Percolation in living neural networks

TL;DR

This study investigates how neural connectivity in living networks disintegrates through a percolation transition, revealing a universal critical behavior independent of neuron type balance and suggesting a Gaussian degree distribution.

Contribution

It demonstrates a percolation transition in living neural networks using graph theory, highlighting universal critical exponents and the nature of the degree distribution.

Findings

Connectivity undergoes a percolation transition as synaptic strength decreases.

The critical exponent for the transition is approximately 0.65.

Degree distribution is Gaussian, not scale-free.

Abstract

We study living neural networks by measuring the neurons' response to a global electrical stimulation. Neural connectivity is lowered by reducing the synaptic strength, chemically blocking neurotransmitter receptors. We use a graph-theoretic approach to show that the connectivity undergoes a percolation transition. This occurs as the giant component disintegrates, characterized by a power law with critical exponent $\beta \simeq 0.65$ is independent of the balance between excitatory and inhibitory neurons and indicates that the degree distribution is gaussian rather than scale free