Seven properties of self-organization in the human brain

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

Separating Agent-Functioning and Inter-Agent Coordination by Activated Modules: The DECOMAS Architecture

The embedding of self-organizing inter-agent processes in distributed software applications enables the decentralized coordination system elements, solely based on concerted, localized interactions. The separation and encapsulation of the activities that are conceptually related to the coordination, is a crucial concern for systematic development practices in order to prepare the reuse and systematic integration of coordination processes in software systems. Here, we discuss a programming model that is based on the externalization of processes prescriptions and their embedding in Multi-Agent Systems (MAS). One fundamental design concern for a corresponding execution middleware is the minimal-invasive augmentation of the activities that affect coordination. This design challenge is approached by the activation of agent modules. Modules are converted to software elements that reason about and modify their host agent. We discuss and formalize this extension within the context of a generic coordination architecture and exemplify the proposed programming model with the decentralized management of (web) service infrastructures

The modern versus extended evolutionary synthesis : sketch of an intra-genomic gene's eye view for the evolutionary-genetic underpinning of epigenetic and developmental evolution

Studying the phenotypic evolution of organisms in terms of populations of genes and genotypes, the Modern Synthesis (MS) conceptualizes biological evolution in terms of 'inter-organismal' interactions among genes sitting in the different individual organisms that constitute a population. It 'black-boxes' the complex 'intra-organismic' molecular and developmental epigenetics mediating between genotypes and phenotypes. To conceptually integrate epigenetics and evo-devo into evolutionary theory, advocates of an Extended Evolutionary Synthesis (EES) argue that the MS's reductive gene-centrism should be abandoned in favor of a more inclusive organism-centered approach. To push the debate to a new level of understanding, we introduce the evolutionary biology of 'intra-genomic conflict' (IGC) to the controversy. This strategy is based on a twofold rationale. First, the field of IGC is both ‘gene-centered’ and 'intra-organismic' and, as such, could build a bridge between the gene-centered MS and the intra-organismic fields of epigenetics and evo-devo. And second, it is increasingly revealed that IGC plays a significant causal role in epigenetic and developmental evolution and even in speciation. Hence, to deal with the ‘discrepancy’ between the ‘gene-centered’ MS and the ‘intra-organismic’ fields of epigenetics and evo-devo, we sketch a conceptual solution in terms of ‘intra-genomic conflict and compromise’ – an ‘intra-genomic gene’s eye view’ that thinks in terms of intra-genomic ‘evolutionarily stable strategies’ (ESSs) among numerous and various DNA regions and elements – to evolutionary-genetically underwrite both epigenetic and developmental evolution, as such questioning the ‘gene-de-centered’ stance put forward by EES-advocates

The relation between language and theory of mind in development and evolution

Considering the close relation between language and theory of mind in development and their tight connection in social behavior, it is no big leap to claim that the two capacities have been related in evolution as well. But what is the exact relation between them? This paper attempts to clear a path toward an answer. I consider several possible relations between the two faculties, bring conceptual arguments and empirical evidence to bear on them, and end up arguing for a version of co-evolution. To model this co-evolution, we must distinguish between different stages or levels of language and theory of mind, which fueled each other’s evolution in a protracted escalation process

A Goal-Directed Bayesian Framework for Categorization

Categorization is a fundamental ability for efficient behavioral control. It allows organisms to remember the correct responses to categorical cues and not for every stimulus encountered (hence eluding computational cost or complexity), and to generalize appropriate responses to novel stimuli dependant on category assignment. Assuming the brain performs Bayesian inference, based on a generative model of the external world and future goals, we propose a computational model of categorization in which important properties emerge. These properties comprise the ability to infer latent causes of sensory experience, a hierarchical organization of latent causes, and an explicit inclusion of context and action representations. Crucially, these aspects derive from considering the environmental statistics that are relevant to achieve goals, and from the fundamental Bayesian principle that any generative model should be preferred over alternative models based on an accuracy-complexity trade-off. Our account is a step toward elucidating computational principles of categorization and its role within the Bayesian brain hypothesis

Science as systems learning. Some reflections on the cognitive and communicational aspects of science

This paper undertakes a theoretical investigation of the 'learning' aspect of science as opposed to the 'knowledge' aspect. The practical background of the paper is in agricultural systems research – an area of science that can be characterised as 'systemic' because it is involved in the development of its own subject area, agriculture. And the practical purpose of the theoretical investigation is to contribute to a more adequate understanding of science in such areas, which can form a basis for developing and evaluating systemic research methods, and for determining appropriate criteria of scientific quality. Two main perspectives on science as a learning process are explored: research as the learning process of a cognitive system, and science as a social, communicational system. A simple model of a cognitive system is suggested, which integrates both semiotic and cybernetic aspects, as well as a model of selfreflective learning in research, which entails moving from an inside 'actor' stance to an outside 'observer' stance, and back. This leads to a view of scientific knowledge as inherently contextual and to the suggestion of reflexive objectivity and relevance as two related key criteria of good science