Difference between LASSO penalty in neural network and just LASSO regression

Question

I wonder whether those two have any significant differences.

I think in neural network, the lasso penalty put on the loss function makes the model simpler and introduces more sparsity by automatically reducing some of the features to 0. I assume that's basically the lasso regression is doing?