RI: Medium:Collaborative Research: Through synapses to spatial learning: a topological approach

RI：媒介：协作研究：通过突触进行空间学习：拓扑方法

基本信息

批准号：
1901360
负责人：
Facundo Memoli
金额：
$ 43.5万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901360&HistoricalAwards=false
关键词：
RI Medium Collaborative Research Through

项目摘要

There is a tension in neuroscience between the emergent phenomena of interested, such as learning and memory, and the level at which most data are acquired. For example, numerous experimental labs study how the strengths of synaptic connections and their dynamics affect cognition by establishing empirical correlations between in vitro electrophysiology measurements and data collected in animal behavioral experiments. However, these correlations fall short of causal explanations: to date, there exist no mechanisms connecting recordings in individual neurons and synapses with cognitive learning dynamics. The problem is not due to a lack of observations at either the neuronal or the systemic level; rather, it reflects a principal gap in our ability to link these two scales. Even if a full description of every neuron in the brain could be produced, there would still be a gap in our ability to transition from local data to making qualitative conclusions about how it combines to produce systemic cognitive outcomes. Addressing this problem requires a conceptual framework encompassing a computational model that would link the experimentally derived characteristics of individual cells with effects of those characteristics at the ensemble level. The proposed research aims to provide a way to establish such a connection: developing a data-driven, systemic model of hippocampal spatial learning based on the parameters of the hippocampal synaptic architecture, including the parameters of synaptic plasticity, using novel topological and geometric techniques. Recent developments in Algebraic Topology will be used to integrate the parameters of synaptic connectivity and synaptic plasticity (e.g., long- and short-term potentiation and depression), to study structure of this map, the mechanisms of its formation and deterioration, and to evaluate the time required to produce a spatial map of a given environment, etc. This project is a natural evolution of prior work done by the Dabaghian group on modeling the mechanisms of spatial learning, based on algebraic topology methods developed by the M?moli group. The theory-based insight into learning phenomena will produce a qualitatively better understanding of how to interpret data, how to design new experiments, what variables should be targeted in measurements, as well as how to minimize use of animals, and in general how to optimize use physical and intellectual resources.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

感兴趣的新兴现象（例如学习和记忆）与获得大多数数据的水平之间存在神经科学的张力。例如，众多实验实验室研究突触连接及其动力学的强度如何通过在动物行为实验中收集的体外电生理测量和收集的数据之间建立经验相关性来影响认知。然而，这些相关性不足以说明因果解释：迄今为止，没有机制将单个神经元中的记录和与认知学习动力学的突触连接起来。问题不是由于在神经元或系统水平上缺乏观察结果。相反，它反映了我们链接这两个量表的能力的主要差距。即使可以完全描述大脑中的每个神经元的完整描述，我们的能力仍然存在差距，从本地数据过渡到对其如何结合产生系统性认知结果的定性结论。解决此问题需要一个概念框架，其中包含一个计算模型，该模型将将单个单元的实验得出的特征与集合级别的这些特征的影响联系起来。拟议的研究旨在提供一种建立这种联系的方法：基于海马突触体系结构的参数，开发数据驱动的，全身的空间学习模型，包括使用新颖的拓扑和几何学技术，包括突触可塑性的参数。代数拓扑的最新发展将用于整合突触连通性和突触可塑性的参数（例如，长期和短期的增强和抑郁症），研究该地图的结构，形成和变质的机制，其形成的机制，并评估该项目的机制，以前的机制，该项目的自然图是由自然图像进行的。空间学习，基于M？moli组开发的代数拓扑方法。基于理论的学习现象的洞察力将在定性上更好地理解如何解释数据，如何设计新实验，哪些变量应针对测量中的变量，以及如何最大程度地减少动物的使用以及一般如何优化物理和知识资源。该奖项反映了NSF的法定任务和范围的构成范围，以反映了构成构成的范围和范围的范围。