Quorum percolation in living neural networks
Department of Physics of Complex Systems, Weizmann Institute of Science - Rehovot 76100, Israel
2 Departament d'ECM. Facultat de Física, Universitat de Barcelona - Av. Diagonal 647, 08028 Barcelona, Spain, EU
Corresponding author: firstname.lastname@example.org
Accepted: 16 December 2009
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.
PACS: 87.19.L- – Neuroscience / 87.19.ll – Models of single neurons and networks / 64.60.ah – Percolation
© EPLA, 2010