Sure, parameters are not updated, but ANNs are universal approximates, so they can model whatever it is you envision this “scratch space” doing. Think about it

Sure, parameters are not updated, but ANNs are universal approximates, so they can model whatever it is you envision this “scratch space” doing. Think about it like this: whatever gets put in to the scratch space would need to be deterministic based on the inputs, i.e. it would just be a store if some intermediate value computed by the network. So how would it fundamentally differ from the network itself. I guess what I’m saying is that I’d want an explanation how how this scratch space fundamentally differs from the network itself. It’s almost like you’re assuming the network is “thinking” and that giving it a pad of paper would help it reason better.