In a standard multi-layer model, the output of a given layer serves as the direct input to the next, creating a sequential chain of processing. Consider an alternative architecture where the input to any given layer (beyond the first) is a combination of the initial input to the entire network and the output of the immediately preceding layer. What is the primary computational difference introduced by this alternative design compared to the standard sequential model?

A multi-layer model processes information sequentially. Given an initial input matrix of hidden states, denoted as $\mathbf{H}^{0}$, and the outputs of three subsequent layers, $\mathbf{H}^{1}$, $\mathbf{H}^{2}$, and $\mathbf{H}^{3}$, arrange these matrices in the correct order of their generation and processing within the model, from start to finish.

In a deep, multi-layer model, a computational error occurs during the processing of the 5th layer, causing its output matrix of hidden states, $\mathbf{H}^{5}$, to become corrupted. Based on the standard sequential processing flow where the output of one layer becomes the input for the next, which subsequent layers will be directly impacted by this corrupted data?