Seven properties of self-organization in the human brain

TL;DR

This paper identifies seven key properties of self-organization in the human brain, linking neurobiological insights with principles for designing adaptable and resilient artificial intelligence systems.

Contribution

It defines seven fundamental properties of brain self-organization and illustrates their role in stability and plasticity through empirical examples and interdisciplinary insights.

Findings

Modular connectivity enables flexible brain functions.

Unsupervised learning contributes to adaptive behavior.

Dynamic system growth supports neural plasticity.

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.