IEEE Systems, Man and Cybernetics Magazine - January 2020 - 10

CIM, which efficiently reduces the extreme complexity to
explain the structural and functional organs of the brain
and their interactions. CIM and LRMB models provide a
logical model for clarifying a set of conventionally overlapped and even contradicting empirical observations.
Both CIM and LRMB provide a blueprint for the emerging
brain-inspired intelligence computers known as cognitive
computers [29]. These computers transfer traditional data
processing to knowledge processing, enabling the next generation of computing machines to fit human needs rather
than requiring humans to fit the machines. The basic studies
based on CIM will lead to cognitive computers that perceive,
think, inference, and learn by mimicking the brain [22], [29].
A Theory of Abstract Intelligence
Underpinning Brain-Inspired Systems
Exploring and modeling the brain is recognized to be a
highly recursive problem that remains a constant challenge
to almost all scientific disciplines. A rigorous study of the
cognitive foundations of natural intelligence may shed light
on the general mechanisms of AI and computational intelligence toward pervasive BCS, explaining how intelligence is
generated from neurophysiological structures.
Intelligence ^ Io h is a human ability or a system function
that transforms information ^ I h into behaviors ^ B h or
knowledge ^ K h as
Io D fb : I " B i fk : I " K .

(4)

Cognitive informatics and cybernetics [22], [28], [31]
reveal that natural intelligence may be explained by a hierarchical framework that maps the brain through the
embodiments at neurological, physiological, cognitive, and
logical layers from the bottom up. On the basis of exploring
the cognitive mechanisms and neurophysiological structures of the brain in "The Neurological and Cognitive Foundations of the Brain" section, models for the general forms
of intelligence and BCSs, which are known as the theory of
aI, may be derived. aI is a mathematical model of the core
properties across all forms of natural intelligence and AI.
In the theory of aI, the hierarchy of cognitive objects
l represented in the human brain is a four-tuple in the
categories of data (D), I, K, and Io from the bottom up,
according to their levels of abstraction:
Z D = fd : O " Q
]
] I = fi : D " S
o
l D _ D, I, K, I i = [
,
]] K = fk : I " C
oI = fio : I " B
\

(5)

where the symbols denote object (O), quantity (Q), semantics (S ), concept (C ), and behavior (B ), respectively.
Let l 0 through l 4 be the hierarchical layers of human
cognitive objects in the brain corresponding to D, I, K,
and Io , respectively. The relationship between the cognitive objects in the brain may be formalized in the following principle.
10

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Janu ar y 2020

Transformability among the cognitive objects,
l = _ D, I, K, Io i, in the brain is a recursive structure S N:
1

n

k=4

i=0

S N = R l k (l k - 1), l 0 = R d iu Ti
= l 4 (l 3 (l 2 (l 1 (l 0))))

= Io a K a I a D a R d i Ti kkkk,
n

i=0

(6)

where the terminal structure at the bottom layer of the
hierarchical framework is given as n-dimensional data,
R in= 0 d iuTi where uTi denotes the ith suffix of data type
for data object d i .
Based on (6), the framework of aI may be embodied by
the four types of intelligence: perceptive ^Io p h, cognitive
^ Io c h, instructive ^Io i h, and reflexive ^ I r h.
Zo
] I p = fp : D " I
] Io c = fc : I " K
aI D _ Io p, Io c, Io i, Io r i = [ o
]] I i = fi : I " B
Io = fr : D " B
\ r

// perceptive
// cognitive
,
// instructive
// reflexive

(7)

where specific forms of cognitive objects and associated
memories in the brain have been defined in (1) and (5), respectively. Behavior (B ) denotes a type of cognitive process that
transfers an abstract stimulus to an observable action.
The aI theory reveals that the structures and configurations of a human memory system may be modeled as being
logically classified into the categories of sensory buffer
memory (SBM), short-term memory (STM), long-term memory (LTM), action buffer memory (ABM), and CSM. Therefore,
certain cognitive entities, i.e., data, information, knowledge,
and intelligence, are embodied in different memories on the
basis of the CMM model as given in (1): SBM ^ D h, STM ^ I h,
LTM ^ K h, CSM ^ Io h, and ABM ^ Io h, respectively.
Based on LRMB, the structural model of the brain
(SMB) as an autonomous intelligence system encompasses
a set of parallel interactions between the natural intelligent system (NIS) and the CMM, as well as the lower structures underpinning them:
/

SMB = NIS < CMM
= (BOS < BApp) < (STM < LTM < SBM < ABM < CSM ),

(8)

where BOS and BApp represents the brain's operating system and applications of NIS, respectively.
SMB reveals the relationship between NIS and CMM,
where NIS serves the natural intelligent engine and CMM is
essential to enable NIS to function properly. In other words,
although memory is not the central power of the brain or
any AI system, it is a necessary foundation due to no memory, no intelligence, according to the aI and LRMB theories.
The aI theory provides a foundation to enable a multidisciplinary enquiry of the brain and intelligence science.
To better explain the architectures and functions of the
brain, as well as their intricate relations and interactions,
systematic logical models become indispensable across



IEEE Systems, Man and Cybernetics Magazine - January 2020

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