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- W2202935427 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.KeywordsPrediction ErrorLearning RateGradient DescentParameter DynamicForward ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W2202935427 date "2011-01-01" @default.
- W2202935427 modified "2023-09-26" @default.
- W2202935427 title "A General Approach to Self-Organization — Homeokinesis" @default.
- W2202935427 doi "https://doi.org/10.1007/978-3-642-20253-7_5" @default.
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