Intuition: why are ReLu activations boundary lines linear? What is the reasoning behind ReLu boundary lines appearing linear when plotting in 2D? Does this generalize to higher dimensions in that boundary lines in large dimensions are linear hyperplanes as well?

For instance, in tensorflow's playground any time ReLu is chosen as the activation function, the resulting boundary line is made of lines.

I'm failing to see the connection between the underlying neurons outputting only z or 0 and how that connects to the output plot.

Thanks very much for any thoughts/help!
As far as i understand, relu gives linear boundary because it is linear for X&gt;0. If you expand it to write it in the form of an equation like Z=f1(x1)+f2(x2)....fn(xn), we simply get a linear equation even if we consider the dead neurons. The part where x&lt;0 makes sure that no -ve signals are carried forward but dosent add any non linearity to the final equation. The effect of dead neurons probaly the the final coefficients of the equaion will have coefficients with slightly less value than had the activation been just a normal fuction since there is no negative component which is added to a neuron.