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Percolation in Living Neural Networks
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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 an exponent β ≃ 0.65 . β 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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BRESKIN, Ilan, et al. Percolation in Living Neural Networks. Physical Review Letters. 2006. Vol. 97, num. 18, pags. 188102. ISSN 0031-9007. [consulted: 2 of June of 2026]. Available at: https://hdl.handle.net/2445/135297