title: 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 an exponent beta approximately or = 0.65. Beta is independent of the balance between excitatory and inhibitory neurons and indicates that the degree distribution is Gaussian rather than scale free.
authors.affiliation: Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel.
authors.forename: Ilan
authors.initials: I
authors.lastname: Breskin
authors.forename: Jordi
authors.initials: J
authors.lastname: Soriano
authors.forename: Elisha
authors.initials: E
authors.lastname: Moses
authors.forename: Tsvi
authors.initials: T
authors.lastname: Tlusty
mesh_terms: D000818:Animals; D002478:Cells, Cultured; D004558:Electric Stimulation; D009415:Nerve Net; D009474:Neurons / Q000502:physiology*; D051381:Rats; D009435:Synaptic Transmission / Q000502:physiology*