Highlights•Relating network connectivity to dynamics poses a serious theoretical
challenge.•Network science concepts may have limited relevance in a neuroscience …

As in many naturally formed networks, the brain exhibits an inherent modular architecture
that is the basis of its rich operability, robustness, and integration-segregation capacity …

Sequences of neural activity arise in many brain areas, including cortex, hippocampus, and
central pattern generator circuits that underlie rhythmic behaviors like locomotion. While …

A cornerstone of theoretical neuroscience is the circuit model: a system of equations that
captures a hypothesized neural mechanism. Such models are valuable when they give rise …

This article is part of the Physical Review Research collection titled Physics of
Neuroscience. Neural computation in biological and artificial networks relies on the …

Combinatorial threshold-linear networks (CTLNs) are a special class of inhibition-dominated
TLNs defined from directed graphs. Like more general TLNs, they display a wide variety of …

Threshold-linear networks (TLNs) are models of neural networks that consist of simple,
perceptron-like neurons and exhibit nonlinear dynamics determined by the network's …

Goal-driven selective attention (GDSA) refers to the brain's function of prioritizing the activity
of a task-relevant subset of its overall network to efficiently process relevant information …

The asymptotic phase θ of an initial point x in the stable manifold of a limit cycle (LC)
identifies the phase of the point on the LC to which the flow φt (x) converges as t→∞. The …

Oscillations in the brain are one of the most ubiquitous and robust patterns of activity and
correlate with various cognitive phenomena. In this work, we study the existence and …