Garson proposed an algorithm, later modified by Goh (1995) for determining the relative importance of an input node to a network. In the case of a single layer of hidden units, the equation is
$$ Q_{ik} = \frac{ \sum_{j=1}^L | w_{ij} v_{jk} |/ \sum_{r=1}^N | w_{rj}|}{\sum_{i=1}^N \sum_{j=1}^L(|w_{ij}v_{jk}|/ \sum_{r=1}^N|w_{rj}|} $$$$ Q_{ik} = \frac{ \sum_{j=1}^L | w_{ij} v_{jk} |\ /\ \sum_{r=1}^N | w_{rj}|}{\sum_{i=1}^N \sum_{j=1}^L\big(|w_{ij}v_{jk}|\ /\ \sum_{r=1}^N|w_{rj}|\big)} $$
where $w_{ij}$ is the weight between the $i$th input and the $j$th hidden unit, and $v_{jk}$ is the weight between the $j$th hidden unit and the $k$th output.
I am interested in the case where the neural network is fully connected and has a single output. In this case, the only difference between the $Q_i$s for each input $i$ is the $\sum_{j=1}^L |w_{ij}|$, and so if we only care about the relative importance between the inputs, we can define $$ Q_{ik} = \sum_{j=1}^L |w_{ij}|.$$ That is, the only thing that matters are the inputs weights leaving that hidden unit, even if this is generalized to a multi-hidden layer neural network.
I was wondering if the same would hold if the hidden layer was replaced by a layer of LSTM units? My rationale is that since LSTMs are fully connected, we would still be able to say that $$ Q_{ik} = \sum_{j=1}^L |w_{ij}|.$$
