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MesoNet: automated scaling and segmentation of mouse mesoscale cortical maps using machine learning
| Description: | Raw wide-field calcium imaging data was recorded from male awake head-fixed GCaMP6 mice. This dataset is used to discovery cortical activity motifs and generate motif based functional maps. Usage: The main demo code to run: MesoNet_motif_discovery_Democode.m MesoNet_MBFM_multirun_Democode.m The codes are available on the OSF repository: “OSF Storage/4_Data_code” at https://osf.io/svztu/ The demo code was tested on: Operating systems: Windows10 Versions the software has been tested on: Matlab 2020a Expected run time for demo on a "normal" desktop computer: the code may need several hours or days to run. |
| Notes: | Item exited embargo and became publicly available on 2024-04-01 |
| Authors: | Murphy, Timothy; University of British Columbia;  0000-0002-0093-4490Xiao, Dongsheng; University of British Columbia; 0000-0002-1669-0021 |
| Keywords: | mesoscale GCAMP mouse cortex |
| Field of Research: | Basic medicine and life sciences > Neurosciences, medical and physiological and health aspects > Neurosciences, medical and health and physiological aspects, not elsewhere classified
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| Publication Date: | 2024-04-01 |
| Publisher: | Federated Research Data Repository / dépôt fédéré de données de recherche |
| Funder: | Canadian Institutes of Health Research (CIHR) Canadian Open Neuroscience Platform (CONP) |
| URI: | https://doi.org/10.20383/101.0310 |
| Related Identifiers: |
This dataset is a supplement to
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| Geographic Coverage: | City Vancouver Province / Province / Territory British Columbia Territory Country Canada |
| Appears in Collections: | UBC Brain Circuits |
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Access to this dataset is subject to the following terms:
Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
Citation
Murphy, T., Xiao, D. (2024). MesoNet: automated scaling and segmentation of mouse mesoscale cortical maps using machine learning. Federated Research Data Repository. https://doi.org/10.20383/101.0310
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