Characterizing the complexity of brain and mind networks

Gorka Zamora-López, Eleonora Russo, Pablo M. Gleiser, Changsong ZHOU*, Jürgen Kurths

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

15 Citations (Scopus)


Recent studies of brain connectivity and language with methods of complex networks have revealed common features of organization. These observations open a window to better understand the intrinsic relationship between the brain and the mind by studying how information is either physically stored or mentally represented. In this paper, we review some of the results in both brain and linguistic networks, and we illustrate how modelling approaches can serve to comprehend the relationship between the structure of the brain and its function. On the one hand, we show that brain and neural networks display dynamical behaviour with optimal complexity in terms of a balance between their capacity to simultaneously segregate and integrate information. On the other hand, we show how principles of neural organization can be implemented into models of memory storage and recognition to reproduce spontaneous transitions between memories, resembling phenomena of memory association studied in psycholinguistic experiments.

Original languageEnglish
Pages (from-to)3730-3747
Number of pages18
JournalPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences
Issue number1952
Publication statusPublished - 13 Oct 2011

Scopus Subject Areas

  • Mathematics(all)
  • Engineering(all)
  • Physics and Astronomy(all)

User-Defined Keywords

  • Brain networks
  • Complexity
  • Free association
  • Memory latching
  • Semantic networks


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