LSTMS on auditory cortex data

this repo forms the bulk of my neural data analysis on auditory cortical spiking activity -- I run a shallow LSTM using Keras (only one hidden layer) and find auditory cortex neurons in trained animals become more discriminable towards a target word (stimulus representing reward) over competing non-target probe words (stimuli w/no associated meaning).
Apologies in advance as this repo is still currently a mess. I will be cleaning it up soon.

Name		Name	Last commit message	Last commit date
Latest commit History 674 Commits
__pycache__		__pycache__
cv_loocv_lstmdecoder		cv_loocv_lstmdecoder
cvacrossanimalslstmdecoder		cvacrossanimalslstmdecoder
decoding_over_time		decoding_over_time
decoding_scripts		decoding_scripts
helpers		helpers
json_files		json_files
lfads_prep_save		lfads_prep_save
visualisation		visualisation
Binned_data.zip		Binned_data.zip
binned_spikes.npy		binned_spikes.npy
choices.npy		choices.npy
loadscoresandvisualise.py		loadscoresandvisualise.py
readME.md		readME.md
requirements.txt		requirements.txt
rundecodingshuffledseq.sh		rundecodingshuffledseq.sh
windolene_example_rasters_andpsth_grid_readfromcsv.py		windolene_example_rasters_andpsth_grid_readfromcsv.py

carlacodes/neuraldecoding

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LSTMS on auditory cortex data

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