分。看似相似性的生物学和人工神经网络之间的联系，以及深度神经网络（DNN）中网格细胞的应用结构域，期望进一步探索。这项工作表明，在适当的训练参数设置下，可以在基于纯视觉的路径集成DNN中复制网格单元。我们还表明，对于非空间任务的馈电DNN中会产生类似网格的行为。我们的发现支持网格编码是生物和人工网络的有效表示。
Grid cells are believed to play an important role in both spatial and non-spatial cognition tasks. A recent study observed the emergence of grid cells in an LSTM for path integration. The connection between biological and artificial neural networks underlying the seemingly similarity, as well as the application domain of grid cells in deep neural networks (DNNs), expect further exploration. This work demonstrated that grid cells could be replicated in either pure vision based or vision guided path integration DNNs for navigation under a proper setting of training parameters. We also show that grid-like behaviors arise in feedforward DNNs for non-spatial tasks. Our findings support that the grid coding is an effective representation for both biological and artificial networks.
