Antonio Politi

The paper demonstrates chaotic synchronization in a mean-field model of two heterogeneous populations of pulse-coupled oscillators with adaptive coupling strength.

The paper proposes a robust nonlinear balancing mechanism for spiking neural networks that maintains stability in the absence of strong external currents.

This paper analyzes dynamical regimes in balanced neural networks using QIF neurons, showing that asynchronous activity or periodic oscillations can emerge depending on parameters, with mean field models capturing these behaviors.

The paper examines how finite pulse-widths affect collective dynamics in neuronal networks, finding that wider inhibitory pulses can lead to full synchrony.

The paper analyzes the stability of synchronous states in neuronal networks with inhibitory and excitatory neurons, finding that stability depends on the relative pulse-width.

The paper analyzes asynchronous neural activity in a sparse network of leaky integrate-and-fire neurons, showing that strong synaptic coupling leads to bursting activity with long-tailed interspike intervals.

The paper studies neural networks of leaky integrate-and-fire neurons, finding collective irregular dynamics as a distinct thermodynamic phase.

The paper shows that balanced networks of spiking neurons exhibit collective irregular dynamics in the thermodynamic limit under various conditions.