Next Article in Journal
Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks
Next Article in Special Issue
#lockdown: Network-Enhanced Emotional Profiling in the Time of COVID-19
Previous Article in Journal
A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem
Previous Article in Special Issue
Text Mining in Big Data Analytics
Open AccessConcept Paper

Seven Properties of Self-Organization in the Human Brain

ICube Lab UMR 7357 Centre National de la Recherche Scientifique, University of Strasbourg, 67085 Strasbourg, France
Big Data Cogn. Comput. 2020, 4(2), 10; https://doi.org/10.3390/bdcc4020010
Received: 24 March 2020 / Revised: 7 May 2020 / Accepted: 8 May 2020 / Published: 10 May 2020
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
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. View Full-Text
Keywords: self-organization; computational philosophy; brain; synaptic learning; adaptation; functional plasticity; activity-dependent resonance states; circular causality; somatosensory representation; prehensile synergies; robotics self-organization; computational philosophy; brain; synaptic learning; adaptation; functional plasticity; activity-dependent resonance states; circular causality; somatosensory representation; prehensile synergies; robotics
Show Figures

Figure 1

MDPI and ACS Style

Dresp-Langley, B. Seven Properties of Self-Organization in the Human Brain. Big Data Cogn. Comput. 2020, 4, 10. https://doi.org/10.3390/bdcc4020010

AMA Style

Dresp-Langley B. Seven Properties of Self-Organization in the Human Brain. Big Data and Cognitive Computing. 2020; 4(2):10. https://doi.org/10.3390/bdcc4020010

Chicago/Turabian Style

Dresp-Langley, Birgitta. 2020. "Seven Properties of Self-Organization in the Human Brain" Big Data Cogn. Comput. 4, no. 2: 10. https://doi.org/10.3390/bdcc4020010

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop