dropout: forward prop VS back prop in machine learning Neural Network

Regarding dropout, we know that in the forward propagation some neurons are put to "zero" (i.e., turned off). How about back propagation ?

Are these dropped out neurons also zeros (turned off) during back-prop ?

Thank

Refer to this link, which seems to be not very clear ... : Dropout backpropagation implementation

• In forward propagation, inputs are set to zero with probability $p$, and otherwise scaled up by $\frac{1}{1 - p}$.
• In backward propagation, gradients for the same dropped units are zeroed out; other gradients are scaled up by the same $\frac{1}{1-p}$.