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Abstract：
Topological data analyses are widely used for describing and conceptualizing large volumes of neurobiological data, e.g., for quantifying spiking outputs of large neuronal ensembles and thus understanding the functions of the corresponding networks. Below we discuss an approach in which convergent topological analyses produce insights into how information may be processed in mammalian hippocampus-a brain part that plays a key role in learning and memory. The resulting functional model provides a unifying framework for integrating spiking data at different timescales and following the course of spatial learning at different levels of spatiotemporal granularity. This approach allows accounting for contributions from various physiological phenomena into spatial cognition-the neuronal spiking statistics, the effects of spiking synchronization by different brain waves, the roles played by synaptic efficacies and so forth. In particular, it is possible to demonstrate that networks with plastic and transient synaptic architectures can encode stable cognitive maps, revealing the characteristic timescales of memory processing.
摘要：
拓扑数据分析广泛用于描述和概念化大量的神经生物学数据，例如，用于量化大型神经元集合的尖峰输出，从而了解相应网络的功能。下面我们讨论一种方法，在这种方法中，收敛拓扑分析产生了如何在哺乳动物海马中处理信息的见解，海马是在学习和记忆中起关键作用的大脑部分。所得到的功能模型提供了一个统一的框架，用于在不同的时间尺度上集成尖峰数据，并遵循不同时空粒度级别的空间学习过程。这种方法可以考虑各种生理现象对空间认知的贡献-神经元尖峰统计，不同脑电波对尖峰同步的影响，突触功效发挥的作用等等。特别是,有可能证明具有可塑性和瞬态突触结构的网络可以编码稳定的认知图，揭示了记忆处理的特征时间尺度。
