RI: Small: Collaborative Research: Robustness of Spatial Learning in Flickering Networks: The Case of the Hippocampus

RI：小型：协作研究：闪烁网络中空间学习的鲁棒性：海马体的案例

• 批准号: 1422438
• 负责人: Yuri Dabaghian
• 金额: $ 26.97万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422438&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Robustness

The reliability of our memories is nothing short of remarkable. Thousands of neurons die every day, synaptic connections appear and disappear, and the networks formed by these neurons constantly change due to various forms of synaptic plasticity. How can the brain develop a reliable representation of the world, learn and retain memories despite, or perhaps due to, such complex dynamics? Answering such questions is the natural evolution of recent work by the Dabaghian lab, which has been studying spatial cognition by modeling mechanisms of learning, based on algebraic topology methods developed by the Memoli group. This approach rests on the insight that the animal brain must first construct a rough-and-ready map of the environment before being able to fill it in with geometric details, which would be too computationally costly in light of typical navigational goals such as evading predators, returning to a nest, or finding a cafe. This basic map pays particular attention to the connectivity between places in the environment and is thus based on spatial topology; as such, the investigators hypothesized, it would be amenable to analysis by topological methods.By simulating exploratory movements through different environments Dabaghian and Memoli will study how stable topological features arise in assemblies of simulated neurons operating under a wide range of conditions, including variations in firing rate, the size of the space each cell "senses," the number of cells in the population, and electrical oscillations in the brain that alter the behavior of the ensemble. They will use several novel methods from Persistent Homology Theory to understand how connections between cells (synapses) influence the speed and reliability of spatial learning. One might assume that learning would be enhanced if synapses never disappeared, but biology has clearly evolved to favor great synaptic plasticity. One reason may be that the loss of certain connections allows more room for mistakes to be unlearned. The objectives of this project are to study synaptic plasticity in a computational model, which will allow the influences of different parameters on the outcome of learning to be studied in detail. Principles that emerge on spatial learning in the hippocampus should be translatable to spatial cognition in machines.

我们记忆的可靠性是非常惊人的。每天有成千上万的神经元死亡，突触连接出现和消失，这些神经元形成的网络由于各种形式的突触可塑性而不断变化。大脑如何发展出对世界的可靠表征，如何学习和保留记忆，尽管，或者可能是由于，如此复杂的动态？提出这些问题是Dabaghian实验室最近工作的自然演变，该实验室一直在通过建模学习机制来研究空间认知，基于Memoli小组开发的代数拓扑方法。这种方法基于这样一种认识，即动物大脑必须首先构建一个粗略的环境地图，然后才能用几何细节来填充它，这对于典型的导航目标来说太昂贵了，比如躲避捕食者，返回巢穴，或者找到咖啡馆。这一基本地图特别注意环境中各地点之间的连通性，因此以空间拓扑结构为基础;因此，研究人员假设，它将适合于拓扑方法的分析。通过模拟在不同环境中的探索性运动，Dabaghian和Memoli将研究在各种条件下运行的模拟神经元组件中稳定的拓扑特征是如何出现的，包括放电率的变化、每个细胞“感知”的空间大小、群体中细胞的数量以及改变整体行为的大脑中的电振荡。他们将使用持久同源理论中的几种新方法来了解细胞（突触）之间的连接如何影响空间学习的速度和可靠性。人们可能会认为，如果突触永远不会消失，学习就会得到加强，但生物学显然已经进化到有利于突触可塑性的程度。原因之一可能是，某些联系的丧失为错误提供了更多的忘却空间。本计画的目标是在一个计算模型中研究突触可塑性，这将允许详细研究不同参数对学习结果的影响。海马体空间学习的原理应该可以转化为机器的空间认知。