  The dynamics of neural networks is often characterized by collective behavior
and quasi-synchronous events, where a large fraction of neurons fire in short
time intervals, separated by uncorrelated firing activity. These global
temporal signals are crucial for brain functioning. They strongly depend on the
topology of the network and on the fluctuations of the connectivity. We propose
a heterogeneous mean--field approach to neural dynamics on random networks,
that explicitly preserves the disorder in the topology at growing network
sizes, and leads to a set of self-consistent equations. Within this approach,
we provide an effective description of microscopic and large scale temporal
signals in a leaky integrate-and-fire model with short term plasticity, where
quasi-synchronous events arise. Our equations provide a clear analytical
picture of the dynamics, evidencing the contributions of both periodic (locked)
and aperiodic (unlocked) neurons to the measurable average signal. In
particular, we formulate and solve a global inverse problem of reconstructing
the in-degree distribution from the knowledge of the average activity field.
Our method is very general and applies to a large class of dynamical models on
dense random networks.
