Living organisms must actively maintain themselves in order to continue existing. Autopoiesis is a key concept in the study of living organisms, where the boundaries of the organism are not static but dynamically regulated by the system itself. To study the autonomous regulation of a self-boundary, we focus on neural homeodynamic responses to environmental changes using both biological and artificial neural networks. Previous studies showed that embodied cultured neural networks and spiking neural networks with spike-timing dependent plasticity (STDP) learn an action as they avoid stimulation from outside. In this article, as a result of our experiments using embodied cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: If the agent cannot learn an action to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes, as if to ignore the uncontrollable input. We also show such a behavior is reproduced by spiking neural networks with asymmetric STDP. We consider that these properties are to be regarded as autonomous regulation of self and nonself for the network, in which a controllable neuron is regarded as self, and an uncontrollable neuron is regarded as nonself. Finally, we introduce neural autopoiesis by proposing the principle of stimulus avoidance.

中文翻译：

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神经自创生：在生物和人工神经网络中通过回避刺激来组织自边界

生物体必须积极维持自己才能继续存在。自创生是生物体研究中的一个关键概念，其中生物体的边界不是静态的，而是由系统本身动态调节的。为了研究自边界的自主调节，我们使用生物和人工神经网络专注于对环境变化的神经动态响应。先前的研究表明，具身培养神经网络和具有尖峰时间依赖可塑性 (STDP) 的尖峰神经网络在避免来自外部的刺激时会学习一个动作。在本文中，作为我们使用具身培养神经元进行实验的结果，我们发现还有第二个特性可以让网络避免刺激：如果智能体无法学习避免外部刺激的动作，它倾向于减少刺激诱发的尖峰，好像忽略了无法控制的输入。我们还展示了这种行为是通过使用非对称 STDP 刺激神经网络来重现的。我们认为这些特性被认为是网络对自我和非自我的自主调节，其中将可控神经元视为自我，将不可控神经元视为非自我。最后，我们通过提出刺激回避原则来介绍神经自创生。一个无法控制的神经元被视为非我。最后，我们通过提出刺激回避原则来介绍神经自创生。一个无法控制的神经元被视为非我。最后，我们通过提出刺激回避原则来介绍神经自创生。