NDLI: Basic theories for neuroinformatics and neurocomputing

Content Provider	IEEE Xplore Digital Library
Author	Yingxu Wang
Copyright Year	2013
Description	Author affiliation: Int. Inst. of Cognitive Inf. & Cognitive Comput. (ICIC), Univ. of Calgary, Calgary, AB, Canada (Yingxu Wang)
Abstract	Summary form only given. A fundamental challenge for almost all scientific disciplines is to explain how natural intelligence is generated by physiological organs and what the logical model of the brain is beyond its neural architectures. According to cognitive informatics and abstract intelligence, the exploration of the brain is a complicated recursive problem where contemporary denotational mathematics is needed to efficiently deal with it. Cognitive psychology and medical science were used to explain that the brain works in a certain way based on empirical observations on related activities in usually overlapped brain areas. However, the lack of precise models and rigorous causality in brain studies has dissatisfied the formal expectations of researchers in computational intelligence and mathematics, because a computer, the logical counterpart of the brain, might not be explained in such a vague and empirical approach without the support of formal models and rigorous means. In order to fonnally explain the architectures and functions of the brain, as well as their intricate relations and interactions, systematic models of t he brain are s ought for revealing the principles and mechanisms of the brain at the neural, physiological, cognitive, and logical (abstract) levels. Cognitive and brain informatics investigate into the brain via not only inductive syntheses through these four cognitive levels from the bottom up in order to form theories based on empirical observations, but also deductive analyses from the top down in order to explain various functional and behavioral instances according to the abstract intelligence theory. This keynote lecture presents systematic models of the brain from the facets of cognitive informatics, abstract intelligence, brain Informatics, neuroinformatics, and cognitive psychology. A logical model of the brain is introduced that maps the cognitive functions of the brain onto its neural and physiological architectures. This work leads to a coherent abstract intelligence theory based on both denotational mathematical models and cognitive psychology observations, which rigorously explains the underpinning principles and mechanisms of the brain. On the basis of the abstract intelligence theories and the logical models of the brain, a comprehensive set of cognitive behaviors as identified in the Layered Reference Model of the Brain (LRMB) such as perception, inference and learning can be rigorously explained and simulated.The logical model of the brain and the abstract intelligence theory of natural intelligence will enable the development of cognitive computers that perceive, think and learn. The functional and theoretical difference between cognitive computers and classic computers are that the latter are data processors based on Boolean algebra and its logical counterparts; while the former are knowledge processors based on contemporary denotational mathematics. A wide range of applications of cognitive computers have been developing in ICIC and my laboratory such as, inter alia, cognitive robots, cognitive learning engines, cognitive Internet, cognitive agents, cognitive search engines, cognitive translators, cognitive control systems, and cognitive automobiles.
Starting Page	3
Ending Page	4
File Size	145041
Page Count	2
File Format	PDF
e-ISBN	9781479907830
DOI	10.1109/ICCI-CC.2013.6622217
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Abstracts Brain modeling Computational modeling Data models Engines Informatics Lead applications Cognitive informatics cognitive computing abstract intelligence denotational mathematics cognitive computers knowledge processors inference engines learning engines perception engines computational intelligence
Content Type	Text
Resource Type	Article

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