Fractional cyber-neural systems -- a brief survey

TL;DR

This survey reviews the role of fractional-order dynamical systems in modeling cyber-neural systems, highlighting their advantages and outlining key analysis, control, and application aspects for future neurotechnology development.

Contribution

It provides a comprehensive overview of fractional CNS, including foundational definitions, analysis methods, and potential applications, emphasizing their importance in advancing neurotechnology.

Findings

Fractional CNS effectively models neural long-range memory.

Fractional systems enhance system identification and control.

Future research directions include improved modeling and control techniques.

Abstract

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. Ultimately, advancements in these three areas will be critical in developing the next generation of CNS, which will, ultimately, improve people's quality of life.