ABSTRACT Many researchers have tried to model how environmental knowledge is learned by the brain and used in the form of cognitive maps. However, previous work was limited in various important ways: there was little consensus on how these cognitive maps were formed and represented, the planning mechanism was inherently limited to performing relatively simple tasks, and there was little consideration of how these mechanisms would scale up. This paper makes several significant advances. Firstly, the planning mechanism used by the majority of previous work propagates a decaying signal through the network to create a gradient that points towards the goal. However, this decaying signal limited the scale and complexity of tasks that can be solved in this manner. Here we propose several ways in which a network can can self-organize a novel planning mechanism that does not require decaying activity. We also extend this model with a hierarchical planning mechanism: a layer of cells that identify frequently-used sequences of actions and reuse them to significantly increase the efficiency of planning. We speculate that our results may explain the apparent ability of humans and animals to perform model-based planning on both small and large scales without a noticeable loss of efficiency.

中文翻译：

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大脑中分层认知图的形成和使用：一种神经网络模型

摘要 许多研究人员试图模拟大脑如何学习环境知识并以认知地图的形式使用。然而，之前的工作在各种重要方面受到限制：对于这些认知地图的形成和表示方式几乎没有达成共识，规划机制本质上仅限于执行相对简单的任务，并且很少考虑这些机制如何扩大规模。本文取得了几项重大进展。首先，大多数先前工作使用的规划机制通过网络传播衰减信号以创建指向目标的梯度。然而，这种衰减信号限制了可以通过这种方式解决的任务的规模和复杂性。在这里，我们提出了几种网络可以自组织不需要衰减活动的新型规划机制的方法。我们还使用分层规划机制扩展了这个模型：一个单元层，用于识别常用的动作序列并重用它们以显着提高规划效率。我们推测我们的结果可以解释人类和动物在小规模和大规模上执行基于模型的规划而没有明显效率损失的明显能力。