Deep neural network models

…to create specific, image-computable, deep neural network models intended to appropriately abstract, emulate and explain the mechanisms of primate ventral visual processing, up to its deepest neural level, the inferior temporal cortex (IT). Because these leading neuroscientific emulation models — aka “digital twins” — are fully observable and machine-executable, they offer predictive and potential application power that our field’s prior conceptual models did not. Our team’s ongoing work is aimed at ask

Similarity between neural representations is often quantified by measuring alignment of the representations over a set of natural stimuli that are relatively far apart in stimulus space (Kriegeskorte et al. 2008). However, systems with similar global structure can have strikingly different sensitivities to local stimulus distortions (Szegedy et al. 2013), suggesting a need for metrics that compare local sensitivities of representations. We propose a framework for comparing a set of image representations in terms of their sensitivities to local distortions. We quantify the local geometry of a r