  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
