Modeling Multi-Order Adaptive Processes by Self-Modeling Networks

This paper covers the contents of the Keynote Speech with the same title. The paper addresses the use of self-modeling networks to model adaptive biological, mental, and social processes of any order of adaptation. A self-modeling network for some base network is a network extension that represents part of the base network structure by a self-model in terms of added network nodes and connections for them. A network structure, in general, involves network characteristics for connectivity (connections between nodes), aggregation (combining multiple incoming impacts on a node), and timing (node state dynamics speed). By representing some of these network characteristics by a self-model using dynamic node states, these characteristics become adaptive. By iterating this construction, multi-order network adaptation is easily obtained. A dedicated software environment for self-modeling networks that has been developed supports the modeling and simulation processes. This will be illustrated for a number of adaptation principles from a number of application domains, for example, for Cognitive Neuroscience by a second-order adaptive network model to model plasticity of connections and node excitability, and metaplasticity to control such plasticity.