Assessments of the mouse visual system based on spatial-frequency analysis imply that its
visual capacity is low, with few neurons responding to spatial frequencies greater than 0.5 …

The retina and primary visual cortex (V1) both exhibit diverse neural populations sensitive to
diverse visual features. Yet it remains unclear how neural populations in each area partition …

Generalization to unseen data is a key desideratum for deep networks, but its relation to
classification accuracy is unclear. Using a minimalist vision dataset and a measure of …

Invoking the manifold assumption in machine learning requires knowledge of the manifold's
geometry and dimension, and theory dictates how many samples are required. However, in …

The rapid development of multi-electrode and imaging techniques is leading to a data
explosion in neuroscience, opening the possibility of truly understanding the organization and …

The static synaptic connectivity of neuronal circuits stands in direct contrast to the dynamics
of their function. As in changing community interactions, different neurons can participate …

Over the past years, network science has proven invaluable as a means to better understand
many of the processes taking place in the brain. Recently, interareal connectivity data of …

Understanding circuit properties from physiological data presents two challenges:(i) recordings
do not reveal connectivity, and (ii) stimuli only exercise circuits to a limited extent. We …

Deep neural network modeling of biological visual processing is widespread: brains are
archetypal pattern analyzers and deep CNNs are currently the best object classifiers. Implicit is …

Shading and material effects are coupled in causing brightness variations in natural images,
which is problematic for shape-from-shading inferences. Given the importance of …