Fractional cyber-neural systems — A brief survey

Emily Reed*, Sarthak Chatterjee, Guilherme Ramos, Paul Bogdan, S.D. Gonçalves Melo Pequito

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

4 Citations (Scopus)
4 Downloads (Pure)


Neurotechnology has made great strides in the last 20 years. However, we still have a long way to go to commercialize many of these technologies as we lack a unified framework to study cyber-neural systems (CNS) that bring the hardware, software, and the neural system together. Dynamical systems play a key role in developing these technologies as they capture different aspects of the brain and provide insight into their function. Converging evidence suggests that fractional-order dynamical systems are advantageous in modeling neural systems because of their compact representation and accuracy in capturing the long-range memory exhibited in neural behavior. In this brief survey, we provide an overview of fractional CNS that entails fractional-order systems in the context of CNS. In particular, we introduce basic definitions required for the analysis and synthesis of fractional CNS, encompassing system identification, state estimation, and closed-loop control. Additionally, we provide an illustration of some applications in the context of CNS and draw some possible future research directions. Advancements in these three areas will be critical in developing the next generation of CNS, which will, ultimately, improve people's quality of life.

Original languageEnglish
Pages (from-to)386-408
JournalAnnual Reviews in Control
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Cyber-neural systems
  • Fractional-order systems
  • Neurotechnology

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