  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.
