Seven properties of self-organization in the human brain

Abstract

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: modular connectivity, unsupervised learning, adaptive ability, functional resiliency, functional plasticity, from-local-to-global functional organization and 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.

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Cited by in corpus (7)

• Grip force as a functional window to somatosensory cognition
• Spatiotemporal modeling of grip forces captures proficiency in manual robot control
• Unsupervised classification of cell imaging data using the quantization error in a Self Organizing Map
• From Biological Synapses to Intelligent Robots
• Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map
• The Grossberg Code: Universal Neural Network Signatures of Perceptual Experience
• Correlating grip force signals from multiple sensors highlights prehensile control strategies in a complex task-user system