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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.
Authors: Murphy, Timothy; University of British Columbia; https://orcid.org/0000-0002-0093-4490 ORCID iD
Xiao, Dongsheng; University of British Columbia; https://orcid.org/0000-0002-1669-0021 ORCID iD
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
Date: 1-Apr-2024
Publisher: Federated Research Data Repository / dépôt fédéré de données de recherche
URI: https://doi.org/10.20383/101.0310
Related Identifiers: 
This dataset is a supplement to
Geographic Coverage: 
City
Vancouver
Province /
Province / Territory
British Columbia
Territory
 
Country
Canada
Appears in Collections:UBC Brain Circuits

Files in Dataset 
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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