How does the mind organize thoughts? The hippocampal-entorhinal complex is thought to support domain-general representation and processing of structural knowledge of arbitrary state, feature and concept spaces. In particular, it enables the formation of cognitive maps, and navigation on these maps, thereby broadly contributing to cognition. It has been proposed that the concept of multi-scale successor representations provides an explanation of the underlying computations performed by place and grid cells. Here, we present a neural network based approach to learn such representations, and its application to different scenarios: a spatial exploration task based on supervised learning, a spatial navigation task based on reinforcement learning, and a non-spatial task where linguistic constructions have to be inferred by observing sample sentences. In all scenarios, the neural network correctly learns and approximates the underlying structure by building successor representations. Furthermore, the resulting neural firing patterns are strikingly similar to experimentally observed place and grid cell firing patterns. We conclude that cognitive maps and neural network-based successor representations of structured knowledge provide a promising way to overcome some of the short comings of deep learning towards artificial general intelligence.
翻译:心智是如何组织思想的? 河马阵列-内心综合体被认为支持对任意状态、特征和概念空间的广域代表性和结构知识的处理,特别是它能够形成认知图和在这些地图上进行导航,从而广泛地有助于认知;有人提议,多尺度后继代表的概念可以解释由地点和网格细胞进行的基本计算。在这里,我们提出了一个神经网络方法,以了解这种表达方式及其在不同情景中的应用:基于有监督的学习的空间探索任务,基于强化学习的空间导航任务,以及非空间性任务,在这种任务中,语言构造必须通过观察抽样判决推断。在所有情况下,神经网络正确地通过建立后继代表来学习和接近基本结构。此外,由此产生的神经发射模式与实验性观测到的地点和网格单元发射模式极为相似。我们的结论是,认知地图和以神经网络为基础的结构知识的后继表达提供了一种很有希望的方法,可以克服一些通过观察人造一般智能的短短的深学习。