OUTDATED (Version 0.91) Systems Neurology (the only objective is My CAREER, o...EmadfHABIB2
This document proposes a simplified functional architecture of the human brain based on systems theory perspectives. It discusses three approaches to studying the brain: 1) neurology, which focuses on neurons and neuronal functions; 2) systems neurology, which views the brain as a complex system of neuronal networks; and 3) behavior and functions, which examines how the brain links needs to behavior. It then presents the meso-scale approach involving classifying brain theories and frameworks based on 18 "complexity universal aspects". Finally, it provides conclusions regarding the multi-scale nature of studying the complex brain system across micro, meso, and macro levels.
The document discusses various research projects involving the automated design and optimization of complex physical, chemical, and biological systems using evolutionary algorithms and machine learning techniques. It describes current and planned usage of computer clusters to run simulations and experiments for protein structure prediction, software self-assembly, and modeling physico-chemical systems through evolutionary optimization of parameters. The research requires significant computational resources to process large datasets and evaluate models in parallel.
Possible Worlds Explorer: Datalog & Answer Set Programming for the Rest of UsBertram Ludäscher
PWE: Datalog & ASP for the Rest of Us discusses using Possible Worlds Explorer (PWE) to make Datalog and Answer Set Programming (ASP) more accessible to non-experts. It covers topics like using provenance to explain query results, capturing rule firings to track provenance, representing provenance as a graph, using states to track derivation rounds, and declarative profiling of Datalog programs. The presentation advocates for tools like PWE that wrap Datalog/ASP engines to combine them with Python ecosystems and allow interactive use in Jupyter notebooks. This makes the languages more approachable and helps users build on existing work by experimenting further.
OUTDATED (Version 0.91) Systems Neurology (the only objective is My CAREER, o...EmadfHABIB2
This document proposes a simplified functional architecture of the human brain based on systems theory perspectives. It discusses three approaches to studying the brain: 1) neurology, which focuses on neurons and neuronal functions; 2) systems neurology, which views the brain as a complex system of neuronal networks; and 3) behavior and functions, which examines how the brain links needs to behavior. It then presents the meso-scale approach involving classifying brain theories and frameworks based on 18 "complexity universal aspects". Finally, it provides conclusions regarding the multi-scale nature of studying the complex brain system across micro, meso, and macro levels.
The document discusses various research projects involving the automated design and optimization of complex physical, chemical, and biological systems using evolutionary algorithms and machine learning techniques. It describes current and planned usage of computer clusters to run simulations and experiments for protein structure prediction, software self-assembly, and modeling physico-chemical systems through evolutionary optimization of parameters. The research requires significant computational resources to process large datasets and evaluate models in parallel.
Possible Worlds Explorer: Datalog & Answer Set Programming for the Rest of UsBertram Ludäscher
PWE: Datalog & ASP for the Rest of Us discusses using Possible Worlds Explorer (PWE) to make Datalog and Answer Set Programming (ASP) more accessible to non-experts. It covers topics like using provenance to explain query results, capturing rule firings to track provenance, representing provenance as a graph, using states to track derivation rounds, and declarative profiling of Datalog programs. The presentation advocates for tools like PWE that wrap Datalog/ASP engines to combine them with Python ecosystems and allow interactive use in Jupyter notebooks. This makes the languages more approachable and helps users build on existing work by experimenting further.
The document discusses potential areas of collaboration between Metron and the Science of Evolving Adaptive Systems (SEAS) Lab. It describes three main possibilities: 1) Working on parallel geometric algorithms and SPEEDES framework, 2) Developing a framework similar to SPEEDES for GPU hardware, 3) Leveraging the Starcat framework for distributed and parallel computation. The document provides background on the director of SEAS Lab and his research interests. It outlines initial plans to pursue the different collaboration opportunities through publications, applications, and follow-on funding.
Object Oriented Approach Within Siebel BoundariesRoman Agaev
This document discusses implementing object-oriented principles within the Siebel environment. It defines key object-oriented concepts like hierarchy, inheritance, abstraction, encapsulation, generalization, polymorphism, and persistence. The document argues that Siebel supports these concepts through mechanisms like business components, services, and classes. It emphasizes that developers must adhere to object-oriented patterns like strong typing and encapsulation when building applications in Siebel. Following these principles helps reuse code and align development with object-oriented analysis and design.
Bra a bidirectional routing abstraction for asymmetric mobile ad hoc networks...Mumbai Academisc
This document summarizes a paper that presents a framework called BRA that provides a bidirectional abstraction of asymmetric mobile ad hoc networks to enable off-the-shelf routing protocols to work. BRA maintains multi-hop reverse routes for unidirectional links, improves connectivity by using unidirectional links, enables reverse route forwarding of control packets, and detects packet loss on unidirectional links. Simulations show packet delivery increases substantially when AODV is layered on BRA in asymmetric networks compared to regular AODV.
Machine learning in science and industry — day 4arogozhnikov
- tabular data approach to machine learning and when it didn't work
- convolutional neural networks and their application
- deep learning: history and today
- generative adversarial networks
- finding optimal hyperparameters
- joint embeddings
A consistent and efficient graphical User Interface Design and Querying Organ...CSCJournals
We propose a software layer called GUEDOS-DB upon Object-Relational Database Management System ORDMS. In this work we apply it in Molecular Biology, more precisely Organelle complete genome. We aim to offer biologists the possibility to access in a unified way information spread among heterogeneous genome databanks. In this paper, the goal is firstly, to provide a visual schema graph through a number of illustrative examples. The adopted, human-computer interaction technique in this visual designing and querying makes very easy for biologists to formulate database queries compared with linear textual query representation.
For non-grid 3D images like point clouds and meshes, and inherently graph-based data.
Inherently graph-based data include for example brain connectivity analysis, scientific article citation networks, (social) network analysis, etc.
Alternative download link:
https://www.dropbox.com/s/2o3cofcd6d6e2qt/geometricGraph_deepLearning.pdf?dl=0
This document discusses techniques for analyzing unstructured text data from computer data inspection. It discusses using clustering algorithms like K-means and hierarchical clustering to automatically group related documents without supervision. The goal is to help computer examiners analyze large amounts of text data more efficiently. Prior work on clustering ensembles, evolving gene expression clusters, self-organizing maps, and thematically clustering search results is reviewed as relevant to this problem. The problem is how to identify and cluster documents stored across multiple remote locations during computer inspections when existing algorithms make this difficult.
This document proposes developing a system to display data structures and algorithms in a graphical user interface. It will allow users to visualize changes in data structures as algorithms execute through animated graphical objects. The system will be implemented using the Genesis programming language and JavaFX for the interface. It will follow a MVC design pattern with classes to represent runtime objects, control interaction, and define the interface. This system aims to help novice students learn programming and algorithms visually through manipulating objects.
The document discusses parallel computing over the past 25 years and challenges for using multicore chips in the next decade. It aims to provide context to scale applications effectively to 32-1024 cores. Key challenges include expressing inherent application parallelism while enabling efficient mapping to hardware through programming models and runtime systems. Future work includes developing methods to restore lost parallelism information and tradeoffs between programming effort, generality and performance.
This document discusses challenges and opportunities for integrating large, heterogeneous biological data sets. It outlines the types of analysis and discovery that could be enabled, such as comparing data across studies. Technical challenges include incompatible identifiers and schemas between data sources. Common solutions attempt standardization but have limitations. The document examines Amazon's approach as a model, with principles like exposing all data through programmatic interfaces. It argues for a "platform" approach and combining data-driven and model-driven analysis to gain new insights. Developing services with end users in mind could help maximize data reuse.
The document discusses using a behavior-based approach to build bioinformatics pipelines that is inspired by robotics. It summarizes the challenges of next generation sequencing data and describes how behavior-based robots operate in a distributed, modular way without centralized control. The document advocates designing bioinformatics pipelines with autonomous modules that can opportunistically process data and adapt as needed. It provides an example pipeline built with a behavior-based approach using Google Spreadsheets and autonomous processing agents.
A General Purpose Extensible Scanning Query Architecture for Ad Hoc AnalyticsFlurry, Inc.
We present Burst, an analytic query system with a scalable and flexible approach to performing lowlatency ad hoc analysis over large complex datasets. The architecture consists of hardwareefficient scan techniques and a language facility to transform an extensible set of ad hoc declarative queries into imperative physical scan plans. These plans are multicast across all nodes/cores of a two level sharded/distributed ingestion, storage, and execution topology and executed. The first release of this system is the query engine behind the Flurry Explorer product. Here we explore the design details of that system as well as the incremental ingestion pipeline enhancement currently being implemented for the next major release.
The document discusses two NSF-funded research projects on intelligence and security informatics:
1. A project to filter and monitor message streams to detect "new events" and changes in topics or activity levels. It describes the technical challenges and components of automatic message processing.
2. A project called HITIQA to develop high-quality interactive question answering. It describes the team members and key research issues like question semantics, human-computer dialogue, and information quality metrics.
Artificial Neural Networks is a calculation method that builds several processing units based on
interconnected connections. The network consists of an arbitrary number of cells or nodes or units
or neurons that connect the input set to the output. It is a part of a computer system that mimics how
the human brain analyzes and processes data. Self-driving vehicles, character recognition, image
compression, stock market prediction, risk analysis systems, drone control, welding quality analysis,
computer quality analysis, emergency room testing, oil and gas exploration and a variety of other
applications all use artificial neural networks. Predicting consumer behavior, creating and
understanding more sophisticated buyer segments, marketing automation, content creation and
sales forecasting are some applications of the ANN systems in the marketing. In this paper, a review
in recent development and applications of the Artificial Neural Networks is presented in order to move
forward the research filed by reviewing and analyzing recent achievements in the published papers.
Thus, the developed ANN systems can be presented and new methodologies and applications of the
ANN systems can be introducedArtificial Neural Networks (ANNs), or more simply neural networks, are new systems and computational
methods for machine learning, knowledge demonstration, and finally the application of knowledge
gained to maximize the output responses of complex systems (Chen et al. 2019). An Artificial Neural
Network (ANN) is a data processing model based on the way biological nervous systems, such as the
brain, process data. They're focused on the neuronal structure of the mamalian cerebral cortex, but at
a much smaller scale. Many artificial intelligence experts believe that artificial neural networks are the Artificial neural networks are designed in the same way as the human brain, with neuron nodes
interconnected in a web-like fashion. Neurons are billions of cells that make up the human brain. Each
neuron is made up of a cell body that processes information by bringing it to and from the brain (inputs
and outputs) (Van Gerven and Bohte 2017). The main idea of such networks is (to some extent) inspired
by the way the biological neural system works, to process data, and information in order to learn and
create knowledge. The key element of this idea is to create new structures for the information
processing system. The Artificial neural network architecture is shown in the figure 2 (Bre, Gimenez,
and Fachinotti 2018).The system is made up of a large number of highly interconnected processing elements called neurons
that work together to solve a problem and transmit information through synapses (electromagnetic
connections). The neurons are interconnected closely and organized into layer. The input layer receives the data, while the output layer generates the final result. Between the two, one or more secret layers are typically sandwiched. This arrangement makes predicting
Artificial Neural Networks is a calculation method that builds several processing units based on
interconnected connections. The network consists of an arbitrary number of cells or nodes or units
or neurons that connect the input set to the output. It is a part of a computer system that mimics how
the human brain analyzes and processes data. Self-driving vehicles, character recognition, image
compression, stock market prediction, risk analysis systems, drone control, welding quality analysis,
computer quality analysis, emergency room testing, oil and gas exploration and a variety of other
applications all use artificial neural networks.
Feature Extraction and Analysis of Natural Language Processing for Deep Learn...Sharmila Sathish
This document discusses using deep learning techniques for multi-modal feature extraction. It proposes a multi-modal neural network with independent sub-networks for each data mode. It also discusses using a bi-directional GRU network for English word segmentation to effectively solve long-distance dependency issues while reducing training and prediction time compared to bi-directional LSTM. Experimental results showed the proposed multi-modal fusion model can effectively extract low-dimensional fused features from original high-dimensional multi-modal data.
Executive Summary
Introduction
Protein engineering is a burgeoning field within the life sciences promising targeted therapeutics, enhanced agricultural yield, and more efficient manufacturing. Various models and analysis paradigms employed by scientists and engineers leverage statistics and cutting-edge machine learning models to guide desirable functional changes. While notable advancements have been made concerning modeling protein tertiary structure as AlphaFold’s attention network has accomplished, there is room for simpler graphical models with better feature extractability to quickly inform scientists of key functional associations.(Senior, 2020).
Biological Background
Proteins are a polymer consisting of amino acids (of which there are 20) in a linear chain. An amino acid is composed of one nitrogen and two carbon atoms and is bound to various hydrogen and oxygen molecules, as shown in Figure 1. The central carbon Cα is linked to the unit “R” or residue, which distinguishes the amino acid. Amino acids bind through the loss of water molecules and the remaining parts of the amino acids are known as amino acid residues. Amino acids bind to form chains of hundreds to thousands of amino acids, forming the primary structure of proteins.
Figure 1: Amino acid structure. Retrieved from https://study.com/academy/lesson/what-is-amino-acid-residue.html
Amino acids in the chain can also interact with other non-adjacent amino acids in the same chain. This can cause the folding of the amino acid chain and lead to varying three-dimensional structures (secondary and tertiary structures). The two common forms of secondary structure include alpha helices and beta sheets. Proteins are essential in every cellular process. Many proteins are functional as monomers. Other proteins often form complexes (protein-protein interaction) to achieve specific functions. This is known as the quaternary structure of proteins. The four levels of protein structures are visually represented in Figure 2.
Figure 2: Protein structure: Primary, secondary, tertiary, and Quaternary. Retrieved from https://www.thoughtco.com/protein-structure-373563
Protein-protein or residue-residue interactions are the heart of biological processes. They give the protein its structure, which brings us to the key idea of biology: “structure equals function”. Thus, it is crucial to be able to identify these interaction sites or interface residues, as they can indicate the functionality of proteins. In this case, a protein can be modeled graphically where nodes are referred to as the 3D residue position, and the edges in the graph illustrate the spatial neighborhood of the residue.
Modeling
There are many tasks involving predicting the large numbers of variables that depend on pairwise associations. The method of a structured prediction is critical in graphical modeling and a combination of classification. (Athar, 2018). These pairwise as ...
From Simulation to Online Gaming: the need for adaptive solutions Gabriele D'Angelo
In many fields such as distributed simulation and online gaming the missing piece is adaptivity. There is a strong need for dynamic and adaptive solutions that can improve performances and react to problems.
These slides were presented at a meetup in Kansas City by Bahador Khaleghi of H2O.ai.
More details can be viewed here: https://www.meetup.com/Kansas-City-Artificial-Intelligence-Deep-Learning/events/265662978/
The document discusses potential areas of collaboration between Metron and the Science of Evolving Adaptive Systems (SEAS) Lab. It describes three main possibilities: 1) Working on parallel geometric algorithms and SPEEDES framework, 2) Developing a framework similar to SPEEDES for GPU hardware, 3) Leveraging the Starcat framework for distributed and parallel computation. The document provides background on the director of SEAS Lab and his research interests. It outlines initial plans to pursue the different collaboration opportunities through publications, applications, and follow-on funding.
Object Oriented Approach Within Siebel BoundariesRoman Agaev
This document discusses implementing object-oriented principles within the Siebel environment. It defines key object-oriented concepts like hierarchy, inheritance, abstraction, encapsulation, generalization, polymorphism, and persistence. The document argues that Siebel supports these concepts through mechanisms like business components, services, and classes. It emphasizes that developers must adhere to object-oriented patterns like strong typing and encapsulation when building applications in Siebel. Following these principles helps reuse code and align development with object-oriented analysis and design.
Bra a bidirectional routing abstraction for asymmetric mobile ad hoc networks...Mumbai Academisc
This document summarizes a paper that presents a framework called BRA that provides a bidirectional abstraction of asymmetric mobile ad hoc networks to enable off-the-shelf routing protocols to work. BRA maintains multi-hop reverse routes for unidirectional links, improves connectivity by using unidirectional links, enables reverse route forwarding of control packets, and detects packet loss on unidirectional links. Simulations show packet delivery increases substantially when AODV is layered on BRA in asymmetric networks compared to regular AODV.
Machine learning in science and industry — day 4arogozhnikov
- tabular data approach to machine learning and when it didn't work
- convolutional neural networks and their application
- deep learning: history and today
- generative adversarial networks
- finding optimal hyperparameters
- joint embeddings
A consistent and efficient graphical User Interface Design and Querying Organ...CSCJournals
We propose a software layer called GUEDOS-DB upon Object-Relational Database Management System ORDMS. In this work we apply it in Molecular Biology, more precisely Organelle complete genome. We aim to offer biologists the possibility to access in a unified way information spread among heterogeneous genome databanks. In this paper, the goal is firstly, to provide a visual schema graph through a number of illustrative examples. The adopted, human-computer interaction technique in this visual designing and querying makes very easy for biologists to formulate database queries compared with linear textual query representation.
For non-grid 3D images like point clouds and meshes, and inherently graph-based data.
Inherently graph-based data include for example brain connectivity analysis, scientific article citation networks, (social) network analysis, etc.
Alternative download link:
https://www.dropbox.com/s/2o3cofcd6d6e2qt/geometricGraph_deepLearning.pdf?dl=0
This document discusses techniques for analyzing unstructured text data from computer data inspection. It discusses using clustering algorithms like K-means and hierarchical clustering to automatically group related documents without supervision. The goal is to help computer examiners analyze large amounts of text data more efficiently. Prior work on clustering ensembles, evolving gene expression clusters, self-organizing maps, and thematically clustering search results is reviewed as relevant to this problem. The problem is how to identify and cluster documents stored across multiple remote locations during computer inspections when existing algorithms make this difficult.
This document proposes developing a system to display data structures and algorithms in a graphical user interface. It will allow users to visualize changes in data structures as algorithms execute through animated graphical objects. The system will be implemented using the Genesis programming language and JavaFX for the interface. It will follow a MVC design pattern with classes to represent runtime objects, control interaction, and define the interface. This system aims to help novice students learn programming and algorithms visually through manipulating objects.
The document discusses parallel computing over the past 25 years and challenges for using multicore chips in the next decade. It aims to provide context to scale applications effectively to 32-1024 cores. Key challenges include expressing inherent application parallelism while enabling efficient mapping to hardware through programming models and runtime systems. Future work includes developing methods to restore lost parallelism information and tradeoffs between programming effort, generality and performance.
This document discusses challenges and opportunities for integrating large, heterogeneous biological data sets. It outlines the types of analysis and discovery that could be enabled, such as comparing data across studies. Technical challenges include incompatible identifiers and schemas between data sources. Common solutions attempt standardization but have limitations. The document examines Amazon's approach as a model, with principles like exposing all data through programmatic interfaces. It argues for a "platform" approach and combining data-driven and model-driven analysis to gain new insights. Developing services with end users in mind could help maximize data reuse.
The document discusses using a behavior-based approach to build bioinformatics pipelines that is inspired by robotics. It summarizes the challenges of next generation sequencing data and describes how behavior-based robots operate in a distributed, modular way without centralized control. The document advocates designing bioinformatics pipelines with autonomous modules that can opportunistically process data and adapt as needed. It provides an example pipeline built with a behavior-based approach using Google Spreadsheets and autonomous processing agents.
A General Purpose Extensible Scanning Query Architecture for Ad Hoc AnalyticsFlurry, Inc.
We present Burst, an analytic query system with a scalable and flexible approach to performing lowlatency ad hoc analysis over large complex datasets. The architecture consists of hardwareefficient scan techniques and a language facility to transform an extensible set of ad hoc declarative queries into imperative physical scan plans. These plans are multicast across all nodes/cores of a two level sharded/distributed ingestion, storage, and execution topology and executed. The first release of this system is the query engine behind the Flurry Explorer product. Here we explore the design details of that system as well as the incremental ingestion pipeline enhancement currently being implemented for the next major release.
The document discusses two NSF-funded research projects on intelligence and security informatics:
1. A project to filter and monitor message streams to detect "new events" and changes in topics or activity levels. It describes the technical challenges and components of automatic message processing.
2. A project called HITIQA to develop high-quality interactive question answering. It describes the team members and key research issues like question semantics, human-computer dialogue, and information quality metrics.
Artificial Neural Networks is a calculation method that builds several processing units based on
interconnected connections. The network consists of an arbitrary number of cells or nodes or units
or neurons that connect the input set to the output. It is a part of a computer system that mimics how
the human brain analyzes and processes data. Self-driving vehicles, character recognition, image
compression, stock market prediction, risk analysis systems, drone control, welding quality analysis,
computer quality analysis, emergency room testing, oil and gas exploration and a variety of other
applications all use artificial neural networks. Predicting consumer behavior, creating and
understanding more sophisticated buyer segments, marketing automation, content creation and
sales forecasting are some applications of the ANN systems in the marketing. In this paper, a review
in recent development and applications of the Artificial Neural Networks is presented in order to move
forward the research filed by reviewing and analyzing recent achievements in the published papers.
Thus, the developed ANN systems can be presented and new methodologies and applications of the
ANN systems can be introducedArtificial Neural Networks (ANNs), or more simply neural networks, are new systems and computational
methods for machine learning, knowledge demonstration, and finally the application of knowledge
gained to maximize the output responses of complex systems (Chen et al. 2019). An Artificial Neural
Network (ANN) is a data processing model based on the way biological nervous systems, such as the
brain, process data. They're focused on the neuronal structure of the mamalian cerebral cortex, but at
a much smaller scale. Many artificial intelligence experts believe that artificial neural networks are the Artificial neural networks are designed in the same way as the human brain, with neuron nodes
interconnected in a web-like fashion. Neurons are billions of cells that make up the human brain. Each
neuron is made up of a cell body that processes information by bringing it to and from the brain (inputs
and outputs) (Van Gerven and Bohte 2017). The main idea of such networks is (to some extent) inspired
by the way the biological neural system works, to process data, and information in order to learn and
create knowledge. The key element of this idea is to create new structures for the information
processing system. The Artificial neural network architecture is shown in the figure 2 (Bre, Gimenez,
and Fachinotti 2018).The system is made up of a large number of highly interconnected processing elements called neurons
that work together to solve a problem and transmit information through synapses (electromagnetic
connections). The neurons are interconnected closely and organized into layer. The input layer receives the data, while the output layer generates the final result. Between the two, one or more secret layers are typically sandwiched. This arrangement makes predicting
Artificial Neural Networks is a calculation method that builds several processing units based on
interconnected connections. The network consists of an arbitrary number of cells or nodes or units
or neurons that connect the input set to the output. It is a part of a computer system that mimics how
the human brain analyzes and processes data. Self-driving vehicles, character recognition, image
compression, stock market prediction, risk analysis systems, drone control, welding quality analysis,
computer quality analysis, emergency room testing, oil and gas exploration and a variety of other
applications all use artificial neural networks.
Feature Extraction and Analysis of Natural Language Processing for Deep Learn...Sharmila Sathish
This document discusses using deep learning techniques for multi-modal feature extraction. It proposes a multi-modal neural network with independent sub-networks for each data mode. It also discusses using a bi-directional GRU network for English word segmentation to effectively solve long-distance dependency issues while reducing training and prediction time compared to bi-directional LSTM. Experimental results showed the proposed multi-modal fusion model can effectively extract low-dimensional fused features from original high-dimensional multi-modal data.
Executive Summary
Introduction
Protein engineering is a burgeoning field within the life sciences promising targeted therapeutics, enhanced agricultural yield, and more efficient manufacturing. Various models and analysis paradigms employed by scientists and engineers leverage statistics and cutting-edge machine learning models to guide desirable functional changes. While notable advancements have been made concerning modeling protein tertiary structure as AlphaFold’s attention network has accomplished, there is room for simpler graphical models with better feature extractability to quickly inform scientists of key functional associations.(Senior, 2020).
Biological Background
Proteins are a polymer consisting of amino acids (of which there are 20) in a linear chain. An amino acid is composed of one nitrogen and two carbon atoms and is bound to various hydrogen and oxygen molecules, as shown in Figure 1. The central carbon Cα is linked to the unit “R” or residue, which distinguishes the amino acid. Amino acids bind through the loss of water molecules and the remaining parts of the amino acids are known as amino acid residues. Amino acids bind to form chains of hundreds to thousands of amino acids, forming the primary structure of proteins.
Figure 1: Amino acid structure. Retrieved from https://study.com/academy/lesson/what-is-amino-acid-residue.html
Amino acids in the chain can also interact with other non-adjacent amino acids in the same chain. This can cause the folding of the amino acid chain and lead to varying three-dimensional structures (secondary and tertiary structures). The two common forms of secondary structure include alpha helices and beta sheets. Proteins are essential in every cellular process. Many proteins are functional as monomers. Other proteins often form complexes (protein-protein interaction) to achieve specific functions. This is known as the quaternary structure of proteins. The four levels of protein structures are visually represented in Figure 2.
Figure 2: Protein structure: Primary, secondary, tertiary, and Quaternary. Retrieved from https://www.thoughtco.com/protein-structure-373563
Protein-protein or residue-residue interactions are the heart of biological processes. They give the protein its structure, which brings us to the key idea of biology: “structure equals function”. Thus, it is crucial to be able to identify these interaction sites or interface residues, as they can indicate the functionality of proteins. In this case, a protein can be modeled graphically where nodes are referred to as the 3D residue position, and the edges in the graph illustrate the spatial neighborhood of the residue.
Modeling
There are many tasks involving predicting the large numbers of variables that depend on pairwise associations. The method of a structured prediction is critical in graphical modeling and a combination of classification. (Athar, 2018). These pairwise as ...
From Simulation to Online Gaming: the need for adaptive solutions Gabriele D'Angelo
In many fields such as distributed simulation and online gaming the missing piece is adaptivity. There is a strong need for dynamic and adaptive solutions that can improve performances and react to problems.
These slides were presented at a meetup in Kansas City by Bahador Khaleghi of H2O.ai.
More details can be viewed here: https://www.meetup.com/Kansas-City-Artificial-Intelligence-Deep-Learning/events/265662978/
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Breast cancer: Post menopausal endocrine therapyDr. Sumit KUMAR
Breast cancer in postmenopausal women with hormone receptor-positive (HR+) status is a common and complex condition that necessitates a multifaceted approach to management. HR+ breast cancer means that the cancer cells grow in response to hormones such as estrogen and progesterone. This subtype is prevalent among postmenopausal women and typically exhibits a more indolent course compared to other forms of breast cancer, which allows for a variety of treatment options.
Diagnosis and Staging
The diagnosis of HR+ breast cancer begins with clinical evaluation, imaging, and biopsy. Imaging modalities such as mammography, ultrasound, and MRI help in assessing the extent of the disease. Histopathological examination and immunohistochemical staining of the biopsy sample confirm the diagnosis and hormone receptor status by identifying the presence of estrogen receptors (ER) and progesterone receptors (PR) on the tumor cells.
Staging involves determining the size of the tumor (T), the involvement of regional lymph nodes (N), and the presence of distant metastasis (M). The American Joint Committee on Cancer (AJCC) staging system is commonly used. Accurate staging is critical as it guides treatment decisions.
Treatment Options
Endocrine Therapy
Endocrine therapy is the cornerstone of treatment for HR+ breast cancer in postmenopausal women. The primary goal is to reduce the levels of estrogen or block its effects on cancer cells. Commonly used agents include:
Selective Estrogen Receptor Modulators (SERMs): Tamoxifen is a SERM that binds to estrogen receptors, blocking estrogen from stimulating breast cancer cells. It is effective but may have side effects such as increased risk of endometrial cancer and thromboembolic events.
Aromatase Inhibitors (AIs): These drugs, including anastrozole, letrozole, and exemestane, lower estrogen levels by inhibiting the aromatase enzyme, which converts androgens to estrogen in peripheral tissues. AIs are generally preferred in postmenopausal women due to their efficacy and safety profile compared to tamoxifen.
Selective Estrogen Receptor Downregulators (SERDs): Fulvestrant is a SERD that degrades estrogen receptors and is used in cases where resistance to other endocrine therapies develops.
Combination Therapies
Combining endocrine therapy with other treatments enhances efficacy. Examples include:
Endocrine Therapy with CDK4/6 Inhibitors: Palbociclib, ribociclib, and abemaciclib are CDK4/6 inhibitors that, when combined with endocrine therapy, significantly improve progression-free survival in advanced HR+ breast cancer.
Endocrine Therapy with mTOR Inhibitors: Everolimus, an mTOR inhibitor, can be added to endocrine therapy for patients who have developed resistance to aromatase inhibitors.
Chemotherapy
Chemotherapy is generally reserved for patients with high-risk features, such as large tumor size, high-grade histology, or extensive lymph node involvement. Regimens often include anthracyclines and taxanes.
Travel vaccination in Manchester offers comprehensive immunization services for individuals planning international trips. Expert healthcare providers administer vaccines tailored to your destination, ensuring you stay protected against various diseases. Conveniently located clinics and flexible appointment options make it easy to get the necessary shots before your journey. Stay healthy and travel with confidence by getting vaccinated in Manchester. Visit us: www.nxhealthcare.co.uk
5-hydroxytryptamine or 5-HT or Serotonin is a neurotransmitter that serves a range of roles in the human body. It is sometimes referred to as the happy chemical since it promotes overall well-being and happiness.
It is mostly found in the brain, intestines, and blood platelets.
5-HT is utilised to transport messages between nerve cells, is known to be involved in smooth muscle contraction, and adds to overall well-being and pleasure, among other benefits. 5-HT regulates the body's sleep-wake cycles and internal clock by acting as a precursor to melatonin.
It is hypothesised to regulate hunger, emotions, motor, cognitive, and autonomic processes.
Know the difference between Endodontics and Orthodontics.Gokuldas Hospital
Your smile is beautiful.
Let’s be honest. Maintaining that beautiful smile is not an easy task. It is more than brushing and flossing. Sometimes, you might encounter dental issues that need special dental care. These issues can range anywhere from misalignment of the jaw to pain in the root of teeth.
NAVIGATING THE HORIZONS OF TIME LAPSE EMBRYO MONITORING.pdfRahul Sen
Time-lapse embryo monitoring is an advanced imaging technique used in IVF to continuously observe embryo development. It captures high-resolution images at regular intervals, allowing embryologists to select the most viable embryos for transfer based on detailed growth patterns. This technology enhances embryo selection, potentially increasing pregnancy success rates.
These lecture slides, by Dr Sidra Arshad, offer a simplified look into the mechanisms involved in the regulation of respiration:
Learning objectives:
1. Describe the organisation of respiratory center
2. Describe the nervous control of inspiration and respiratory rhythm
3. Describe the functions of the dorsal and respiratory groups of neurons
4. Describe the influences of the Pneumotaxic and Apneustic centers
5. Explain the role of Hering-Breur inflation reflex in regulation of inspiration
6. Explain the role of central chemoreceptors in regulation of respiration
7. Explain the role of peripheral chemoreceptors in regulation of respiration
8. Explain the regulation of respiration during exercise
9. Integrate the respiratory regulatory mechanisms
10. Describe the Cheyne-Stokes breathing
Study Resources:
1. Chapter 42, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 36, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 13, Human Physiology by Lauralee Sherwood, 9th edition
Discover the benefits of homeopathic medicine for irregular periods with our guide on 5 common remedies. Learn how these natural treatments can help regulate menstrual cycles and improve overall menstrual health.
Visit Us: https://drdeepikashomeopathy.com/service/irregular-periods-treatment/
“Psychiatry and the Humanities”: An Innovative Course at the University of Mo...Université de Montréal
“Psychiatry and the Humanities”: An Innovative Course at the University of Montreal Expanding the medical model to embrace the humanities. Link: https://www.psychiatrictimes.com/view/-psychiatry-and-the-humanities-an-innovative-course-at-the-university-of-montreal
Giloy in Ayurveda - Classical Categorization and SynonymsPlanet Ayurveda
Giloy, also known as Guduchi or Amrita in classical Ayurvedic texts, is a revered herb renowned for its myriad health benefits. It is categorized as a Rasayana, meaning it has rejuvenating properties that enhance vitality and longevity. Giloy is celebrated for its ability to boost the immune system, detoxify the body, and promote overall wellness. Its anti-inflammatory, antipyretic, and antioxidant properties make it a staple in managing conditions like fever, diabetes, and stress. The versatility and efficacy of Giloy in supporting health naturally highlight its importance in Ayurveda. At Planet Ayurveda, we provide a comprehensive range of health services and 100% herbal supplements that harness the power of natural ingredients like Giloy. Our products are globally available and affordable, ensuring that everyone can benefit from the ancient wisdom of Ayurveda. If you or your loved ones are dealing with health issues, contact Planet Ayurveda at 01725214040 to book an online video consultation with our professional doctors. Let us help you achieve optimal health and wellness naturally.
PGx Analysis in VarSeq: A User’s PerspectiveGolden Helix
Since our release of the PGx capabilities in VarSeq, we’ve had a few months to gather some insights from various use cases. Some users approach PGx workflows by means of array genotyping or what seems to be a growing trend of adding the star allele calling to the existing NGS pipeline for whole genome data. Luckily, both approaches are supported with the VarSeq software platform. The genotyping method being used will also dictate what the scope of the tertiary analysis will be. For example, are your PGx reports a standalone pipeline or would your lab’s goal be to handle a dual-purpose workflow and report on PGx + Diagnostic findings.
The purpose of this webcast is to:
Discuss and demonstrate the approaches with array and NGS genotyping methods for star allele calling to prep for downstream analysis.
Following genotyping, explore alternative tertiary workflow concepts in VarSeq to handle PGx reporting.
Moreover, we will include insights users will need to consider when validating their PGx workflow for all possible star alleles and options you have for automating your PGx analysis for large number of samples. Please join us for a session dedicated to the application of star allele genotyping and subsequent PGx workflows in our VarSeq software.
UPDATED (Version 1.0) Systems Neurology (the only objective is My CAREER, only , Eng.EmadFaragHABIB)- Ver 1.0.pdf
1. Systems Neurology
Functions of the Human Brain
Proposed Approaches to Study the Structure & Functions
of the Human Brain
Based on a Systems & Complexity Perspective
Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Eng. Emad Farag HABIB
Presentation is Downloadable
(and is : Virus, Malignancy, and Macro Free)
VERSION 1.0 August 17th 2023
To get the Latest Version: Open https://www.slideshare.net/EmadfHABIB2/
You will Find ONLY ONE File Named :
“UPDATED (Version <whatever>) Systems Neurology … “ ,
While other files are named “Outdated” or have a Completely Different Name (Other Subjects)
2. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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
are both ( Correct & Relevant ) to Your Query. If this is not the case , you can simply Skip this
Presentation ( with Author’s apology for any inconvenience )
( Apology: the “one-page shift” problem in PDF file Format: is still being tackled )
3. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Conclusion ! (5 Slides):
- 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 .
4. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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
5. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
"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-
4-7 digits 4-7 digits
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
6. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Human Brain Networks Topologies : How Neuronal Populations form "Large-scale Networks" , 20230600 Eng. Emad Farag HABIB
# Name
diagram Name Relevant Function 2D: [Global Efficiency VS Clustering]
aka Notes (Fn)
5 Spatial
(Integrative)
SUBJECTIVE Complex Fns:
Requires Information Instantiation & Probabilistic
Modeling: Consciousness
HH High Global , High
Clustering
Effective Function-
wise
Importance of Info "Instances"
(Copies, Mapping), Probabilistic
Modeling and Updating (Bayesian
Inference)
(4B) VSCS MYRIAD of Fns:
Requiring System-Str to change according to
Function's varying Signals/Inputs.
HH High Global , High
Clustering
Variable-structure
Control-system
Context-sensitive integration =
“task-related responses” / akas:
SemiautonomousSubsystems,
shifting hierarchy
4 Hub structure COGNITIVE Fns:
Sequential (Linking/Attributing) to/of Specialized
Hub-regions
HH High Global , High
Clustering
core–periphery
architecture
Learning = bridges between
distinct communities
(3B) Hierarchical
structure
ORGANIZATIONAL Fns:
Optimized Reach/Access: Better (Time/Chain of
Command) to Address a certain node
HM High Global ,
Medium
Clustering
Nestedness NSTD,
Inclusion-
Embedding
Order & Formality
3 Small-world
structure
PRIORITIZED Fns:
Optimized-Performance: Min Total number of
computational steps
MH Medium Global ,
High Clustering
SW, high clustering Min. steps needed to
process external stimuli
(2B) (Lattice) ROUTINE Fns:
Equal-Importance Task-items
LH Low Global , High
Clustering
nearest neighbours (Default, Equi-Probable)
Setting
2 Community SPECIALIZED Fns/Tasks:
Specialized Brain cognitive Areas (Communities,
Sensory Modalities)
MM Medium Global ,
Medium
Clustering
stochastic block
model/ Probability/
subnetworks with specific
cognitive functions
1 Random NON-STRUCTURED Fns/Tasks/Activities:
Possibly suiting the initial (Learning/ Trials&Error)
phases.
HL High Global , Low
Clustering
fixed probability P ~ 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
7. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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 :
8. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
- End of Conclusion ! :
9. Next
Approach #2 : meso-scale
Complexity Theory: 18 Aspects
Approach #1 : micro-scale
Neurons
Approach #3 : macro-scale
Functions
Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
10. Next
Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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
11. 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]
Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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
12. 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
Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
13. 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 ]
Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
14. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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 .
15. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
- 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.
16. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
- 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).
17. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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.
18. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
- 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).
19. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
- 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) .
20. Approach #2 : meso-scale
Complexity Theory
Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
21. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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”
22. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Complexity Theory
- Complexity is 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
23. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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 .
24. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Complexity 18 Aspects
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
25. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Complexity 18 Aspects
- 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 ? )
26. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
First 5 Aspects of Complexity
5 Aspects of :
[Numerousity, Clustering, Diversity, Nestedness, and Modularity]
27. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
# 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)
28. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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)
SubNets: Don’t care/
Connections: Must be
Different/ Topologies: Must be
Different InterLinking
H-Intricacy: Don’t care/ V-
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)
SubNets: Don’t care/
Topologies: : must be
Hierarchy
H-Intricacy: Don’t care/ V-
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
29. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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
30. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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 ROI )
<> (ROI) Non-connected
<> (ROI) Uniformly-connected=ALL are Equally-connected
<> (ROI) "dyadly-edged"=TWO-nodes per edge
<> Classification, <> Regression
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 scales "S
<> 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 Function(
Abbrev.: ROI region of interest / NTRC: Intricacy, H Horizontal, V Vertical L Links (LNKX)/ / s.c. special case/ wrt with respect to/ D
CONTEXTS: 4 Contexts, and with different 4-terms for the notoin of "Entity" : [Item, Node, SubNetwork, Element] : ["Item": vs "s
31. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Complexity Theory: Principal 5 TERMS: in 4 Relevant Contexts: Eng.Emad Farag HABIB , 20230614
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
<> Classification, <> Regression Ambiguity of: "clustering" (mere grouping) <> Put Element into set or group, <> Prediction
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
Abbrev.: CMX: Complexity/ NE NodeEdge (Discrete Math)/ VS = Versus / #Varities = Number of V. / Str Structur(al), Fn Function(al)/ ICT Information Communication Technolo
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
32. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Complexity 18 Aspects
Axes-Values for the 18 Aspects
33. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Axes-Values for the 18 Aspects
- Values range from 0 to 10 ( including 0 and 10) :
- Value 10 = *“Meta”, beyond, better than] this scale
- Value 5 = [Typical, Nominal, Average, Normal] Value
- Value 4 = Sort of
- Value 3 = General and Mixed
- Value 0 = No, Non, Nil, Not, Never
- When reading the Chart: start from bottom value : 0 , to the
top value : 10
34. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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 ) :
35. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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.
36. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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
37. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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 ?
38. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
A1-A5 (The a/m )
[Numerousity, Clustering,
Diversity, Nestedness, and
Modularity]
A 1 A 2 A 3 A 4 A 5
Numerousity
Clustering Diversity Nestedness
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
39. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Next: 18 Aspects x 11 Values
( In 4-Slides )
Slide#1: Aspects 1-9 : Values 1-5
Slide#2: Aspects 1-9 : Values 5-10
Slide#3: Aspects 10-18 : Values 1-5
Slide#4: Aspects 10-18 : Values 5-10
40. A 1 A 2 A 3 A 4 A 5 A 6 A 7 A 8 A 9
Numerousity
Clustering Diversity Nestedness
ModularityCriticality OptimalityQuantized (μ)
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
Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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
41. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
A 1 A 2 A 3 A 4 A 5 A 6 A 7 A 8 A 9
Numerousity
Clustering Diversity Nestedness
ModularityCriticality OptimalityQuantized (μ)
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
42. A 10 A 11 A 12 A 13 A 14 A 15 A 16 A 17 A 18
Correlation (Info)
Causality Substantiation
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
8-12 digits 8-12 digits
Adaptive
&
Contextu
al
Adapting/
Develop
ment
Balancing
Feedback
Correlatio
n:
(Circural
time-
domain
Solutions,
c(t), ..
Compreh
ended
Functiona
l Macro-
4-7 digits 4-7 digits
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
Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
~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:
43. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
A 10 A 11 A 12 A 13 A 14 A 15 A 16 A 17 A 18
Correlation (Info)
Causality Substantiation
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
8-12 digits 8-12 digits
Adaptive
&
Contextu
al
Adapting/
Develop
ment
Balancing
Feedback
Correlatio
n:
(Circural
time-
domain
Solutions,
c(t), ..
Compreh
ended
Functiona
l Macro-
4-7 digits 4-7 digits
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
44. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
"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
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-
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
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:
45. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Complexity 18 Aspects
Charting (or Plotting)
Some of the Famous Theories & Frameworks of the Brain :
PCT, IWMT, and Malsow
Versus these 18-Aspects :
46. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
"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-
4-7 digits 4-7 digits
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
47. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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 Media, and Social Networks Analysis,
- 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 ! )
48. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
CPC “Complexity Profiling Chart”
- 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 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 pursuing 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 .
49. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
CPC “Complexity Profiling Chart”
- Concluding Notes (Draft):
- 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 .
50. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
CPC “Complexity Profiling Chart”
- Concluding Notes (Draft):
- The Complexity Profiling Chart : uses the smart approach of “Anatomical” Annotation ,
prevailing the Medical Literature Terminology, rather than using a Functional annotation
approach, in describing the Complexity Issue Aspects ( and the word “Anatomical” here
means adopting more “Descriptive” terms rather than “Prescriptive” ones ) .
- 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 .
51. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
CPC “Complexity Profiling Chart”
- Concluding Notes (Draft):
- Limitations : Our Current Limitations in Dealing with Complex Systems : is due to our
Weak Reliance on Visual Representations & Visualization Tools !! :
- We must Rely more on such means : if we are to succeed in tasks like : Profiling,
Understanding, and Controlling Complexity , and tasks like tackling AI Transparency &
Log Reports ( Representations of “Network Topologies” will be an indispensible tool in
such tasks ) .
52. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Network Topologies (CNS Context)
Ref: cf table doi, Draft on 0531
53. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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.
54. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Human Brain Networks Topologies : How Neuronal Populations form "Large-scale Networks" , 20230600 Eng. Emad Farag HABIB
# Name
diagram Name Relevant Function 2D: [Global Efficiency VS Clustering]
aka Notes (Fn)
5 Spatial
(Integrative)
SUBJECTIVE Complex Fns:
Requires Information Instantiation & Probabilistic
Modeling: Consciousness
HH High Global , High
Clustering
Effective Function-
wise
Importance of Info "Instances"
(Copies, Mapping), Probabilistic
Modeling and Updating (Bayesian
Inference)
(4B) VSCS MYRIAD of Fns:
Requiring System-Str to change according to
Function's varying Signals/Inputs.
HH High Global , High
Clustering
Variable-structure
Control-system
Context-sensitive integration =
“task-related responses” / akas:
SemiautonomousSubsystems,
shifting hierarchy
4 Hub structure COGNITIVE Fns:
Sequential (Linking/Attributing) to/of Specialized
Hub-regions
HH High Global , High
Clustering
core–periphery
architecture
Learning = bridges between
distinct communities
(3B) Hierarchical
structure
ORGANIZATIONAL Fns:
Optimized Reach/Access: Better (Time/Chain of
Command) to Address a certain node
HM High Global ,
Medium
Clustering
Nestedness NSTD,
Inclusion-
Embedding
Order & Formality
3 Small-world
structure
PRIORITIZED Fns:
Optimized-Performance: Min Total number of
computational steps
MH Medium Global ,
High Clustering
SW, high clustering Min. steps needed to
process external stimuli
(2B) (Lattice) ROUTINE Fns:
Equal-Importance Task-items
LH Low Global , High
Clustering
nearest neighbours (Default, Equi-Probable)
Setting
2 Community SPECIALIZED Fns/Tasks:
Specialized Brain cognitive Areas (Communities,
Sensory Modalities)
MM Medium Global ,
Medium
Clustering
stochastic block
model/ Probability/
subnetworks with specific
cognitive functions
1 Random NON-STRUCTURED Fns/Tasks/Activities:
Possibly suiting the initial (Learning/ Trials&Error)
phases.
HL High Global , Low
Clustering
fixed probability P ~ 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
55. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
# 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)
56. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
(( FCBPSS Modeling Framework ))
57. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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 ]
58. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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
59. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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
60. Approach #1 : micro-scale
Neurons
Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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67. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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
68. Approach #3 : macro-scale
Functions
Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
69. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
DAC Theory
70. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
DAC
Distributed Adaptive Control (DAC)
DAC
Conforms Reasonably
with a Months-ago, Self-developed
Similar Diagram
( cf the To-Be vs T-Do “2D Diagram“ )
71. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Next Slides :
Details of “Generic Behavior” or “Affective Behavior”
72. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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
73. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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)]
Motivation details : [ Need/ Search/ Choice/ Enact/ Experience/ Reassess ]/ draft schematic
Cognitive, Affective, and Volitional Constructs
Emotions
[ Positive/ Negative ]
Attitudes
Learned Behavior(s)
[Reinforce/ Avoid ]
74. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Moods
Behavior / Action
Motivation details : [ Need/ Search/ Choice/ Enact/ Experience/ Reassess ]/ draft schematic
Emotions
[ Positive/ Negative ]
Attitudes
Learned Behavior(s)
[Reinforce/ Avoid ]
Search Choice Enact
Experience
Reassess
Need
(as a Drive)
Need
[ Maslow/ ERG/ ..]
Regulators , Controllers
Needs / Behaviors
Cognitive, Affective, and Volitional Constructs
Diagram #3: BOTH : INTRA & EXTRA -Motivational Constructs:
75. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
Motivation details : [ Need/ Search/ Choice/ Enact/ Experience/ Reassess ]/ draft schematic
Search Choice Enact
Experience
Reassess
Behavior
Need
(as a Drive)
Emotions
[ Positive/ Negative ]
Cognitive, Affective, and Volitional Constructs
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)
76. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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
[ Positive/ Negative ]
[ 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 )
77. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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
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(( PCT : Perceptual Control Theory ))
79. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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” )
80. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
(( 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” ) actively
processes information about its Environment & some
concerned “Perceived Object” : this info comprises both *
Easy information (Environ-available Pure Sensory Signals)
and Important information ] (Self-generated Deductions
from the a/m Sensory Information) .
then SECOND the Percipient Brain uses these Info to reach 3
logical conclusions ( “Classifying” or Classing the Object, as
per some known categorization system).
then THIRD the Percipient Brain engages in higher Functions
in terms of its own environment : seeking “Guiding
Principles” that possibly govern the situation , seeking
counter-manipulation, in addition to pursing conformality
with the Prevailing 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. of 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
81. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
#
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
82. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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
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
flame
increasingly Discrete {Digital.Binary}
5
Fifth
Order
Events Expansion / O. is
Changing its 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
83. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
PCT Difficult Example: Language Learning Phases
# Hierarchical
level: (from
periphery)
PCT Level
(Order)
Name
Linguistic (Organs & terms) Linguistic O/P = Content & Form = Product Language Learning Phases
(12)
Eleventh
Order (12th
?)
System
Concepts
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 throughout childhood and well into adulthood
(11)
( new
Eleventh
Order)
Conflict ~ Learning against-all-odds ? (counter
counter-Learning)
~ Learning against-all-odds ? ( against
manipulative Objects = counter counter-
Learning)
~ Malignant Manipulator: responding by some incorrect Feedbacks ! When the P. (infant) produces a Correct
Syllable, word, or phrase ! // opposite reward ( = conflict ) !// P. has to "counteract" the "counter-Learning"
responses and Feedbacks !!!
10
Tenth Order Principles 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),
9
Ninth Order Programs 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 choice
points
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 inward passive vocabulary with few words
spoken, to describe to themselves what they are doing, and to recite the logical recipe that guides their
present purpose.
8
Eighth Order Sequences recognize and control Sequence
perceptions : child fascinated with
Sequences (incl. O/P)
words / phonemic contrasts (Sequences) ,
with same "parameters of contrast " /
analyze syllable-Events into phonemic
contrasts
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 into phonemic contrasts that
recur in different combinations. Prior to this, syllables differed from each other as unitary Events; now,
they contrast with each other at identifiable initial, interior, and final points, and the parameters of
contrast are the same across many syllables. Controlling the phonemic distinctions makes possible a rapid
increase in the passive learning of vocabulary during the 9 weeks that the child is learning to recognize and
control Sequence perceptions
7
Seventh
Order
Categories 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 complex relationships
6
Sixth Order Relationships Canonical "Babbling" Canonical "Babbling" : Repeated (Consonant/
Vowel) segments : (percieved as) in a
plausible context
Canonical babbling: Event perceptions come under control of the Relationship level : the child produces the
Event perceptions that we recognize as simple syllables : Adults perceive some of these syllables as words
when they occur in a plausible context
5
Fifth Order Events by diaphragm and larynx resemble canonical (vowels) syllables: fully
resonant vowels : Intonation contours / plays
with variations of pitch and amplitude: incl
contours of squeals, yells /
Syllable or Word: fully resonant vowels: perceptions begin to resemble canonical syllables: able to produce
clear vocalizations with the diaphragm and larynx / constricting the vocal tract so as to interrupt the flow of
vocalization = sounds something like consonants : ~ skills Expansion : better control of sounds produced by
the larynx : the child plays with the contours of squeals, yells, and other variations of pitch and amplitude:
4
Fourth Order Transitions Transitions & changes ( in
configurations and Patterns)
Smooth Transitions in Gooing emergence of Transitions (smooth changes in configurations). Linguistic Context
3
Third Order Configurations vocal organs coordinated control Gooing: ~ coordinated vowel Gooing : vocal organs are played within a more coordinated way [ lips, tongue, velum, epiglottis, and
larynx]
2 Second
Order
Sensations Tone, Frequency/Tenor ~ ‘quasi-vowel’ Child's competence : strengthens in the world of Sensations at about 5 weeks
1 First Order Intensities Amplitude, volume Phonation : ‘quasi-vowel’ : non-smooth Phonation : ‘quasi-vowel’ sounds without the smooth onset and clear sound of adult vowels
84. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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, Rivalry)
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 )
85. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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/
86. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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 …
87. Systems Neurology Ver 1.0 August 17th 2023 HABIB’s Complexity 3D Perspective Eng. Emad Farag HABIB
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