Regular Article

The European Physical Journal B

, 85:358

First online:

Statistical characterization of an ensemble of functional neural networks

  • B. B. M. SilvaAffiliated withInstituto de Física, Universidade Federal da Bahia
  • , J. G. V. MirandaAffiliated withInstituto de Física, Universidade Federal da Bahia
  • , G. CorsoAffiliated withDepartamento de Biofísica e Farmacologia, Centro de Biociências, Universidade Federal do Rio Grande do Norte
  • , M. CopelliAffiliated withDepartamento de Física, Universidade Federal de Pernambuco
  • , N. VasconcelosAffiliated withDepartamento de Sistemas e Computação, Universidade Federal de Campina GrandeInstituto do Cérebro, Universidade Federal do Rio Grande do Norte
  • , S. RibeiroAffiliated withInstituto do Cérebro, Universidade Federal do Rio Grande do Norte
  • , R. F. S. AndradeAffiliated withInstituto de Física, Universidade Federal da Bahia Email author 

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This work uses a complex network approach to analyze temporal sequences of electrophysiological signals of brain activity from freely behaving rats. A network node represents a neuron and a network link is included between a pair of nodes whenever their firing rates are correlated. The framework of time varying graph (TVG) is used to deal with a very large number (>30 000) of time dependent networks, which are set up by taking into account correlations between neuron firing rates in a moving time lag window of suitable width. Statistical distributions for the following network measures are obtained: size of the largest connected cluster, number of edges, average node degree, and average minimal path. We find that the number of networks with highly correlated activity in distinct brain areas has a fat-tailed distribution, irrespective of the behavioral state of the animal. This contrasts with short-tailed distributions for surrogates obtained by shuffling the original data, and reflects the fact that neurons in the neocortex and hippocampus often act in precise temporal coordination. Our results also suggest that functional neuronal networks at the millimeter scale undergo statistically nontrivial rearrangements over time, thus delimitating an empirical constraint for models of brain activity.


Statistical and Nonlinear Physics