What is the difference between a **Residual** Neural Net and a **Recurrent** Neural Net?

As I understand,

[Residual Neural Networks][1] are very deep networks that implement 'shortcut' connections across multiple layers in order to preserve context and eliminate exploding/vanishing gradients. Layers in a residual neural net have input from the layer before it and the optional, less processed data, from *X* layers higher. This prevents early or deep layers from 'dieing' due to vanishing/exploding gradients.

[Recurrent Neural Networks][2] are networks that contain a directed cycle which when computed is 'unrolled' through time. Layers in recurrent neural network have input from the layer before it and the optional, *time dependent* extra input. This provides situational context for things like natural language processing.

Therefor, a recurrent neural network can be used to generate a basic residual network if the input remains the same with respect to time.

Is this correct?


  [1]: https://arxiv.org/pdf/1512.03385v1.pdf
  [2]: https://arxiv.org/pdf/1604.03640.pdf