Cerebellar granule cell axons support high dimensional representations

DOI

This page provides the links to the raw data and analysis scripts for the figures in the following publication:Lanore, F., Cayco-Gajic, N.A., Gurnani, H., Coyle, C., Silver, R.A. (2021). Cerebellar granule cell axons support high dimensional representations. Nature Neuroscience 24(8):1142-1150. doi: 10.1038/s41593-021-00873-xIn this study we used Acousto-Optic Lens (AOL) 3D two-photon microscopy to image large populations of granule cell axons (parallel fibres) in the cerebellar cortex of spontaneously behaving mice. We examined how different behavioral states and sensorimotor variables are represented in granule cell population activity. We characterized the manifold structure of the coding subspace and estimated dimensionality of the representations. Main project page:https://figshare.com/projects/Cerebellar_granule_cell_axons_support_high_dimensional_representations/101456The fibre direction data is here:https://figshare.com/articles/dataset/Fibre_Direction_Data/14976822Functional data after grouping is here:https://figshare.com/articles/dataset/Functional_Data/14976891The Metadata is here:https://figshare.com/articles/dataset/Metadata_Files/14976837The code used for data processing and analysis can be found on GitHub here: https://github.com/SilverLabUCL/ParallelFibresPlease cite the paper if you use this code in your research. For any questions, contact Alex Cayco-Gajic (natasha.cayco.gajic@ens.fr) or Angus Silver (a.silver@ucl.ac.uk).

Identifier
DOI https://doi.org/10.5522/04/14482977.v1
Metadata Access https://api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:figshare.com:article/14482977
Provenance
Creator Lanore, Frederic ORCID logo; Silver, Angus ORCID logo; Cayco Gajic
Publisher University College London UCL
Contributor Figshare
Publication Year 2021
Rights https://creativecommons.org/licenses/by/4.0/
OpenAccess true
Contact researchdatarepository(at)ucl.ac.uk
Representation
Language English
Resource Type Dataset
Discipline Life Sciences; Medicine; Neurosciences