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

在神经科学中，人们感兴趣的突发现象（如学习和记忆）与获得大多数数据的水平之间存在紧张关系。例如，许多实验实验室通过在体外电生理测量和动物行为实验中收集的数据之间建立经验相关性来研究突触连接的强度及其动态如何影响认知。然而，这些相关性缺乏因果解释：迄今为止，还没有将单个神经元和突触的记录与认知学习动态联系起来的机制。这个问题不是由于缺乏在神经元或系统水平上的观察；相反，它反映了我们将这两个尺度联系起来的能力的主要差距。即使可以对大脑中的每个神经元进行完整的描述，我们从局部数据过渡到关于如何结合产生系统认知结果的定性结论的能力仍然存在差距。解决这一问题需要一个概念框架，其中包括一个计算模型，该模型将单个细胞的实验衍生特征与这些特征在整体水平上的影响联系起来。本研究旨在提供一种建立这种联系的方法：利用新颖的拓扑和几何技术，基于海马突触结构参数，包括突触可塑性参数，开发一个数据驱动的海马空间学习系统模型。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，长期和短期增强和抑制），研究该地图的结构，其形成和恶化的机制，并评估生成给定环境的空间地图所需的时间等。这个项目是Dabaghian小组先前在空间学习机制建模方面所做的工作的自然演变，该工作基于M？moli组。以理论为基础的对学习现象的洞察将在定性上更好地理解如何解释数据，如何设计新的实验，测量中应该针对哪些变量，以及如何最大限度地减少动物的使用，以及总体上如何优化使用体力和智力资源。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.