Habituation and sensitization (nonassociative learning) are among the most fundamental forms of learning and memory behavior present in organisms that enable adaptation and learning in dynamic environments. Emulating such features of intelligence found in nature in the solid state can serve as inspiration for algorithmic simulations in artificial neural networks and potential use in neuromorphic computing. Here, we demonstrate nonassociative learning with a prototypical Mott insulator, nickel oxide (NiO), under a variety of external stimuli at and above room temperature. Similar to biological species such as Aplysia, habituation and sensitization of NiO possess time-dependent plasticity relying on both strength and time interval between stimuli. A combination of experimental approaches and first-principles calculations reveals that such learning behavior of NiO results from dynamic modulation of its defect and electronic structure. An artificial neural network model inspired by such nonassociative learning is simulated to show advantages for an unsupervised clustering task in accuracy and reducing catastrophic interference, which could help mitigate the stability–plasticity dilemma. Mott insulators can therefore serve as building blocks to examine learning behavior noted in biology and inspire new learning algorithms for artificial intelligence.

习惯和敏感（非联想学习）是生物体中最基本的学习和记忆行为形式之一，可以在动态环境中适应和学习。模拟在自然界中发现的固态智能的这些特征可以为人工神经网络中的算法模拟和神经形态计算的潜在用途提供灵感。在这里，我们展示了在室温及高于室温的各种外部刺激下，使用原型 Mott 绝缘体氧化镍 (NiO) 进行的非关联学习。类似于海兔等生物物种，NiO的习惯化和敏化具有依赖于强度和刺激之间的时间间隔的时间依赖性可塑性。实验方法和第一性原理计算的结合表明，NiO 的这种学习行为是由其缺陷和电子结构的动态调制引起的。模拟了受这种非关联学习启发的人工神经网络模型，以显示无监督聚类任务在准确性和减少灾难性干扰方面的优势，这有助于缓解稳定性-可塑性困境。因此，莫特绝缘体可以作为构建块来检查生物学中注意到的学习行为并激发人工智能的新学习算法。