A General Approach to Self-Organization — Homeokinesis

Abstract

In this chapter we will introduce the concept of homeokinesis, formulate it in mathematical terms, and develop a first understanding of its functionality. The preceding chapter on homeostasis made clear that the objective of “keeping things under control” cannot lead to a system which has a drive of its own to explore its behavioral options in a self-determined manner. This is not surprising since the homeostatic objective drives the controller to minimize the future effects of unpredictable perturbations. This chapter uses a different objective, the so called time-loop error, derives learning rules by gradient descending that error and discusses first consequences of the new approach. Minimizing the time-loop error is shown to generate a dynamical entanglement between state and parameter dynamics that has been termed homeokinesis since it realizes a dynamical regime jointly involving the physical, the neural, and the synaptic dynamics of the brain-body system.