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@@ -7,12 +7,14 @@ Data and code for Crombie et al. "Spiking activity in the visual thalamus is cou
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### Figures
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To generate figures from already processed data, copy the `*.pkl` data files out of `data/original_cooked/` and `data/original_raw` into `data/` and then run the corresponding cells in the `figures_*.ipynp` Jupyter notebooks. Note that notebook cells should be run in order to avoid variable name conflicts. Figures will by default be stored in the `figures/` folder, which also contains sub-folder
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-**Note** : Because many of the analyses involve randomization (e.g. comparison to random permutations of the data), the original figures may only be *exactly* reproduced by using the data files from the `original_cooked` folder.
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-
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### Analyses
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-Data for this project is stored as "pickled" Pandas DataFrame objects. The "raw" data, including `pupil_*.pkl`, `spikes_*.pkl`, `ball_*.pkl`, and `trials_*.pkl`, are minimally-processed data to be used in further analyses.
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+Data for this project is stored as pickled Pandas DataFrame objects. The "raw" data files, including `pupil_*.pkl`, `spikes_*.pkl`, `ball_*.pkl`, and `trials_*.pkl`, contain minimally-processed data to be used in further analyses.
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+
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+To re-generate the processed ("cooked") data, run the corresponding `*.py` file with the desired arguments.
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-To re-generate the processed ("cooked") data, run the corresponding `*.py` file with the desired arguments. The positional argument `e_name` specifies the experiment name, and is required for all scripts. It can be one of
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+**Note** : Because many of the analyses involve randomization (e.g. comparison to random permutations of the data), the original figures may only be *exactly* reproduced by using the data files from the `original_cooked` folder.
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+
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+The positional argument `e_name` specifies the experiment name, and is required for all scripts. It can be one of
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- 'spontaneous' : experiments where the monitor displayed a uniform gray screen
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- 'sparsenoise' : experiments with sparse noise visual stimulation
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@@ -30,9 +32,24 @@ Spike type : --spk_type -s
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- 'spk' : all spikes, i.e. any spike regardless of whether it is part of a burst or not
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Time ranges : --tranges -t
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-- 'run'
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-- 'sit'
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-- 'nosaccade'
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-- 'noopto'
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-- 'desync'
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+- 'run' : bouts of locomotion
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+- 'sit' : behavioral quiescence (no locomotion)
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+- 'nosaccade' : periods with no saccadic eye movements
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+- 'noopto' : trials without optogenetic stimulation
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+- 'desync' :
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- 'sizematched'
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+
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+**Note** : the `imf_decoding.py` script will automatically look for `phasetuning` data from the `noopto` time ranges.
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+
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+Analysis and plotting parameters are stored in `parameters.py`. Useful, general-purpose functions are stored in the `util.py` module.
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+
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+## Nomenclature
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+Experimental units are defined using a MSEU nomenclature: mouse (the strain, year, and ID of a mouse, e.g. 'PVCre_2020_0001'), series (recording session, during which several experimental stimulation protocols are carried out, e.g. 's01'), experiment (a single experiment, e.g. e01), unit (a putative neuron identified by spike sorting, e.g. 'u10').
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+
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+The folloing terms are used interchangeably:
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+
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+Intrinsic mode function (IMF) == component of pupil dynamics (CPD) == timescale
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+- a component resulting from application of Empirical Mode Decomposition (EMD) to the pupil size timeseries
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+
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+Characteristic frequency == frequency == timescale
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+- the value (1/seconds) used to characterise a CPD by taking the mean amplitude-weighted instantaneous frequency
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