At the moment, the way they are trained is not a good theory. But that is the compact human interpretable way of thinking of these models. It seems like if we k

At the moment, the way they are trained is not a good theory. But that is the compact human interpretable way of thinking of these models. It seems like if we keep iterating on this then we could arrive at a compact description of the neural network which is its learning rules, architecture and environment. Why is it important to have a compact explanation of the trained resulting model if the learning rule, architecture and data are a fairly compact description? It seems like for vision there are a few simple theories of learning: having layers of nonlinearities weight sharing across space and some way of doing credit assignment on the loss from a visual task Which taken together are enough to explain a large amount of the explainable variance in the neural data. I agree that the models could get more biologically realistic in the way they learn, but I disagree that it's important to explain how the learned model functions in a compact way, since there may be no such explanation better than the one based on learning.