Homeostatic adaptive networks

Homeostasis is constancy in the face of perturbation. The concept was originally developed to describe the fixed internal environment of an organism and this descriptive view of homeostasis has been prevalent in literature. However, homeostasis cal also be seen as the dynamic process of self-regulation and as such it is an organising principle by which systems adapt their behaviour over time. In this thesis we adopt this casual view of homeostasis and develop a theory of homeostatic adaptive systems. We study homeostatic adaptive networks by looking at specific examples of homeo-static systems: the Homeostat, homeostatic plasticity in neural networks, and homeostatic regulation of the environment by the biota. Investigation of these cases studies forms the basis for the development of a generalised theory of homeostatic adaptive systems. The Homeostat was an electromechanical device designed by W.A. Ross Ashby to demonstrate the principle of ultra stability, where the stability of a system requires homeostasis of essential variables. Ashby put forward a theory of mammalian learning as a process of homeostatic adaptation that was based on the idea of the ultra stable system. Here we develop a simulated Homeostat and explore its properties as a homeostatic adaptive system, looking at its ultra stable nature and its ability to adapt to external perturbations. The second case study, neural homeostasis, has recently been a topic much interest in the neurosciences, with new data being presented concerning the existence and functioning of a variety of mechanisms by which neural activity is regulated. Homeostatic plastic mechanisms prevent long term quiescence or hyper-excitation in biological neurons and this suggests that such mechanisms may be used to solve the problem of node saturation in artificial neuron networks. Here we develop homeostatic plastic mechanisms for use in continuous-time recurrent neural networks; a kind of network often used in evolutionary robotics, and studies the effect of these mechanisms on network behaviour. Node saturation effects can make these networks difficult to evolve as robot controllers and we also look at the effect of homeostatic plasticity on evolvabilty. The third case study is the evolution of homeostatic regulation of the physical environment by the biota. The Gaia theory state that life regulates the entire biosphere to conditions suitable for life, but the general concept of biological regulation of the environment is applicable on a variety of scales. However, there are major theoretical issues concerning the compatibility of environmental regulation with environmental theory. Here we develop a modified version of the Daisyworld model and use it to determine the compatibility of global regulation with individual selection. We show that regulation in Daisyworld depends on several key assumptions and fails if these assumptions are removed. We develop the Flask model, in which environmental regulation by microbial communities evolves as a result of multi-level selection, in order to show how regulation can occur when the core assumptions of Daisyword are relaxed. At the end of the thesis we try to draw some general conclusions concerning homeostatic adaptive systems. We consider the adaptive and homeostatic properties of each of the case study systems, and then generalise from these to give some principles of homeostatic adaptation. Our analysis shows that the perturbations to a system can be classified in terms of their effects on homeostasis, and that the ability of a system to adapt to a perturbation and maintain homeostasis depends on the variety of responses it can produce. We argue that the parameter change caused by a loss of homeostasis depends on the variety of responses it can produce. We argue that parameter change caused by a loss of homeostasis causes 'organisation death' in a homeostatic adaptive system, where the system does not survive in its current form. This suggests a view of learning and evolution of organisms as second order homeostatic adaptive process.