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Deep Neural Networks (DNNs) became the standard tool for functionapproximation with most of the introduced architectures being developed forhigh-dimensional input data. However, many real-world problems havelow-dimensional inputs for which standard Multi-Layer Perceptrons (MLPs) arethe 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-RBFlayer processes each individual input dimension with a population of neuronswhose activations depend on different preferred input values. We verify itseffectiveness compared to MLPs in low-dimensional function regressions andreinforcement learning tasks. The results show that the U-RBF is especiallyadvantageous when the target function becomes complex and difficult toapproximate.

Univariate Radial Basis Function Layers: Brain-inspired Deep Neural Layers for Low-Dimensional Inputs