从拓扑分析到功能建模：海马的案例
From topological analyses to functional modeling: the case of hippocampus
摘要：
拓扑数据分析正迅速成为量化大量神经生物学数据的关键工具，例如，用于组织大型神经元集合的尖峰输出，从而深入了解各种网络产生的信息。下面我们讨论一个案例，其中几个收敛拓扑分析不仅提供了数据结构的描述，而且还产生了对这些数据如何在海马体中处理的见解-海马体是在学习和记忆中起关键作用的大脑部分。由此产生的功能模型提供了一个统一的框架，整合尖峰信息在不同的时间尺度和理解的过程中，在不同层次的时空粒度的空间学习。特别地，该模型允许量化各种生理现象的贡献-脑电波、突触强度、突触架构等，空间认知。
英文摘要：
Topological data analyses are rapidly turning into key tools for quantifying large volumes of neurobiological data, e.g., for organizing the spiking outputs of large neuronal ensembles and thus gaining insights into the information produced by various networks. Below we discuss a case in which several convergent topological analyses not only provide a description of the data structure, but also produce insights into how these data may be processed in the hippocampus---a brain part that plays a key role in learning and memory. The resulting functional model provides a unifying framework for integrating spiking information at different timescales and understanding the course of spatial learning at different levels of spatiotemporal granularity. In particular, the model allows quantifying contributions of various physiological phenomena---brain waves, synaptic strengths, synaptic architectures, etc., into spatial cognition.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
