Extended homeostatic adaptation model with metabolic causation in plasticity mechanism—toward constructing a dynamic neural network model for mental imagery

This study presents an extended dynamic neural network model of homeostatic adaptation as the first step toward constructing a model of mental imagery. In the homeostatic adaptation model, higher-level dynamics internally self-organized from sensorimotor dynamics are associated with desired behaviors. These dynamics are regenerated when drastic changes occur, which might break the internal dynamics. Due to the weak link between desired behavior and internal homeostasis in the original homeostatic adaptation model, adaptivity is limited. In this paper, we improve on the homeostatic adaptation model to create a stronger link between desired behavior and internal homeostasis by introducing a metabolic causation in a plasticity mechanism and show that it becomes more adaptive. Our results show that our model has three different time scales in the adaptive behaviors, which are discussed with our cognition and mental imagery.

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