Before revision:  Armed with such activation functions, deep neural networks can reproduce traditional approximation tools and, therefore, approximate target functions with a smaller number of parameters than traditional neural networks. In terms of trainingdynamics of deep learning, reproducing activation functions can generate neural tangent kernels  with a better condition number than traditional activation functions lessening the spectral bias of deep learning.
After revision:  Armed with such activation functions, deep neural networks can reproduce traditional approximation tools and, therefore, approximate target functions with a smaller number of parameters than traditional NNs. In NN training, RAFs can generate neural tangent kernels with a better condition number than traditional activation functions lessening the spectral bias of deep learning.