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: (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.
Highlights
The principle of self-organization [1] governs both structure and function, which co-evolve in self-organizing systems
In line with previous attempts at a comprehensive definition of the concept [1], the author proposes that self-organization may be defined in terms of a general principle of functional organization that ensures a system’s auto-regulation, stability, adaptation to new constraints, and functional autonomy
The fields of neuroscience and artificial intelligence in particular share a history of interaction in the theoretical development of both the concept of self-organization and self-organizing systems, and many of the current advances in Artificial Intelligence (AI) were inspired by the study of neural processes in humans and other living species [3,5,13]
Summary
The principle of self-organization [1] governs both structure and function, which co-evolve in self-organizing systems. Neuroscience may help conceive new algorithms, architectures, functions, and codes of representation for the design of biologically plausible AI by using a way of thinking about similarities and analogies between natural and artificial intelligence [15] Such two-way conceptual processes acquire a particular importance in the newly emerging field of computational philosophy, which regroups a wide range of approaches relating to all fields of science. This article is a conceptual essay written from the viewpoint of computational philosophy It highlights seven general functional key properties related to the principle of self-organization, which are discussed under the light of a specific example from sensory neuroscience, backed by empirical data. Comput. 2020, 4, 10 provides a biologically plausible conceptual support for the design of autonomous self-organization (AI) in soft robotics and illustrates why the seven key properties brought forward here in this concept paper are conducive to advancing the development of “strong AI” [5], as pin-pointed in the conclusions
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