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.

人类从稀缺信息中就世界上实体之间的关系提供了丰富的信息。例如，我们可以在看到几条街道数字后找到一个新颖的目的地，或者在词典中查看其他页面中的几个单词，从而在字典中找到页面。理论上的考虑表明，大脑通过构建环境中关系的“内部模型”（行动与世界状态之间的关系），并通过精神上模拟这些模型来制定此类信息。但是，尚不清楚心理模拟的神经底物和机制。我们的总体目标是将理论和建模与行为和电生理学的见解整合到清醒中，表现猴子以了解内部模型的心理模拟如何支持关系推断。我们将为猴子开发一个行为任务，在该猴子中，它们在一个维离散刺激的一维抽象空间（即，图像序列）上从一种刺激到另一种刺激进行了“导航”。我们将评估动物的行为特征是否暴露了精神模拟的标志。然后，我们将创建一个大型的神经元网络模型库，以生成用于替代计算策略（包括心理模拟）的假设，大脑可能用于导航抽象空间。接下来，我们将从猴子顶和内嗅皮层中的候选大脑区域记录，并分析单细胞和人群的数据。寻找心理模拟签名的水平。最后，我们将采用一种迭代方法，涉及基于模型的数据分析和数据驱动的模型修订，其最终目标是创建模型，这些模型仅成功执行与任务相关的计算（即行为）并说明了观察到的神经响应。最后，我们将通过评估新行为任务中的行为和电生理学的模型预测来验证我们的框架。