Etienne Thoret, Thomas Andrillon, Damien Léger, Daniel Pressnitzer
Abstract
Many scientific fields now use machine-learning tools to assist with complex classification tasks. In neuroscience, automatic classifiers may be useful to diagnose medical images, monitor electrophysiological signals, or decode perceptual and cognitive states from neural signals. Tools such as deep neural networks regularly outperform humans with such large and high-dimensional datasets.
However, such tools often remain black-boxes: they lack interpretability. A lack of interpretability has obvious ethical implications for clinical applications, but it also limits the usefulness of machine-learning tools to formulate new theoretical hypotheses. Here, we propose a simple and versatile method to help characterize and understand the information used by a classifier to perform its task. The method is inspired by the reverse correlation framework familiar to neuroscientists. Specifically, noisy versions of training samples or, when the training set is unavailable, custom-generated noisy samples are fed to the classifier. Variants of the method using uniform noise and noise focused on subspaces of the input representations, so-called “bubbles”, are presented. Reverse correlation techniques are then adapted to extract both the discriminative information used by the classifier and the canonical information for each class.
We provide illustrations of the method for the classification of written numbers by a convolutional deep neural network and for the classification of speech versus music by a support vector machine. The method itself is generic and can be applied to any kind of classifier and any kind of input data. Compared to other, more specialized approaches, we argue that the noise-probing method could provide a generic and intuitive interface between machine-learning tools and neuroscientists.