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arXiv · 1007.5022

Percolation in living neural networks

Ilan Breskin·Jordi Soriano·Elisha Moses·Tsvi Tlusty
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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

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Ilan Breskin, Jordi Soriano, Elisha Moses, Tsvi Tlusty. 2010-07-28. Percolation in living neural networks. https://doi.org/10.1103/physrevlett.97.188102

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