OUTDATED (Version 0.95) Systems Neurology (the only objective is My CAREER, only , Eng.EmadFaragHABIB)- Ver 0.95.pdf

Systems Neurology
Functions of the Human Brain
Proposed Study Approaches to the Structure & Functions
of the Human Brain
Based on a Systems & Complexity Perspectives
Systems Neurology Ver 0.95 July 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Eng. Emad Farag HABIB
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Please Note:
This Presentation is NOT a Professional presentation
from a Professional Author at all ! ,
Rather it is exactly the opposite !!
This Presentation is Just a set of “Draft Proposed Ideas” !!!!
Stated just to ease Ideas & Notions Discussions :
And it rises more questions than providing answers,
Intended to be used among those interested, specialists and/or
professionals.
If You happen to be Scientifically interested in such “Inter-disciplinary” Topic : you may find
such presentation useful , specially if you can consult some Specialist to advice which slides
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Presentation ( with Author’s apology for any inconvenience )
( Apology: the “one-page shift” problem in PDF file Format: is still being tackled )

Conclusion ! :
- The Human Brain is a typical Complex System that can be studied by 3 Approaches
matching the Brain’s micro-meso-Macro Scales that comprise its Structural and
Functional Complexity.
- Next 3 slides show Conclusions drawn from the 3 Approaches
- Approach #1 : the micro-scale: Conclusions drawn from Scientific Research Articles
Studying Brain Structural to Functional issues are shown .
- Approach #2 : the meso-scale: Conclusions drawn from “Complexity Profiling
Chart” (CPC) Famous Applications are shown .
- Approach #3 : the Macro-scale: Conclusions drawn from Brain’s “Hypothetical
Constructs” (Behavior vs Dispositions) sorted as a 2-Dimensional “Conceptual
Map”! is shown .

Executive Functions / Memory / Motor/ Emotional Regulation/ Olfactory
Attention/ visual/ sound/ Somatosensory/ Not well understood
Brodmann’s Areas : [ olfaction 34 / auditory 22, 41,42 / visual 17,18,19 / attention 7, 39 /
memory 21,20,37 , 36, 28, 23 / motor 4,6,8, 32 / somatosensory 3,1,2 , 5, 40, 43, 31 /
emotional 38, 11,12, 47,25 , 13 / executive 44,45, 46, 10, 9 ]
Focusing more on Higher Functions :
Hence, Areas-groups are prioritized as follows :
Executive Functions / Emotional Regulation/ Attention/
Memory / visual/ sound/ Olfactory/ Somatosensory/ Motor/ Not well understood
Brodmann’s Areas

"Complexity Profiling Chart" (CPC Ver 1.1): Complexity & Brain Theories & Frameworks Plotted against "CPC" , 20230516
A 1 A 2 A 3 A 4 A 5 A 6 A 7 A 8 A 9 A 10 A 11 A 12 A 13 A 14 A 15 A 16 A 17 A 18
Numerousity
Clustering Diversity Nestedness
ModularityCriticality OptimalityQuantized (μ)
Investigation
Correlation (Info)
Causality Substantiation
Formulation
Structured (M)
Quantized (M)
States(#VRTY)
Subjectivity
Higher Functions
10
>13 digits Meta- Meta- Meta- Meta- Meta- Meta- >13 digits Meta- Meta- Meta- Meta- Meta- Meta- >13 digits >13 digits Meta- Meta-
9
8-12 digits
~Social
Diversity
8-12 digits
Edge
Technolo
gies and
Methodol
Positive
Feedback
Correlatio
n: (incl
Contexte
d by some
Universal-
Law(s)
Mathema
tical
Formulati
on
8-12 digits 8-12 digits
Adaptive
&
Contextu
al
Adapting/
Develop
ment
Balancing
PCT
8
4-7 digits
Compreh
ended
Complex
Clustering
Compreh
ended
Complex
Nestedne
Compreh
ended
SOC: Self-
Organized
Compreh
ended
Complex
Optimalit
4-7 digits
Feedback
Correlatio
n:
(Circural
time-
domain
Solutions,
c(t), ..
Compreh
ended
Functiona
l Macro-
Reinforce
ment
Motivatio
n-
Values,
Beliefs,
incl
Affiliative
7.1 ± 2
7
3 digits
Existing
Complex
Clustering
/Emergen
Neuronal
Diversity:
incl (n
Neurons
Existing
Complex
Nestedne
ss:
Integrativ
e (plus
a/m)
Existing
Complex
Criticality
(but fairly-
Existing
Complex
Optimalit
y (but
3 digits
Modified/
Customiz
ed/
Tailored/
Direct
Causality
Correlatio
n: incl
Effective
Functiona
l
Causality,
Dynamica
l-systems
Formulati
on,
Analytical
Formulati
on
Existing
Topology,
Fully-
Structure
3 digits 3 digits
Process
Motivatio
n-
Theories
MSG(Thin
king
Styles),
Managing
6
2 digits
Advanced
Modularit
y ( incl
cross-
2 digits
Cause-
Effect:
Incl Direct
Causally ,
2 digits 2 digits
Content
Motivatio
nTheories
Learning,
Language,
Tacit
Knowledg
5
One Digit
Clustering
(reasonab
le
Complex
Diversity
(reasonab
le: incl:
Distinct
Nestedne
ss
(reasonab
le N.:
Modularit
y
(reasonab
le M.: of
Criticality
(reasonab
le C. )
Optimalit
y
(reasonab
le O. )
One Digit
fMRI,
EEG,
BOLD,
MEG
Informati
on Flow/
Directed/
Predictive
Functiona
l Causality
Substanti
ated:
Nominal
Modeling
Semi-
Analytical
Formulati
on
Semi-
Structure
d Macro-
Construct
One Digit One Digit
Conscious
ness,
Awarenes
s=
Higher
Functions
:
~PanFacul
IWMT
4 ~Numero
usity-
aspect
(some
Clustered
Regulator
y
Aggregate
~Nestedn
ess-
aspect
(some
Modularit
y-aspect
(some
sort of it)
~Criticalit
y-aspect
(some
form of it:
~Optimali
ty-aspect
(some
sort of it)
~Quantita
tive-
aspect
(some
Anatomic
al,
Dissectio
n, Dyes,
~Causality-
aspect
(some
sort of it)
Dual
Anatomic
al-
Functiona
Diagrams
(plus
possibly
less)
~Structur
ed-aspect
(some
sort of it)
~Quantita
tive-
aspect
(some
~"System-
State"-
aspect
(some
~Subjecti
vity-
aspect
(some
Affective/
Intellectu
al,
5.9 ± 1.2
3
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
2 Clustered
Bonding
Aggregate
s
Clinical
Examinati
on, Skills,
Obeserva
Correlate
d or
Depende
nt Info:
Descriptiv
e
(somwho
w
Structure
d Article,
Manuscri
pt,
Cognitive
Fns,
Thoughts,
Judgeme
Maslow
1 Clustered
Physical-
Matter
Aggregate
Primal
Methodo
gies: for
Investgati
Structural
Connectiv
ity/Causal
ity (only)
Seminal
Works,
Referenc
es, yet
Text,
Plain Raw
Articulati
on
Soma &
Reactive :
SUBJECTI
VE
Needs-
Behavior,
Condition
ing, incl.
3.1 ± 2.4
0 No
NUMERO
USITY in
Connectio
Non-
CLUSTERE
D
SubSyste
Non-
DIVERSITY
in
SubSyste
Non-
NESTED
SubSyste
ms: ~
Non-
MODULA
R
SubSyste
Non-
CRITICALI
ZED (SOC)
SubSyste
Non-
OPTIMAL
SubSyste
ms (=
Non-
QUANTIZ
ED
SubSyste
No
INVESTIG
ATION
Method(s
Non-
CORRELAT
ED
SubSyste
Non-
CAUSAL
Connectiv
ity(Effecti
No
SUBSTAN
TIATION,
or
No
FORMULA
TION: incl
Heuristic
Non-
STRUCTU
RED
Macro-
Non-
QUANTIZ
ED Macro-
Construct
No
System-
STATES!
(Macro
Non-
SUBJECTI
VE
Dynamics
No
HIGHER
Functions
(links:
0
10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Environ
(Nourishment,
Needs)
OTHERNESS
Functional-Architecture of the Human Brain: Systems Theory
3 Emotional/ 4 Cognitive/ 5 Afflictive/ 6 Social / 7 Volitional Functions : Threats & Regulators
ADAPTATION:
LTM
Knowledge, Information, Data
Beliefs
Habits
VII. Being Controller (Constructive Memory)
Instinctual
Algorithms
0. Temperature & Pain
1. Reflexes, Senses/ Posture & Movement/ SensoryMotor, SomatoSensory
2. Survival
#1: #2 Physiological Fns: [Physical]
I. Basal Controller (of BMR) :
II. Threats-Survival Controller (Innate) :
STM
Basic
Biological
Behaviors
Threats-Survival
Responses
I/P
Inputs
O/P
Outputs
To-Do: Action, Needs-Behavior: Complexity in Action
To-Be:
Development
/
Functional
Dominance,
Abstraction/
Complexity
SOC
Threats
Personal-DEVELOPMENT:
VI. Adaptation Controller(Cooperation)
7. VOLITON: ( incl. Character & Preferences )
#6: #7 Social-Volitional Fns: [(non)-Cognitive Dissonance]
4. Cognitive
3. Emotional
#3: #4 Emotional-Cognitive Fns: [Affective Action]
Moods
Feeling & Affective Constructs
MSG & Thinking Styles Portfolio Social
Action
Behaviors
Mental & Intellectual Constructs
Thoughts
Reward System
Past Episodes ~Impressions
ReInforcement
Mental & Intellectual Constructs Feeling & Affective Constructs
III. Affective Controller :
Needs *Maslow, ERG, … +
5. Self Actualisation
4. Esteem
3. Affiliation
2. Safety & Security
1. Biological
6. Social Interaction: (Incl. Personality & Traits)
Personal
Development
Behaviors
Social Behavior – Concordance
( Self: Facts, Norms, and Culture)
Social Evironment
Social Facts
Social Norms
Societal Culture
Judgment, Learning, Memory
Desires
Behavior:
[6 Domains]
[Bio/ Survival/ Generic/ Afflictive/ Social/ Developmental] Fns
Wisdom, Sagaciousness
#5 Affiliation Fns: [Friendship, Acquaintances] IV. Friendship Controller : Afflictive
Behaviors
Empathy
Conflict-of-Wills
Motivation
Personal
[
Developmental
/
Adapt
]
Balance
Behavior [ Inhibitory / Excitatory ] Balance
Skills, ,Tacit Knowledge Generic
Behaviors
Perception
Consciousness
Language
Attitudes
Values
V. Generic Behavior Controller :

- End of Conclusion ! :

Next
Approach #1 :
micro-scale Neurons
Approach #2 :
meso-scale Complexity Theory: 18 Aspects
Approach #3 :
macro-scale Functions

Next
3 Approaches to Study the Brain : [micro/ meso/ Macro] "SCALES" of "Complexity Theory" : Eng. Emad Farag H
Approach # 1 2 3
System-Scale micro-scale meso-scale macro-scale
Content Neurology Anatomy &
Functions
Networks & Connectivity Higher Functions
Chart/Diagra
m
Brain-Areas Functions Complexity Profiling Chart (CPC) 2D Diagram
Description Neurology Brain Functions
plotted on Brain
(Areas/Regions)
“n-Dimensional” Comparison
Table : Complexity 18 Aspects
Brain Functions as a Links
between (Needs and
Behavior) : 6 Behavioral
Domains
Other Brodmann's Areas Different Topologies of Brain-
networks/ FCBPSS Systems-
Modeling Framework
DAC theory
Details
Lists& Notes Topics of[Neurons, Neuronal
Populations, and N. Dynamics and
Function]
Notions of: [Neuronal-Synapses/ Tracts/
Pathways/ Circuits/ Networks]: aka: Brain
Networks and SubNetworks:
4 Domains : [Soma/ Reactive/
Adaptive/ Contextual ]
Abbrev.: DAC Distributed Adaptive Control/ 2D: two-dimensional / n= >1(M ath.)/ FCBPSS: Function, Context, Behavior, Principle, State, Structure/ aka also known

So: if You Are : Then Do : [ reading order ]
A NOVICE to Complexity: [ 3, 1, 2]
An Expert in Neurology: [ 1, 3, 2]
An Expert in Complexity: [ 2, 1, 3]
3 Approaches to Study the Brain : [micro/ meso/ Macro] "SCALES" of "Complexity Theory" : Eng. Emad Farag H
Approach # 1 2 3
System-Scale micro-scale meso-scale macro-scale
Content Neurology Anatomy &
Functions
Networks & Connectivity Higher Functions
Chart/Diagra
m
Brain-Areas Functions Complexity Profiling Chart (CPC) 2D Diagram
Description Neurology Brain Functions
plotted on Brain
(Areas/Regions)
“n-Dimensional” Comparison
Table : Complexity 18 Aspects
Brain Functions as a Links
between (Needs and
Behavior) : 6 Behavioral
Domains
Other Brodmann's Areas Different Topologies of Brain-
networks/ FCBPSS Systems-
Modeling Framework
DAC theory
Details
Lists& Notes Topics of[Neurons, Neuronal
Populations, and N. Dynamics and
Function]
Notions of: [Neuronal-Synapses/ Tracts/
Pathways/ Circuits/ Networks]: aka: Brain
Networks and SubNetworks:
4 Domains : [Soma/ Reactive/
Adaptive/ Contextual ]
Abbrev.: DAC Distributed Adaptive Control/ 2D: two-dimensional / n= >1(M ath.)/ FCBPSS: Function, Context, Behavior, Principle, State, Structure/ aka also known

Next
(TOC : Abstract, Introduction, then 2,1,3 not 1,2,3)
Approach #2 : meso-scale
Complexity Theory: 18 Aspects
Approach #1 : micro-scale
Neurons
Approach #3 : macro-scale
Functions

More Specific: if You Are: Then Do: [ Starting-slide / Ending Slide ]
Interested in Complexity Only : [ Slide 20 / Slide 30 ]
Interested in CPC Only : [ Slide 44 / Slide 49 ]
Interested in Neurons & Brain Fns : [ Approach #1 : micro-scale Neurons / Slide 65 ]

Abstract
- As of June 2023, Literature Review of Neurology & Systems
Neurology works shows that :
- Most Researches fall into 3 categories
1 - Neurology: Concerned mainly of Neurons, Neuronal
Populations, and N. Anatomy and Function.
2 - Systems Neurology: Concerned mainly of the Brain as a
“Complex System” with groups of Neuronal Connections
(Synapses) forming [Tracts, Pathways, Circuits, and Networks].
3 – Behavior & Functions: Concerned mainly of How the
Brain Functions as a Links between Needs and Behavior .

- Studies of Neurology: Concerned mainly of Neurons, can span
a good range of the Brain Functions, starting from Basic Fns:
[Pain and Sensorymotor] to [Awareness, Perception] to
Higher functions [Language and Intellectual functions].
- Studies of Systems Neurology: Concerned mainly of How the
Brain Functions as a “Complex System” entailing Neuronal
Connections: [Tracts, Pathways, Circuits, and Networks] that
can be studied by Theories of: [Complexity Theory, Dynamic
Systems, and ICT & Networks Theories].
- Studies of Behavior & Functions: Concerned mainly of Needs
and Behavior . Usually entail “Macro Constructs” to tackle
such macro issues.

- Regarding #2: This article presents a simple method to gain a
preliminary evaluation of “Level of Complexity”, When a
Researcher is concerned about any Complexity Issue or System
(Literature Review, Research Topic, or Human System).
- The “Level of Complexity” Evaluator : structures the
mysterious topic of Complexity into 18 Aspects ( or Axes or
Dimensions ) , along with their “Axes-Values” .
- This simple Evaluator is termed the “Complexity Profiling
Chart” (CPC).

Introduction
- This article will present a simple method to gain a preliminary
evaluation of the “Level of Complexity” of any “Complexity”
Issue or System in any Research: Be it a [Complexity Theory
Literature Review, Complex System, Complexity Topic in Some
InterDisciplinary Context, Complexity Topic or Issue ] when a
Researcher is faced by such a challenge.

- Such Evaluation is made by using a “Level of Complexity”
“Profiling Chart” : that “structures” complexity into 18 Aspects
or Dimensions, along with their Axes-Values [ aka:
Dimensions-Degrees = Hallmarks-Shades ].
- This Profiling Chart provides a simple “Visualization “ of the
concerned Complexity by using Universal Aspects as the
“Background” for plotting the Complexity Chart .
- ( Similar somehow to plotting Student’s SCORE in different study subjects ,
- or Graphing the “ECG” Curve on papers in Medical Investigation,
- Or Plotting (Thermodynamic Properties Curves & Surfaces ) on PVT Axes chart
(Pressure, Volume, and Temperature) to “visualize” “Thermodynamic Processes” incl.
phase transitions “SOC” .
- or plotting Engineering systems’ FREQUENCY-RESPONSE Curves, the Frequency Domain
dynamics, on a “Semi-Log-scale” paper: the “Bode Plot” representation in engineering).

- Article Starts by first presenting Approach #2: the a/m meso-
scale: Then providing a quick review of Approaches #1 and #3 :
Approach #2 details the CPC, then Approach #1: Neurology
(Neurons, Neuronal Populations, and N. Dynamics and
Function), then Approach #3: Behavior & Functions: Brain
Functions as a Links between (Needs and Behavior) .

Approach #2 : meso-scale
Complexity Theory

Complexity Theory
Quotes
“Complexity is A MULTI-FACETED Phenomenon,
involving a variety of features .. “
James Ladyman (University of Bristol) & Karoline Wiesner (Universität Potsdam),
August 2020 : Author’s book “What is a complex system?” (published with Yale University
Press)
“A variety of DIFFERENT MEASURES would be required
to capture all our intuitive ideas
about what is meant by complexity”
The late Physics Nobel Laureate : “Murray Gell-Mann”

Complexity Theory
- Complexity is indeed a Complex Phenomenon ! , but its study and use would be much
eased if we are able to get some preliminary evaluation of the “Level of Complexity” of
any Complexity Issue or Complex System. A Scientific Researcher is usually faced by
such challenges when tackling tasks like : Literature Review, exploring some unknown
Topic, prioritizing his research Sub-topics, or investigating some Human-related
Complex System .
- Scientific Researchers have many “Complexity Measures”
- Dealing with available “data-series” representing
- “information flow” among system entities
- on different system scales, Like the shown table.
- But researchers have no overall Measure(s).
- Such Preliminary Evaluation or Profiling is made possible by using a “Level of
Complexity” Profiler that “structures” complexity into 18 Aspects along with their Axes-
Values. This Profiling Chart provides a simple visualization/representation of the
concerned Complexity when used as a “Background” for plotting the Complexity
Aspects.
Axis X Y Z
Axis-Title Orderness Causality (Feedback) Intricacy
System Part
("Scope")
Environ / Sys Sys / Subsys Subsys / Subsys
Main
Phenomena
Macro Properties,
Pattern formation.
Feedback
(Coded Symbolic).
Self-Organization
(Subsys, Elements).
Examples Thermodynamics(PV=
nRT),Fractals,
Swarms, Flocks
Comm: Sampling
Rates (2X), mRNA,
Physiology: Regulatory
(=Signaling)
Pathways?
Immune Antibodies
Diversification (@ Germinal
Centers)/ Brain Learning Neurons
(N. Populations Connectivity}
Quantification Entropy measure:
(T.D., Shannon)
Hard!, Indirect via: [Non-
Linearity & (Info-
)Agents Formation]
Measures of: Sophistication,
Hierarchical C., Tree subgraph.
Main Feature Notion of ~Gestalt Notion of ~Classes Notion of ~Elements

Complexity 18 Aspects
- Multi-faceted : Complexity is indeed a Complex Topic ! described as a “Multi-faceted”
phenomenon, with multiple Facets, Aspects, Features, Hallmarks altogether forming the
phenomenon.
- micro-meso-Macro : one good way to arrange or sort these aspects, is by viewing the
overall system as composed of 3 Scales :
- #1: the micro-scale of Brain-internals : Elements or SubNetworks,
- #3: the Macro-scale of Brain-externals : Observable Functions,
- #2: an intermediate or inbetween scale ( called the Complexity “meso-scale” ) where
Information flows between the system’s Macro and micro scales .

Basic Complexity A 1 A 2 A 3 A 4 A 5
Numerousity Clustering Diversity Nestedness Modularity
SOC (that creates CMX) A 6 A 7 A 8
Criticality Optimality Quantized (μ)
Research & Formulation A 9 A 10 A 11 A 12 A 13
Investigation Correlation (Info) Causality Substantiation Formulation
Observable Macro Constructs A 14 A 15 A 16 A 17 A 18
Structured (M) Quantized (M) States(#VRTY) Subjectivity Higher Functions
Abbrev: SOC: Self-Organized Criticality/ CMX: Complexity/ μ:micro/ Ino: Information/ M:Macro/ VRTY: Varities

- First 5 Aspects (Basic Complexity) :
- A1: Does system-components have “Numerous” Connections ?
( “The” Basic Aspect of any Complex system ! )
- A2: Does system-components have “Clustering” ?
( Is there some “Differently Edged-nodes” in these Edges/Connections? Is the
Connections Distribution the same for all SubNetworks Nodes or is it different ? )
- A3: Does System-components have “Diversity” ?
( Are System Entities Different ?)
- A4: Does system Network Topology have “Nestedness” ?
(Does the system have some form of Inclusion-embedding, Hierarchy, Ranking,
Tree, Supervisor, … )
- A5: Does system Network Topology have “Modularity” ?
( Does the system have some repeated pattern? “scale-free” SubNetworks ? )

First 5 Aspects of Complexity
3 Whats ! Regarding the 5 Aspects :
[Numerousity, Clustering, Diversity, Nestedness, and Modularity]
What they : ARE
What they’r: NOT
What: AMBIGUITY & Sub-Types exits

# Name Feature Description
( Format: <Entity>:<Property> )
1 Numerousity Connections: Plenty Massive Linking (overall System-
wise)
2 Clustering (Differently "Edged-
Nodes")
Dense Linking (Some
SubNetwork-wise)
3 Diversity SubNets or
Connections: Different
Different Entities [Items/
Nodes/ SubNets/ Entities] or
4 Nestedness Topologies: Hierarchy Different Entities' Layers (Tiers)
5 Modularity SubNets or
Connections: Similar
Similar Patterns = Scale-free
(Entities or SubNetworks)

Complexity Theory: Principal 5 TERMS: in 4 Relevant Contexts: Eng.Emad Farag HABIB , 20230614
NAME [Math/ DiscreteMath, Networks/ Complexity] CONTEXTS
# Name
Items-Classing-Set:
Mathematics (Set Theory)
Nodes-Edges-Graph:
Discrete Math
SubNetworks-Connections-
Topologies: Networks
H-Intricacy/ V-Intricacy/
Links: Complexity.Intricacy Notes
Q!=ALL are "Don’t care"(Boolean-wise)/ Different1=At least one is Different, Similar1=At least one is Similar / N=Node, E=Edge, G=Graph
1 Numerousity Q! Edges: Plenty Connections: Plenty Links: Dense
Items: Don’t care / Classing:
Don’t care / Sets: Don’t care
Nodes: Don’t care / Edges:
Must be Plenty/ Graph: Must
be Densily InterLinked
SubNets: Don’t care/
Connections: Must be Plenty/
Topologies: Don’t care
H-Intricacy: Don’t care/ V-
Intricacy: Don’t care/ Links:
must have Dense InterLinks
Brain: 1Neuron
connects to ~1
000
Neurons !! (average) :
1
0^9 N.: 1
0^1
2 synapses
2 Clustering Different Classing (Differently "Edged-Nodes")
Different Connections Different InterLinks
Items: Don’t care / Classing:
must have Classing / Sets:
ditto
Nodes: Don’t care / Edges:
must have some Edges/
Graph: Must Have some
Differently "Edged-Nodes"
(InterLinked)
Connections: Must be
Different/ Topologies: Must be
Different InterLinking
Intricacy: Don’t care/ Links:
must have Different InterLinks
Clustering is easily
detected by Clustering
Algorithms / Links to
"Emeregence" in
Complex System
3 Diversity Different1 Different N or E SubNets or Connections: Different
Different H-Intricacy or V-Intricacy
Items, Classing, or Sets:
(Either) must be Different
Nodes & Edges: (Either) must
be different/ Graph: Don’t
care
SubNets & Connections:
(Either) Must be different/
Topologies: Don’t care
H-Intricacy & V-Intricacy:
(Either) must be different/
Links: Don't care
NTX: certain Network
Topologies: ~Non-
DVRS: [ Line?/ Bus?/
Star/ Ring/ Lattice/
M esh/ Fractals/ .. ]
4 Nestedness Q! Graph: Tiers Topologies: Hierarchy V-Intricacy: Layers (Tiers)
Items: Don’t care/ Classing:
Don’t care/ Sets: Don’t care
Both Nodes & Edges: Don’t
care / Graph: Must have Tiers
(Hierarchy)
Topologies: : must be
Hierarchy
Intricacy: Must have Layers
(Tiers) / Links: Don't care
usually: Structural only
5 Modularity Similar1 Similar N or G SubNets or Connections: Similar
Similar H-Intricacy or V-Intricacy:
Items, Classing, or Sets:
(Either) must be Same
Nodes, or Graph: (Either)
must be Same // Edges: Don’t
care
SubNets: must be similar/
Connections & Topologies:
Don’t care
H-Intricacy & V-Intricacy:
(Either) must be Same/ Links:
Don’t care
usually: Fn only ( but s.c.:
also exists: Str
M odularity)
Abbrev.: CMX: Complexity/ NE NodeEdge (Discrete Math)/ VS = Versus / #Varities = Number of V. / Str Structur(al), Fn Function(al)/ ICT Information Communication Technology/
Abbrev.: ROI region of interest / NTRC: Intricacy, H Horizontal, V Vertical L Links (LNKX)/ / s.c. special case/ wrt with respect to/ DAG Directed Acyclic Graph/
CONTEXTS: 4 Contexts, and with different 4-terms for the notoin of "Entity" : [Item, Node, SubNetwork, Element] : ["Item": vs "set", Math.] VS ["Node" vs Edge: Networks ] VS ["Sub

Complexity Theory: Principal 5 TERMS: in 4 Relevant Contexts: Eng.Emad Farag HABIB , 20230614
NAME
# Name Aka(s)
Versus, <>
1 Numerousity Densily InterLinked, Multitude of Connections, Plenty of Edges ( Generally, System-wise, e.g. Brain! )
<> (System) Sparsly-connected
2 Clustering InterLinked, Interwined, Interweaved, Meshed, Adjoined ( Specifically, "Nodes"-wise, e.g. Specific RO
<> (ROI) Non-connected
<> (ROI) Uniformly-connected=ALL are Equally-connected
<> (ROI) "dyadly-edged"=TWO-nodes per edge
3 Diversity Heterogenity, Speciality, Atypicality, Community/ aka: Speciality (yet: Cooperation) / Horizontal CMX
<> Homogenity, Generality, Typicality,
<> Vertical Intricacy
-
-
4 Nestedness Hierarchy, Embedding (Inclusion-E.)/ Tiers, Ranks, Tree / Vertical CMX,
<> Flat
<> Horizontal Intricacy
<> General Relational Entities
<> DAG
-
5 Modularity Patternity!, Repertoirity ! / Repeated (Configuration Formations Assemblies Molds) at different scale
<> Scale-dependant (Non-repeated)
<> Novelity (of Entities and Connections)
<> SubNetwork
-
Abbrev.: CMX: Complexity/ NE NodeEdge (Discrete Math)/ VS = Versus / #Varities = Number of V. / Str Structur(al), Fn Functi
Abbrev.: ROI region of interest / NTRC: Intricacy, H Horizontal, V Vertical L Links (LNKX)/ / s.c. special case/ wrt with respect t
CONTEXTS: 4 Contexts, and with different 4-terms for the notoin of "Entity" : [Item, Node, SubNetwork, Element] : ["Item":

NAME NOTES
# Name Aka(s) Abbrev. Description Notes
Versus, <> Ambiguity/ Wrong Use/ (Types) ( SubTypes, Lists, And Ambiguities )
1 Numerousity
Densily InterLinked, Multitude of Connections, Plenty of Edges ( Generally, System-wise, e.g. Brain! ) NUMRS Massive Linking (overall Sys
<> (System) Sparsly-connected Ambiguity of Sparsly-connected Entities VS Numerousity
2 ClusteringInterLinked, Interwined, Interweaved, Meshed, Adjoined ( Specifically, "Nodes"-wise, e.g. Specific ROI ) CLSTR Dense Linking (Some SubNe
<> (ROI) Non-connected Ambiguity of non connected Entities VS Clustering
<> (ROI) Uniformly-connected=ALL are Equally-connected
Ambiguity of uniformly-connected Entities , while "clustering" necessitates differences in connections-density
<> (ROI) "dyadly-edged"=TWO-nodes per edge
Ambiguity of dyadly-edged Nodes , while "clustering" necessitates 3 or more nodes per cluster
3 Diversity Heterogenity, Speciality, Atypicality, Community/ aka: Speciality (yet: Cooperation) / Horizontal CMX DVRS Different Entities [Items/ No
<> Homogenity, Generality, Typicality,
<> Vertical Intricacy [H vs V] Intricacy: H: +(System Disorder)/ V: -(System Order) Intricacy: 3: [ Horizontal Intricacy / Vertical Intricacy /
Horizontal
- 3: [Intra vs Inter vs Community] Diversity Diversity: 3: [ intra-type/ inter-types / Community Co
- 2: [Atypicality vs "Typicality"] Notions Typically: 2: [ Atypicality (items, sets)= Non-typical /
high A.=no
4 Nestedness
Hierarchy, Embedding (Inclusion-E.)/ Tiers, Ranks, Tree / Vertical CMX, NSTD
<> Flat
<> Horizontal Intricacy [H vs V] Intricacy: H: +(System Disorder)/ V: -(System Order) Intricacy: 3: [ Horizontal Intricacy / Vertical Intricacy /
Horizontal
<> General Relational Entities General Relational Entities (ICT.Database Context!) vs Nestedness
<> DAG Ambiguity of more advanced network topology than NSTD, e.g. DAG
- 2: [ Embodied-Embedded ] Nestedness
5 Modularity
Patternity!, Repertoirity ! / Repeated (Configuration Formations Assemblies Molds) at different scales "Scale-free" MDLR
<> Scale-dependant (Non-repeated) Ambiguity of Scale-dependant (Non-repeated) SubNetworks vs Repeated
<> Novelity (of Entities and Connections)
Ambiguity of Novelity (of Entities and Connections) vs Repeated
<> SubNetwork Ambiguity of naming a (general) SubNetwork: a "Module", "Modular Level" vs Neuronal !
- 2: [ Str/ Fn ] Modularity

Axes-Values for the 18 Aspects

Axes-Values for the 18 Aspects
- Values range from 0 to 10 ( including 0 and 10) :
- Value 10 = “Meta”, better than : this scale
- Value 5 = Typical, Nominal, Average, Normal Value
- Value 4 = Sort of
- Value 3 = General and Mixed
- Value 0 = No, Non
- When reading the Chart: start from bottom value : 0 , to the
top value : 10

Generating the Profiling Chart
( Simple MCQ List )
- Generating the CPC Chart is very simple, via a List of Universal
MCQ ( 18 Questions ) , with each Question having a maximum
of a/m 11 Possible Answers ( 0 to 10 ) .
- This simple procedure “generates” the Profile Chart for ANY
System or Complexity Issue !
- Next Slides: Examples on such MCQ:
- for 2 “Complexity Aspects” that are common for any
researcher: the Scientific Substantiation and Formulation :
A#12, A#13 ( in addition to the a/m 5 basic Complexity
Aspects : A#1 to A#5 ) :

A12: Scientific Substantiation
And A13: Scientific Formulation
- 2 easy ( Non-controversial ) Axes are : D12 and D13 : How far is
the Complex System Mathematically-Modeled :
- i.e. the levels of “Scientific Substantiation” And “Scientific
Formulation” of the system Model.

A12 Scientific Substantiation
A12: Substantiation:
No SUBSTANTIATION ! /
Seminal Works/
Descriptive/
(General & Mixed)/
Anatomical-Functional/
(Nominally) Substantiated/
Cause-Effect/
Dynamical-systems/
time-domain Solutions/
Universal-Law(s)-Contexted/
Meta-
Aka : Substantiation & Rigor of the Investigation & Findings
A 12
Substantiation
10 Meta-
9 Contexted by some Universal-Law(s)
[Uniformity, Entropy, Conservation,
Homeostasis], hence easily follows
Some Analytical Formulation and
8 time-domain Solutions, c(t), ..
7 Dynamical-systems Formulation,
Including Laplace Transform, C(S)
6 Cause-Effect: Incl Direct Causally ,
"Causally Effective Information"
5 Substantiated: Nominal
Modeling/Formulation : (both Evidence-
based and Conformal to Human &
Biological Organisms contexts)
4 Dual Anatomical-Functional
substantiation, Pathological
Affirmations ?
3 (General & Mixed)
2 Descriptive (somwhow structured)
1 Seminal Works, References, yet not
fully-substantiated, taken for granted
0 No SUBSTANTIATION, or Unknown !,
Proposed, speculative, provisional,
Draft Articles, Amatuers

A13 Scientific Formulation
A13 : Formulation:
No FORMULATION ! /
Text/
Structured Article/
(General & Mixed) /
Diagrams/
Semi-Analytical/
Analytical/
Mathematical/
Meta-
Aka: Formulation of the Investigation & Findings
A 13
Formulation
10 Meta-
9 Mathematical Formulation
8
7 Analytical Formulation
6
5 Semi-Analytical Formulation
4 Diagrams (plus possibly less)
3 (General & Mixed)
2 Structured Article, Manuscript,
Narrative?
1 Text, Plain Raw Articulation
0 No FORMULATION: incl Heuristic ?

A1-A5 (The a/m )
[Numerousity, Clustering,
Diversity, Nestedness, and
Modularity]
A 1 A 2 A 3 A 4 A 5
Numerousity
Modularity
10
>13 digits Meta- Meta- Meta- Meta-
9
8-12 digits
~Social
Diversity
8
4-7 digits
Compreh
ended
Complex
Clustering
Compreh
ended
Complex
Nestedne
7
3 digits
Existing
Complex
Clustering
/Emergen
Neuronal
Diversity:
incl (n
Neurons
Existing
Complex
Nestedne
ss:
Integrativ
e (plus
a/m)
6
2 digits
Advanced
Modularit
y ( incl
cross-
5
One Digit
Clustering
(reasonab
le
Complex
Diversity
(reasonab
le: incl:
Distinct
Nestedne
ss
(reasonab
le N.:
Modularit
y
(reasonab
le M.: of
4 ~Numero
usity-
aspect
(some
Clustered
Regulator
y
Aggregate
~Nestedn
ess-
aspect
(some
Modularit
y-aspect
(some
sort of it)
3
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
2 Clustered
Bonding
Aggregate
s
1 Clustered
Physical-
Matter
Aggregate
0 No
NUMERO
USITY in
Connectio
Non-
CLUSTERE
D
SubSyste
Non-
DIVERSITY
in
SubSyste
Non-
NESTED
SubSyste
ms: ~
Non-
MODULA
R
SubSyste

Next: 18 Aspects x 11 Values
In 4-Slides
Slide#1: Aspects 1-9 : Values 5-10

A 1 A 2 A 3 A 4 A 5 A 6 A 7 A 8 A 9
Numerousity
Investigation
10
>13 digits Meta- Meta- Meta- Meta- Meta- Meta- >13 digits Meta-
9
8-12 digits
~Social
Diversity
8-12 digits
Edge
Technolo
gies and
Methodol
8
4-7 digits
Compreh
ended
Complex
Clustering
Compreh
ended
Complex
Nestedne
Compreh
ended
SOC: Self-
Organized
Compreh
ended
Complex
Optimalit
4-7 digits
7
3 digits
Existing
Complex
Clustering
/Emergen
Neuronal
Diversity:
incl (n
Neurons
Existing
Complex
Nestedne
ss:
Integrativ
e (plus
a/m)
Existing
Complex
Criticality
(but fairly-
Existing
Complex
Optimalit
y (but
3 digits
Modified/
Customiz
ed/
Tailored/
6
2 digits
Advanced
Modularit
y ( incl
cross-
2 digits
5
One Digit
Clustering
(reasonab
le
Complex
Diversity
(reasonab
le: incl:
Distinct
Nestedne
ss
(reasonab
le N.:
Modularit
y
(reasonab
le M.: of
Criticality
(reasonab
le C. )
Optimalit
y
(reasonab
le O. )
One Digit
fMRI,
EEG,
BOLD,
MEG

A 1 A 2 A 3 A 4 A 5 A 6 A 7 A 8 A 9
Numerousity
Investigation
10
>13 digits Meta- Meta- Meta- Meta- Meta- Meta- >13 digits Meta-
9
8-12 digits
~Social
Diversity
8-12 digits
Edge
Technolo
gies and
Methodol
8
4-7 digits
Compreh
ended
Complex
Clustering
Compreh
ended
Complex
Nestedne
Compreh
ended
SOC: Self-
Organized
Compreh
ended
Complex
Optimalit
4-7 digits
7
3 digits
Existing
Complex
Clustering
/Emergen
Neuronal
Diversity:
incl (n
Neurons
Existing
Complex
Nestedne
ss:
Integrativ
e (plus
a/m)
Existing
Complex
Criticality
(but fairly-
Existing
Complex
Optimalit
y (but
3 digits
Modified/
Customiz
ed/
Tailored/
6
2 digits
Advanced
Modularit
y ( incl
cross-
2 digits
5
One Digit
Clustering
(reasonab
le
Complex
Diversity
(reasonab
le: incl:
Distinct
Nestedne
ss
(reasonab
le N.:
Modularit
y
(reasonab
le M.: of
Criticality
(reasonab
le C. )
Optimalit
y
(reasonab
le O. )
One Digit
fMRI,
EEG,
BOLD,
MEG
4 ~Numero
usity-
aspect
(some
Clustered
Regulator
y
Aggregate
~Nestedn
ess-
aspect
(some
Modularit
y-aspect
(some
sort of it)
~Criticalit
y-aspect
(some
form of it:
~Optimali
ty-aspect
(some
sort of it)
~Quantita
tive-
aspect
(some
Anatomic
al,
Dissectio
n, Dyes,
3
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
2 Clustered
Bonding
Aggregate
s
Clinical
Examinati
on, Skills,
Obeserva
1 Clustered
Physical-
Matter
Aggregate
Primal
Methodo
gies: for
Investgati
0 No
NUMERO
USITY in
Connectio
Non-
CLUSTERE
D
SubSyste
Non-
DIVERSITY
in
SubSyste
Non-
NESTED
SubSyste
ms: ~
Non-
MODULA
R
SubSyste
Non-
CRITICALI
ZED (SOC)
SubSyste
Non-
OPTIMAL
SubSyste
ms (=
Non-
QUANTIZ
ED
SubSyste
No
INVESTIG
ATION
Method(s

A 10 A 11 A 12 A 13 A 14 A 15 A 16 A 17 A 18
Correlation (Info)
Formulation
Structured (M)
Quantized (M)
States(#VRTY)
Subjectivity
Higher Functions
Meta- Meta- Meta- Meta- Meta- >13 digits >13 digits Meta- Meta-
Positive
Feedback
Correlatio
n: (incl
Contexte
d by some
Universal-
Law(s)
Mathema
tical
Formulati
on
Adaptive
&
Contextu
al
Adapting/
Develop
ment
Balancing
Feedback
Correlatio
n:
(Circural
time-
domain
Solutions,
c(t), ..
Compreh
ended
Functiona
l Macro-
Reinforce
ment
Motivatio
n-
Values,
Beliefs,
incl
Affiliative
Direct
Causality
Correlatio
n: incl
Effective
Functiona
l
Causality,
Dynamica
l-systems
Formulati
on,
Analytical
Formulati
on
Existing
Topology,
Fully-
Structure
3 digits 3 digits
Process
Motivatio
n-
Theories
MSG(Thin
king
Styles),
Managing
Cause-
Effect:
Incl Direct
Causally ,
2 digits 2 digits
Content
Motivatio
nTheories
Learning,
Language,
Tacit
Knowledg
Informati
on Flow/
Directed/
Predictive
Functiona
l Causality
Substanti
ated:
Nominal
Modeling
Semi-
Analytical
Formulati
on
Semi-
Structure
d Macro-
Construct
One Digit One Digit
Conscious
ness,
Awarenes
s=
Higher
Functions
:
~PanFacul

A 10 A 11 A 12 A 13 A 14 A 15 A 16 A 17 A 18
Correlation (Info)
Formulation
Structured (M)
Quantized (M)
States(#VRTY)
Subjectivity
Higher Functions
Meta- Meta- Meta- Meta- Meta- >13 digits >13 digits Meta- Meta-
Positive
Feedback
Correlatio
n: (incl
Contexte
d by some
Universal-
Law(s)
Mathema
tical
Formulati
on
Adaptive
&
Contextu
al
Adapting/
Develop
ment
Balancing
Feedback
Correlatio
n:
(Circural
time-
domain
Solutions,
c(t), ..
Compreh
ended
Functiona
l Macro-
Reinforce
ment
Motivatio
n-
Values,
Beliefs,
incl
Affiliative
Direct
Causality
Correlatio
n: incl
Effective
Functiona
l
Causality,
Dynamica
l-systems
Formulati
on,
Analytical
Formulati
on
Existing
Topology,
Fully-
Structure
3 digits 3 digits
Process
Motivatio
n-
Theories
MSG(Thin
king
Styles),
Managing
Cause-
Effect:
Incl Direct
Causally ,
2 digits 2 digits
Content
Motivatio
nTheories
Learning,
Language,
Tacit
Knowledg
Informati
on Flow/
Directed/
Predictive
Functiona
l Causality
Substanti
ated:
Nominal
Modeling
Semi-
Analytical
Formulati
on
Semi-
Structure
d Macro-
Construct
One Digit One Digit
Conscious
ness,
Awarenes
s=
Higher
Functions
:
~PanFacul
~Causality-
aspect
(some
sort of it)
Dual
Anatomic
al-
Functiona
Diagrams
(plus
possibly
less)
~Structur
ed-aspect
(some
sort of it)
~Quantita
tive-
aspect
(some
~"System-
State"-
aspect
(some
~Subjecti
vity-
aspect
(some
Affective/
Intellectu
al,
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
Correlate
d or
Depende
nt Info:
Descriptiv
e
(somwho
w
Structure
d Article,
Manuscri
pt,
Cognitive
Fns,
Thoughts,
Judgeme
Structural
Connectiv
ity/Causal
ity (only)
Seminal
Works,
Referenc
es, yet
Text,
Plain Raw
Articulati
on
Soma &
Reactive :
SUBJECTI
VE
Needs-
Behavior,
Condition
ing, incl.
Non-
CORRELAT
ED
SubSyste
Non-
CAUSAL
Connectiv
ity(Effecti
No
SUBSTAN
TIATION,
or
No
FORMULA
TION: incl
Heuristic
Non-
STRUCTU
RED
Macro-
Non-
QUANTIZ
ED Macro-
Construct
No
System-
STATES!
(Macro
Non-
SUBJECTI
VE
Dynamics
No
HIGHER
Functions
(links:

"Complexity Profiling Chart" (CPC Ver 1.1): Complexity & Brain Theories & Frameworks Plotted against "CPC" , 20230516
A 1 A 2 A 3 A 4 A 5 A 6 A 7 A 8 A 9 A 10 A 11 A 12 A 13 A 14 A 15 A 16 A 17 A 18
Numerousity
Investigation
Correlation (Info)
Formulation
Structured (M)
Quantized (M)
States(#VRTY)
Subjectivity
Higher Functions
10
>13 digits Meta- Meta- Meta- Meta- Meta- Meta- >13 digits Meta- Meta- Meta- Meta- Meta- Meta- >13 digits >13 digits Meta- Meta-
9
8-12 digits
~Social
Diversity
8-12 digits
Edge
Technolo
gies and
Methodol
Positive
Feedback
Correlatio
n: (incl
Contexte
d by some
Universal-
Law(s)
Mathema
tical
Formulati
on
Adaptive
&
Contextu
al
Adapting/
Develop
ment
Balancing
8
4-7 digits
Compreh
ended
Complex
Clustering
Compreh
ended
Complex
Nestedne
Compreh
ended
SOC: Self-
Organized
Compreh
ended
Complex
Optimalit
4-7 digits
Feedback
Correlatio
n:
(Circural
time-
domain
Solutions,
c(t), ..
Compreh
ended
Functiona
l Macro-
Reinforce
ment
Motivatio
n-
Values,
Beliefs,
incl
Affiliative
7
3 digits
Existing
Complex
Clustering
/Emergen
Neuronal
Diversity:
incl (n
Neurons
Existing
Complex
Nestedne
ss:
Integrativ
e (plus
a/m)
Existing
Complex
Criticality
(but fairly-
Existing
Complex
Optimalit
y (but
3 digits
Modified/
Customiz
ed/
Tailored/
Direct
Causality
Correlatio
n: incl
Effective
Functiona
l
Causality,
Dynamica
l-systems
Formulati
on,
Analytical
Formulati
on
Existing
Topology,
Fully-
Structure
3 digits 3 digits
Process
Motivatio
n-
Theories
MSG(Thin
king
Styles),
Managing
6
2 digits
Advanced
Modularit
y ( incl
cross-
2 digits
Cause-
Effect:
Incl Direct
Causally ,
2 digits 2 digits
Content
Motivatio
nTheories
Learning,
Language,
Tacit
Knowledg
5
One Digit
Clustering
(reasonab
le
Complex
Diversity
(reasonab
le: incl:
Distinct
Nestedne
ss
(reasonab
le N.:
Modularit
y
(reasonab
le M.: of
Criticality
(reasonab
le C. )
Optimalit
y
(reasonab
le O. )
One Digit
fMRI,
EEG,
BOLD,
MEG
Informati
on Flow/
Directed/
Predictive
Functiona
l Causality
Substanti
ated:
Nominal
Modeling
Semi-
Analytical
Formulati
on
Semi-
Structure
d Macro-
Construct
One Digit One Digit
Conscious
ness,
Awarenes
s=
Higher
Functions
:
~PanFacul
4 ~Numero
usity-
aspect
(some
Clustered
Regulator
y
Aggregate
~Nestedn
ess-
aspect
(some
Modularit
y-aspect
(some
sort of it)
~Criticalit
y-aspect
(some
form of it:
~Optimali
ty-aspect
(some
sort of it)
~Quantita
tive-
aspect
(some
Anatomic
al,
Dissectio
n, Dyes,
~Causality-
aspect
(some
sort of it)
Dual
Anatomic
al-
Functiona
Diagrams
(plus
possibly
less)
~Structur
ed-aspect
(some
sort of it)
~Quantita
tive-
aspect
(some
~"System-
State"-
aspect
(some
~Subjecti
vity-
aspect
(some
Affective/
Intellectu
al,
3
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
2 Clustered
Bonding
Aggregate
s
Clinical
Examinati
on, Skills,
Obeserva
Correlate
d or
Depende
nt Info:
Descriptiv
e
(somwho
w
Structure
d Article,
Manuscri
pt,
Cognitive
Fns,
Thoughts,
Judgeme
1 Clustered
Physical-
Matter
Aggregate
Primal
Methodo
gies: for
Investgati
Structural
Connectiv
ity/Causal
ity (only)
Seminal
Works,
Referenc
es, yet
Text,
Plain Raw
Articulati
on
Soma &
Reactive :
SUBJECTI
VE
Needs-
Behavior,
Condition
ing, incl.
0 No
NUMERO
USITY in
Connectio
Non-
CLUSTERE
D
SubSyste
Non-
DIVERSITY
in
SubSyste
Non-
NESTED
SubSyste
ms: ~
Non-
MODULA
R
SubSyste
Non-
CRITICALI
ZED (SOC)
SubSyste
Non-
OPTIMAL
SubSyste
ms (=
Non-
QUANTIZ
ED
SubSyste
No
INVESTIG
ATION
Method(s
Non-
CORRELAT
ED
SubSyste
Non-
CAUSAL
Connectiv
ity(Effecti
No
SUBSTAN
TIATION,
or
No
FORMULA
TION: incl
Heuristic
Non-
STRUCTU
RED
Macro-
Non-
QUANTIZ
ED Macro-
Construct
No
System-
STATES!
(Macro
Non-
SUBJECTI
VE
Dynamics
No
HIGHER
Functions
(links:

Charting (or Plotting)
The Known Theories/Frameworks of :
PCT, IWMT, and Malsow
on these 18-Aspects

CPC “Complexity Profiling Chart”
- Concluding Notes (Draft):
- Typical Applications :
- CPC can significantly ease studying the following Complex Systems:
- ICT Networks Dynamics
- AI & AI Training Progress
- Climate Change Dynamics and Mitigation Strategies.
- Immune System Dynamics
- Swarms, Flocks, .. : Natural or Artificial
- Social Structures, Social Networks, and Social Media, ..
- Global Conflict: World Order&Organizations, States&Relationships,
Parties&Ideologies, Factions&Divisions, ..
- Brain Structure&Function , and Brain Theories&Frameworks
- ( It is note-worthy that the CPC was inspired while studying this particular complex system,
- which may prove to be the most complex of all systems ! )

- Regarding our Contemporary Knowledge of the Theory of Complexity : it is undeniable
that we are somewhere close to the ( Pre-Newtonian Era ) in Mechanics!!! . We are
hardly spelling the ABC’s of Complexity , and in this very situation: such “Complexity
Profiling Chart” CPC can be helpful as long as we are still pusuing Descriptive Notions.
- Moreover: Amid a Global Boom in AI Technology and its Uses , our General &
CONSTRUCTIVE Use of AI capabilities in the Vast Applications of [Non-pattern-
recognition, Non-pure-responsive, Non-Executive, and Non-protective] may turn out to
be hugely dependent on having a Structured-Knowledge of the phenomenon of
Complexity ( in addition to being also dependent on having a Structured-Knowledge of
the concerned Macro Application ) .
- CPC is suitable for Both Complex and Complicated System .

- Difficulties faced when first encounter with The Complexity Profiling Chart is a fact .
- Almost a decade ago: Difficulties were encountered by Systems-Engineers while studying
FREQUENCY-RESPONSE of Dynamical Systems .
- It was very hard to shift the human comprehension
- from the ( easy, real-world, intuitive) TIME-Domain,
- to the ( hard, imaginary, counter-intuitive) FREQUENCY-Domain.
- In the 21st Century: same Difficulty is encountered : when studying Complex Systems :
- It is time for shifting our their comprehension
- from Anatomical-Functional , MINUTE-Domain, Reductionistic-Approach,
- to Information & System, COMPLEXTY-Domain, Synthetic-Approach .

- The Complexity Profiling Chart : uses the smart “Anatomical” approach prevailing the
Medical Literature Terminology, rather than using a Functional approach, in describing
the Complexity Issue Aspects ( and the word “Anatomical” here means adopting a more
“Descriptive” Perspective rather than “Prescriptive”) .
- Examples:
- First 5 Aspects: follows ICT algorithms exact detection-sequence ! for exploring complexity.
- The crucial process/function of “SOC” is "scattered" ! among ~5 Aspects ! : [Numerousity, Modularity, Criticality,
Optimization, and (the resulting) Macro Constructs] !!!
- Substantiation & Formulation: for the Macro scale only !, rather than micro or meso, where a “Descriptive" approach
usually prevails . Noting that this does not undermine the objectivity of the evaluation, because the micro scale
(SubSystems and connections ) and the meso scale (Information flow) : are both inherently-analytic if they are ( at all,
ever, in the first place) were to be tackled by the under-study Complexity Issue.
- It is also noteworthy that the 18 Complexity Aspects are arranged [i.e.: Ordered (x-axis
wise) , Valued (y-axis wise), and Termed (axes-names-wise)] in the same Arrangements
used to “Describe” Complex systems (and in particular the Human Brain). Such
Arrangement would support further Advancements & Progresses in our knowledge of
Complexity Theory & Complex Systems .

Network Topologies (CNS Context)
Ref: cf doi, Draft on 0531

Network Topologies (CNS Context)
- Draft Notes :
- It’s all about “SubNetworks” !
- ( aka: the “Topology” or ~shape : of the “Interconnections” between “Nodes” ) .
- Understanding how these SubNetworks function is crucial
- What TYPES of SubNetworks exists ?
- How “Self-Organized Criticality” (SOC) affects these SubNetworks : as evident in their
[Constrains, Optimization, and Balancing] .
- How SubNetworks Optimizations & Efficiencies [ both Global and Local-clustering ] differs ( in
particular: increases ) with more complex SubNetworks types .
- What are the Relevant Functions to each of these SubNetowrks Types ?
- Noting that: this is NOT an “Exclusive List” of SubNetworks types : but rather : this is just a mere
proposed set of types that are both Main & Easily-describable, hence apt to staffing in a Tabulated
Linear-List of Types.

Very Important
20230610
Human Brain Networks Topologies : How Neuronal Populations form "Large-scale Networks" , 20230600 Eng. Emad Farag HABIB
# Name
diagram Name aka 2D: [Global Efficiency VS Clustering]
Relevant Function Notes (Fn)
5 Spatial
(Integrative)
Effective Function-
wise
HH High Global , High
Clustering
SUBJECTIVE Complex Fns:
Requires Information Instantiation & Probabilistic
Modeling: Consciousness
Importance of Info "Instances"
(Copies, Mapping), Probabilistic
Modeling and Updating (Bayesian
Inference)
(4B) VSCS Variable-structure
Control-system
Clustering
MYRIAD of Fns:
Requiring System-Str to change according to
Function's varying Signals/Inputs.
Context-sensitive integration =
“task-related responses” / akas:
SemiautonomousSubsystems,
shifting hierarchy
4 Hub structure core–periphery
architecture
Clustering
COGNITIVE Fns:
Sequential (Linking/Attributing) to/of Specialized
Hub-regions
Learning = bridges between
distinct communities
(3B) Hierarchical
structure
Nestedness NSTD,
Inclusion-
Embedding
HM High Global ,
Medium
Clustering
ORGANIZATIONAL Fns:
Optimized Reach/Access: Better (Time/Chain of
Command) to Address a certain node
3 Small-world
structure
SW, high clustering MH Medium Global ,
High Clustering
PRIORITIZED Fns:
Optimized-Performance: Min Total number of
computational steps
Min. steps needed to
process external stimuli
(2B) (Lattice) nearest neighbours LH Low Global , High
Clustering
ROUTINE Fns:
Equal-Importance Task-items
2 Community stochastic block
model/ Probability/
MM Medium Global ,
Medium
Clustering
SPECIALIZED Fns/Tasks:
Specialized Brain cognitive Areas (Communities,
Sensory Modalities)
subnetworks with specific
cognitive functions
1 Random fixed probability P HL High Global , Low
Clustering
NON-STRUCTURED Fns/Tasks/Activities:
Possibly suiting the initial (Learning/ Trials&Error)
phases.
~ Heurestic & Explorative
Fns/Tasks/Activities
Abbrev: VSCS: Variable-structure Control-system // NTX Network(s)/ High, Medium, Low/ Probability Distribution, P./ Versus/ Very Important/ also known as/ Fun
NTX 5: [ RND/ CMNT / (Lattice)/ SW/ (Hierarchical)/ Hub/ (VSCS) / Spatial ] , NTX.3D : Ref: 2019, https:/doi.org/10.1038/s42254-019-0040-8

# Name Constraints
Constraints Optimality Balance ( Cost VS Benefit ) Math Model Notes
5 Spatial
(Integrative)
BOTH physical and metabolic
constraints
MINIMUM overall @ CNS Level
MINIMUM total wiring distance (Metabolically driven
to Minimize & Physically constrained to exist within a
tight 3D volume) While Max Organism-level Fn
Metabolically &
Physically-Constrained
Performing Organism-
level Higher Fn
P: Min. total
wiring
distance
Diagram
backgrou
nd =
"Brain
Organ"
(4B) VSCS ~ Effcicieny (in performing
other non-VSCS Fns)
constraints ?
~ Fn-based Optimalty @ CNS Level
MAXIMUM Functional Performance, wrt the Fn itself,
not the CNS Constraints
Same Organism-Entities
are to perform n Fns
Performing Organism-
level n Fns
P: Max.
Functional
Performance
4 Hub structure Functional-Constraints:
Sequential Progress: mandates
Linking to the (MAXIMALLY
Linked Node)
MINIMUM overall @ Network Level
MINIMUM overall path lengths across the network
Info Processing
Objective Limitation(s)
of a Learning/Objective
Organism
Efficient Clustering :
(Pinpointing/Addressing
) Relevant Nodes
P: ( Max n
Nodes )
(3B) Hierarchical
structure
More Effcient than mere
clustering, by mandating
TIERED Links
MINIMUM minimum @ Community L.
MINIMUM minimum path lengths to some sought
Organogram node = Better communication
Info Processing On-need-
basis Organizational
Limitation(s) of an
Execuitive Organization
Efficient Clustering :
(Pinpointing/Addressing
) Organogram Nodes
( ~ Default
Organized
Structure)
3 Small-world
structure
More Effcient than Lattice, by
adding few TRANSVERSE Links
MINIMUM average @ Community L.
MINIMUM average path lengths between all pairs of
nodes = efficient communication
Equal Likelihood
(opposes far-links)
Fulfill Some
Specific/Local
Linking/Bonding Force
Watts-
Strogatz
model
(2B) (Lattice) Uniformity: ANY Node to be
connected to ALL its
neighborhoods
MAXIMUM Strength @ Community Level
~Uniform P.Distr. ?!
Equal Likelihood
(hinders far-links)
Fulfill Some
General/Global
Linking/Bonding Force
Uniform
P.Distr. (α→∞)
2 Community Many Nodes Link to Many
NEIGHBORHOODS
AVERAGE average @ Pairs Level
AVERAGE average path lengths between all pairs of
nodes
Metabolic-Constrants
(prevents far-links)
( some Benefit due to
Linking )
stochastic
block model
1 Random NIL ! ~~RND P.Distr. ?! Link Probability satisfy
some "P" of a Binomial
P.Distr.
( some Random Benefit
due to Linking )
Erdös-Rényi
model (α→0)
(Non)

(( FCBPSS Modeling Framework ))

FCBPSS : [ Function/ Context/ Behavior/ Principle/ State/ Structure ] / draft schematic 0406
Example: Needs  Drives  Directed Behavior  Reinforcement  Emotions  Limbic
System (,Brain Stem) ( cf next slide )
FCBPSS:
Arranged Operation-wise: [Structure/ State/ Principle/ Behavior/Context/ Function ]

Systems-Neurology: FCBPSS Framework: "CONCEPTUAL MAP" of the common "Hypothetical Constructs" arranged in an FCBPSS Framework layout: 20230406, 0
FCBPSS S (SubStr) S P B C F VSC
(( Personal-Development Gonstructs ))
II: Cognitive
I: Emotional
II. Cognition/ Thoughts/ K.I.D. / MSG, Portfolio/ Values/ Beliefs/ ..
I. Emotions/ Moods/ Habbits/ Attitudes/ Social Behavior/ ,,
Intrinsic Algorithms ( Needs )
Organism-Environment Interactions
System-Structures, "Hypothetical Constructs" :
System-Structures(Physical): [ Brain/ Senses&Motor/ Glandular Control/ Body! ] 0410
System-Structures("Hypothetical Constructs") : (cf)
System-States: Need, Desires, Tensions, .. :
System-Principle(s): ~Motivation Theory: Priorization of a Motivation-Principles (cf)
( B. ) System-Behaviors: more (Elements & System) than (Organism/Environ) Behavior : TODO0409 so the 6-listed
System-Contexts:
(Fns) System-Functions :
FCBPSS.SubConstructs: 0409 // also: linked notion
~ 2 TYPES 3 TYPES = 11 SubTypes MTX-theories4 Contexts:"Brain States" // plus? [Body, Environ] contexts/states?
2: [Satisfaction/ Dissatisfaction(Deprivation)] ? 0409 , of the a/m needs
3: [ CONTENT/ PROCESS/ REINFORCEMENT ] = MTX-theories-types
11: [[[ Hierarchy (Maslow) / ERG/ two-factor/ Acquired Needs // Equity/ Goal-setting/ Expectancy // Positive/ Avoid
4: [ Alert(aroused)/ Awake/ DMN(defauly-mode Network, relaxed)/ asleep] ? , aka "Brai
Motivations: [[[ KINDS 4 // How to (get) Ultimate Motivation 6 // CHANGE_BHX 5 // .. ]]]
202305200todo UPDATE as per PPT SubPrinciple(s): 0410 = Tier Layer #3+
~Motivation Theory: ~Principles Priorization 6: [ Need/ Search/ Choice/ Enact/ Experience/ Reasses] : NSCEER , 0410
6: [Bio/ Survival/ Affiliative/ Generic/ Adapting/ Development]
# Maslow Pyramid ( N. = Need )
5 Self-Actualization N. for Self-Actualization
is Personal-Development B. Personal-Development Fns.
4 Esteem N. for Esteem to Social B. Social Fns.
CCN: Collective Control Networks ~4: Affliation (SOX) // some Aspects of BHX ? // .. .. // Non-standard: Subjectivity ??! // SACT ( cf SACT.TOC in DOC
( Links to )
'~ Need for Voluntry Action
needs- Generic Action B. Cognitive Fns. II. Cognition/ Thoughts/ K.I.D. / MSG, Portfolio
Emotional Fns. I. Emotions/ Moods/ Habbits/ Attitudes/ Social
3 Affliations N. for Affliationspursue Affliations B. Affliations Fns.
ADC: Adaptive Distributed Control ?
2 Safety & Security N. for Safety & Security
Satisfaction Survivial B. ( Spontaneous & Instinctive )
1 Basic Bioloical Needs N. for Basic Bioloical Needs
As-much-as-possible Biological B. Biological Fns.
( Number of ) 5 6 n 6 n 7
Abbrev.: Function/ Context/ Behavior/ Principle/ State/ Structure /// Variable-Structure Control /// Knowledge, Information, Data// Mental Self Gov// n=many/ N. Neuron

Systems-Neurology: FCBPSS Framework : ~ Information-Notion wise : i.e Information-Theoretic Notions, arranged as per an FCBPSS framework, Eng. E. F. HA
FCBPSS S S P B C F VSC
???? : ( former) (( Personal-Development Gonstructs )) ( Links to )
todo: Stuctures VS "Hypothetical Constructs" : Master-details 0409
???? : Information Issues ?
???? : Matter-Energy Issues ?
???? : Information-Hierarchy ? [ Organelles, .. Neurons, .. Systems, .. Organism, .. ], Macro-meso-micro
???? : (Gestalt, Systems-theory Perspective) vs ( Reductionism )
# ~Advanced Notions of: Wholism ( Nature-wise) / Teleology / CZL / ....
Rivals: "Changes" : #1 Im learning the subject / #2 recent researches, where even TERMINOLOGY is not sharply defined yet (2-3- terms for 1 meaning / 2-3 meaning for 1 term) / 0521
9 Universals Universal Principles: Organism Strive to (Organism <> Environment) : [ UNIFORMITY/ P.Distr, PowerLaws/ MAS , CAS, SOC, EMRG
8 Organism/(Society, Otherness, Affiliation, ....) :
7 Organism/Environment Suvival : incl (Memory Links Experience links Processing !) / .. ) // standard Notions: Interaction: Awareness, Alterness, // .. // De
Perception (Non-Reorganization) : PCT, LOOP: same, no NEW layers: Information.Pyramid:
PCT.11 : 11 levels of perceptions : [ intensity/ sensation/ configuration/ transition/ event/ relationship/ cate
Links to: SOC causes the EMERGENCE of new layers of PCT
SOC,PCT: Reorganization (Emerging, “Con
SOC,PCT: Reorganization (Emerging, “Construction” !): PCT: SOC causes NEW Layers *Nodes Groups / ~Edges+ to emerge // links: P
6 Organism Learning for Surival Repeated Pattern Information : Organism uses "Memory" ?? [ Flip-flop // .. ]
Learning, Memory, Habituation, Conditioning, Priming/ Experience , ..
Draft List of ((ASPECTS)) : VIMP: [ notion of "Motivated Behavior" BHX, MTX theories-types 3 // "Affective Behavior" //Self-awareness , Attention, Alterness // REINF, Sujectivity// H
5 System Info.sys Notions : incl * Information.TOC, “VSCS” : Aspects/Manifestations : 7 / CNS Features 5 / .. +
4 Organ: "Functioning" Modules: and SemiAutonomus, e.g. [Modules (= ICNs) / TFMs] Info CARRIERS & FORM = Info.Sys.HW: Information.Carriers ? (4: Circuits&Signals
VIMP: incl.: subsystems : rank order is variable !! (and semiau
NTX.3D : 6: [[[ RND// CMNT// (Lattice)// SW// Hub// Spatial// (VSCS) ]]] = "Large-scale Brain Networks" // a
Node-Edge: N: 6: [ Neurons/ Networks/ Nodes/ Rich-club Hubs/ Modules (= ICNs
3 Tissue: NE : ["submodules" , "Nodes" , N. Population, Modules] & [Connections & Connectivity , "Coupling"] / terms contexts #1: Computational Neurology #2 Math ,
SubModules Connectivity : [[[Weight// Timing // Range]]] = submodules.CouplingParameters // aka synapt
2 Cell: Neurons : N. [Number(incl Connections)/ Type/ Connections] = ~CMX 3D Perspective !! , D
1 Organelles / Support!: SubCellular [ {VIMP: includes : ) Synapses, Gap Junctions, .. ] / MacroMolecules / Molcular /// VIMP: Tissue [ Glia, other support Cells, .. ] [ Glia, oth
Electrical Conduction ( as a mandate for Electric Info Propagation)
Info.Sys.Components Tactics: Saltatory Conduction , Summation , // Synapses types and dynamics
N. as a Living Cell Support Functions: ~Norishment/ Growth, Developmental / ..
( Number of ) 9 ?? ?? ?? ?? ??
Abbrev.: Function/ Context/ Behavior/ Principle/ State/ Structure /// Variable-Structure Control /// Knowledge, Information, Data// Mental Self Gov// n=many/ N. Neuron

Approach #1 : micro-scale
Neurons

Approach #3 : macro-scale
Functions

DAC Theory
NOTES 0531

DAC
Distributed Adaptive Control (DAC)
DAC
Conforms Reasonably
with a Months-ago Self-developed
Similar Diagram

Next Slides :
Details of “Generic Behavior” or “Affective Behavior”

Search Choice Enact
Experience
Reassess
Behavior
Need
(as a Drive)
“Affective Behavior”
Aka: The Motivation Process (6)
Diagram #1: INTRNALS: Intra-Motivational Constructs : 6
Items :
[ Need (as a state) / Search (for Remedial Actions )
Choice (Action Selection)/ Enact (Implementation)/
Experience (Experiencing Consequences) / Reassess (Reinforcement) ]
Motivation details : [ Need/ Search/ Choice/ Enact/ Experience/ Reassess ]/ draft schematic
Needs

Need
[ Maslow/ ERG/ ..]
Moods
“Affective Behavior”
Aka: “Generic Behavior”
, The Motivation Process
Behavior / Action
Regulators , Controllers
Needs / Behaviors
Diagram #2: EXTRNALS: Extra-Motivational Constructs : 4
Groups:
[ Input (needs) / Output (Behavior + Learned B.)
Affected By (Cognitive Controllers)/ Affects (Emotions, Moods + Attitudes)]
Cognitive, Affective, and Volitional Constructs
Emotions
[ Positive/ Negative ]
Attitudes
Learned Behavior(s)
[Reinforce/ Avoid ]

Moods
Behavior / Action
Emotions
Attitudes
Learned Behavior(s)
[Reinforce/ Avoid ]
Search Choice Enact
Experience
Reassess
Need
(as a Drive)
Need
[ Maslow/ ERG/ ..]
Regulators , Controllers
Needs / Behaviors
Diagram #3: BOTH : INTRA & EXTRA -Motivational Constructs:

Search Choice Enact
Experience
Reassess
Behavior
Need
(as a Drive)
Emotions
Learned Behavior(s)
[Reinforce/ Avoid ]
Needs
(as a Structure)
[ Maslow/ ERG/ ..]
C1: Need
Exists?
[ Y/N ]
C2: Behavior
Fulfilled the Need?
[ Y/N ]
C4: Behavior
Efficacy?
[ Effective / Ineffective ]
C3: Need
Urgency/ Importance
Satisfied
[ Y/ N ]
Causality: Adaptation, Action, to-do
Functional
Abstraction
Layer/
Dominance/
Complexity/
to-be
Behavior [ Inhibitory / Excitatory ] Balance
Personal
[
Developmental
/
Adapt
]
Balance
Moods
C5: Need
Necessitates
Caged-Emotions ?
[ Y/ N ]
Attitudes
This is NOT “Graphics” nor “Art” , but “Systems-Neurology” :
This is NOT a Graphical Piece of Art, with regular and equally-spaced items ! ,
rather: Items are arranged as-per the a/m “2-D Perspective” .
Abbrev. : C = “Controller, Regulator”
Diagram #4: BOTH (detailed): INTRA & EXTRA -Motivational
Constructs :
Learned Behavior(s)

Search
Remedial Actions ,
Alternatives, Ways
Certain Outcomes Attractive
Choice,
Goal-directed Behavior,
Will, Intentions
Action Selection
Enact,
Implementation
Experiencing
Probing :
Behavior Consequences
Reassess,
Reinforcement
Feedback
Behavior/ Actio
Need
(Desire/ Tension/ Drive )
Felt-Deprivation
Emotions
[ Happiness: ~2: Pride, Joy / Non- : n-emotions ]
Cognitive, Affective, and Volitional (Existing) Dispositions / (observed) Constructs
[Judgment ]
Reasoning, Judgment, Perceptions/
Beliefs, Concepts / Values, Morals//
Moods and Emotions Affects, Emotions//
Will
Learned Behavior(s)
Past Episodes
[Reinforce/ Avoid ]
Needs
(as a Structure)
Wants / Dreams/ Interests
4 CONTENT + 3 PROCESS + 4 INFORCEMENT Theories
C1: Need
Exists?
[ Y/N ]
C2: Behavior
Continue/ Cease
[ Y/N ]
C4: Behavior
Reward/ Punishment (Conflict)
C3: Need
Satisfaction/ Dissatisfaction
Tension/Drive Reduced?
Extent of S.
[ Y/ N ]
Moods = Longterm Emotions-Abstraction
C5: Need
Necessitates
Behavioral Apathy
[ Y/ N ]
Attitudes = meta Caged-emotions ~ Behavioral Apathy
Diagram #5: BOTH: INTRA & EXTRA -Motivational Constructs : AKAS
( with apology for smaller-font )

MOTIVATION: in Different Contexts: 20230400
MOTIVATION: in Different Contexts : Motivational Processes 6: [ Need / Search / Choice/ Enact / ~Experiencing / Reassess ]: 20230400
Apology: "Sparse-table = Obligatory SmallFonts" !
A: Motivation:
Psychology-Context:
1 2 3 4 5 6
Need Search Choice Enact Experience Reassess
Akas1 (Common) Desires Alternatives/
Remedies
Will Implement Experiencing
(Rewards vs
Punishment)
Reinforcement
Inbetweens!, Details Inclination? / Tentative
Action
B: Motivation: Business-
Context: Employee
4 Steps 4: Goal (Wants) (~Attitude?) 1: Effort 2: Performance 3: Reward
Inbetweens!, Details "Goal-
directed
BHX"
Opportunity ? [ Abilities / OBJECTIVE
Performance Evaluation System
] // Competence //
Involvement // Mobilization,
Participatory // Devotion ,
Confidence in Others
Performance
Evaluation
Criteria
Dominant
Needs
By?, Action By Whom? DIYK Employee-
Environ
Employee Workplace
setting , Work-
Environ
Company,
Administration
C: Motivation: Business-
Context: Company
3: [Expectancy,
Instrumentality, Valence]
1. Expectancy 2. Instrumentality 3. Valence
Employee Queries Can I ACHIEVE the
desired level of
Performance?
What work
OUTCOMES will
be received as
a result of the
Performance?
How Highely do
I VALUE Work
outcomes?
more Motivation = E x I x V Match [ Needs -
Rewards ] :
Employee-
needs vs
Company-
Rewards

(( PCT : Perceptual Control Theory ))

Our Brains
Tackles ANY Perception Process
in the Following Order
( Starting from
Level 1 : at the “table-bottom” to Level 12 : at the “table-top” )

(( PCT : Perceptual Control Theory ))
PCT : in Narrative Format: Living Creatures Brain is
organized in a very Logical way to give it the ability to deal
effectively with a varying environment. Human Brain
organizes its “Neuronal Populations” in the following way to
be able to deal effectively with such variations :
FIRST The Human Brain ( the “Percipient” ) Collects all
possible easy information (Sensory Signals) from the
Environment that relates to some “Perceived Object” : then
collects 4 other important information,
then the Percipient Brain SECOND uses these 6 Info to reach
3 logical and Rational conclusions: ( starting by “Classifying”
or Classing the Object, a c.a.t. some known categorization
system).
then the Percipient Brain THIRD engages in some final
higher Functions related to its own environment : seeking
“Guiding Principles” that possibly govern the situation ,
seeking counter-manipulation, in addition to pursing
conformality with the Whole Cultural system.
At the Neuronal Level: the Living Creature achieves all this
by having its “Actuating Signal” equal to the Difference
between Two Signals : ( The Reference S. – The Perception S.
), rather than ( Reference S. – Output-Feedback S. ) in non-
living systems.
# PCT Level
(Order)
Name
~Survival
Context: Links
To :
Perception
PARTY
Examples
(12)
System
Concepts
Conformity
Percipient/
Environ-
"Systems"
Physics, Government
(11) Conflict Malignancy
~Object
.Rivalry
Manipulation
10 Principles Guidance
Percipient/
Environ
the precept “honesty"
9 Programs
Contingencie
s
Percipient/
Environ
choosing a menu item,
driving to a venue
8 Sequences Action Percipient
Recipe steps, map
directions
7 Categories
Species
{ Biology
Context }
Object/
Class
Generalization,
abstraction, analogy
6 Relationships
~Prepositions
/ Interiors
Object/
Environ
Under, inside, adjacent,
equal
5 Events Hostility
~Object
.Hostility!
Expansion / O. is
Changing its Form or
Flow
4 Transitions Threat
~Object.
Potentiality
Rising, rotating
3 Configurations Pattern
Object.
Configuration
Extent of Limb-Bend,
Weather, Road Strait &
Narrowness
2 Sensations Quality Signal
Color Green,
cantaloupe odor
1 Intensities Scale Signal Brightness, loudness

#
PCT Level
(Order)
~Survival
Context: Links
Quotes
More Examples
12 Our Brains Tackles ANY Perception Process in that Order : from 1 to 12
(12)
System
Concepts
Conformity
A good system shortens the road to the GOAL
( Orison Swett Marden )
Participation in gatherings/ Social Role / Tax
paying? /
(11) Conflict Malignancy
" There is No rampart that will hold out against MALICE "
( Moliere )
Peril-type3: Rivalry / Dirty Competition / Sport
Wrestles/ Malignant & Irrational Personalities /
Manipulative & Submissive Control Relations/
Son is playing sick to push return home quickly /
10 Principles Guidance
" The Value of a PRINCIPLE is the number of things it will
explain "
( Ralph Waldo Emerson )
Adequate Sport, Good Fitness/ Honesty and
Fidelity / Being On-time vs being Late / ATM
withdraw limits ! / ..
9 Programs Contingencies
“The more INFORMED you are, the less arrogant and
aggressive you are”
(Nelson Mandela)
Car Problem/ Computer fault troubleshooting/
Job Interview/ Sales Plan
8 Sequences Action
"Don't learn SAFETY by accident"
{ Jerry Smith )
Reactions to a sudden wind storm / improvised
tactical solutions to sudden small problems /
routine dressing undressing/ ..
7 Categories
Species
{ Biology
Context }
"It is Human Nature to instinctively rebel at OBSCURITY or
ORDINARINESS"
( Taylor Caldwell )
Types of Berries, Sparrows, Sharks, ..
6 Relationships
~Prepositions/
Interiors
The MULTITUDE of sheep frightens not the wolf
( Unknown )
Business Firm Intra (Internal) Relationships/
caged wild animals/ Fruit at tree-top
5 Events Hostility
"Once HARM has been done, even a fool understands it "
( Homer )
Peril-type2: Wild Animal, Forest Fire (mass), ..
4 Transitions Threat
Life is the DYNAMIC, Creative Edge of Reality
( Eric Parslow )
Peril-type1: a Baseball , a Frisbee, ,,
3 Configurations Pattern
Mouse PERCIEVES cat as a Lion
( Unknown )
Forest landscape/ venue map
2 Sensations Quality
“NOT everything that can be counted counts,
and NOT everything that counts can be counted.”
( Albert Einstein :1879-1955 )
Colors, Sounds, Odors/ (Normal) Weather
1 Intensities Scale
COMPARE apple to apple
( Unknown )
Apples count, Fruit Weight/ Temperature

Hierarchi
cal level:
PCT Level
(Order)
Examples Type of perception Bill Powers' Campfire
Ex.
McClelland (2011)
(12)
Eleventh Order
(1
2th ?)
System
Concepts
Physics,
Government
Sense of organized
unities
enriching marriage by
enjoying time together
~ Gray-scale? : ~How % "Conformal" ?
(11)
( new Eleventh
Order)
Conflict Manipulation Conflict-of-Wills
Manipulation
( Person X had put water
on coal ! / Phone-caller Y
~ Gray-scale? : ~How % "Manipulative"
?
10
Tenth
Order
Principles the precept
“honesty"
Guiding heuristics a nice evening ~ Gray-scale? : ~How % "Principled" ?
9
Ninth
Order
Programs choosing a menu
item, driving to a
Networks of
contingencies
if no bubbling water,
more heat
Discrete: and "the whole of the
sequence is either completed or not" /
8
Eighth
Order
Sequences Recipe steps, map
directions
Serial orderings bigger fire, boiling water/
hot coffee
7
Seventh
Order
Categories Generalization,
Class memberships sputtering vs roaring
campfire
Discrete, but changeable / symbols ..
6
Sixth
Order
Relationships Under, inside,
adjacent, equal
Co-variations lots of kindling, near
flame
increasingly Discrete {Digital.Binary}
5
Fifth
Order
Events Expansion / O. is
Temporal
segmentations
stoking, placing firewood increasingly Discrete {Digital.Binary}
4
Fourth
Order
Transitions Rising, rotating Paths, rates of change flickering Contrasts Scalar {Analogue} variables
3
Third
Order
Configuration
s
Extent of Limb-
Bend, Weather,
Collections of
attributes
fire vs unburnt wood Scalar {Analogue} variables
2
Second
Order
Sensations Color Green,
cantaloupe odor
Attributes, weighted
sums
yellow, crackling Scalar {Analogue} variables
1
First
Order
Intensities Brightness, loudness Magnitudes, amounts Brightness Scalar {Analogue} variables

Hierarchi
cal level:
PCT Level
(Order)
Examples Type of perception Bill Powers' Campfire
Ex.
McClelland (2011)
(12)
Eleventh Order
(1
2th ?)
System
Concepts
Physics,
Government
Sense of organized
unities
enriching marriage by
enjoying time together
~ Gray-scale? : ~How % "Conformal" ?
(11)
( new Eleventh
Order)
Conflict Manipulation Conflict-of-Wills
Manipulation
( Person X had put water
on coal ! / Phone-caller Y
~ Gray-scale? : ~How % "Manipulative"
?
10
Tenth
Order
Principles the precept
“honesty"
Guiding heuristics a nice evening ~ Gray-scale? : ~How % "Principled" ?
9
Ninth
Order
Programs choosing a menu
item, driving to a
Networks of
contingencies
if no bubbling water,
more heat
8
Eighth
Order
Sequences Recipe steps, map
directions
Serial orderings bigger fire, boiling water/
hot coffee
7
Seventh
Order
Categories Generalization,
Class memberships sputtering vs roaring
campfire
Discrete, but changeable / symbols ..
6
Sixth
Order
Relationships Under, inside,
adjacent, equal
Co-variations lots of kindling, near
flame
increasingly Discrete {Digital.Binary}
5
Fifth
Order
Events Expansion / O. is
Temporal
segmentations
stoking, placing firewood increasingly Discrete {Digital.Binary}
4
Fourth
Order
Transitions Rising, rotating Paths, rates of change flickering Contrasts Scalar {Analogue} variables
3
Third
Order
Configuration
s
Extent of Limb-
Bend, Weather,
Collections of
attributes
fire vs unburnt wood Scalar {Analogue} variables
2
Second
Order
Sensations Color Green,
cantaloupe odor
Attributes, weighted
sums
yellow, crackling Scalar {Analogue} variables
1
First
Order
Intensities Brightness, loudness Magnitudes, amounts Brightness Scalar {Analogue} variables
Linguistic (Organs & terms) Linguistic O/P = Content
& Form = Product
Language Learning Phases
developing self image and a concept
of family
~ Family : Dialogue, Chat,
..
System concepts, including a developing self image and a
concept of family (beginning at about 17 months). System
Concepts are continuously developed and refined
~ Learning against-all-odds ?
(counter counter-Learning)
~ Learning against-all-odds
? ( against manipulative
Objects = counter counter-
~ Malignant Manipulator: responding by some incorrect
Feedbacks ! When the P. (infant) produces a Correct
Syllable, word, or phrase ! // opposite reward ( = conflict )
selects an APPROPRIATE program !
(heuristics and other Principles)
~ Language Selection
matches Context
infant selects an appropriate program : via heuristics and
other Principles (at about 15 months),
talk to themselves a lot = recite the
logical recipe that guides their
present purpose.
phrases, sentences : talk
to themselves / use word-
like logical operations and
combine Sequences into Program-level perceptions: as the
child begins to use word-like logical operations and choice
points. Children talk to themselves a lot, at first using
recognize and control Sequence
perceptions : child fascinated with
Sequences (incl. O/P)
words / phonemic
contrasts (Sequences) ,
with same "parameters of
The child becomes fascinated with how one event follows
another, and with what the steps are to fit objects together
in a certain way. The child can now analyze syllable-Events
perceptions: categorizing ..
Increasingly complex relationships
~ set of syllables ( but still
are "unitary Events" ) /
passive vocabulary
Words learned as passive vocabulary .. a kind of matrix ..
child to sort their experiences into different kinds,
categorizing perceptions according to more and more
Canonical "Babbling" Canonical "Babbling" :
Repeated (Consonant/
Vowel) segments :
Canonical babbling: Event perceptions come under control
of the Relationship level : the child produces the Event
perceptions that we recognize as simple syllables : Adults
by diaphragm and larynx resemble canonical
(vowels) syllables: fully
resonant vowels :
Syllable or Word: fully resonant vowels: perceptions begin
to resemble canonical syllables: able to produce clear
vocalizations with the diaphragm and larynx / constricting
Transitions & changes ( in
configurations and Patterns)
Smooth Transitions in
Gooing
emergence of Transitions (smooth changes in
configurations). Linguistic Context
vocal organs coordinated control Gooing: ~ coordinated
vowel
Gooing : vocal organs are played within a more coordinated
way [ lips, tongue, velum, epiglottis, and larynx]
Tone, Frequency/Tenor ~ ‘quasi-vowel’ Child's competence : strengthens in the world of Sensations
at about 5 weeks
Amplitude, volume Phonation : ‘quasi-vowel’ :
non-smooth
Phonation : ‘quasi-vowel’ sounds without the smooth onset
and clear sound of adult vowels
PCT Difficult Example: Language Learning Phases

A Proposed Opinion : Some PCT : Perceptual Control Theory ”Missing Layer?”
A higher levels Perception Layer of ( Conflict of Wills ) ? Very Draft Notes: 0427
“... While many computer demonstrations of principles have been developed, the proposed higher
levels are difficult to model because too little is known about how the brain works at these levels.
Isolated higher-level control processes can be investigated, but models of an extensive hierarchy of
control are still only conceptual, or at best rudimentary … ”
( Ref: wikipedia PCT )
A Missing Perception Layer of ( Conflict of Wills )
= Perception of some Adversary that is beyond ( Threat and Hostility )
= Perception of a “Manipulative” Disturbing Object !
= The Percipient perceives the Error Signal E (= R – P ) as the Difference Signal between : the Reference
Signal & the Disturbance stemming from an (Intentionally, Deliberately, Willing) (Counter, Anti, Rivaly)
Object
= Comparing ( Output Behavior ) to the ( Already-learned Behaviors ) = the “Reassessment Signal”,
ReInforcement in Motivation Theory : indicates the existence of some “Manipulative” Disturbance.
= A Situation of (Self-organized Criticality ) in Complexity Theory
= Hence follows: the well-known Motive for the Emergence of a new Abstraction Layer (similar to what
happens in the Development & Abstraction of all levels )

The term "CONFLICT" : A Clarification (Motivation vs PCT Contexts)/ 20230500, Eng. E
# Item "CONFLICT" in PCT11 Context "CONFLICT" in PCT12 Context Details Notes
1 Brief Conflict within 1 Person: within Same
Person, regarding Motives & Actions
Conflict between 2 Persons: A "Percipeint"
and a Perceptive-Manipulator
2 Involved-Party mainly a one person setting mainly a two-persons setting (at least)
3 Topic Context Pathology : Method of Levels Perception: Social Behavior
4 Term Context Motivation Theory: Reward-Punishment
reinforcement: aka: Reward-conflict
Percipient-Object interaction: that involves
a "Manipulative" Object
5 Term
Disambiguity
- VS: Conflict vs Reward: for an intended
action
- AKA: Conflict aka Punishment
- VS: Conflict vs cooperative: same Principles
& Values
- AKA: Conflict aka Manipulation, Malignant
Maneuvering, Deception, "Conflict of Will"
(more precisly "Conflcit of Wills")
6 Importance Guides Persons Acts & Motivation Protects against Malice & Conflict of Will
7 Theory &
History
Motivation Theory: known since ~1900's PCT Theory: Item ("Manipulative" Level) is
Proposed in 2023
Abbrev: PCT: Perceptual Control theory / VS: Versus/ AKA: also known as/

VIMP:
"Self-Organized Criticality" (SOC )
SOC is the Neuronal State that “Generates” the Abstraction Layers
[ In other words: The Definition of a “New” Layer or Order in the PCT Theory: is the Creation of a
New Abstraction Layer by/of Neurons/Synapses, to be able to “Cancel, Mitigate, Compensate,
nullify, neutralize” the Neuronal SOC State ]
Next Slide :
Hence, elaborating the SOC Process for the 11-Orders (Levels) indicates that a “Manipulation Layer”
is missing , without which: ALL the lower 10 levels will be permanently prone to Malice and
Manipulation as the whole System is unable to achieve its Goals amid having its “Perceptual
Control” being in fact “Controlled” !
The Slide Details (at the rightmost column) :
The possible “ERRORS” or Criticality at each Perception Level which
eventually Causes the Brain to Grow …

PCT Level
(Order)
Name
~Survival
Context: Links
To :
Perception
PARTY
ERRORS: Misperception Possibilities ( =Error SubTypes ) [ S.=Signal,
O.=Object, P.= Percipient , E. =Environment, Prx = Perception]
notes
(12) System Concepts Conformity Percipient/ Environ-"Systems" - P., E. (Over)-Selfish
- P., E. Frenzied, chaos
- P., E. Alien
0
(11) Conflict Malignancy ~Object
.Rivalry
- (O,P) Benign Object (towards P.)
- (O,P) Unintentional Disturbance
- (O,P) Non-Malignant Behavior
- (O,P),E. P. gets most info from O. (Only)
TODO Q
10 Principles Guidance Percipient/ Environ - P. Anomalous, Unruled, lawless
- P. Norms violating/ Lawbreaking
9 Programs Contingencies Percipient/ Environ - P. Haphazard, unplanned, ad hoc Actions
- P. Incorrect Plan
- P. Unanticipated Contingencies
Conting
8 Sequences Action Percipient - P. Inaction (vs Dread)
- P. Incorrect action
"And pr
7 Categories Species
{ Biology Context }
Object/
Class
- O. Ambiguity: Different "Set" (Uncertainity.hard)
- O. Match, Fit, Conformal (vs Misfit)
Guilford
6 Relationships ~Prepositions/
Interiors
Object/
Environ
- O. Isolated
- O. Non-Related, Non-Contained
- O., E. Not Grouped, No Covariance, No Plot!
5 Events Hostility ~Object
.Hostility!
- O. Solid, firm
- O. Stable, robust
- O. Non-aggressive/ Non-wild/ friendly
4 Transitions Threat ~Object.
Potentiality
- O. Static, status-quo
- O. Non-harming/ Neutral
3 Configurations Pattern Object.
Configuration
- O. Vagueness: Different "Item" (Uncertainity.easy)
2 Sensations Quality Signal - S. Different: [Variety, sort, nature] prx
1 Intensities Scale Signal - S. unusual/uncommon Multiple/Mass O.
- S. Disproportionate prx
Intensit

VIMP:
"Self-Organized Criticality" (SOC )
[ SOC is the Neuronal State that is responsible for Abstraction Layers Genesis ]
( Includes 3-Tables in 3 Slides )
1- The 11 Perception Levels vs Introductory (Summary) Aspects
of Misperception
[ #1 Errors (Possibilities)/ #2 Briefs / #3 Neuronal Level Issues]
2- 11 Perception Levels vs 3 Basic Aspects of Misperception
[ #1 Errors (Percipient)/ #2 Errors (Interface)/ #3 Reality]
3- Perception Levels vs 3 Other Aspects of Misperception
[ #4 Consequences/ #5 Learned Lessons/ #6 Emerged Layer]

OUTDATED (Version 0.95) Systems Neurology (the only objective is My CAREER, only , Eng.EmadFaragHABIB)- Ver 0.95.pdf

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