Jeroen Olieslagers

Representational Dissimilarity Metric Spaces for Stochastic Neural Networks

Quantifying similarity between neural representations---e.g. hidden layer activation vectors---is a perennial problem in deep learning and neuroscience research. Existing methods compare deterministic responses (e.g. artificial networks that lack stochastic layers) or averaged responses (e.g., trial…