Event-driven simulations of a plastic, spiking neural network

TL;DR

This paper models a fully-connected spiking neural network with plasticity, revealing how activity regimes and emergent structures depend on the plasticity parameter through event-driven simulations.

Contribution

It introduces a simulation framework for analyzing plasticity-driven dynamics and emergent structures in spiking neural networks, bridging theoretical predictions and finite-size network behavior.

Findings

Low plasticity leads to noise-dominated activity

High plasticity results in self-sustaining activity

Emergent structures include paths and hubs in transition regions

Abstract

We consider a fully-connected network of leaky integrate-and-fire neurons with spike-timing-dependent plasticity. The plasticity is controlled by a parameter representing the expected weight of a synapse between neurons that are firing randomly with the same mean frequency. For low values of the plasticity parameter, the activities of the system are dominated by noise, while large values of the plasticity parameter lead to self-sustaining activity in the network. We perform event-driven simulations on finite-size networks with up to 128 neurons to find the stationary synaptic weight conformations for different values of the plasticity parameter. In both the low and high activity regimes, the synaptic weights are narrowly distributed around the plasticity parameter value consistent with the predictions of mean-field theory. However, the distribution broadens in the transition region between the two regimes, representing emergent network structures. Using a pseudophysical approach for visualization, we show that the emergent structures are of "path" or "hub" type, observed at different values of the plasticity parameter in the transition region.