Spyglass: a framework for reproducible and shareable neuroscience research DOI Creative Commons
Kyu Hyun Lee, Eric L. Denovellis, Ryan Ly

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Янв. 26, 2024

Abstract Scientific progress depends on reliable and reproducible results. Progress can also be accelerated when data are shared re-analyzed to address new questions. Current approaches storing analyzing neural typically involve bespoke formats software that make replication, as well the subsequent reuse of data, difficult if not impossible. To these challenges, we created Spyglass , an open-source framework enables analyses sharing both intermediate final results within across labs. uses Neurodata Without Borders (NWB) standard includes pipelines for several core in neuroscience, including spectral filtering, spike sorting, pose tracking, decoding. It easily extended apply existing newly developed datasets from multiple sources. We demonstrate features context a cross-laboratory replication by applying advanced state space decoding algorithms publicly available data. New users try out Jupyter Hub hosted HHMI 2i2c: https://spyglass.hhmi.2i2c.cloud/ .

Язык: Английский

The future of neurotechnology: From big data to translation DOI
Jinhyun Kim, Thomas J. McHugh, Chul Hoon Kim

и другие.

Neuron, Год журнала: 2025, Номер 113(6), С. 814 - 816

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Two-Phase Coding Strategy by CA1 Pyramidal Neurons: Linking Spatiotemporal Integration to Predictive Behavior DOI Open Access

Raphael Heldman,

Dongyan Pang,

Colin Porter

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Март 16, 2025

ABSTRACT Space and time are fundamental components of memory, yet how the brain encodes these dimensions to guide behavior remains unclear. Using virtual-reality environments, we uncovered a two-phase neural code in hippocampus CA1 that represents or distance through two functional pyramidal subpopulations, PyrUp PyrDown. In Phase I, activity synchronously increases mark initiation encoding; II, their decays at heterogeneous, neuron-specific rates, creating gradual divergence across-population firing rates scales with elapsed time. Conversely, PyrDown initially decreases before gradually rising. The crossover point, where rising surpasses declining activity, precedes predictive licking behavior. Combining optogenetics computational modeling, provided circuit-level evidence neurons primarily process locomotion-related inputs regulated by somatostatin-positive interneurons, whereas mainly receive reward-related gated parvalbumin-positive interneurons. These findings advance our understanding hippocampal circuits compute spatiotemporal information inform

Язык: Английский

Процитировано

0
Is a Human-scale Mouse Brain Model enough as a Human Brain Model? DOI
Tadashi Yamazaki

The Brain & Neural Networks, Год журнала: 2025, Номер 32(1), С. 3 - 11

Опубликована: Март 5, 2025

Язык: Английский

Процитировано

0
Aversive experience drives offline ensemble reactivation to link memories across days DOI Creative Commons
Yosif Zaki, Zachary T. Pennington, Denisse Morales-Rodriguez

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Март 14, 2023

Abstract Memories are encoded in neural ensembles during learning and stabilized by post-learning reactivation. Integrating recent experiences into existing memories ensures that contain the most recently available information, but how brain accomplishes this critical process remains unknown. Here we show mice, a strong aversive experience drives offline ensemble reactivation of not only memory also neutral formed two days prior, linking fear from to previous memory. We find specifically links retrospectively, prospectively, across days. Consistent with prior studies, period following learning. However, increases co-reactivation period. Finally, expression context is associated shared between memories. Taken together, these results demonstrate can drive retrospective memory-linking through providing mechanism which be integrated

Язык: Английский

Процитировано

10
Spyglass: a framework for reproducible and shareable neuroscience research DOI Creative Commons
Kyu Hyun Lee, Eric L. Denovellis, Ryan Ly

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Янв. 26, 2024

Abstract Scientific progress depends on reliable and reproducible results. Progress can also be accelerated when data are shared re-analyzed to address new questions. Current approaches storing analyzing neural typically involve bespoke formats software that make replication, as well the subsequent reuse of data, difficult if not impossible. To these challenges, we created Spyglass , an open-source framework enables analyses sharing both intermediate final results within across labs. uses Neurodata Without Borders (NWB) standard includes pipelines for several core in neuroscience, including spectral filtering, spike sorting, pose tracking, decoding. It easily extended apply existing newly developed datasets from multiple sources. We demonstrate features context a cross-laboratory replication by applying advanced state space decoding algorithms publicly available data. New users try out Jupyter Hub hosted HHMI 2i2c: https://spyglass.hhmi.2i2c.cloud/ .

Язык: Английский

Процитировано

3