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Code for paper: Systematic errors in connectivity inferred from activity in strongly recurrent networks [Das & Fiete 2020] (https://www.nature.com/articles/s41593-020-0699-2)

GPL-3.0 license

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Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
GLM Kijung		GLM Kijung
GLM_pillow		GLM_pillow
GLMpillow_mine		GLMpillow_mine
L1General		L1General
L1GeneralExamples		L1GeneralExamples
MPF mine		MPF mine
MPF original		MPF original
inferred_sb		inferred_sb
nsb-entropy-oct_1.13		nsb-entropy-oct_1.13
nsb_entropy		nsb_entropy
synchrony_inference		synchrony_inference
synchrony_inference_RSA		synchrony_inference_RSA
.gitattributes		.gitattributes
.gitignore		.gitignore
1D Grid Cells.nb		1D Grid Cells.nb
GC GLM.zip		GC GLM.zip
GLM.m		GLM.m
GLM2.m		GLM2.m
GLM3.m		GLM3.m
GLM_cv.m		GLM_cv.m
GLM_cv.mat		GLM_cv.mat
GLM_data6.mat		GLM_data6.mat
GLM_exp_on_thresh.m		GLM_exp_on_thresh.m
GLM_final.m		GLM_final.m
GLM_gen_exp.m		GLM_gen_exp.m
GLM_gen_thresh.m		GLM_gen_thresh.m
GLM_inference_data.mat		GLM_inference_data.mat
GLM_inference_data_flat.mat		GLM_inference_data_flat.mat
GLM_likelihood.m		GLM_likelihood.m
GLM_likelihood_node.m		GLM_likelihood_node.m
GLM_likelihood_ratio.m		GLM_likelihood_ratio.m
GLM_multiple.m		GLM_multiple.m
GLM_pillow.zip		GLM_pillow.zip
GLM_quick.m		GLM_quick.m
GLM_quick_exp_on_GLM_exp.mat		GLM_quick_exp_on_GLM_exp.mat
GLM_sideband_test.m		GLM_sideband_test.m
GLM_spatiotemporal_exp_on_GLM_exp.mat		GLM_spatiotemporal_exp_on_GLM_exp.mat
GLM_try.m		GLM_try.m
GLMspiketools-master.zip		GLMspiketools-master.zip
IBIHistogram.m		IBIHistogram.m
Jsubs.mat		Jsubs.mat
KLdiv_calculate.m		KLdiv_calculate.m
KLdiv_calculate_concatenated.m		KLdiv_calculate_concatenated.m
KLdiv_calculate_ising.m		KLdiv_calculate_ising.m
KLdivs.mat		KLdivs.mat
KLdivs_binsize_50.mat		KLdivs_binsize_50.mat
LICENSE		LICENSE
LL_data.mat		LL_data.mat
LL_data_sliding.mat		LL_data_sliding.mat
LNP_pattern.mat		LNP_pattern.mat
README.md		README.md
W.mat		W.mat
W100.mat		W100.mat
W_chaotic.mat		W_chaotic.mat
W_chaotic_sparse.mat		W_chaotic_sparse.mat
W_glm_sb.mat		W_glm_sb.mat
W_rand_sparse.mat		W_rand_sparse.mat
W_rand_symm.mat		W_rand_symm.mat
W_sb.m		W_sb.m
W_sb.mat		W_sb.mat
W_sb_ising.m		W_sb_ising.m
W_up_sb.mat		W_up_sb.mat
W_up_sb_13.mat		W_up_sb_13.mat
W_up_sb_23.mat		W_up_sb_23.mat
W_up_sb_ising.mat		W_up_sb_ising.mat
a_gap.m		a_gap.m
acc_p.m		acc_p.m
accuracyvstemp.m		accuracyvstemp.m
accuracyvstemp_bydatasize.m		accuracyvstemp_bydatasize.m
accuracyvstemp_bytemp.m		accuracyvstemp_bytemp.m
accuracyvstemp_bytemp100.m		accuracyvstemp_bytemp100.m
accuracyvstemp_bytemp100_check.m		accuracyvstemp_bytemp100_check.m
activations.mat		activations.mat
autocorrelation.m		autocorrelation.m
autocorrelation2.m		autocorrelation2.m
autocorrelation3.m		autocorrelation3.m
autocorrs.mat		autocorrs.mat
autocorrs2.mat		autocorrs2.mat
autocorrs_ising.mat		autocorrs_ising.mat
bias_error.m		bias_error.m
bias_error_data.mat		bias_error_data.mat
bias_error_dyn_glm.mat		bias_error_dyn_glm.mat
bias_error_dyn_ising.mat		bias_error_dyn_ising.mat
bias_error_ising_ising.m		bias_error_ising_ising.m
bias_error_old.m		bias_error_old.m
bin.m		bin.m
binnedspikes_GLM_gen.mat		binnedspikes_GLM_gen.mat
binnedspikes_glm.mat		binnedspikes_glm.mat
binsize_err.mat		binsize_err.mat
binsizesweep.m		binsizesweep.m
ccg_graph_asymm.m		ccg_graph_asymm.m
ccg_graph_chaotic_sparse.m		ccg_graph_chaotic_sparse.m
ccg_graph_symm.m		ccg_graph_symm.m
chaotic_randomnet.m		chaotic_randomnet.m
circular_shift.m		circular_shift.m
convert_inf_errs_to_newest.m		convert_inf_errs_to_newest.m
create_sparse_asymmetric_random_circuit.m		create_sparse_asymmetric_random_circuit.m
create_symmetric_random_circuit.m		create_symmetric_random_circuit.m
cxcorr.m		cxcorr.m
cxcov.m		cxcov.m

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neural-circuit-inference

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Code for paper: Systematic errors in connectivity inferred from activity in strongly recurrent networks [Das & Fiete 2020] (https://www.nature.com/articles/s41593-020-0699-2)

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