Towards the Synergy of Cognitive Informatics, Neural Informatics, Brain Informatics, and Cognitive Computing

Towards the Synergy of Cognitive Informatics, Neural Informatics, Brain Informatics, and Cognitive Computing

Yingxu Wang (University of Calgary, Canada)
DOI: 10.4018/jcini.2011010105
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Abstract

The contemporary wonder of sciences and engineering recently refocused on the starting point: how the brain processes internal and external information autonomously rather than imperatively as those of conventional computers? This paper explores the interplay and synergy of cognitive informatics, neural informatics, abstract intelligence, denotational mathematics, brain informatics, and computational intelligence. A key notion recognized in recent studies in cognitive informatics is that the root and profound objective in natural, abstract, and artificial intelligence, and in cognitive informatics and cognitive computing, is to seek suitable mathematical means for their special needs. A layered reference model of the brain and a set of cognitive processes of the mind are systematically developed towards the exploration of the theoretical framework of cognitive informatics. A wide range of applications of cognitive informatics and denotational mathematics are recognized in the development of highly intelligent systems such as cognitive computers, cognitive knowledge search engines, autonomous learning machines, and cognitive robots.
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1. Introduction

Cognitive informatics studies the natural intelligence and the brain from a theoretical and a computational approach, which rigorously explains the mechanisms of the brain by a fundamental theory known as abstract intelligence, which formally models the brain by contemporary denotational mathematics. The contemporary wonder of sciences and engineering has recently refocused on the starting point of them: how the brain processes internal and external information autonomously and cognitively rather than imperatively as those of conventional computers? The latest advances and engineering applications of CI have led to the emergence of cognitive computing and the development of cognitive computers that perceive, learn, and reason (Wang, 2006, 2009d, 2009f, 2010a, 2011). CI has also fundamentally contributed to autonomous agent systems (Wang, 2009a) and cognitive robots (Wang, 2010a). A wide range of applications of CI are identified such as in the development of cognitive computers, cognitive robots, cognitive agent systems, cognitive search engines, cognitive learning systems, and artificial brains. The work in CI may also lead to a fundamental solution to computational linguistics, Computing with Natural Language (CNL), and Computing with Words (CWW) (Zadeh, 1975, 1999).

Cognitive Informatics is a term coined by Wang in the first IEEE International Conference on Cognitive Informatics (ICCI 2002) (Wang, 2002a). Cognitive informatics (Wang, 2002a, 2003, 2007b; Wang & Wang, 2006; Wang & Kinsner, 2006; Wang, Jonston, & Smith, 2002; Wang, Wang, Patel, & Patel, 2006; Wang, Zhang, Latombe, & Kinsner, 2008; Wang, Kinsner, & Zhang, 2009; Wang, Zhang, & Tsumoto, 2009; Wang et al., 2009, Wang & Chiew, 2010) studies the natural intelligence and the brain from a theoretical and a computational approach, which rigorously explains the mechanisms of the brain by a fundamental theory known as abstract intelligence. Cognitive informatics formally models the brain by contemporary denotational mathematics such as concept algebra (Wang, 2008b), real-time process algebra (RTPA) (Wang, 2002b, 2008d), system algebra (Wang, 2008c; Wang, Zadeh, & Yao, 2009), and visual semantic algebra (VSA) (Wang, 2009e). The latest advances in CI have led to a systematic solution for explaining brain informatics and the future generation of intelligent computers.

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