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

机译：可塑性机制中具有代谢因果关系的扩展稳态适应模型-构建心理影像动态神经网络模型

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摘要

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.
机译：这项研究提出了稳态适应的扩展动态神经网络模型，这是构建心理意象模型的第一步。在稳态适应模型中，从感觉运动动力学内部自组织的更高层次的动力学与期望的行为相关。发生剧烈变化时，这些动态会重新生成，这可能会破坏内部动态。由于在原始稳态适应模型中所需行为与内部稳态之间的联系较弱，因此适应性受到限制。在本文中，我们对稳态适应模型进行了改进，通过在可塑性机制中引入代谢因果关系，从而在所需行为和内部稳态之间建立了更牢固的联系，并表明它变得更具适应性。我们的结果表明，我们的模型在适应行为中具有三个不同的时间尺度，并与我们的认知和心理意象进行了讨论。

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