CRII: RI: Navigational Circuitry of Brain: Novel Neural Codes with Diversity for Robust and Adaptive Location Processing

CRII：RI：大脑导航电路：具有多样性的新颖神经代码，用于鲁棒和自适应位置处理

• 批准号: 1464349
• 负责人: Onur Ozan Koyluoglu
• 金额: $ 11.52万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2018-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1464349&HistoricalAwards=false
• 关键词: CRII; RI; Navigational; Circuitry; Brain

The main objectives of this project are to develop a novel coding framework for the navigational circuitry of the brain, and in the context of this model, to investigate how neural diversity and neurally plausible information processing mechanisms contribute to a robust navigation system. Robust localization techniques based on neural coding principles may have applications in robotics. In addition, the framework developed here will lead to a better understanding of brain's navigational circuitry and associated brain areas, which are implicated in Alzheimer's disease and other brain disorders.The brain's navigational circuitry includes cells whose activities are modulated depending on where an animal is in its environment. Place cells in the hippocampus are typically active over a certain region within an environment, but remain mostly silent otherwise. Grid cells in the entorhinal cortex, on the other hand, respond at multiple regions, where the activity of each cell follows a striking periodic pattern, forming a hexagonal tiling of the space. Both place and grid populations exhibit diversity (e.g., firing fields of neurons typically increase along the dorsoventral axis) and adaptivity (e.g., cells change their activity levels in response to changes in environment). One theory of how these cells are related is that grid cells form place cells, which in turn are responsible for performing navigational computations, but this view contradicts recent neurophysiological data. The central hypothesis of the investigator, in contrast, is that the brain has a neural code consisting of both place cells and grid cells, and, when processed with neurally plausible, low-complexity algorithms, codewords of this code form a robust and adaptive navigational processing mechanism. This project focuses on coding models consisting of place cells that provide side information in decoding of the grid cell activity, and separate read-out populations that perform computations with place and grid cells iteratively. Utilizing these models, the investigator proposes to study the role of neural diversity in achieving robustness, to analyze the modular processing capacity of the code, to understand neural adaptivity through obtaining broader estimate by lowering the code rate, and to compare these findings with experimental observations.

该项目的主要目标是为大脑导航电路开发一种新颖的编码框架，并在该模型的背景下研究神经多样性和神经合理的信息处理机制如何有助于构建强大的导航系统。基于神经编码原理的鲁棒定位技术可能在机器人技术中得到应用。此外，这里开发的框架将有助于更好地了解大脑的导航电路和相关的大脑区域，这些区域与阿尔茨海默病和其他大脑疾病有关。大脑的导航电路包括一些细胞，这些细胞的活动根据动物在其环境中的位置进行调节。海马体中的位置细胞通常在环境中的某个区域活跃，但在其他区域大多保持沉默。另一方面，内嗅皮质中的网格细胞在多个区域做出反应，其中每个细胞的活动遵循引人注目的周期性模式，形成空间的六边形平铺。地点和网格群体都表现出多样性（例如，神经元的发射场通常沿着背腹轴增加）和适应性（例如，细胞响应环境变化而改变其活动水平）。关于这些细胞如何相关的一种理论是，网格细胞形成位置细胞，位置细胞反过来负责执行导航计算，但这种观点与最近的神经生理学数据相矛盾。相比之下，研究者的中心假设是，大脑有一个由位置细胞和网格细胞组成的神经代码，当用神经上合理的、低复杂度的算法进行处理时，该代码的码字形成了一个强大的、自适应的导航处理机制。该项目重点关注由位置单元组成的编码模型，位置单元在网格单元活动的解码中提供辅助信息，以及使用位置和网格单元迭代执行计算的单独读出群体。利用这些模型，研究人员建议研究神经多样性在实现鲁棒性方面的作用，分析代码的模块化处理能力，通过降低代码率获得更广泛的估计来理解神经适应性，并将这些发现与实验观察结果进行比较。