In living organisms, homeostasis is the natural regulation of internal states aimed at maintaining conditions compatible with life. Typical artificial systems are not equipped with comparable regulatory features. Here, we introduce an artificial neural network that incorporates homeostatic features. Its own computing substrate is placed in a needful and vulnerable relation to the very objects over which it computes. For example, artificial neurons performing classification of MNIST digits or Fashion-MNIST articles of clothing may receive excitatory or inhibitory effects, which alter their own learning rate as a direct result of perceiving and classifying the digits. In this scenario, accurate recognition is desirable to the agent itself because it guides decisions to regulate its vulnerable internal states and functionality. Counterintuitively, the addition of vulnerability to a learner does not necessarily impair its performance. On the contrary, self-regulation in response to vulnerability confers benefits under certain conditions. We show that homeostatic design confers increased adaptability under concept shift, in which the relationships between labels and data change over time, and that the greatest advantages are obtained under the highest rates of shift. This necessitates the rapid un-learning of past associations and the re-learning of new ones. We also demonstrate the superior abilities of homeostatic learners in environments with dynamically changing rates of concept shift. Our homeostatic design exposes the artificial neural network's thinking machinery to the consequences of its own "thoughts", illustrating the advantage of putting one's own "skin in the game" to improve fluid intelligence.

翻译：在生物体中，稳态是维持生命兼容条件的内部状态自然调节机制。典型的人工系统不具备可比的调节特性。本文提出一种融合稳态特性的人工神经网络。其计算载体与计算对象之间建立了一种需求依赖且脆弱的关系。例如，执行MNIST手写数字分类或Fashion-MNIST服装分类的人工神经元，可能因感知和分类数字而接受兴奋性或抑制性效应，从而直接改变其自身的学习率。在此场景中，智能体本身需要精确识别，因为这能指导其调节脆弱内部状态和功能的决策。反直觉的是，为学习器增加脆弱性并不必然损害其性能。相反，在特定条件下，针对脆弱性的自我调节能带来益处。我们证明稳态设计能增强模型在概念漂移（即标签与数据间关系随时间变化）下的适应能力，且最大优势出现在漂移速率最高时。这要求快速消除旧关联并重新学习新关联。我们还展示了稳态学习器在概念漂移速率动态变化环境中的卓越能力。我们的稳态设计使人工神经网络的"思维机制"暴露于自身"思想"的后果之中，阐明了通过承担切身风险来提升流体智能的优势。