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Monroe, E.

Publications and source records attributed to Monroe, E..

2 recordsLinked to original sources

Disruption of theta-timescale spiking impairs learning but spares hippocampal replay

The ability to rapidly learn and retrieve salient information about new environments is critical for survival. In mammals, the hippocampus plays a crucial role in that learning. Specialized features of hippocampal population coding, including network-level theta oscillatory activity, location-specific firing of principal cells, and reactivation of experience during immobility (replay), have been implicated in rapid storage and retrieval of spatial information. Disruptions of theta and replay jointly, or replay alone, are sufficient to impair learning; however, the specific contribution of theta-associated temporal structure during locomotion remains unknown. In this study, we manipulated hippocampal spiking activity in rats specifically during locomotion by optogenetically activating septal parvalbumin-expressing GABAergic neurons. We developed a closed-loop, theta phase-specific stimulation protocol that reliably reduced theta power shortly after stimulation onset. This manipulation preserved the place codes of individual cells but disrupted the fine temporal structure of endogenous spatio-temporal representations (i.e., theta sequences) at the pairwise and population level. Theta disruption during locomotion was also sufficient to cause pronounced deficits in learning the more cognitively challenging component of a spatial alternation task, even though disruption was applied on only [~]66% of trials. Notably, network effects accompanying theta disruption were restricted to locomotor periods; we did not observe changes in replay rate, length, or content during immobility. Together, these results demonstrate the importance of the precise temporal microstructure of locomotion-associated spatial representations in the hippocampus for learning.

neuroscience↗

Spyglass: a data analysis framework for reproducible and shareable neuroscience research

Scientific progress depends on reliable and reproducible results. Progress can be accelerated when data are shared and re-analyzed to address new questions. Current approaches to storing and analyzing neural data involve bespoke formats and software that make replication and reuse of data difficult. To address these challenges, we created Spyglass, an open-source data management and analysis framework written in Python. Spyglass provides reproducible pipelines for common neuroscience analyses and sharing of raw data, intermediate analyses, and final results within and across labs. Spyglass uses the Neurodata Without Borders (NWB) standard and includes pipelines for spectral filtering, spike sorting, pose tracking, and neural decoding. Spyglass can be extended to apply existing and newly developed pipelines to datasets from multiple sources. We demonstrate these features in the context of a cross-laboratory replication by applying advanced state space decoding algorithms to publicly available data. New users can try out Spyglass on a Jupyter Hub hosted by HHMI and 2i2c: https://spyglass.hhmi.2i2c.cloud/.

neuroscience↗