A DyCoN is a KFM-type network, where each neuron contains an individual PerPot-based self-control of its learning behaviour. The DyCoN-concept enables for continuous learning and therefore supports continuous training and testing, training in phases and with generated data, online-adaptation during tests and analyses, and flexible adaptation to new information patterns (see Perl, 2002 ).
Published in Chapter:
Physiologic Adaptation by Means of Antagonistic Dynamics
Juergen Perl (University of Mainz, Germany)
Copyright: © 2009
|Pages: 7
DOI: 10.4018/978-1-60566-026-4.ch492
Abstract
In particular in technical contexts, information systems and analysing techniques help a lot for gathering data and making information available. Regarding dynamic behavioral systems like athletes or teams in sports, however, the situation is difficult: data from training and competition do not give much information about current and future performance without an appropriate model of interaction and adaptation. Physiologic adaptation is one major aspect of targetoriented behavior, in physical training as well as in mental learning. In a simplified way it can be described by a stimulus- response-model, where external stimuli change situation or status of an organism and so cause activities in order to adapt. This aspect can appear in quite different dimensions like individual biochemical adaptation that needs only milliseconds up to selection of the fittest of a species, which can last millions of years. Well-known examples can be taken from learning processes or other mental work as well as from sport and exercising. Most of those examples are characterized by a phenomenon that we call antagonism: The input stimulus causes two contradicting responses, which control each other and – by balancing out – finally enable to reach a given target. For example, the move of a limb is controlled by antagonistic groups of muscles, and the result of a game is controlled by the efforts of competing teams. In order to understand and eventually improve such adaptation, models are necessary that make the processes transparent and help for simulating dynamics like for example, the increase of heart rate as an reaction of speeding up in jogging. With such models it becomes possible not only to analyze past processes but also to predict and schedule indented future ones. In the Background section, main aspects of modeling antagonistic adaptation systems are briefly discussed, which is followed by a more detailed description of the developed PerPot-model and a number of examples of application in the Main Focus section.