Large Language Models (LLMs) have been shown to be effective models of the human
language system, with some models predicting most explainable variance of brain activity in …

Many cognitive neuroscience studies use large feature sets to predict and interpret brain
activity patterns. Feature sets take many forms, from human stimulus annotations to …

Decades of neuroscientific research has sought to understand medial temporal lobe (MTL)
involvement in perception. Apparent inconsistencies in the literature have led to competing …

Recent progress in artificial intelligence has renewed interest in building machines that
learn like animals. Almost all of the work comparing learning across biological and artificial …

A key feature of cortical systems is functional organization: the arrangement of functionally
distinct neurons in characteristic spatial patterns. However, the principles underlying the …

Deep neural networks, inspired by information processing in the brain, can achieve human-
like performance for various tasks. However, research efforts to use these networks as …

Visual stimuli are known to vary in their perceived duration. Some visual stimuli are also
known to linger for longer in memory. Yet, whether these two features of visual processing …

The visual object category reports of artificial neural networks (ANNs) are notoriously
sensitive to tiny, adversarial image perturbations. Because human category reports (aka …

The analogy between the brain and deep neural networks (DNNs) has sparked interest in
neuroscience. Although DNNs have limitations, they remain valuable for modeling specific …

We developed a tool for visualizing and analyzing large pre-trained vision models by
mapping them onto the brain thus exposing their hidden inside. Our innovation arises from a …