Graph theory is now becoming a standard tool in system-level neuroscience. However,
endowing observed brain anatomy and dynamics with a complex network structure does not …

Neurons in the brain represent information in their collective activity. The fidelity of this
neural population code depends on whether and how variability in the response of one …

Neural population equations such as neural mass or field models are widely used to study
brain activity on a large scale. However, the relation of these models to the properties of …

The dimensionality of a network's collective activity is of increasing interest in neuroscience.
This is because dimensionality provides a compact measure of how coordinated network …

In the brain, patterns of neural activity represent sensory information and store it in non‐
random synaptic connectivity. A prominent theoretical hypothesis states that assemblies …

There is an increasing expectation that advanced, computationally expensive machine
learning (ML) techniques, when applied to large population‐wide neuroimaging datasets …

The neural dynamics generating sensory, motor, and cognitive functions are commonly
understood through field theories for neural population activity. Classic neural field theories …

The Wilson–Cowan equations represent a landmark in the history of computational
neuroscience. Along with the insights Wilson and Cowan offered for neuroscience, they …

An essential step toward understanding neural circuits is linking their structure and their
dynamics. In general, this relationship can be almost arbitrarily complex. Recent theoretical …

The interpretation of neuronal spike train recordings often relies on abstract statistical
models that allow for principled parameter estimation and model selection but provide only …