Spurious self-feedback of mean-field predictions inflates infection curves.

The susceptible-infected-recovered (SIR) model and its variants form the foundation of our understanding of the spread of diseases. Here, each agent can be in one of three states (susceptible, infected, or recovered), and transitions between these states follow a stochastic process. The probability of an agent becoming infected depends on the number of its infected neighbors, hence all agents are correlated.

Changes in maerl-associated macroalgal community dynamics as evidence of anthropogenic pressure.

Background And Aims: Maerl-associated communities have received considerable attention due to their uniqueness, biodiversity and functional importance. Although the impacts of human activities are well documented for maerl-associated macrofauna, the spatio-temporal variations of macroalgae have comparatively been neglected, and the drivers that influence their dynamics are poorly known. We investigate the links between maerl-associated macroalgal communities, anthropogenic pressures and environmental conditions, and hypothesize that sites under human pressure would exhibit different dynamics when compared to reference sites.

NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models.

Mean-field theory of neuronal networks has led to numerous advances in our analytical and intuitive understanding of their dynamics during the past decades. In order to make mean-field based analysis tools more accessible, we implemented an extensible, easy-to-use open-source Python toolbox that collects a variety of mean-field methods for the leaky integrate-and-fire neuron model. The Neuronal Network Mean-field Toolbox (NNMT) in its current state allows for estimating properties of large neuronal networks, such as firing rates, power spectra, and dynamical stability in mean-field and linear response approximation, without running simulations.

Erratum: Self-consistent formulations for stochastic nonlinear neuronal dynamics [Phys. Rev. E 101, 042124 (2020)].

This corrects the article DOI: 10.1103/PhysRevE.101.

Gell-Mann-Low Criticality in Neural Networks.

Criticality is deeply related to optimal computational capacity. The lack of a renormalized theory of critical brain dynamics, however, so far limits insights into this form of biological information processing to mean-field results. These methods neglect a key feature of critical systems: the interaction between degrees of freedom across all length scales, required for complex nonlinear computation.