Probing machine-learning classifiers using noise, bubbles, and reverse correlation

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 […]