OUTDATED (Version 0.93) Systems Neurology (including Proposed ''Complexity Profiling Chart'' CPC, Eng.EmadFaragHABIB)- Ver 0.93.pdf

Systems Neurology
Functions of the Human Brain
A Proposed Simplified Functional-Architecture of the Human Brain
Based on a Systems-Theory Perspectives
Systems Neurology Ver 0.93 June 21st 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 summary presentation from a
Professional Author at all !
But rather it is exactly the opposite !!
This Presentiation 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 of science : you
will find such presentation useful . If this is not the case , you can simply skip it ( with Author’s
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( Apology: the PDF “one-page shift” problem: is still being tackled )

Conclusion ! :
- The Human Brain -as a Complex System- can be studied by 3 Approaches matching the
micro-meso-Macro scales of its Complexity.
- Next 3 slides show Conclusions drawn from the 3 Approaches
- Approach #1 : the micro-scale: concluded views of scientific works that span Brain
issues from Anatomical to Functional issues are shown .
- Approach #2 : the meso-scale: a concluded “Complexity Profiling Chart” (CPC ) is
shown .
- Approach #3 : the Macro-scale: a concluded view of the 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 1.0): 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(#Var)
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 Compreh
ended
Complex
Clustering
~Social
Diversity
Compreh
ended
Complex
Nestedne
Compreh
ended
SOC
Compreh
ended
Complex
Optimalit
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
PCT
8 4-7 digits 4-7 digits Feedback
Correlatio
n:
(Circural
time-
domain
Solutions,
c(t), ..
4-7 digits 4-7 digits Reinforce
ment
Motivatio
n-
Values,
Beliefs,
incl
Affiliative
7 ± 2.6
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
Fully-
Structure
d Macro-
Construct
3 digits 3 digits Process
Motivatio
n-
Theories
MSG,
Knowledg
e/ Info/
Data
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
form of it)
~Criticalit
y-aspect
(some
form of it:
~Optimali
ty-aspect
(some
form of it)
~Quantita
tive-
aspect
(some
Anatomic
al,
Dissectio
n, Dyes,
~Causality-
aspect
(some
form of it)
Dual
Anatomic
al-
Functiona
Diagrams
(plus
possibly
less)
~Structur
ed-aspect
(some
form of it)
~Quantita
tive-
aspect
(some
~"System-
State"-
aspect
(some
~Subjecti
vity-
aspect
(some
Affective/
Intellectu
al,
5.9 ± 1.2
3 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
2 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.
Maslow
1 (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)
2.9 ± 2.5
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
Causality: Adaptation, Action, to-do
Functional
Abstraction
Layer/
Dominance/
Complexity/
to-be
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
Social Evironment
Social Facts
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)
Social Norms
Personal
Development
Behaviors
Social Behavior – Concordance
( Self: Facts, Norms, and Culture) 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 as an organ
“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 as an organ
“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)
Complexity Theory: 18 Aspects
Neurons
Functions

Abstract
- As of June 2023, Literature Review of Neurology & Systems
Neurology works shows that most studies 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 Higher functions [Awareness,
Perception, and Intellectual functions ].
- Studies of Systems Neurology: Concerned mainly of How the
Brain Functions as a “Complex System” entailing Neuronal
[Tracts, Pathways, Circuits, and Networks] that can be studied
by theories of Dynamic Systems, Information Networks, and
Complexity Theory.
- Studies of Behavior & Functions: Concerned mainly of Needs
and Behavior . Usually entail “Macro Constructs” to tackle
such macro issues.

- - This article presents a simple method to gain a preliminary
evaluation of “Level of Complexity”, When a Researcher is
concerned about “Complexity” in any Research, Literature
Review, System, Discipline, Topic or Issue.
- 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 (Literature Review, Research Topic, or Human
System), 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 = 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” dynamics on papers in Medicine ,
- or plotting Engineering system FREQUENCY-DOMAIN dynamics on a “Semi-Log-scale”
paper: the “Bode Plot” in engineering ,
- Or Plotting (Thermodynamic Properties Curves & Surfaces ) on PVT Axes -Pressure,
Volume, and Temperature- to “visualize” “Thermodynamic Processes” incl. phase
transitions “SOC” ).
- 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) , Approach #3: Behavior & Functions: Brain
Functions as a Links between (Needs and Behavior) .

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 Phenomena ! , 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 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 Elements or SubNetworks,
- #3: the Macro-scale with 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 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
What are?
[Numerousity, Clustering, Diversity, Nestedness, and Modularity]

# Name Description
1 Numerousity Connections: Plenty Massive Linking
2 Clustering Differently "Edged-
Nodes"
Different Links-Distribution
3 Diversity Different SubNets or
Connections
Different Entities [Items/ Nodes/
SubNets/ Entities] or Connections
4 Nestedness Topologies: Hierarchy Different Entities' Layers (Tiers)
5 Modularity SimilarSubNets Similar Patterns = Scale-free
(Entites 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 Different SubNets or Connections
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 SimilarSubNets 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)/ #Varities = Number of V. / Str Structur(al), Fn Function(al)/ ICT Information Communication Techn
Abbrev.: 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 Ed

First 5 Aspects of Complexity
What
[Numerousity, Clustering, Diversity, Nestedness, and Modularity]
are NOT !! :
And what AMBIGUITY is there
for these terms :

0 NAME
# Name Aka(s)
Versus, <>
1 Numerousity Densily InterLinked, Multitude of Connections, Plenty of Edges
<> Sparsly-connected
2 Clustering InterLinked, Interwined, Interweaved, Meshed, Adjoined
<> Non-connected
<> Uniformly-connected= ALL are Equally-connected
3 Diversity Heterogenity, Speciality, Atypicality, Community/ aka: Speciality (yet: Cooperation) / Horizontal CM
<> 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!, .. / Repeated (Configuration Formations Assemblies Molds) at different scales "Scale-f
<> Scale-dependant (Non-repeated)
<> Novelity (of Entities and Connections)
-
Abbrev.: CMX: Complexity/ NE NodeEdge (Discrete Math)/ #Varities = Number of V. / Str Structur(al), Fn Function(al)/ ICT
Abbrev.: NTRC: Intricacy, H Horizontal, V Vertical L Links (LNKX)/ / s.c. special case/ wrt with respect to/ DAG Directed Acyc
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/ Use ( SubTypes, Lists, And Ambiguities ) QQ
1 Numerousity Densily InterLinked, Multitude of Connections, Plenty of Edges NUMRS Massive Linking 0
<> Sparsly-connected Ambiguity of Sparsly-connected Entites
2 Clustering InterLinked, Interwined, Interweaved, Meshed, Adjoined CLSTR Different Links-Distribution
0
<> Non-connected Ambiguity of non connected Entites
<> Uniformly-connected= ALL are Equally-connected
Ambiguity of uniformly-connected Entites , while "clustering" necessitates differences in connections-density
3 Diversity Heterogenity, Speciality, Atypicality, Community/ aka: Speciality (yet: Cooperation) / Horizontal CMX DVRS Different Entities [Items/ No
0
<> Homogenity, Generality, Typicality,
<> Vertical Intricacy [H vs V] Intricacy: H: +(System Disorder)/ V: -(System Order) Intricacy: 3: [ Horizontal Intricacy / Vertical Intricacy /
Horizontal D
- 3: [Intra vs Inter vs Community DVRS] Diversity: 3: [ intra-type/ inter-types / Community Co
- 2: [Atypicality vs "Typicality"] Typically: 2: [ Atypicality (items, sets)= Non-typical /"
high A.=non
4 Nestedness Hierarchy, Embedding (Inclusion-E.)/ Tiers, Ranks, Tree / Vertical CMX, NSTD 0
<> Flat
<> Horizontal Intricacy [H vs V] Intricacy: H: +(System Disorder)/ V: -(System Order) Intricacy: 3: [ Horizontal Intricacy / Vertical Intricacy /
Horizontal D
<> General Relational Entities General Relational Entites (ICT.Database Context!)
<> DAG Ambiguity of more advanced network topology than NSTD, e.g. DAG
- 2: [ Embodied-Embedded ]
5 Modularity Patternity!, .. / Repeated (Configuration Formations Assemblies Molds) at different scales "Scale-free" MDLR 0
<> Scale-dependant (Non-repeated) Ambiguity of Scale-dependant (Non-repeated) SubNetworks
<> Novelity (of Entities and Connections)
Ambiguity of Novelity (of Entities and Connections)
- 2: [ Str/ Fn ]
Abbrev.: CMX: Complexity/ NE NodeEdge (Discrete Math)/ #Varities = Number of V. / Str Structur(al), Fn Function(al)/ ICT Information Communication Technology/
Abbrev.: 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

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 1 = General and Mixed
- Value 0 = 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, a List of MCQ (18
Standard Questions) , with each Question having a maximum
of 11 Possible Answers .
- This simple procedure “generates” the Profile Chart for ANY
System or Complexity Issue !
- Next: 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 ! / (General & Mixed)/ Seminal Works/
Descriptive/ Anatomical-Functional/ (Nominally) Substantiated/ Cause-Effect/ Dynamical-
systems/ time-domain Solutions/ Universal-Law(s)-Contexted/ Meta- ]
, i.e. : Substantiation of the Investigation & Findings
5 Substantiated: Nominal
Modeling/Formulation : (both Evidence-
based and Conformal to Human &
Biological Organisms contexts)
4 Dual Anatomical-Functional
substantiation, Pathological
Affirmations ?
3 Descriptive (somwhow structured)
2 Seminal Works, References, yet not
fully-substantiated, taken for granted
1 (General & Mixed)
0 No SUBSTANTIATION, or Unknown !,
Proposed, speculative, provisional,
Draft Articles, Amatuers
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"

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

The a/m A1-A5
[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-
ended
Complex
Clustering
~Social
Diversity
Compreh
ended
Complex
Nestedne
8 4-7 digits
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-
(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
form of it)
3 Clustered
Bonding
Aggregate
s
2 Clustered
Physical-
Matter
Aggregate
1 (General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
(General
& Mixed)
0 No
NUMERO
USITY in
Connectio
Non-
CLUSTERE
D
SubSyste
Non-
DIVERSITY
in
SubSyste
Non-
NESTED
SubSyste
ms: ~
Non-
MODULA
R
SubSyste

"Complexity Profiling Chart" (CPC Ver 1.0): 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
ModularityCriticality OptimalityQuantized (μ)
Investigation
Correlation (Info)
Causality Substantiation
Formulation
Structured (M)
Quantized (M)
States(#Var)
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-
ended
Complex
Clustering
~Social
Diversity
Compreh
ended
Complex
Nestedne
Compreh
ended
SOC
Compreh
ended
Complex
Optimalit
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
8 4-7 digits 4-7 digits Feedback
Correlatio
n:
(Circural
time-
domain
Solutions,
c(t), ..
4-7 digits 4-7 digits 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
Fully-
Structure
d Macro-
Construct
3 digits 3 digits Process
Motivatio
n-
Theories
MSG,
Knowledg
e/ Info/
Data
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
(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
form of it)
~Criticalit
y-aspect
(some
form of it:
~Optimali
ty-aspect
(some
form of it)
~Quantita
tive-
aspect
(some
Anatomic
al,
Dissectio
n, Dyes,
~Causality-
aspect
(some
form of it)
Dual
Anatomic
al-
Functiona
Diagrams
(plus
possibly
less)
~Structur
ed-aspect
(some
form of it)
~Quantita
tive-
aspect
(some
~"System-
State"-
aspect
(some
~Subjecti
vity-
aspect
(some
Affective/
Intellectu
al,
3 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
2 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.
1 (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)
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)
PCT, IWMT, and Malsow
Theories/Frameworks
on these 18-Aspects

- Concluding Notes (Draft):
- The Complexity Universal Aspects Evaluator: 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”) .
- The 18 Complexity Aspects are arranged [i.e.: Ordered, Valued, and Termed ] in the same
Arrangements used to “Describe” Complex systems (and in particular the Human Brain),
descriptions that are based on our contemporary knowledge of Complexity Theory.
- 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.

- Concluding Notes (Draft):
- Regarding our Contemporary Knowledge of the Theory of Complexity : it is undeniable
that we are somewhere close to the ( Pre-Newtonian Era ) in Complexity !!! . We are
hardly spelling the ABC’s of Complexity , and in this very situation: such “Complexity
Profiling Chart” CPC can be helpful .

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

Network Topologies (CNS Context)
- Draft Notes :
- It’s all about “SubNetworks” .
- 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 ?

Human Brain Networks Topologies : How Neuronal Populations form "Large-scale Networks" , 20230600 Eng. Emad Farag HABIB
# Name Constraints
Constraints Optimality Balance Efficiency [Global-VS-Clustering]
Function Math Model Notes
5 Spatial
(Integrative)
BOTH physical and metabolic
constraints
MINIMUM overall @ CNS Level
MINIMUM total wiring distance (metabolically
driven to ..) // Physically constrained to exist
within a tight 3D volume
Physically-constrained to
exist within a tight 3D
volume
HH SUBJECTIVE Complex Fns:
Information Instantiation &
Probabilistic Modeling are
required
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
Optimum performanceFn
VS performing other Fns
HH MYRIAD of Fns: requiring System-
Str to change according to
Function's varying Signals/Inputs.
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
Functionally-constrained
to exist within a Learning
Creature
HH COGNITIVE Fns: Sequential
(Linking/Attributing) to/of
Specialized Hub-regions
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 node = fast communication
Balance Efficient
Clustering with fast
locating nodes
HM SWIFT Fns: Optimized Reach-
time:
Min Time to locate a certain node
( ~ 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
Balance Efficient
Clustering with short
average path length
MH PRIORITIZED Fns: Optimized-
Performance:
Min Total number of
computational steps
Watts-
Strogatz
model
(2B) (Lattice) Uniformity: ANY Node to be
connected to ALL its
neighborhoods
MAXIMUM Strength @ Community Level
~Uniform P.Distr. ?!
( Equal Likelihood : to
reach the "Most
Probable" State )
LH ROUTINE Fns: Equal-Importance
Task-items
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
Balance Link-Objective
with Metabolic-
Constrants
MM SPECIALIZED Fns/Tasks:
specialized Brain cognitive Areas
(Communities, Sensory
Modalities)
stochastic
block model
1 Random NIL ! ~~RND P.Distr. ?! No Balance, just Link
Probability satisfy some
"P" of a Binomial P.Distr.
HL NON-STRUCTURED Fns/Tasks:
possibly suiting the initial
(Learning/ Trials&Error) phases.
Erdös-Rényi
model (α→0)
(Non)
determ
Abbrev: VSCS: Variable-structure Control-system // NTX Network(s)/ Random/ Modular(Community, Clusters)/ Lattice(Crystals)/ SmallWorld/ Hub/ Spatial 3D/ High, Medium, Low/ Probability Distribution, P./ Versus/ Very Important/ also known as/ Function/ Directed Acy
NTX 5: [ RND// CMNT // (Lattice)// SW// (Hierarchical)// Hub// (VSCS) // Spatial ] , NTX.3D : Ref: 2019, https://doi.org/10.1038/s42254-019-0040-8
Very Important
20230610

(( 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 Causality-wise: [Structure/ State/ Principle/ Behavior/Context/ Function ]

FCBPSS : [ Function/ Context/ Behavior/ Principle/ State/ Structure ] / draft schematic 0406
Arranged Causality-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, 0504 f
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 "BHX"
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/ Avoidance/
4: [ Alert(aroused)/ Awake/ DMN(defauly-mode Network, relaxed)/ asleep] ? , aka "Brain State
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 DOC4 file)
( Links to )
'~ Need for Voluntry Action
needs- Generic Action B. Cognitive Fns. II. Cognition/ Thoughts/ K.I.D. / MSG, Portfolio/ Value
Emotional Fns. I. Emotions/ Moods/ Habbits/ Attitudes/ Social Behav
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/ Type/ Connections] = CMX 3D Perspective !! , D#3 in particualr : Sub
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

Term “Conflict”
NOTES 0531

The term "CONFLICT" : A Clarification (Motivation vs PCT Contexts)/ 20230500, Eng. E
# Item PCT11 "CONFLICT" PCT12 "CONFLICT" 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: Proposed 2023
Abbrev: PCT: Perceptual Control theory / VS: Versus/ AKA: also known as/

Neurons

Functions

DAC Theory
NOTES 0531

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

2D Diagram
For Brain Functions
“To-Do” VERSUS “To-Be”

Functional
Abstraction
Layer/
Dominance/
Complexity/
to-be
Behavioral [ Inhibitory / Excitatory ] Balance
Next Slides :
Proposed Idea of
Sketching The Brain Functions
In a 2D Diagram :
“To-Do X To-Be” !
Behavior X Needs
Action X Learn
Flow X Fountain
Cash X Credit
Causality X Dominance
Where the well- Known “Neurological Constructs “
Like [ Beliefs/ Values/ Attitudes/ Motives ]
are laid in a 2D Arrangement
that works as a “Conceptual Map”
Personal
[
Developmental
/
Adaptation
]
Balance
More Accurately:
Behavior X Needs = System (Oranism-Envirom)
( To-Do X To-Be ) = Essence
Action X Learn = Growth
Flow X Fountain = Homo sapiens
Cash X Credit = Personality, Character
Causality X Dominance = Shortterm, Longterm
( Complexity X Maslow ) = FCBPSS
Needs *Maslow, ERG, … +
5. Self Actualisation
4. Esteem
3. Affiliation
2. Safety & Security
1. Biological

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 ]
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),
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
Object/
Class
Generalization,
abstraction, analogy
6 Relationships
~Prepositions
/ Interiors
Object/
Environ
Under, inside, adjacent,
equal
5 Events Hostility
~Object
.Hostility!
Expansion / O. Changing
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
System
Concepts
Physics,
Government
Sense of organized
unities
enriching marriage by
enjoying time together
~ Gray-scale? : ~How % "Conformal" ?
(11)
0 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
Discrete: and "the whole of the
sequence is either completed or not" /
7
Seventh
Order
Categories Generalization,
abstraction, analogy
Class memberships sputtering vs roaring
campfire
Discrete, but changeable / symbols ..
6
Sixth
Order
Relationships Under, inside,
adjacent, equal
Co-variations lots of kindling, near
frame
increasingly Discrete {Digital.Binary}
5
Fifth
Order
Events Expansion / O.
Changing Form or
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

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, Rival)
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 )

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, to be able to “Cancel, Mitigate, Compensate, nullify,
neutralize” the SOC Neuronal 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: Advanced Aspects for each Level : including "ERRORS" or Misperceptions
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. 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]

Misperception : ERRORS = Misperception Possibilities / DEVELOPMENTAL : in Weeks / Notable BRIEF Extracts ( Concepts )
# PCT Level
(Order)
Perceptio
n
PARTY
ERRORS: Misperception Possibilities ( =Error
SubTypes ) [ S.=Signal, O.=Object, P.=
Percipient , E. =Environment, Prx =
Wee
ks:
Nor
Notable Brief Extracts Neuronal Level
[ Information Carriers for CONTENT ]
(12)
System
Concepts
Percipient/
Environ-
"Systems"
- P., E. (Over)-Selfish
- P., E. Alien
75
System Concepts … Conformity … Organized unities ..
Brain Areas of: Identify social emotions and Moral
Judgment .
Abstraction of SYSTEM, SOCITY
, Links to: Identify social emotions and Moral Judgment Brain Areas
(11)
Conflict ~Object
.Rivalry
- (O,P) Benign Object (towards P.)
- (O,P),E. P. gets most info from O. (Only)
?
Conflict .. Malignancy .. Perceived "O. Rivalry" ..
Counter-manipulation .. "Information Pyramid" .
Abstraction of COUNTER-MALIGNANCY . Neuronal Populations exhibits
Coordinated "Interaction" // possibly utilizes all the 6 levels of "Information
Pyramid" , many Neuronal networks and "maps" ?
10
Principles Percipient/
Environ
- P. Anomalous, Unruled, lawless
- P. Norms violating/ Law breaking 64
Principles ... Guidance … Conceptual constructs and
Moral Judgment .
Abstraction of PRINCIPLES
, Links to: Conceptual constructs and Moral Judgment Brain Areas
9
Programs Percipient/
Environ
- P. Haphazard, unplanned, ad hoc Actions
- P. Incorrect Plan
55
Programs ... Planning .. Situation Contingencies ... Brain
Areas of : Planning .
Abstraction of Action PLANNING,
( Links to Brain Areas of Planning )
8
Sequences Percipient - P. Inaction (vs Dread)
- P. Incorrect action 46
Sequences ... Action … P. Responses .. Brain Areas of:
Vasomotor planning, Motor planning and execution .
Abstraction of SensoryMotor Areas, to enable MOVEMENT, Vasomotor planning,
Motor planning and execution, Speech, Further Sensing?, ..
7
Categories Object/
Class
- O. Ambiguity: Different "Set"
(Uncertainity.hard)
37
Categories.. Class .. O. "Set" ..Ambiguity ..
Inaccuracy..Importance of "Labels" .
Abstraction of Planning to infer CLASS,
( Incl. Importance of Language and "Labels")
6
Relationships Object/
Environ
- O. Isolated
- O., E. Not Grouped, No Covariance, No Plot!
No Collaborative Efforts
26
Relationships ... ~Prepositions ... Context .. O.Environ ..
O.Internal & SubSystems
- Abstraction to infer RELATIONSHIPS, ( Includes Filtering Object/Environment,
Relationships, Companionship types, .. )
5
Events ~Object
.Hostility!
- O. Solid, firm
- O. Stable, robust
19
Events .. Temporal Segmentations .. Perceived Prospect
of "O. Hostility" hence pursues non Aggressive .
Abstraction : for EPISODES ( Incl. Episodic Memory ) , HOSTILITY, ( Incl.
Semiautonomous SubSystems )
4
Transitions ~Object.
Potentiality
- O. Non-harming/ Neutral 12
Transitions .. Paths .. Perceived Prospect of "O.
Potentiality" hence pursues Evading Threats .
Abstraction of Topographical Mapping to infer RATES
- Abstraction to infer POTENTIALITY(incl. Routing and gating of information flow /
Rates mandates “Coherent firing”of Neurons )
3
Configuration
s
Object.
Configuration
- O. Vagueness: Different "Item"
(Uncertainity.easy) 8
Configuration.. Pattern .. O. "Item" ..Vagueness ..
Imprecision.. "Combinatorial" Storage .
Abstraction: where Patterns are stored in "Combinatorial PATTERN Coding" ( Incl.
Literal "SOC" @ Information.Reconstruction of images / =meta AP, Fluctuations, ..
)
2
Sensations Signal - S. Different: [Variety, sort, nature] prx
5
Sensations … Quality .. Brain Areas of: Sensory
Association Areas .
SENSORY Association Areas
1
Intensities Signal - S. unusual/uncommon Multiple/Mass O.
- S. Disproportionate prx 0
Intensities … Scale … Quantity Prx . First (PRIMITIVE) Abstraction

Misperception : #1: Percipient (Inference) Errors / #2: Percipient-Object (Interface) Errors / #3: Reality, Actual, ..
# PCT Level
(Order)
Name
ERRORS: Misperception Possibilities (
=Error SubTypes ) [ S.=Signal, O.=Object,
P.= Percipient , E. =Environment, Prx =
Perception]
#1. Percipient ERRORS #2. Interface ERRORS #3. While Actual/reality was/is--
(12)
System
Concepts
- P., E. Alien
- Errors of proceeding with NON-CONFORMAL Unity - Non-cooperative, Non-Collaborative Actions/Plans.
- Disorganized Unities with the System ( VS Complexity Theory
"Self-organized Criticality": Specialty-Cooperation )
- Non-conformal, Unfitting,
P., E.: P. can be more conformal to the social
system..
(11)
Conflict - (O,P) Benign Object (towards P.)
- Errors of Undetected Malignant INTENTIONS.
- Errors of Undetected MANIPULATION of P.
Behaviors.
- NAÏVE & BENIGN evaluation of O. Rivalry
- Errors of Single Info Source ( from O. only)
O. Practices Malignancy and Manipulation
10
Principles - P. Anomalous, Unruled, lawless
- P. Norms violating/ Law breaking
- Errors of proceeding with UNGUIDED or
Ungrammatical Plans.
- Unguided/Unprincipled Plans
- ~Norms-violating (Ungrammatical) Plans
P. can be guided by Principles
9
Programs - P. Haphazard, unplanned, ad hoc Actions
- P. Incorrect Plan
- Errors of : INCORRECT CHOICE of plans / of
inadequate plans/ of ~limited Contingencies
Consideration
- Mistaken preference of Unorganized Actions (plan) , links to
[prx & volition]
- Mistaken choice of Plan
- Unanticipated (Unaccounted for) Contingencies
P. Situation mandates a correct Plan
8
Sequence
s
- P. Inaction (vs Dread)
- Errors of INCORRECT SELECTION of action(s) / or
inaction.
- Incorrectly leaning towards inaction, (vs dread), links to [prx
& volition]
- Incorrect selection of the Action(s)
P. Situation mandates a correct Act
7
Categories - O. Ambiguity: Different "Set"
(Uncertainity.hard)
- Errors of INACCURATE Set (Class) Recognition
- Errors of Insufficient/Inefficient Population Sample
Inaccurate Identification of the Object's Set
"Non Representative Sample" that defined what is "Normal"
O. Set is [Perceptible/ Memory Stored/
Matchable]
6
Relationsh
ips
- O. Isolated
- O., E. Not Grouped, No Covariance, No
Plot!
'Errors of INSULAR & Limited evaluation of O.
Relationships
- Faulty Inference of Object/Environ Relationships & Context. O., E. : O. experiences relationships
5
Events - O. Solid, firm
- O. Stable, robust
Errors of DORMANT &UNSOPHISTICATED evaluation
of O. Hostility
- Faulty Monitoring / Naïve O. Hostility Assessment
[Inexperienced, ~over-peaceful, .. ]
O. experiences Event-wise Changes
4
Transition
s
Errors of STAGNANT & SERENE evaluation of O.
Potentiality
- Faulty Alertness/ Incautious O. Observation & Potentiality
Assessment [Unquestioning, Trusting, .. ]
O. experiences Transition
3
Configurat
ions
(Uncertainity.easy)
Errors of IMPRECISE Pattern Recognition Imprecise Identification of the Object (as an Item) O. is [Perceptible/ Memory Stored/ Matchable]
2 Sensation
s
- S. Different: [Variety, sort, nature] prx Errors of INCORRECT Attributing & weighted sums Different Quality Prx S. is of different quality or sort
1
Intensities - S. unusual/uncommon Multiple/Mass O.
Errors of First Acquaintance / INSUFFICIENT
Attention & Noise-rejection
Inexact Quantity Prx S. is of different quantity or amount

Misperception : #4: Consequences / #5: Learned Lessons / #6: New Layers (Abstractions)
# PCT Level
(Order)
Name
ERRORS: Misperception Possibilities
( =Error SubTypes ) [ S.=Signal,
O.=Object, P.= Percipient , E.
=Environment, Prx = Perception]
#4. Consequences (SOC)
= Hence Experiencing Incorrect --
#5. Learned Lesson #6. Emerged Layer (L.) : Tackling --
(12)
System
Concepts
- P., E. Alien
P. Experiencing NON-
CONFORMAL Unity with the
System.
Proper Conformity with the
System
New L. for Assuring Proper P. matching with the Society (as a
Social SubSystem in Unity with the system)
(11)
Conflict - (O,P) Benign Object (towards P.)
- (O,P),E. P. gets most info from O.
P. Experiencing UNTRUE
NULLIFICATION Of the Error Signal
!
Proper Countering of the Object's
Malignancy, and Proper Ino Input
from E. ( not only O.)
New L. for Proper Counteraction of Malignancy [ Complex
networks of behaviors perception/anticipation // O.'s Intentions
Probing // More Input from E.// Behaviors Trial and Error //
Higher level interactions ]
10
Principles - P. Anomalous, Unruled, lawless
- P. Norms violating/ Law breaking
P. Experiencing UNPRINCIPLED
Plans
Proper Principles to Guide the
Plans/Actions
New L. for Assuring Proper, Principled and Grammatical Plans, in
addition to adequate consideration of Plans' Contingencies
9
Programs - P. Haphazard, unplanned, ad hoc
Actions
- P. Incorrect Plan
P. Experiencing INCORRECT
CHOICE of plans
Proper Planning and Selection of
Plans
New L. for Proper Planning and Selection of Plans
8
Sequence
s
- P. Inaction (vs Dread)
P. Experiencing INCORRECT
SELECTION of action(s) or
Proper Action(s) New L. for Proper Action Enactment and Selection.
7
Categories - O. Ambiguity: Different "Set"
(Uncertainity.hard)
P. Experiencing Unrecognized
Object SET
Proper Accuracy of the
Recognition Process
New L. for Proper Classing Abstraction:[ Item-set Inference/ Set
Memory and recall/ Set Matching Process ]
6
Relationsh
ips
- O. Isolated
- O., E. Not Grouped, No Covariance,
No Plot!
P. Experiencing Incorrect Prx of
Object's RELATIONSHIPS
Proper contexting of the O.Prx New L. for Proper Relationships Inference
5
Events - O. Solid, firm
- O. Stable, robust
- O. Non-aggressive/ Non-wild/
friendly
Object's KINETICS
Proper Prx and response of/to the
Object's Hostility
New L. for Proper Duration-Alertness/ and for better Cautious
Monitoring
4
Transition
s
Object's KINEMATICS
Proper Alertness/ Observation
and Caution
New L. for Proper Rates-Alertness/ and for good Cautious
Observation
3
Configurat
ions
(Uncertainity.easy)
P. Experiencing Unrecognized
Object (as an ITEM)
Proper Precision of the
Recognition Process
New L. for Proper Configurations-Abstraction: [ Pattern Prx
Process/ Pattern Memory Storage & Recall/ Pattern Matching
Process ]
2
Sensation
s
- S. Different: [Variety, sort, nature]
prx
P. Experiencing Different QUALITY Proper Attributing & weighted
sums
Layer (L.) for Proper weighted sums & Attributing
1
Intensities - S. unusual/uncommon
Multiple/Mass O.
P. Experiencing Different
QUANTITY
Proper Attention and Noise
Rejection
Layer (L.) for Proper Quantity Prx, Attention and Noise Rejection
Tasks.

Abbreviations
Abbreviations:
CNS Central Nervous System
BA Brodmann Areas
MSG: Mental Self Government
PCT: Perceptual Control Theory
CCN: Collective Control Networks
ADC: Adaptive Distributed Control
STM: Short Term Memory
LTM: Long Term Memory
CLTM: Constructive Memory
BMR: Basal Metabolic Rate
RIT: Roget International Thesaurus
Abbreviations: (continued)
Sys: System
Ctrl: Controller (Regulator)
Fn, Fns: Function, Functions
VSCS, VSC: Variable-Structure Control-System Model
FCBPSS: [ Function/ Context/ Behavior/ Principle/ State/ Structure ] Model
Ref: Reference
K: Knowledge
OS: Operating System
(ICT-context : Information, Communication, Technology)
cf: Refer to, please turn over to
e.g.: For Example
a/m: afore-mentioned
Incl.: Including
RCA: Root-cause Analysis
PPT: PowerPoint Presentation ( also PDF file format)
VIMP: Very Important (issue or point)
[ Item#1/ Item#2/ Item#3/ .. ]: Lists of Items
w.r.t.: with respect to

Quotes ( non-cited quotes are Author’s DIY mockups )
Human Brain Development :
“The gap between ape and man
is immeasurably greater than
the one between amoeba and ape. “
Stephen Fleming,
Quantity and Quality :
“Not everything that can be counted counts,
and not everything that counts can be counted.”
Albert Einstein (1879-1955)
Exploration ( Un-mentored Trial & Error .. ) :
“Exploration is not an EFFICIENT Process”
Diagrams & Ideas :
“A Picture Speaks a thousand Words,
a DIAGRAM speaks a thousand IDEAS”
Complexity Theory :
Our Contemporary Knowledge of Complexity is undeniably
somewhere close to the ( pre-Newtonian era ) in
Mechanics, and our CONSTRUCTIVE use of AI capabilities
may prove to be fully dependent on that Knowledge .
Brain & Library :
"A man should keep his little brain attic stocked with all
the furniture that he is likely to use,
and the rest he can put away in the lumber-room of his
library .. “
Sir Arthur Konan Doyle
Malice :
“Never attribute to malice anything that can be attributed
to STUPIDITY“
James W. Haefner
Workings of our Brains:
“The laws of nature are written in the workings of our
brains”
Thomas L. Saaty
Brain’s “Abstraction Layers” (PCT Theory ) :
The Power of MSG Theory is that it is based upon deducing
What Humans Brain “Thinking Styles” are, based on how
they created “Social Governance Systems “ (Politics
Context).
Similarly: PCT Theory is based upon deducing What
Humans Brain “Abstraction Layers” are, based on how
they created “Social Communication Systems” (Linguistic
Context).

86
Brodmann K (1909). "Vergleichende Lokalisationslehre der Grosshirnrinde" (in German).
Leipzig: Johann Ambrosius Barth.
( “Brodmann’s Areas”, Korbinian Brodmann,1909, yes 1909  )
Klemm, William R., “Core Ideas in Neuroscience”,
2013, 2016, Texas A&M University
Hemmen, J. L, van, et al, “23 Problems in Systems Neuroscience”,
OXFORD UNIVERSITY PRESS, 2005 , ISBN-13: 978-0-19-514822-0, ISBN-10: 0-19-514822-3
Sternberg, R. J. (1988). Mental Self-Government: A Theory of Intellectual Styles and Their Development.
https://doi.org/10.1159/000275810
William T. Powers … . Perceptual Control Theory: { Apology, doi details will be added after surviving drowsy toture
by microwaver ray }
Ganong, William F., 1995, “Review of Medical Physiology”,
17th edition, LANGE books
Despopoulos, Agamemmon, 2003, “Color Atlas of Physiology”,
5th Edition, Thieme Flexibooks
Bertalanffy, Ludvig von, 1968, “General System Theory” : Foundations, Development, and Applications”,
Revised Edition, George Braziller, New York
Haefner, James W. , 1996, “Modeling Biological Systems” : Principles and Applications”
Chapman & Hall Pub.
References :
Systems Neurology Ver 0.93 June 21st 2023 HABIB’s Complexity 3D Perspective Eng. Emad Fa

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