Spiking hierarchy in an adaptive exponential integrate-and-fire network synchronization

TL;DR

This study reveals a hierarchical spiking order in adaptive exponential integrate-and-fire neural networks, influenced by initial potentials, network topology, and stimuli, highlighting the layered propagation of neural activity.

Contribution

It uncovers a hierarchical spiking pattern and the factors influencing neuron spiking order within adaptive exponential integrate-and-fire networks.

Findings

Neurons form a layered hierarchy with primary neurons spiking first.

Spiking order within layers depends on stimuli received from upper layers.

Less connected neurons tend to spike earlier when stimuli are similar.

Abstract

Neuronal network synchronization has received wide interests. Network connection structure is known to play a key role in its synchronization. In the present manuscript, we study the influence of initial membrane potentials together with network topology on bursting synchronization, in particular the sequential spiking order in stabilized inter bursts. We find a hierarchical phenomenon on spiking order. We grade neurons into different layers: primary neurons are those initiate the spike and lower-layer neurons are then determined via network connections successively. This constructs a directed graph to indicate spiking propagation among different layers of neurons. Neurons in upper layers spike earlier than those in lower layers. More interestingly, we find that among the same layer, neuron spiking order is mainly associated with stimuli they receive from upper layer via network connections; more stimuli leads to earlier spiking. Furthermore, we find that with effectively the same stimuli from upper layer, neurons with less connections spike earlier.