Abstract
Cooperative effects in neural networks appear because a neuron fires only if a minimal number m>1 of its inputs are excited. The multiple inputs requirement leads to a percolation model termed quorum percolation. The connectivity undergoes a phase transition as m grows, from a network-spanning cluster at low m to a set of disconnected clusters above a critical m. Both numerical simulations and the model reproduce the experimental results well. This allows a robust quantification of biologically relevant quantities such as the average connectivity 
and the distribution of connections pk from different neural densities.