# LSTMs: what is $W_x$ & $U_z$ in $φ(W_x + U_z + b)$?

Despite of their varying characteristics, most of them(RNNs) share a common computational building block, described by the following equation: $φ(W_x + U_z + b)$, where $x ∈ R_n$ and $z ∈ R_m$ are state vectors coming from different information sources, $W ∈ R_{d×n}$ and $U ∈ R_{d×m}$ are state-to-state transition matrices, and $b$ is a bias vector.

Don't get what meaning of $W_x$ and $U_z$. I know that $W$ is typicaly for weights... what does this equation mean?