Neuroscience and artificial intelligence (AI) share a long, intertwined history. It has been
argued that discoveries in neuroscience were (and continue to be) instrumental in driving …

The neuroscience of perception has recently been revolutionized with an integrative reverse-
engineering approach in which 3 computation, brain function, and behavior are linked …

Deep neural networks (DNNs) have revolutionized computer science and are now widely
used for neuroscientific research. A hot debate has ensued about the usefulness of DNNs as …

Despite differing from the human language processing mechanism in implementation and
algorithms, current language models demonstrate remarkable human-like or surpassing …

Understanding how biological visual systems process information is challenging due to the
complex nonlinear relationship between neuronal responses and high-dimensional visual …

Computational modeling plays an increasingly important role in neuroscience, highlighting
the philosophical question of how computational models explain. In the particular case of …

Advances in cognitive neuroscience are often accompanied by an increased complexity in
the methods we use to uncover new aspects of brain function. Recently, many studies have …

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

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 …