Physiologic Adaptation by Means of Antagonistic Dynamics

Physiologic Adaptation by Means of Antagonistic Dynamics

Juergen Perl (University of Mainz, Germany)
DOI: 10.4018/978-1-60566-026-4.ch492
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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.
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Undisturbed limited growth processes in biological systems often are asymptotic, oriented in specific target values. This behavior reflects adaptation to limited resources and delays caused by resource production and transport. Processes of this type theoretically can be modeled rather easily by means of exponential functions – for example, f(t) = c – exp(-s × t/d), where c is the target value, s characterizes the deceleration, and d characterizes the delay (see Banister & Calvert, 1980; Banister, Calvert, Savage & Bach, 1975). In practice, however, situations are more complex (see Lames, 1996; Viru & Viru, 1993): The growth process normally is disturbed (stopped, restarted, reduced, intensified) by external effects; capacity limitations cause changes of the temporary process type (phase changes); buffers cause delays of the internal dynamics; seemingly constant parameters turn out to be time-depending. Therefore, often such processes cannot be modeled using continuous functions (e.g., as solutions of differential equations) but have to be calculated iteratively using discrete level-rate-equations, which only piecewise could be approximated by exponential functions.

Physiologic adaptation is a kind of limited biological growth and therefore can be modeled and simulated using such an iterative approach – as we have successfully done with load-performance-interaction and learning in sport. To this aim we developed an approach (PerPot: Performance Potential Metamodel, see Mester & Perl, 2000; Perl, 2002; Perl & Mester, 2001), the central idea of which is that of antagonism: A load input flow feeds in the same way two internal buffers – the strain potential and the response potential. These buffers are connected with a performance potential, the level of which is decreased by a negative strain flow and increased by a positive response flow. Both flows are delayed. The relations between the strain delay and the response delay characterize the interaction of load input and performance output. In case of training or learning the strain delay can be interpreted as fatigue delay, while response delay stands for the delay of recovery.

Key Terms in this Chapter

Response Potential: The antagonistic counter-part of the strain potential here is called the response potential.

PerPot: PerPot is a model of dynamic adaptation, where an input flow feeds an internal strain potential as well as an internal response potential, from which an output potential is fed by specifically delayed flows. Since the strain flow is negative and the response flow is positive, resulting in an oscillating stabilizing adaptation, the model is called antagonistic (see Perl, 2002 ).

Performance Potential: The result of load input under antagonistic control is modeled by the performance potential, which indicates the current performance state of the modeled physiologic system.

DyCoN: 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 ).

Strain Potential: If a load input stream is fed into an organism, it normally cannot be handled at once but is stored in buffers (e.g.,, organs) for being processed after certain delays. The abstract model of the load buffer here is called strain potential.

Delay: Adaptation processes can be thought of as time-consuming (stepwise or continuous) changes of the system’s state. This means that the reaction on a state disturbance does not takes place at once but shows a certain delay.

Kohonen Feature Map (KFM): A KFM consists of a (normally: 2-dimensional) matrix of neurons, each of which can contain information. During a training phase, those neurons are fed with information, which then is learned and organized by means of training algorithms. In the productive or test phase the learned information can be used for attaching or classifying test input (see Kohonen, 1995 ).

Adaptation: If the situation of a physiologic system is changed to a nonoptimal or unstable one – for example by external stimuli – the system tries to balancing out the disturbance by changing its state. This process is called adaptation.

Antagonism: Normally, in particular in technical systems, a target position or situation is reached by an asymptotic approach “from one side”, where one-directional impulses with decreasing intensity control the adaptation process. In most physiologic (and also in some technical) systems the control is completed by a countercontrol, where the same input activates two contradicting activities, which are controlling each other.

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