Using a generalized random recurrent neural network model, and by extending our recently
developed mean-field approach [J. Aljadeff, M. Stern, and T. Sharpee, Phys. Rev. Lett. 114, …

Dimension reduction is a common strategy to study nonlinear dynamical systems composed
by a large number of variables. The goal is to find a smaller version of the system whose …

The structure in cortical microcircuits deviates from what would be expected in a purely
random network, which has been seen as evidence of clustering. To address this issue, we …

Mean-field theory for networks of spiking neurons based on the so-called diffusion
approximation has been used to calculate certain measures of neuronal activity which can be …

In recurrent networks of leaky integrate-and-fire (LIF) neurons, mean-field theory has proven
successful in describing various statistical properties of neuronal activity at equilibrium, …

In this issue we collect the advances published on March 6, April 14, May 22 and June 22,
together with materials received and composed during July. In each section, We have …

The temporo-spatial relationship between the three organs (lung, spleen and lymph node)
involved during the initial stages of Mycobacterium tuberculosis infection has been poorly …

Structure is fundamental in shaping the types of computations that neuronal circuits can
perform. Explaining the laws that determine the connectivity properties of brain networks and …

The complexity inherent to large, non-linear dynamical systems of interacting units (ecological
communities, neuronal assemblies, etc.) makes them very difficult to study. One of the big …

Background The study of the nervous system underwent a profound transformation after the
work of Camillo Golgi and Santiago Ramón y Cajal during the late nineteenth century. Golgi …