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首页> 外文期刊>Medical Physics >A global bioheat model with self-tuning optimal regulation of body temperature using Hebbian feedback covariance learning.
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A global bioheat model with self-tuning optimal regulation of body temperature using Hebbian feedback covariance learning.

机译：使用Hebbian反馈协方差学习的具有自我调节的最佳体温调节的全局生物热模型。

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In the lower brain, body temperature is continually being regulated almost flawlessly despite huge fluctuations in ambient and physiological conditions that constantly threaten the well-being of the body. The underlying control problem defining thermal homeostasis is one of great enormity: Many systems and sub-systems are involved in temperature regulation and physiological processes are intrinsically complex and intertwined. Thus the defining control system has to take into account the complications of nonlinearities, system uncertainties, delayed feedback loops as well as internal and external disturbances. In this paper, we propose a self-tuning adaptive thermal controller based upon Hebbian feedback covariance learning where the system is to be regulated continually to best suit its environment. This hypothesis is supported in part by postulations of the presence of adaptive optimization behavior in biological systems of certain organisms which face limited resources vital for survival. We demonstrate the use of Hebbian feedback covariance learning as a possible self-adaptive controller in body temperature regulation. The model postulates an important role of Hebbian covariance adaptation as a means of reinforcement learning in the thermal controller. The passive system is based on a simplified 2-node core and shell representation of the body, where global responses are captured. Model predictions are consistent with observed thermoregulatory responses to conditions of exercise and rest, and heat and cold stress. An important implication of the model is that optimal physiological behaviors arising from self-tuning adaptive regulation in the thermal controller may be responsible for the departure from homeostasis in abnormal states, e.g., fever. This was previously unexplained using the conventional "set-point" control theory.
机译：在下部大脑中，尽管周围环境和生理状况的巨大波动不断威胁着人体的健康，但人体的温度却几乎可以得到完美的调节。定义热稳态的潜在控制问题是巨大的问题之一：许多系统和子系统都参与温度调节，并且生理过程本质上是复杂且相互交织的。因此，定义的控制系统必须考虑到非线性的复杂性，系统不确定性，延迟的反馈回路以及内部和外部干扰。在本文中，我们提出了一种基于Hebbian反馈协方差学习的自调整自适应热控制器，该系统要不断进行调节以最适合其环境。假定某些生物的生物系统中的适应性优化行为存在的假设部分支持了这一假设，这些生物面临着对生存至关重要的有限资源。我们证明了使用Hebbian反馈协方差学习作为体温调节中可能的自适应控制器。该模型假定了Hebbian协方差适应作为热控制器中强化学习的一种重要作用。被动系统基于简化的2节点主体的核和壳表示，其中捕获了全局响应。模型预测与观察到的对运动和休息条件以及热和冷压力的温度调节反应一致。该模型的重要含义是，由热控制器中的自调节自适应调节引起的最佳生理行为可能是导致异常状态（例如发烧）中动态平衡偏离的原因。以前使用常规的“设定点”控制理论无法解释这一点。

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