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

CRCNS：内嗅和顶叶皮层心理模拟的计算原理

基本信息

批准号：
10396142
负责人：
Ila R. Fiete
金额：
$ 33.48万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-06 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10396142
关键词：
Adopted Animals Area Behavior Behavioral Brain Cells Characteristics Cues Data Data Analyses Databases Destinations Development Diagnostic Dictionary Dimensions Divorce Electrophysiology (science)Engineering Environment Exhibits Future Goals Human Image Knowledge Learning Lesion Libraries Mental Health Modeling Monkeys Motor Nature Neural Network Simulation Neurobiology Parietal Parietal Lobe Pattern Play Population Psyche structure Resolution Rodent Role Running Sensory Signal Transduction Stimulus Structure System Task Performances Testing Therapeutic Work awake base behavior prediction behavioral response cognitive neuroscience computer program design entorhinal cortex experience experimental study flexibility insight neural circuit neural model neuroimaging neuromechanism neurotransmission nonhuman primate novel relating to nervous system response simulation spatiotemporal theories way finding

项目摘要

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.

人类从稀缺的信息中对世界上的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新的目的地，或者通过浏览其他页面中的几个单词来找到字典中的页面。然而，考虑表明大脑通过构建环境关系（世界行为和状态之间的关系）的“内部模型”并通过心理模拟这些模型来做出此类推论。心理模拟的神经基础和机制并不我们的首要目标是将理论和模型的见解与清醒、行为猴子的行为和电生理学相结合，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，让它们进行心理“导航”。沿着离散刺激的一维抽象空间（即一系列图像）从一个刺激到另一个刺激，我们将评估动物的行为特征是否表现出心理模拟的特征。产生假设接下来，我们将记录猴子顶叶和内嗅皮层的候选大脑区域，并分析单细胞和群体水平的数据，寻找特征。最后，我们将采用基于模型的数据分析和数据驱动的模型修正的迭代方法，最终目标是创建同时成功执行任务相关计算（即行为）并解释观察到的模型。最后，我们将通过评估我们的模型对新行为任务中的行为和电生理学的预测来验证我们的框架。