Here's a problem: you can argue that Gabor filters arise because we design neural nets to encourage them. Gabor filters mostly arise in CNN or things otherwise regularized to be like CNNs. Convolutional layers are a form of regularization that restrict the space of models that a network can conform to. The Gabor filters are learned but none of this is evidence they are globally "optimal" given that a human manually decided whether or not to include the presence of convolutions.

It also goes without saying that the phrase "statistically optimal" is meaningless in this specific context. You can claim they are a part of minimizing the cost function, but, again, you have to be very careful about the chicken and egg problem, because humans are the ones who manually craft the cost function.