The recent striking success of deep neural networks in machine learning raises profound
questions about the theoretical principles underlying their success. For example, what can …

Injury to the central or peripheral nervous systems leads to the loss of cognitive and/or
sensorimotor capabilities, which still lacks an effective treatment. Tissue engineering in the …

Electroencephalography (EEG) has long been studied as a potential diagnostic method for
Alzheimer's disease (AD). The pathological progression of AD leads to cortical …

Many qualitative features of the emerging collective dynamics in neuronal networks, such as
correlated activity, stability, response to inputs, and chaotic and regular behavior, can be …

As a schematic model of the complexity economic agents are confronted with, we introduce
the “Sherrington-Kirkpatrick game,” a discrete time binary choice model inspired from mean …

In neural circuits, synaptic strengths influence neuronal activity by sha** network
dynamics, and neuronal activity influences synaptic strengths through activity-dependent …

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 …

Naturalistic animal behavior exhibits a strikingly complex organization in the temporal
domain, with variability arising from at least three sources: hierarchical, contextual, and …

Neural networks are high-dimensional nonlinear dynamical systems that process
information through the coordinated activity of many connected units. Understanding how …

Recurrent neural networks (RNNs) are powerful dynamical models, widely used in machine
learning (ML) and neuroscience. Prior theoretical work has focused on RNNs with additive …