Our knowledge of sensory processing has advanced dramatically in the last few decades,
but this understanding remains far from complete, especially for stimuli with the large …

We report a new multidimensional measure of visual complexity (GraphCom) that captures
variability in the complexity of graphs within and across writing systems. We applied the …

Highlights•Subunit pooling and normalization are building blocks of hierarchical cortical
models.•Image statistics models predict when normalization is recruited in primary …

Dense local, recurrent connections are a major feature of cortical circuits, yet how they affect
neurons' responses has been unclear, with some studies reporting weak recurrent effects …

One of the key problems in computer vision is adaptation: models are too rigid to follow the
variability of the inputs. The canonical computation that explains adaptation in sensory …

In vision science, cascades of Linear+ Nonlinear transforms are very successful in modeling
a number of perceptual experiences. However, the conventional literature is usually too …

Deep convolutional neural networks (CNNs) trained on visual objects have shown intriguing
ability to predict some response properties of visual cortical neurons. However, the factors …

Computational modeling is a useful method for generating hypotheses about the
contributions of impaired neurobiological mechanisms, and their interactions, to …

Deep convolutional neural networks (CNNs) trained on objects and scenes have shown
intriguing ability to predict some response properties of visual cortical neurons. However, the …

Local divisive normalization provides a phenomenological description of many nonlinear
response properties of neurons across visual cortical areas. To gain insight into the utility of …