IEEE Systems, Man and Cybernetics Magazine - January 2020 - 11

Table 1. The themes of the Technical Committee on Brain-Inspired Cognitive Systems.
Autonomous Systems

Cognitive Systems

Cybernetic Systems

Natural intelligent systems

Cognitive computers

Cognitive cybernetics

Big neural data systems

Computational intelligent systems

Cognitive IoT

Neuroinformatic systems

Cognitive machine learning

Cognitive robots

Neural knowledge bases

Cognitive sensors and networks

Hybrid human-machine societies

Neural image processing systems

Machine knowledge learning systems

Cognitive informatics

Symbiotic and collective intelligent
systems

Natural language comprehension
systems

Human-machine sharable knowledge
bases

Neurocomputing systems

Intelligent unmanned systems

Human-machine symbiosis

the transdisciplinary studies at levels of neurology, physiology, cognition, and denotational mathematics [2], [3], [10],
[14], [16], [17], [21], [23], [26], [31]-[33].
The Mission and Themes of TC-BCS
The TC-BCSs within the IEEE SMCS were established in
2018. The current challenges to cognitive cybernetics and the
increasing demands for brain-inspired systems in society,
industry, and education have advanced BCS studies toward
integrating hybrid human-machine systems. BCSs provide
an overarching link among the three principle technical
areas of the IEEE SMCS based on the rapid advances of AI,
human-machine systems/societies, brain-inspired computational intelligence, and autonomous/unmanned systems.
The mission of TC-BCSs is to promote interdisciplinary
and transdisciplinary theories, methodologies, best practices, and engineering/societal applications of braininspired and cognitive cybernetic systems. TC-BCSs
synergize transdisciplinary theoretical foundations of system, brain, intelligence, cybernetic, and cognitive sciences,
focusing on hybrid human-machine and brain-inspired
systems, as well as their societal implications.
The themes of TC-BCSs include, but are not limited to, a
rich set of research subjects, as summarized in Table 1.
The research and activities of TC-BCS may be classified
into the categories of autonomous, cognitive, and cybernetic systems across SMC technical areas of cybernetics,
human-machine systems, and systems science and engineering. TC-BCSs will not only cover a coherent theme of
the IEEE SMCS on cognitive cybernetics but also establish
an emerging and unique field of BCSs.
TC-BCSs strive to serve the interests and demands of
researchers, practitioners, policy makers, and the general
public on the frontier of brain-inspired systems. Their strategic goals are to address the cutting-edge studies of
brain-inspired systems in cognitive cybernetics and novel
engineering implementations. The TC-BCSs will strengthen
the well-positioned leadership of SMCS in the strategic fields of brain-inspired, cognitive, autonomous, and
unmanned systems. They will establish active collaboration

among several IEEE Societies including those of the SMCS,
Computer, Computational Intelligence, and Robotics and
Automation, as well as the International Institute of Cognitive Informatics and Cognitive Computing and leading
industrial partners.
Conclusion
It is recognized that the brain and natural intelligence should
be well understood before AI can be developed on a rigorous
basis. BCSs study not only the brain to innovate AI and cognitive systems but also the formal models and rigorous theories
necessary for explaining and simulating the brain. We presented the latest developments in basic studies and engineering
applications of BCSs toward novel human-machine systems
and intelligent cybernetic systems. Establishing TC-BCSs will
enhance BCS research and engineering applications in AI,
computational intelligence, cognitive systems, cognitive computers, cognitive robots, autonomous systems, machine
learning systems, cognitive neural networks, semantic comprehension systems, cognitive IoTs, big data systems, intelligent unmanned systems, self-driving vehicles, and hybrid
human-machine systems.
Acknowledgment
We would like to acknowledge the advice and support from
the IEEE System, Man, and Cybernetics Society vice presidents, including Andreas Nuernberger, Rodney Roberts, and
Vladik Kreinovich, as well as Stuart Rubin for establishing
the technical committee on brain-inspired cognitive systems.
We would also like to thank the late Prof. Mo E. El-Hawary
and IEEE Systems, Man, and Cybernetics Magazine Associate Editor Haibin Zhu for their valuable advice.
About the Authors
Yingxu Wang (yingxu@ucalgary.ca) earned his B.Sc. degree
in electrical engineering from the Tongji (Shanghai Tiedao)
University, China, in 1983 and his Ph.D. degree in computer
science from Nottingham Trent University, United Kingdom,
in 1998. He is a professor of computational intelligence,
cognitive systems, brain science, software science, and
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