Fractal geometry meets computational intelligence: future perspectives

Lorenzo Livi, Alireza Sadeghian, Antonio Di Ieva

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

Abstract

Characterizations in terms of fractals are typically employed for systems with complex and multi-scale descriptions. A prominent example of such systems is provided by the human brain, which can be idealized as a complex dynamical system made of many interacting subunits. The human brain can be modeled in terms of observable variables together with their spatio-temporal-functional relations. Computational Intelligence is a research field bridging many nature-inspired computational methods, such as artificial neural networks, fuzzy systems, and evolutionary and swarm intelligence optimization techniques. Typical problems faced by means of Computational Intelligence methods include those of recognition, such as classification and prediction. Although historically conceived to operate in some vector space, such methods have been recently extended to the so-called non-geometric spaces, considering labeled graphs as the most general example of such patterns. Here we suggest that fractal analysis and Computational Intelligence methods can be exploited together in neuroscience research. Fractal characterizations can be used to (i) assess scale-invariant properties and to (ii) offer numeric, feature-based representations to complement the usually more complex pattern structures encountered in neurosciences. Computational Intelligence methods could be used to exploit such fractal characterizations, considering also the possibility to perform data-driven analysis of non-geometric input spaces, hence overcoming the intrinsic limits related to Euclidean geometry.
LanguageEnglish
Title of host publicationThe Fractal geometry of the brain
EditorsAntonio Di Ieva
Place of PublicationNew York
PublisherSpringer, Springer Nature
Pages567-580
Number of pages14
ISBN (Print)9781493939954
DOIs
Publication statusPublished - 2016

Publication series

NameSpringer Series in Computational Neuroscience
PublisherSpringer

Fingerprint

Fractal Geometry
Computational Intelligence
Fractal
Neuroscience
Complex Dynamical Systems
Fractal Analysis
Euclidean geometry
Swarm Intelligence
Scale Invariant
Numerics
Data-driven
Fuzzy Systems
Computational Methods
Optimization Techniques
Vector space
Artificial Neural Network
Complement
Prediction
Graph in graph theory
Human

Keywords

  • computational intelligence
  • complex systems
  • analysis of non-geometric input spaces
  • fractal analysis
  • brain research

Cite this

Livi, L., Sadeghian, A., & Di Ieva, A. (2016). Fractal geometry meets computational intelligence: future perspectives. In A. Di Ieva (Ed.), The Fractal geometry of the brain (pp. 567-580). (Springer Series in Computational Neuroscience). New York: Springer, Springer Nature. https://doi.org/10.1007/978-1-4939-3995-4_36
Livi, Lorenzo ; Sadeghian, Alireza ; Di Ieva, Antonio. / Fractal geometry meets computational intelligence : future perspectives. The Fractal geometry of the brain. editor / Antonio Di Ieva. New York : Springer, Springer Nature, 2016. pp. 567-580 (Springer Series in Computational Neuroscience).
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Livi, L, Sadeghian, A & Di Ieva, A 2016, Fractal geometry meets computational intelligence: future perspectives. in A Di Ieva (ed.), The Fractal geometry of the brain. Springer Series in Computational Neuroscience, Springer, Springer Nature, New York, pp. 567-580. https://doi.org/10.1007/978-1-4939-3995-4_36

Fractal geometry meets computational intelligence : future perspectives. / Livi, Lorenzo; Sadeghian, Alireza; Di Ieva, Antonio.

The Fractal geometry of the brain. ed. / Antonio Di Ieva. New York : Springer, Springer Nature, 2016. p. 567-580 (Springer Series in Computational Neuroscience).

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

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Livi L, Sadeghian A, Di Ieva A. Fractal geometry meets computational intelligence: future perspectives. In Di Ieva A, editor, The Fractal geometry of the brain. New York: Springer, Springer Nature. 2016. p. 567-580. (Springer Series in Computational Neuroscience). https://doi.org/10.1007/978-1-4939-3995-4_36