Data normalization of count data for neural networks

I have a sparse matrix of count data that I'm using as input to a neural network.

I know, usually, the input data should be normalized (e.g. via min-max scaling, $$z$$-score standardization, etc.). But for features that are counts, what is a good approach? Should I $$\log_2(x+1)$$ transform the data and then do a $$z$$-score standardization? Is there another better approach?