Most large-scale network models use neurons with static nonlinearities that produce analog
output, despite the fact that information processing in the brain is predominantly carried out …

Neural networks need the right representations of input data to learn. Here we ask how
gradient-based learning shapes a fundamental property of representations in recurrent …

Highlights•Inhibition-stabilized networks describe the visual cortical operating
point.•Dynamic synapses cause nonlinearity and multi-stability in balanced …

This article is part of the Physical Review Research collection titled Physics of
Neuroscience. Recurrent networks are widely used as models of biological neural circuits …

Autonomous, randomly coupled, neural networks display a transition to chaos at a critical
coupling strength. Here, we investigate the effect of a time-varying input on the onset of …

This is a book about 'Neurodynamics'. What we mean is that this is a book about how ideas
from dynamical systems theory have been developed and employed in recent years to give …

Coupling among neural rhythms is one of the most important mechanisms at the basis of
cognitive processes in the brain. In this study, we consider a neural mass model, rigorously …

Recurrent neural networks (RNNs) have been successfully applied to a variety of problems
involving sequential data, but their optimization is sensitive to parameter initialization …

Typical responses of cortical neurons to identical sensory stimuli appear highly variable. It
has thus been proposed that the cortex primarily uses a rate code. However, other studies …

Most external stimuli, including sound, temperature, and illumination, exhibit spatially
heterogeneous, and different amplitudes of the same signal are received by neurons at …