How do the current input and the output of the previous time step get combined in an LSTM? I am currently looking into LSTMs. I found this nice blog post, which is already very helpful, but still, there are things I don't understand, mostly because of the collapsed layers. 


The input $X_t$, and the output of the previous time step $H_{t-1}$, how do they get combined? Multiplied, added or what? 
The input weights and the weights of the input of the previous time step, those are just the weights of the connections between the time-steps/units, right?
(1) $X_t$ and $H_{t-1}$ are concatenated. The blog you cited explained its notation "Lines merging denote concatenation". For example, if $X_t=[1,2,3]$ and $H_{t-1}=[4,5,6,7]$, then their concatenation is $[1,2,3,4,5,6,7]$

(2) When you say "input weights" or "weights of the input of the previous time step", are you referring to the $W_i$ in your cited blog? If so they are not the weights of the connections between the time-steps/units. They are part of the input gate only. The connections between the time-steps/units do not have weights applied to them.