Supplementary Material for Characterizing emergent representations in a space of candidate learning rules for deep networks

We apply singular value decomposition (SVD) to the dataset's input-output correlation matrix to extract the component of the input-output mapping for different hierarchical levels. To compute the strength of a network's input-output mapping for these hierarchical distinctions This author is now affiliated to University Medical Center Hamburg-Eppendorf, Hamburg, Germany. The task is to link each object's perceptual representation ( However, it seems critical to demonstrate that our framework is robust against a modification of this assumption about input structure. Here, we show that the conclusions presented in the main paper remain unchanged even if we relax the assumption of one-hot vectors (which are similar to grandmother-cell neurons: each object is represented by a dedicated single neuron). The differences in learning dynamics across different learning rules within the 2D space are robust against the shift from localist assumption to the current distributed assumption (Supp.