CRCNS: Computational principles of mental simulation in the entorhinal and parietal cortex

人类从稀缺的信息中对世界上实体之间的关系做出丰富的推论。例如，我们可以在看到几个街道号码后找到一个小说目的地，或者通过浏览其他页面中的几个单词来在词典中找到一页。理论方面的考虑表明，大脑通过构建环境中的关系(行为和世界状态之间的关系)的“内部模型”，并通过在心理上模拟这些模型来做出这样的推论。然而，心理模拟的神经基础和机制尚不清楚。我们的首要目标是将来自理论和建模的见解与清醒的猴子的行为和电生理学结合起来，以了解内部模型的心理模拟如何支持关系推理。我们将为猴子开发一项行为任务，在这项任务中，猴子在大脑中沿着离散刺激(即图像序列)的一维抽象空间从一个刺激“导航”到另一个刺激。我们将评估动物的行为特征是否表现出心理模拟的特征。然后，我们将创建一个大型神经网络模型库，以生成大脑可能用于导航抽象空间的替代计算策略(包括心理模拟)的假设。接下来，我们将记录猴子顶叶和内嗅觉皮质中候选脑区的数据，并在单细胞和种群水平上分析数据，以寻找心理模拟的特征。最后，我们将采用迭代方法，包括基于模型的数据分析和数据驱动的模型修订，最终目标是创建同时成功执行与任务相关的计算(即行为)和解释观察到的神经反应的模型。最后，我们将通过评估我们的模型在新的行为任务中对行为和电生理的预测来验证我们的框架。

Humans make rich inferences about the relationships between entities in the world from scarce information. For example, we can find a novel destination after seeing a few street numbers, or find a page in a dictionary by glancing at a few words in other pages. Theoretical considerations suggest that the brain makes such inferences by constructing "internal models" of the relationships in the environment (relationships between actions and states of the world), and by mentally simulating those models. However, the neural substrates and mechanisms of mental simulation are not understood. Our overarching goal is to integrate insights from theory and modeling with behavior and electrophysiology in awake, behaving monkeys to understand how mental simulation of internal models support relational inference. We will develop a behavioral task for monkeys in which they have “navigate” mentally from one stimulus to another along a one-dimensional abstract space of discrete stimuli (i.e., a sequence of images). We will assess whether animals’ behavioral characteristics exhibit hallmarks of mental simulation. We will then create a large library of neural network models to generate hypotheses for alternative computational strategies (including mental simulations) that the brain might employ for navigating abstract spaces. Next, we will record from candidate brain areas in the parietal and entorhinal cortex of monkeys, and analyze the data at single cell and population levels looking for signatures of mental simulation. Finally, we will adopt an iterative approach involving model-based data analyses and data-driven model revision with the ultimate goal of creating models that simultaneously succeed in performing task-relevant computations (i.e., behavior) and account for observed neural responses. Finally, we will validate our framework by evaluating the predictions of our models for both behavior and electrophysiology in new behavioral tasks.