Abstract Systems neuroscience seeks explanations for how the brain implements a wide
variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to …

Neural responses are noisy, and circuit structure can correlate this noise across neurons.
Theoretical studies show that noise correlations can have diverse effects on population coding, …

A commonly observed neural correlate of working memory is firing that persists after the
triggering stimulus disappears. Substantial effort has been devoted to understanding the many …

Primary visual cortex (V1) is the first stage of cortical image processing, and major effort in
systems neuroscience is devoted to understanding how it encodes information about visual …

Scientists have long conjectured that the neocortex learns the structure of the environment
in a predictive, hierarchical manner. To do so, expected, predictable features are …

The current state-of-the-art object recognition algorithms, deep convolutional neural
networks (DCNNs), are inspired by the architecture of the mammalian visual system, and are …

In alternative theories of gravity, designed to produce cosmic acceleration at the current
epoch, the growth of large scale structure can be modified. We study the potential of upcoming …

Sparse coding algorithms trained on natural images can accurately predict the features that
excite visual cortical neurons, but it is not known whether such codes can be learned using …

Ferroelectric materials, which have a reversible spontaneous polarization and generate an
electric potential when subjected to a mechanical stress (piezoelectricity), have applications …

Sparse coding models of natural scenes can account for several physiological properties of
primary visual cortex (V1), including the shapes of simple cell receptive fields (RFs) and the …