The extensible Data-Brain model: Architecture, applications and directions

• Published in: Journal of computational science 2020-10, Vol.46, p.101103, Article 101103
• Main Authors: Kuai, Hongzhi, Zhong, Ning
• Format: Article
• Language: English
• Subjects: Artificial intelligence (AI); Brain big data; Brain computing; Brain informatics; Data-Brain; Intelligence systems; Web intelligence (WI)
• ISSN: 1877-7503; 1877-7511
• DOI: 10.1016/j.jocs.2020.101103

•The challenges, solutions and directions of brain computing in the connected world.•A Brain Informatics based extensible Data-Brain model for systematic brain computing.•The knowledge-information-data architecture to organize brain big data.•An integrated K-I-D loop to carry out the systematic brain investigation.•The iteration and evolution of model through the never-ending learning. One of the key ideas in realizing human-like intelligence is to understand information-processing mechanisms in the human brain. Brain Informatics is a rapidly expanding interdisciplinary field to systematically utilize brain-related data, information and knowledge coming from the entire research process for in-depth brain investigation. In the past few years, a data-centric conceptual brain model, namely Data-Brain, has been proposed, providing the foundation for the systematic Brain Informatics methodology. The Data-Brain model constitutes a conceptual framework and detailed guideline for managing and analyzing brain big data. The development of Data-Brain model also demands the support from advanced technologies. This paper presents an extensible version of the Data-Brain with advanced computing techniques in the connected world. It provides a global understanding of how multidisciplinary techniques work together to tackle brain computing challenges. Particularly, the integrated K-I-D (Knowledge-Information-Data) loop is proposed, constructing a cycle as the thinking space to help pursue the systematic brain investigation, by which the extensible Data-Brain model continuously iterates and evolves through the never-ending learning. Such synergistic evolvement will power future progress for building intelligence systems and applications connected with the study of complex human brain.