How much do we know about visual representations? Quantifying the dimensionality gap between DNNs and visual cortex'. Abstract: Deep neural networks (DNNs) can explain a large portion of variance in image-evoked cortical responses by accounting for the highest variance latent dimensions in neural data, such as dimensions corresponding to animacy, aspect ratio, and curvature. However, […]

Current MA student Atlas Kazemian will give a talk on "Toward a computational neuroscience of visual cortex without deep learning."