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Topological data analyses are rapidly turning into key tools for quantifyinglarge volumes of neurobiological data, e.g., for organizing the spiking outputsof large neuronal ensembles and thus gaining insights into the informationproduced by various networks. Below we discuss a case in which severalconvergent topological analyses not only provide a description of the datastructure, but also produce insights into how these data may be processed inthe hippocampus---a brain part that plays a key role in learning and memory.The resulting functional model provides a unifying framework for integratingspiking information at different timescales and understanding the course ofspatial learning at different levels of spatiotemporal granularity. Inparticular, the model allows quantifying contributions of various physiologicalphenomena---brain waves, synaptic strengths, synaptic architectures, etc., intospatial cognition.

From topological analyses to functional modeling: the case of hippocampus