RI: Small: Collaborative Research: Robustness of spatial learning in flickering networks: the case of the hippocampus

RI：小：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: 1422400
• 负责人: Facundo Memoli
• 金额: $ 23.02万
• 依托单位: Ohio State 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 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422400&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.

我们记忆的可靠性几乎是惊人的。每天有数以千计的神经元死亡，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。大脑如何才能形成对世界的可靠表征，学习并保持记忆，尽管或可能由于如此复杂的动力学？回答这些问题是达巴吉安实验室最近工作的自然演变，该实验室一直在基于Memoli小组开发的代数拓扑学方法，通过建模学习机制来研究空间认知。这种方法基于这样一种见解，即动物的大脑必须首先构建一张粗略的环境地图，然后才能用几何细节填充它，考虑到典型的导航目标，如躲避捕食者、返回巢穴或找到咖啡馆，这一计算代价太高。这张基本地图特别关注环境中地方之间的连通性，因此是基于空间拓扑的；因此，研究人员假设，它可以通过拓扑方法进行分析。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如何产生稳定的拓扑特征，包括放电率的变化，每个细胞“感觉”的空间的大小，种群中细胞的数量，以及大脑中改变整体行为的电振荡。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)之间的联系如何影响空间学习的速度和可靠性。有人可能会认为，如果突触永远不消失，学习会得到加强，但生物学显然已经进化到有利于突触的巨大可塑性。其中一个原因可能是，失去某些联系会让人们有更多的空间来忘却错误。这个项目的目标是在计算模型中研究突触的可塑性，这将使不同参数对学习结果的影响得到详细的研究。在海马体中出现的空间学习原理应该可以转化为机器的空间认知。