scholarly journals Seven Properties of Self-Organization in the Human Brain

2020 ◽  
Vol 4 (2) ◽  
pp. 10
Author(s):  
Birgitta Dresp-Langley
Keyword(s):  
Adaptive Learning ◽  
Functional Organization ◽  
Dynamic Network ◽  
General Purpose ◽  
Self Organization ◽  
Functional Plasticity ◽  
Resonance Theory ◽  
Network Growth ◽  
Strong Artificial Intelligence ◽  
Computational Philosophy

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: (1) modular connectivity, (2) unsupervised learning, (3) adaptive ability, (4) functional resiliency, (5) functional plasticity, (6) from-local-to-global functional organization, and (7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward.

scholarly journals Patterns of Adaptation in Child-Directed and Child Speech in the Emergence of Hebrew Verbs

2021 ◽  
Vol 12 ◽  
Author(s):  
Elitzur Dattner ◽  
Ronit Levie ◽  
Dorit Ravid ◽  
Orit Ashkenazi
Keyword(s):  
Cognitive Abilities ◽  
Dynamic Network ◽  
Growth Potential ◽  
Node Degree ◽  
Natural Interaction ◽  
Network Growth ◽  
Verb Morphology ◽  
Ambient Language ◽  
Hebrew Verbs ◽  
Parent Child

Children approach verb learning in ways that are specific to their native language, given the differential typological organization of verb morphology and lexical semantics. Parent-child interaction is the arena where children's socio-cognitive abilities enable them to track predictive relationships between tokens and extract linguistic generalizations from patterns and regularities in the ambient language. The current study examines how the system of Hebrew verbs develops as a network over time in early childhood, and the dynamic role of input-output adaptation in the network's increasing complexity. Focus is on the morphological components of Hebrew verbs in a dense corpus of two parent-child dyads in natural interaction between the ages 1;8-2;2. The 91-hour corpus contained 371,547 word tokens, 62,824 verb tokens, and 1,410 verb types (lemmas) in CDS and CS together. Network analysis was employed to explore the changing distributions and emergent systematicity of the relations between verb roots and verb patterns. Taking the Semitic root and pattern morphological constructs to represent linked nodes in a network, findings show that children's networks change with age in terms of node degree and node centrality, representing linkage level and construct importance respectively; and in terms of network density, as representing network growth potential. We put forward three main hypotheses followed by findings concerning (i) changes in verb usage through development, (ii) CS adaptation, and (iii) CDS adaptation: First, we show that children go through punctuated development, expressed by their using individual constructs for short periods of time, whereas parents' patterns of usage are more coherent. Second, regarding CS adaptation within a dynamic network system relative to time and CDS, we conclude that children are attuned to their immediate experience consisting of current CDS usage as well as previous usage in the immediate past. Finally, we show that parents (unintentionally) adapt to their children's language knowledge in three ways: First, by relating to their children's current usage. Second, by expanding on previous experience, building upon the usage their children have already been exposed to. And third, we show that when parents experience a limited network in the speech of their children, they provide them with more opportunities to expand their system in future interactions.

On the Design of Self-Organizing Ad Hoc Networks

2010 ◽  
pp. 248-262 ◽  
Author(s):  
Carlos M. S. Figueiredo ◽  
Antonio Alfredo F. Loureiro
Keyword(s):  
Ad Hoc Networks ◽  
Vehicular Networks ◽  
Ad Hoc ◽  
Dynamic Network ◽  
Self Organization ◽  
Autonomous Operation ◽  
Ubiquitous Systems ◽  
Hoc Networks ◽  
Important Design ◽  
Self Organizing

Self-organization concept has become very important to the vision of pervasive and ubiquitous systems because such systems are expected to be composed by lots of interconnected computing devices immersed in the environments. In particular, general Mobile Ad hoc networks, and their specializations such as Sensor and Vehicular networks can be seen as the main technologies for pervasive infra-structures. These networks were conceived under the self-organization paradigm due to many characteristics such as a high number of devices, dynamic network topology and the need of autonomous operation. Although several mechanism and techniques for achieving self-organizing behavior are already applied, there is still the lack of general methodologies for the design of new self-organizing functions. Thus, this chapter will present an overview of self-organizing networks introducing important functions and techniques, and it will focus on important design aspects that can be useful to new designs.

scholarly journals SELF-ORGANIZATION APPLIED TO DYNAMIC NETWORK LAYOUT

2007 ◽  
Vol 18 (10) ◽  
pp. 1537-1549 ◽  
Author(s):  
MARKUS M. GEIPEL
Keyword(s):  
Design Space ◽  
Dynamic Network ◽  
Systems Design ◽  
Self Organization ◽  
Network Visualization ◽  
Systems Research ◽  
Network Layout ◽  
Repulsive Forces ◽  
New Algorithms ◽  
Dynamic Layout

As networks and their structure have become a major field of research, a strong demand for network visualization has emerged. We address this challenge by formalizing the well established spring layout in terms of dynamic equations. We thus open up the design space for new algorithms. Drawing from the knowledge of systems design, we derive a layout algorithm that remedies several drawbacks of the original spring layout. This new algorithm relies on the balancing of two antagonistic forces. We thus call it arf for "attractive and repulsive forces". It is, as we claim, particularly suited for a dynamic layout of smaller networks (n < 103). We back this claim with several application examples from ongoing complex systems research.

scholarly journals Feedforward Chemical Neural Network: An In Silico Chemical System That Learns xor

2017 ◽  
Vol 23 (3) ◽  
pp. 295-317 ◽  
Author(s):  
Drew Blount ◽  
Peter Banda ◽  
Christof Teuscher ◽  
Darko Stefanovic
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Modular Design ◽  
Building Blocks ◽  
General Purpose ◽  
Feedforward Neural Network ◽  
Chemical System ◽  
Complex Information ◽  
Complex Information Processing ◽  
Network Topologies

Inspired by natural biochemicals that perform complex information processing within living cells, we design and simulate a chemically implemented feedforward neural network, which learns by a novel chemical-reaction-based analogue of backpropagation. Our network is implemented in a simulated chemical system, where individual neurons are separated from each other by semipermeable cell-like membranes. Our compartmentalized, modular design allows a variety of network topologies to be constructed from the same building blocks. This brings us towards general-purpose, adaptive learning in chemico: wet machine learning in an embodied dynamical system.

scholarly journals Self-organization in natural swarms of Photinus carolinus synchronous fireflies

2021 ◽  
Vol 7 (28) ◽  
pp. eabg9259
Author(s):  
Raphaël Sarfati ◽  
Julie C. Hayes ◽  
Orit Peleg
Keyword(s):  
Collective Behavior ◽  
Three Dimensional ◽  
Dynamic Network ◽  
Theoretical Models ◽  
Self Organization ◽  
Three Dimensional Reconstruction ◽  
Visual Occlusion ◽  
Photinus Carolinus ◽  
Dimensional Reconstruction ◽  
Synchronization Mechanisms

Fireflies flashing in unison is a mesmerizing manifestation of animal collective behavior and an archetype of biological synchrony. To elucidate synchronization mechanisms and inform theoretical models, we recorded the collective display of thousands of Photinus carolinus fireflies in natural swarms, and provide the first spatiotemporal description of the onset of synchronization. At low firefly density, flashes appear uncorrelated. At high density, the swarm produces synchronous flashes within periodic bursts. Using three-dimensional reconstruction, we demonstrate that flash bursts nucleate and propagate across the swarm in a relay-like process. Our results suggest that fireflies interact locally through a dynamic network of visual connections defined by visual occlusion from terrain and vegetation. This model illuminates the importance of the environment in shaping self-organization and collective behavior.

scholarly journals Self-organization in natural swarms of Photinus carolinus synchronous fireflies

2021 ◽  
Author(s):  
Raphaël Sarfati ◽  
Julie C. Hayes ◽  
Orit Peleg
Keyword(s):  
Collective Behavior ◽  
Three Dimensional ◽  
Dynamic Network ◽  
Theoretical Models ◽  
Self Organization ◽  
Three Dimensional Reconstruction ◽  
Visual Occlusion ◽  
Photinus Carolinus ◽  
Dimensional Reconstruction ◽  
Synchronization Mechanisms

Fireflies flashing in unison is a mesmerizing manifestation of animal collective behavior and an archetype of biological synchrony. To elucidate synchronization mechanisms and inform theoretical models, we recorded the collective display of thousands of Photinus carolinus fireflies in natural swarms, and provide the first spatiotemporal description of the onset of synchronization. At low firefly density, flashes appear uncorrelated. At high density, the swarm produces synchronous flashes within periodic bursts. Using three-dimensional reconstruction, we demonstrate that flash bursts nucleate and propagate across the swarm in a relay-like process. Our results suggest that fireflies interact locally through a dynamic network of visual connections defined by visual occlusion from terrain and vegetation. This model illuminates the importance of the environment in shaping self-organization and collective behavior.

scholarly journals Neural processor in artificial intelligence advancement

2018 ◽  
Vol 1 (1) ◽  
pp. 2
Author(s):  
Manu Mitra
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Graphical Representation ◽  
Time Frame ◽  
Paper Analysis ◽  
Self Organization ◽  
High Level Architecture ◽  
Neuron Network ◽  
High Level ◽  
Structure And Functions

A neuron network is a computational model based on structure and functions of biological neural networks. Information that flows through the network affects the structure of the neuron network because neural network changes-or learns, in a sense-based on that input and output. Although neural network being highly complex (for example change of weights for every new data within the time frame) an experimental model of high level architecture of neural processor is proposed. Neural Processor performs all the functions that an ordinary neural network does like adaptive learning, self-organization, real time operations and fault tolerance. In this paper, analysis of neural processing is discussed and presented with experiments, graphical representation including data analysis.

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