Self Organized Criticality in a Mesoscopic Model of Excitatory-Inhibitory Neuronal Populations by Short-term and Long-term Synaptic Plasticity

TL;DR

This paper investigates how neural networks self-organize near a critical bifurcation point using a mesoscopic model that incorporates synaptic plasticity mechanisms, explaining scale-free activity patterns observed in the brain.

Contribution

It introduces a stochastic neural field model that demonstrates self-organization near a Bogdanov-Takens bifurcation through synaptic plasticity, linking microscopic plasticity to macroscopic criticality.

Findings

Network self-tunes to the bifurcation point via plasticity mechanisms.

The mesoscopic model aligns with directed percolation at the phase transition.

Scale-free avalanches emerge from the self-organized criticality.

Abstract

In [1], we have shown that the dynamics of an interconnected population of excitatory and inhibitory spiking neurons wandering around a Bogdanov-Takens (BT)bifurcation point can generate the observed scale-free avalanches at the population level and the highly variable spike patterns of individual neurons. These characteristics match experimental findings for spontaneous intrinsic activity in the brain. In this paper, we address the mechanisms causing the system to get and remain near this BT point. We propose an effective stochastic neural field model which captures the dynamics of the mean-field model. We show how the network tunes itself through local long-term synaptic plasticity by STDP and short-term synaptic depression to be close to this bifurcation point. The mesoscopic model that we derive matches the directed percolation model at the absorbing state phase transition.