Order symmetry breaking and broad distribution of events in spiking neural networks with continuous membrane potential

TL;DR

This paper introduces an exactly solvable continuous membrane potential LIF neuron model, revealing how order symmetry breaking leads to chaotic dynamics and scale-free avalanches in neural networks.

Contribution

It presents a novel integrable LIF model with continuous membrane potential and demonstrates how order symmetry breaking causes complex chaotic activity in neural networks.

Findings

Chaotic dynamics emerge at certain coupling strengths.

Order symmetry breaking correlates with broad activity distributions.

Scale-free avalanches are observed in the network activity.

Abstract

We introduce an exactly integrable version of the well-known leaky integrate-and-fire (LIF) model, with continuous membrane potential at the spiking event, the c-LIF. We investigate the dynamical regimes of a fully connected network of excitatory c-LIF neurons in the presence of short-term synaptic plasticity. By varying the coupling strength among neurons, we show that a complex chaotic dynamics arises, characterized by scale free avalanches. The origin of this phenomenon in the c-LIF can be related to the order symmetry breaking in neurons spike-times, which corresponds to the onset of a broad activity distribution. Our analysis uncovers a general mechanism through which networks of simple neurons can be attracted to a complex basin in the phase space.