Quorum percolation in living neural networks

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...

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Bibliographic Details
Published in:Europhysics letters 2010-01, Vol.89 (1), p.18008
Main Authors: Cohen, O, Keselman, A, Moses, E, Rodríguez Martínez, M, Soriano, J, Tlusty, T
Format: Article
Language:English
Subjects:
64.60.ah
87.19.L
87.19.ll
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Summary: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 ${\bar{k}} $ and the distribution of connections pk from different neural densities.
ISSN:0295-5075
1286-4854
DOI:10.1209/0295-5075/89/18008