Coherent periodic activity in excitatory Erdos-Renyi neural networks:The role of network connectivity

TL;DR

This paper investigates how network connectivity influences the emergence of coherent periodic activity in excitatory Erdős-Rényi neural networks, revealing a critical average connectivity for synchronization and effects of synapse reliability.

Contribution

It introduces a scaling parameter for network connectivity in Erdős-Rényi models and analyzes the transition between asynchronous and synchronized states, highlighting the role of connectivity and synapse reliability.

Findings

Critical average connectivity for synchronization saturates in large networks.

Unreliable synapses require lower connectivity to induce macroscopic activity.

Disorder-induced chaos diminishes as network size increases, leading to regular dynamics.

Abstract

We consider an excitatory random network of leaky integrate-and-fire pulse coupled neurons. The neurons are connected as in a directed Erd\"os-Renyi graph with average connectivity $<k>$ scaling as a power law with the number of neurons in the network. The scaling is controlled by a parameter $\gamma$, which allows to pass from massively connected to sparse networks and therefore to modify the topology of the system. At a macroscopic level we observe two distinct dynamical phases: an Asynchronous State (AS) corresponding to a desynchronized dynamics of the neurons and a Partial Synchronization (PS) regime associated with a coherent periodic activity of the network. At low connectivity the system is in an AS, while PS emerges above a certain critical average connectivity $<k>_c$. For sufficiently large networks, $<k>_c$ saturates to a constant value suggesting that a minimal average connectivity is sufficient to observe coherent activity in systems of any size irrespectively of the kind of considered network: sparse or massively connected. However, this value depends on the nature of the synapses: reliable or unreliable. For unreliable synapses the critical value required to observe the onset of macroscopic behaviors is noticeably smaller than for reliable synaptic transmission. Due to the disorder present in the system, for finite number of neurons we have inhomogeneities in the neuronal behaviors, inducing a weak form of chaos, which vanishes in the thermodynamic limit. In such a limit the disordered systems exhibit regular (non chaotic) dynamics and their properties correspond to that of a homogeneous fully connected network for any $\gamma$-value. Apart for the peculiar exception of sparse networks, which remain intrinsically inhomogeneous at any system size.