Name	Name	Last commit message	Last commit date
Latest commit History 444 Commits
.ipynb_checkpoints	.ipynb_checkpoints
Demo	Demo
Figures	Figures
IPRFfunctions	IPRFfunctions
__pycache__	__pycache__
git	git
group_id_79	group_id_79
group_id_all_a_c	group_id_all_a_c
group_id_selected_a_c	group_id_selected_a_c
ladyns	ladyns
(Abandoned) additional layer and test Pytorch.ipynb	(Abandoned) additional layer and test Pytorch.ipynb
(Failed) Try Bayesian optimization to select time warping variables.ipynb	(Failed) Try Bayesian optimization to select time warping variables.ipynb
(Previous) Synthetic dataset; Pillow et al.ipynb	(Previous) Synthetic dataset; Pillow et al.ipynb
.DS_Store	.DS_Store
.gitignore	.gitignore
2022 Oct version all conditions time-warping nonlinear history.ipynb	2022 Oct version all conditions time-warping nonlinear history.ipynb
2022 Sep version time-warping nonlinear history.ipynb	2022 Sep version time-warping nonlinear history.ipynb
Artificial population GLM neurons.ipynb	Artificial population GLM neurons.ipynb
CSD.py	CSD.py
DataLoader.py	DataLoader.py
EIF_params.pickle	EIF_params.pickle
GLM.py	GLM.py
Ground truth from single neuron level models.ipynb	Ground truth from single neuron level models.ipynb
HYDRA version Inference and simulation on the final model f_rf.ipynb	HYDRA version Inference and simulation on the final model f_rf.ipynb
Intensity for six probes.ipynb	Intensity for six probes.ipynb
Justify trial type; six area GLM; excursion test (PADDING and Graphxx) HISTORY.ipynb	Justify trial type; six area GLM; excursion test (PADDING and Graphxx) HISTORY.ipynb
Justify trial type; six area GLM; excursion test (PADDING and Graphxx) HISTORY; TRIAL COEF; REFRACTORY.ipynb	Justify trial type; six area GLM; excursion test (PADDING and Graphxx) HISTORY; TRIAL COEF; REFRACTORY.ipynb
Justify trial type; six area GLM; excursion test (PADDING and Graphxx).ipynb	Justify trial type; six area GLM; excursion test (PADDING and Graphxx).ipynb
Justify trial type; six area GLM; excursion test.ipynb	Justify trial type; six area GLM; excursion test.ipynb
KL divergence; KS distance; Wasserstein distance.ipynb	KL divergence; KS distance; Wasserstein distance.ipynb
KS KL Wasser in practice and Bayes Optimization.ipynb	KS KL Wasser in practice and Bayes Optimization.ipynb
LFP_GrangerCausality2ndMouse.ipynb	LFP_GrangerCausality2ndMouse.ipynb
LaDynsRunning.ipynb	LaDynsRunning.ipynb
LaDynsStationary.ipynb	LaDynsStationary.ipynb
Local population pooling.ipynb	Local population pooling.ipynb
More details on f_{rf}.ipynb	More details on f_{rf}.ipynb
Peak 1.ipynb	Peak 1.ipynb
Peak 2.ipynb	Peak 2.ipynb
README.md	README.md
Running.ipynb	Running.ipynb
STAR Huk et al.ipynb	STAR Huk et al.ipynb
STAR Inference and simulation on the final model f_rf copy.ipynb	STAR Inference and simulation on the final model f_rf copy.ipynb
STAR Inference and simulation on the final model f_rf.ipynb	STAR Inference and simulation on the final model f_rf.ipynb
STAR LFP_GrangerCausality.ipynb	STAR LFP_GrangerCausality.ipynb
STAR Model Selection Coupling Effects (BIC).ipynb	STAR Model Selection Coupling Effects (BIC).ipynb
STAR Model Selection Running effects (BIC).ipynb	STAR Model Selection Running effects (BIC).ipynb
STAR Replication on another mouse.ipynb	STAR Replication on another mouse.ipynb
STAR Synthetic dataset; Pillow et al.ipynb	STAR Synthetic dataset; Pillow et al.ipynb
STAR Use all neurons.ipynb	STAR Use all neurons.ipynb
Simulation and check partial corr with experimental data.ipynb	Simulation and check partial corr with experimental data.ipynb
Superimpose.ipynb	Superimpose.ipynb
Template for initial set up.ipynb	Template for initial set up.ipynb
check a bunch of details.ipynb	check a bunch of details.ipynb
check inhibitory neurons.ipynb	check inhibitory neurons.ipynb
environment.yml	environment.yml
fast.png	fast.png
forward_models.py	forward_models.py
logistic regression with constraint.ipynb	logistic regression with constraint.ipynb
modeling speed's effect.ipynb	modeling speed's effect.ipynb
neural clustering.ipynb	neural clustering.ipynb
new time warping and testing.ipynb	new time warping and testing.ipynb
pairwise coupling filters.ipynb	pairwise coupling filters.ipynb
permutation.py	permutation.py
permutation_79.py	permutation_79.py
running_filter_peak2.pickle	running_filter_peak2.pickle
sample.json	sample.json
sensitivity_p_values.pickle	sensitivity_p_values.pickle
shorter history.ipynb	shorter history.ipynb
smoothing_spline.py	smoothing_spline.py
stationary_filter_peak2.pickle	stationary_filter_peak2.pickle
statistics_null_filter.pickle	statistics_null_filter.pickle
statistics_null_filter_exxact1.pickle	statistics_null_filter_exxact1.pickle
statistics_null_output.pickle	statistics_null_output.pickle
statistics_null_output_exxact1.pickle	statistics_null_output_exxact1.pickle
test new Poisson GLM.ipynb	test new Poisson GLM.ipynb
test_multipy.py	test_multipy.py
time-warping GLM.ipynb	time-warping GLM.ipynb
utility_functions.py	utility_functions.py

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