Open AccessComplexity matching in neural networks
Author(s) -
Javad Usefie Mafahim,
David Lambert,
Marzieh Zare,
Paolo Grigolini
Publication year - 2015
Publication title -
new journal of physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.584
H-Index - 190
ISSN - 1367-2630
DOI - 10.1088/1367-2630/17/1/015003
Subject(s) - criticality , randomness , physics , statistical physics , self organized criticality , artificial neural network , matching (statistics) , topology (electrical circuits) , computer science , artificial intelligence , mathematics , statistics , combinatorics , nuclear physics
This article adopts the complexity matching principle that the maximal efficiency of communication between two complex networks is realized when both of them are at criticality, and uses this principle to establish the value of the neuronal interaction strength at which criticality occurs