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pupil_timescales_dLGN
fork de laura_busse/pupil_timescales_dLGN
DOI
10.12751/g-node.ls6w8h
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Arquivos

Data and code for Crombie et al. "Spiking activity in the visual thalamus is coupled to pupil dynamics across temporal scales"

Tree: d6d62510bc
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pupil_timesc...
Davide Crombie d6d62510bc Upload missing running data in darkness 1 mês atrás
data d6d62510bc Upload missing running data in darkness 1 mês atrás
figures adf8ef5c48 Upload already made panels and full figures 2 meses atrás
LICENSE 5a3fca9261 Initial commit 2 meses atrás
README.md fc76ff6c29 Add basic how-to info to readme 2 meses atrás
datacite.yml 413b17bf92 Add information for publishing with DataCite 2 meses atrás
figures_main.ipynb f3e181be31 Upload jupyter notebook for main figures 2 meses atrás
figures_supplementary.ipynb 468bb019cd Upload jupyter notebook for supplementary figures 2 meses atrás

README.md

pupil_timescales_dLGN

Data and code for Crombie et al. "Spiking activity in the visual thalamus is coupled to pupil dynamics across temporal scales"

How to

To generate figures from already processed data, copy the .pkl files out of data/original_cooked/ into data/ and then run the corresponding cells in the figures notebooks. Note that notebook cells should be run in order to avoid variable name conflicts.

To re-generate the processed data, run the corresponding .py file with the desired arguments. The primary argument is the experiment name, which can be 'spontaneous', 'sparsenoise', 'dark', 'natmov', and 'natmov_opto'.