[D] Hash table aspects of ReLU neural networks

If you collect the ReLU decisions into a diagonal matrix with 0 or 1 entries then a ReLU layer is DWx, where W is the weight matrix and x the input. What then is Wₙ₊₁Dₙ where Wₙ₊₁ is the matrix of weights for the next layer? It can be seen as a (locality sensitive) hash table lookup of a linear mapping (effective matrix). It can also be seen as an associative memory in itself with Dₙ as the key. There is a discussion here: The viewpoints are not fully integrated yet and there are notation problems.