Not all solutions are created equal: An analytical dissociation of functional and representational similarity in deep linear neural networks

This work finds that function and representation are dissociated, allowing representational similarity without functional similarity and vice versa, and suggests that representational alignment reflects computational advantages beyond functional alignment alone.