Artificial Intelligence has advanced significantly in recent years thanks to innovations in the design and training of artificial neural networks (ANNs). Despite these advancements, we still understand relatively little about how elementary forms of ANNs learn, fail to learn, and generate false information without the intent to deceive, a phenomenon known as `confabulation'. To provide some foundational insight, in this paper we analyse how confabulation occurs in reservoir computers (RCs): a dynamical system in the form of an ANN. RCs are particularly useful to study as they are known to confabulate in a well-defined way: when RCs are trained to reconstruct the dynamics of a given attractor, they sometimes construct an attractor that they were not trained to construct, a so-called `untrained attractor' (UA). This paper sheds light on the role played by UAs when reconstruction fails and their influence when modelling transitions between reconstructed attractors. Based on our results, we conclude that UAs are an intrinsic feature of learning systems whose state spaces are bounded, and that this means of confabulation may be present in systems beyond RCs.

本研究针对人工神经网络在学习和虚构能力方面的不足进行探索，尤其关注储层计算机中的虚构现象。通过分析未训练吸引子在重构失败时的作用，提出了它们是学习系统固有特征的观点，指出这种虚构现象可能超越储层计算机的应用。研究结果为理解人工智能中的信息生成和失真提供了新的视角。

储层计算机中的虚构动态：利用未训练吸引子填补空白