We might be forced to listen to a high-frequency tone at our audiologist's office or we might
enjoy falling asleep with a white-noise machine, but the sounds that really matter to us are …

Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded
from the human brain as well as in the computational tools available to analyze this data …

The ability to recognize and predict temporal sequences of sensory inputs is vital for survival
in natural environments. Based on many known properties of cortical neurons, hierarchical …

Neuroscientists classify neurons into different types that perform similar computations at
different locations in the visual field. Traditional methods for neural system identification do …

Our understanding of nonlinear stimulus transformations by neural circuits is hindered by the
lack of comprehensive yet interpretable computational modeling frameworks. Here, we …

Neurons in sensory systems often pool inputs over arrays of presynaptic cells, giving rise to
functional subunits inside a neuron's receptive field. The organization of these subunits …

Dendrites integrate inputs nonlinearly, but it is unclear how these nonlinearities contribute to
the overall input-output transformation of single neurons. We developed statistically …

A key problem in computational neuroscience is to find simple, tractable models that are
nevertheless flexible enough to capture the response properties of real neurons. Here we …

Divisive normalization (DN) is a prominent computational building block in the brain that has
been proposed as a canonical cortical operation. Numerous experimental studies have …

As information flows through the brain, neuronal firing progresses from encoding the world
as sensed by the animal to driving the motor output of subsequent behavior. One of the more …