Percolation in living neural networks

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

PACS numbers: 87.18.Sn, 87.19.La, 64.60.Ak http://www.weizmann.ac.il/complex/tlusty/papers/PhysRevLett2006.pdf

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