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arXiv · 1007.5143

Quorum Percolation in Living Neural Networks

Or Cohen·Anna Keselman·Elisha Moses·María Rodríguez Martínez·Jordi Soriano·Tsvi Tlusty
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Abstract

Cooperative effects in neural networks appear because a neuron fires only if a minimal number $m$ of its inputs are excited. The multiple inputs requirement leads to a percolation model termed {\it 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 $\kbar$ and the distribution of connections $p_k$

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Or Cohen, Anna Keselman, Elisha Moses, María Rodríguez Martínez, Jordi Soriano, Tsvi Tlusty. 2010-07-29. Quorum Percolation in Living Neural Networks. https://doi.org/10.1209/0295-5075%2F89%2F18008

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