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, […]
A group of graduate students and postdoctoral fellows from the Department of Cognitive Science at Johns Hopkins, are offering one-on-one mentoring about applying to our department, with priority given to prospective students from underrepresented groups. The sign up form will remain open until November 15, but we strongly encourage you to respond by November 5 for […]
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."