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, […]
Modeling shared and variable information encoded in fine-scale cortical topographies. ABSTRACT: Information is encoded in fine-scale functional topographies that vary from brain to brain. Hyperalignment models information that is shared across brain in a high-dimensional common information space. Hyperalignment transformations project idiosyncratic individual topographies into the common model information space. These transformations contain topographic basis functions, affording […]
Current MA student Atlas Kazemian will give a talk on "Toward a computational neuroscience of visual cortex without deep learning."
MA applications for Spring 2023 due.