Every day we make decisions critical for adaptation and survival. We repeat actions with known consequences. But we also draw on loosely related events to infer and imagine the outcome of entirely novel choices. These inferential decisions are thought to engage a number of brain regions; however, the underlying neuronal computation remains unknown. Here, we use a multi-day cross-species approach in humans and mice to report the functional anatomy and neuronal computation underlying inferential decisions. We show that during successful inference, the mammalian brain uses a hippocampal prospective code to forecast temporally structured learned associations. Moreover, during resting behavior, coactivation of hippocampal cells in sharp-wave/ripples represent inferred relationships that include reward, thereby “joining-the-dots” between events that have not been observed together but lead to profitable outcomes. Computing mnemonic links in this manner may provide an important mechanism to build a cognitive map that stretches beyond direct experience, thus supporting flexible behavior.

我们每天都会做出对适应和生存至关重要的决定。我们重复具有已知后果的行动。但我们也会利用松散相关的事件来推断和想象完全新颖的选择的结果。这些推理决策被认为涉及许多大脑区域。然而，底层的神经元计算仍然未知。在这里，我们在人类和小鼠中使用多天的跨物种方法来报告推理决策背后的功能解剖学和神经元计算。我们表明，在成功的推理过程中，哺乳动物大脑使用海马前瞻性代码来预测时间结构的学习关联。此外，在静息行为期间，尖波/波纹中海马细胞的共激活代表了包括奖励在内的推断关系，从而将尚未一起观察到但会带来有利结果的事件之间“连接起来”。以这种方式计算助记符链接可能会提供一种重要的机制来构建超越直接经验的认知图，从而支持灵活的行为。