Quorum percolation in living neural networks
Cohen, O.; Keselman, A.; Moses, E.; Rodríguez Martínez, M.; Soriano, J.; Tlusty, T.; Cohen, O.; Department of Physics of Complex Systems, Weizmann Institute of Science - Rehovot 76100, Israel; Keselman, A.; Department of Physics of Complex Systems, Weizmann Institute of Science - Rehovot 76100, Israel; Moses, E.; Department of Physics of Complex Systems, Weizmann Institute of Science - Rehovot 76100, Israel; Rodríguez Martínez, M.; Department of Physics of Complex Systems, Weizmann Institute of Science - Rehovot 76100, Israel; Soriano, J.; Department of Physics of Complex Systems, Weizmann Institute of Science - Rehovot 76100, Israel; Departament d'ECM. Facultat de Física, Universitat de Barcelona - Av. Diagonal 647, 08028 Barcelona, Spain, EU; Tlusty, T.; Department of Physics of Complex Systems, Weizmann Institute of Science - Rehovot 76100, Israel
Журнал:
EPL (Europhysics Letters)
Дата:
2010-01-01
Аннотация:
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 p<sub>k</sub> from different neural densities.
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