Topic: stochastic representations

Comparing high-dimensional neural response patterns across different animals or artificial deep networks is a fundamental problem in computational neuroscience. There are now many methods for quantifying representational dissimilarity including canonical correlations analysis (CCA), centered kernel alignment (CKA), representational similarity analysis (RSA), shape metrics, and so on. Intuitively, these measures quantify similarity in the geometry of neural responses while removing expected forms of invariance across networks (e.g. permutations over arbitrary neuron indices). However, these met