Abstract

We analyze simple dynamical network models which describe the limited capacity of nodes to process the input information. For a proper range of their parameters, the information flow pattern in these models is characterized by exponential distribution of the incoming information and a fat-tailed distribution of the outgoing information, as a signature of the law of diminishing marginal returns. We apply this analysis to effective connectivity networks from human EEG signals, obtained by Granger Causality, which has recently been given an interpretation in the framework of information theory. From the distributions of the incoming versus the outgoing values of the information flow it is evident that the incoming information is exponentially distributed whilst the outgoing information shows a fat tail. This suggests that overall brain effective connectivity networks may also be considered in the light of the law of diminishing marginal returns. Interestingly, this pattern is reproduced locally but with a clear modulation: a topographic analysis has also been made considering the distribution of incoming and outgoing values at each electrode, suggesting a functional role for this phenomenon.

Autore Pugliese

• Angelini Leonardo , Stramaglia Sebastiano

Tutti gli autori

• ANGELINI L.;STRAMAGLIA S.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2012

ISSN

1932-6203

ISBN

Non Disponibile

Numero di citazioni Wos

17

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

15

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile