Deep Neural Networks (DNNs) became the standard tool for function
approximation with most of the introduced architectures being developed for
high-dimensional input data. However, many real-world problems have
low-dimensional inputs for which standard Multi-Layer Perceptrons (MLPs) are
the default choice. An investigation into specialized architectures is missing.
We propose a novel DNN layer called Univariate Radial Basis Function (U-RBF)
layer as an alternative. Similar to sensory neurons in the brain, the U-RBF
layer processes each individual input dimension with a population of neurons
whose activations depend on different preferred input values. We verify its
effectiveness compared to MLPs in low-dimensional function regressions and
reinforcement learning tasks. The results show that the U-RBF is especially
advantageous when the target function becomes complex and difficult to
approximate.

深度神经网络（DNN）已成为高维输入数据函数逼近的标准工具，但许多现实世界中的问题具有低维输入，对于这些问题，标准的多层感知器（MLPs）是默认选择。我们提出了一种称为单变量径向基函数（U-RBF）层的新颖 DNN 层，作为一种替代选择。类似于大脑中的感觉神经元，U-RBF 层使用一组神经元来处理每个单独的输入维度，其激活取决于不同的优先输入值。我们在低维函数回归和强化学习任务中验证了其与 MLPs 相比的效果。结果表明，当目标函数变得复杂且难以逼近时，U-RBF 尤其具有优势。