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Replay of procedural memory is independent of the hippocampus.

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The 19 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › LFP analysis ↔ preprocessing/LFP_spindle_processing.ipynb, lines 1–125 · score 0.92 · spindle events, Gaussian window, upper threshold, lower threshold, LFP, Hilbert
  2. [2] § Methods › Video-tracking analysis ↔ preprocessing/predictor_feature_extraction.ipynb, lines 197–264 · score 0.77 · Hausdorff distances, movement variability, Tracking points, radius, trajectories, segmented
  3. [3] § Methods › LFP analysis ↔ preprocessing/LFP_spindle_processing.ipynb, lines 1–125 · score 0.71 · Event duration, LFP, Hilbert, envelope, Gaussian, peak
  4. [4] § Methods › Statistical analysis ↔ ED_5/ED5_plots.ipynb, lines 159–259 · score 0.68 · pairwise PERMANOVA, signed rank, posthocs, Bonferroni, Wilcoxon, Shapiro
  5. [5] § Methods › Statistical analysis ↔ ED_6/ED6_plots.ipynb, lines 505–605 · score 0.68 · pairwise PERMANOVA, signed rank, posthocs, Bonferroni, Wilcoxon, Shapiro
  6. [6] § Results › DLS is required for a multistep procedural memory task ↔ figure1/Fig1_plots.ipynb, lines 431–485 · score 0.67 · Tukey HSD, way ANOVA, eta squared, variance, Cohen, Boxplots
  7. [7] § Methods › Statistical analysis ↔ ED_8/Utilities/utils.py, lines 12–69 · score 0.66 · canonical mode, variance explained, bootstrap, redundancy, CCA, component
  8. [8] § Methods › PP-Seq replay detection ↔ src/PPSeq.jl, lines 43–102 · score 0.66 · collapsed Gibbs sampling, PP Seq, likelihood, posterior, probabilistic, latent
  9. [9] § Methods › LFP analysis ↔ ED_6/ED6_plots.ipynb, lines 11–79 · score 0.64 · frequency band, spectral power, LFP, transform, spindle, 0.5 Hz
  10. [10] § Results › DLS is required for a multistep procedural memory task ↔ ED_1/ED1_plots.ipynb, lines 107–170 · score 0.61 · way ANOVA, post hoc, eta squared, Tukey, Cohen, DLS
  11. [11] § Methods › LFP analysis ↔ ED_5/ED5_plots.ipynb, lines 11–99 · score 0.56 · sleep state, LFP, velocity, ratio, NREM, broadband
  12. [12] § Results › An unsupervised approach for replay detection ↔ src/PPSeq.jl, lines 43–102 · score 0.55 · collapsed Gibbs sampling, latent events, PP Seq, spikes, model
  13. [13] § Results › Procedural memory formation and replay are independent of the hippocampus ↔ figure5/Fig5_plots.ipynb, lines 255–332 · score 0.54 · pairwise PERMANOVA, warp factors, Bonferroni, boxplots, permutation, reverse
  14. [14] § Methods › Tissue processing and image analysis ↔ code/@BT/BT.m, lines 1–47 · score 0.53 · scanImage, BakingTray, wrapper, microscope, serial
  15. [15] § Results › Procedural memory formation and replay are independent of the hippocampus ↔ ED_5/ED5_plots.ipynb, lines 159–259 · score 0.53 · pairwise PERMANOVA, warp factors, Bonferroni, R2, boxplots, permutation
  16. [16] § Methods › LFP analysis ↔ preprocessing/predictor_feature_extraction.ipynb, lines 13–121 · score 0.52 · tracking points, noisy, camera, Epochs, split, movement
  17. [17] § Methods › PP-Seq replay detection ↔ src/model/model.jl, lines 148–198 · score 0.51 · background firing, firing rate, width, log, model, Seq
  18. [18] § Methods › PP-Seq replay detection ↔ src/utils/config.jl, the whole file · a weak match · score 0.51 · background firing, firing rate, width, spikes, Seq, neuron
  19. [19] § Methods › Bayesian decoders ↔ preprocessing/PPseq_awake_postprocess.ipynb, lines 312–420 · score 0.50 · tracking position, task space, radius, bins, port, spiking

Paper

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The authors' code

Jupyter notebook · 538 lines · 20 KB · MIT · 3 matches

  1. # %% [markdown]
  2. # # import
  4. # %%
  5. from Utilities.utils import *
  7. # Get the current working directory
  8. current_working_dir = os.path.join(Path(os.getcwd()).parent,'data')
  9. ED7_data_dict = load_h5(os.path.join(current_working_dir,r"ED7_data.h5"))
  11. # %% [markdown]
  12. # # figure A
  14. # %%
  15. data= ED7_data_dict['spectrogram_data']
  17. log_power = data["log_power"]
  18. frequencies = data["frequencies"]
  19. times = data["times"]
  20. t_points_cut = data["t_points_cut"]
  21. delta_cut = data["delta_cut"]
  22. theta_r_cut = data["theta_r_cut"]
  23. average_velocity_cut = data["average_velocity_cut"]
  24. nrem_spans = data["nrem_spans"]
  25. rem_spans = data["rem_spans"]
  26. spectrogram_params = data["spectrogram_params"]
  27. span_params = data["span_params"]
  29. # Figure
  30. fig, [ax, ax1, ax2, ax3] = plt.subplots(4, 1, figsize=(5, 8))
  32. # --- Spectrogram ---
  33. im = ax.imshow(
  34. log_power,
  35. aspect="auto",
  36. origin="lower",
  37. cmap=spectrogram_params["cmap"],
  38. extent=[
  39. 0,
  40. times.max(),
  41. frequencies.min(),
  42. frequencies.max()
  43. ],
  44. vmin=spectrogram_params["vmin"],
  45. vmax=spectrogram_params["vmax"]
  46. )
  48. ax.set_ylabel("Frequency (Hz)")
  49. ax.set_xlabel("Time (s)")
  50. ax.set_title("Spectrogram of LFP Data")
  51. ax.set_ylim(*spectrogram_params["freq_ylim"])
  53. # --- Delta ---
  54. ax1.plot(
  55. t_points_cut,
  56. delta_cut / np.std(delta_cut),
  57. c="k"
  58. )
  59. ax1.set_title("blue = NREM | red = REM")
  60. ax1.set_ylabel("delta power")
  62. # --- Theta ratio ---
  63. ax2.plot(
  64. t_points_cut,
  65. theta_r_cut / np.std(theta_r_cut),
  66. c="k"
  67. )
  68. ax2.set_ylabel("theta power (relative to broadband)")
  70. # --- Movement ---
  71. ax3.plot(
  72. t_points_cut,
  73. average_velocity_cut / np.std(average_velocity_cut),
  74. c="k"
  75. )
  76. ax3.set_xlabel("time (10s of seconds)")
  77. ax3.set_ylabel("movement")
  79. # --- Sleep state shading ---
  80. axs = [ax1, ax2, ax3]
  82. shade_spans(
  83. axs,
  84. nrem_spans,
  85. color=span_params["nrem_color"],
  86. bin_width=span_params["bin_width"],
  87. alpha=span_params["alpha"]
  88. )
  90. shade_spans(
  91. axs,
  92. rem_spans,
  93. color=span_params["rem_color"],
  94. bin_width=span_params["bin_width"],
  95. alpha=span_params["alpha"]
  96. )
  98. plt.tight_layout()
  99. plt.show()
  102. # %% [markdown]
  103. # # figure B
  105. # %%
  106. nrem_reactivations_per_min = ED7_data_dict['nrem_master']['reactivations_per_min']
  107. rem_reactivations_per_min = ED7_data_dict['rem_master']['reactivations_per_min']
  109. # replace nan values with 0
  110. nrem_reactivations_per_min = [0 if np.isnan(item) else item for item in nrem_reactivations_per_min]
  111. rem_reactivations_per_min = [0 if np.isnan(item) else item for item in rem_reactivations_per_min]
  113. ## PLOT
  114. fig, ax = plt.subplots(figsize=(2, 5))
  115. ax.plot(np.zeros(len(nrem_reactivations_per_min)), nrem_reactivations_per_min, 'o', color = '#69BD45')
  116. ax.boxplot(nrem_reactivations_per_min, positions=[0.3], widths=0.1, patch_artist=True, boxprops=dict(facecolor='#69BD45', color='#69BD45'), medianprops=dict(color='#EE7832'))
  117. ax.plot(np.ones(len(rem_reactivations_per_min)), rem_reactivations_per_min, 'o', color = '#33495C')
  118. ax.boxplot(rem_reactivations_per_min, positions=[0.7], widths=0.1, patch_artist=True, boxprops=dict(facecolor='#33495C', color='#33495C'), medianprops=dict(color='#EE7832'))
  119. ax.set_ylabel('Reactivations per minute')
  120. ax.set_title('nrem vs rem')
  121. ## STATS
  122. a = [x for x in nrem_reactivations_per_min if not np.isnan(x)]
  123. b = [x for x in rem_reactivations_per_min if not np.isnan(x)]
  124. print(scipy.stats.shapiro(a))
  125. print(scipy.stats.shapiro(b))
  126. # p is not greater than 0.05 for both, so we reject the null hypothesis that the data is normally distributed
  127. # non parametric t test, using scipy Wilcoxon Rank-Sum test
  128. print(scipy.stats.wilcoxon(a, b))
  129. effect_size(x=a, y=b, test='wilcoxon')
  131. # %% [markdown]
  132. # # figure C
  134. # %%
  135. nrem_event_lens = ED7_data_dict['nrem_master']['event_lens']
  136. rem_event_lens = ED7_data_dict['rem_master']['event_lens']
  137. nrem_event_lens = [np.nanmean(item) for item in nrem_event_lens]
  138. rem_event_lens = [np.nanmean(item) for item in rem_event_lens]
  140. ### PLOT
  141. fig, ax = plt.subplots(figsize=(2, 5))
  142. ax.plot(np.zeros(len(nrem_event_lens)), nrem_event_lens, 'o', color = '#69BD45')
  143. ax.boxplot(nrem_event_lens, positions=[0.3], widths=0.1, patch_artist=True, boxprops=dict(facecolor='#69BD45', color='#69BD45'), medianprops=dict(color='#EE7832'))
  144. ax.plot(np.ones(len(rem_event_lens)), rem_event_lens, 'o', color = '#32495C')
  145. ax.boxplot([x for x in rem_event_lens if not np.isnan(x)], positions=[0.7], widths=0.1, patch_artist=True, boxprops=dict(facecolor='#32495C', color='#32495C'), medianprops=dict(color='#EE7832'))
  146. ax.set_ylabel('Reactivations event lengths (s)')
  147. ax.set_title('nrem vs rem')
  149. ### STATS
  150. a = [x for x in nrem_event_lens if not np.isnan(x)]
  151. b = [x for x in rem_event_lens if not np.isnan(x)]
  152. print(scipy.stats.shapiro(a))
  153. print(scipy.stats.shapiro(b))
  154. # p is greater than 0.05 for both, so we fail to reject the null hypothesis that the data is normally distributed
  155. print(scipy.stats.wilcoxon(nrem_event_lens, rem_event_lens,nan_policy='omit'))
  156. print(effect_size(x=nrem_event_lens, y=rem_event_lens, test='wilcoxon'))
  159. # %% [markdown]
  160. # # figure D
  162. # %%
  163. bins_ = [0.1, 0.2, 1,2, 5, 10,20,40]
  164. animals_list = ED7_data_dict['nrem_master']['animals']
  166. nrem_f_warp_factors, nrem_r_warp_factors, nrem_forward_total, nrem_reverse_total = extract_mean_warps(pd.DataFrame(ED7_data_dict['nrem_master']['regression']), bins_,animals_list)
  167. nrem_proportion_forward_v_reverse = np.array(nrem_forward_total)/np.array(nrem_reverse_total)
  168. rem_f_warp_factors, rem_r_warp_factors, rem_forward_total, rem_reverse_total = extract_mean_warps(pd.DataFrame(ED7_data_dict['rem_master']['regression']), bins_,animals_list)
  169. rem_proportion_forward_v_reverse = np.array(rem_forward_total)/np.array(rem_reverse_total)
  171. ############################################################### PLOT
  173. fig,ax = plt.subplots(1, 1,figsize=(10, 5))
  174. color_ = '#69BD45'
  175. plot_warp_factors(ax, nrem_f_warp_factors, nrem_r_warp_factors, bins_, color_)
  176. color_ = '#32495C'
  177. plot_warp_factors(ax, rem_f_warp_factors, rem_r_warp_factors, bins_, color_)
  178. ax.set_title('nrem vs rem')
  179. plt.show()
  181. ## STATS
  183. print(scipy.stats.shapiro([item for sublist in nrem_f_warp_factors for item in sublist]))
  184. print(scipy.stats.shapiro([item for sublist in nrem_r_warp_factors for item in sublist]))
  185. print(scipy.stats.shapiro([item for sublist in rem_f_warp_factors for item in sublist]))
  186. print(scipy.stats.shapiro([item for sublist in rem_r_warp_factors for item in sublist]))
  187. print('----------------------------')
  188. print('permanova - forward reverse')
  189. print('----------------------------')
  190. permanova_forward_vs_reverse(nrem_f_warp_factors, nrem_r_warp_factors)
  191. print('-------------------')
  192. permanova_forward_vs_reverse(rem_f_warp_factors, rem_r_warp_factors)
  193. print('----------------------------')
  194. print('permanova - group differences')
  195. print('----------------------------')
  196. # PERMANOVA late vs early diff
  197. el_grouping = group_difference_permanova(nrem_f_warp_factors, nrem_f_warp_factors, rem_f_warp_factors, rem_f_warp_factors)
  198. print('----------------------------')
  199. print('posthoc - group differences')
  200. print('----------------------------')
  201. # Combine:
  202. combined_data = np.vstack([
  203. np.asarray(nrem_f_warp_factors),
  204. np.asarray(nrem_r_warp_factors),
  205. np.asarray(rem_f_warp_factors),
  206. np.asarray(rem_r_warp_factors)
  207. ])
  208. combined_labels = np.array(el_grouping)
  209. feature_results = pairwise_permanova_by_feature(
  210. combined_data,
  211. combined_labels,
  212. method='bonferroni',
  213. permutations=10000
  214. )
  215. print("Significant feature-wise pairwise PERMANOVA results (α=0.05):")
  216. for res in feature_results:
  217. p = res["p_value"]
  218. if p < 0.05:
  219. stars = '***' if p < 0.001 else '**' if p < 0.01 else '*'
  220. print(f"Feature {res['feature']}: {res['group1']} vs {res['group2']}")
  221. print(f" statistic = {res['test_stat']:.4f}, p (corr.) = {p:.4g} {stars}")
  222. print(f"R2 = {res['R2']}")
  223. print("---")
  226. #### PLOT inset
  227. fig, ax = plt.subplots(figsize=(2, 4))
  228. ax.plot(np.zeros(len(nrem_proportion_forward_v_reverse)),nrem_proportion_forward_v_reverse,'o', color = '#69BD45')
  229. ax.boxplot([x for x in nrem_proportion_forward_v_reverse if not np.isnan(x)], positions=[0.3], widths=0.1, patch_artist=True, boxprops=dict(facecolor='#69BD45', color='#69BD45'), medianprops=dict(color='#EE7832'))
  230. ax.plot(np.ones(len(rem_proportion_forward_v_reverse)),rem_proportion_forward_v_reverse,'o', color = '#32495C')
  231. ax.boxplot([x for x in rem_proportion_forward_v_reverse if not np.isnan(x)], positions=[0.7], widths=0.1, patch_artist=True, boxprops=dict(facecolor='#32495C', color='#32495C'), medianprops=dict(color='#EE7832'))
  232. ax.set_title('nrem vs rem')
  233. plt.show()
  235. ### STATS for inset
  236. print('Inset: Difference from expected ------------------------')
  237. # Perform one-sample t-test
  238. statistic, p_value = ttest_1samp(nrem_proportion_forward_v_reverse, 1.0)
  239. # Print the test statistic and p-value
  240. print("Test statistic:", statistic)
  241. print("P-value:", p_value)
  242. print(effect_size(x=nrem_proportion_forward_v_reverse,test='one_sample_ttest',popmean = 1))
  244. print('--------------------')
  245. # Perform one-sample t-test
  246. statistic, p_value = ttest_1samp(rem_proportion_forward_v_reverse, 1.0, nan_policy="omit")
  247. # Print the test statistic and p-value
  248. print("Test statistic:", statistic)
  249. print("P-value:", p_value)
  250. rem_proportion_forward_v_reverse_nanr = rem_proportion_forward_v_reverse[~np.isnan(rem_proportion_forward_v_reverse)]
  251. print(effect_size(x=rem_proportion_forward_v_reverse_nanr,test='one_sample_ttest',popmean = 1))
  253. print('----------------------')
  254. print('Inset: comparison forward/reverse ------------------------')
  255. print(scipy.stats.shapiro(nrem_proportion_forward_v_reverse))
  256. print(scipy.stats.shapiro(rem_proportion_forward_v_reverse))
  257. # not all normal so use wilcoxn signed rank
  258. print(scipy.stats.wilcoxon(nrem_proportion_forward_v_reverse, rem_proportion_forward_v_reverse,nan_policy='omit'))
  259. print(effect_size(x=nrem_proportion_forward_v_reverse, y=rem_proportion_forward_v_reverse, test='wilcoxon'))
  261. # %% [markdown]
  262. # # figure E
  264. # %%
  266. group1_data, group2_data = plot_decay(ED7_data_dict['nrem_master']['binned_rate'] ,ED7_data_dict['nrem_master']['bins_relative_so'],ED7_data_dict['rem_master']['binned_rate'] ,ED7_data_dict['rem_master']['bins_relative_so'], '#69BD45','#32495C','nrem vs rem')
  267. plt.show()
  270. print('-----------------------------------------')
  271. ###################### STATS for inset
  272. ###################### STATS
  273. df1 = pd.DataFrame(group1_data)
  274. df2 = pd.DataFrame(group2_data)
  275. # Fit linear regression for Group 1
  276. model_group1 = sm.OLS.from_formula('y ~ x', data=df1).fit()
  277. model_group2 = sm.OLS.from_formula('y ~ x', data=df2).fit()
  278. print('PLOT 2:')
  279. # Print the summary of each model
  280. print("Group 1 (NREM):")
  281. # print(model_group1.summary())
  282. print('model p value (f statistic) = ')
  283. print(model_group1.f_pvalue)
  284. print('r2 = ')
  285. print(model_group1.rsquared)
  286. print("Group 2 (REM):")
  287. # print(model_group1.summary())
  288. print('model p value (f statistic) = ')
  289. print(model_group2.f_pvalue)
  290. print('r2 = ')
  291. print(model_group2.rsquared)
  293. ## STATS
  294. # Combine the data into one DataFrame
  295. data = pd.concat([df1, df2], axis=0)
  296. # Create a grouping variable
  297. groups = np.array(['nrem'] * len(df1) + ['rem'] * len(df2))
  298. # Perform MANOVA
  299. manova = MANOVA.from_formula('x + y ~ groups', data=data)
  300. # Print the MANOVA results
  301. print(manova.mv_test())
  302. # Compute partial eta-squared
  303. eta_squared = compute_partial_eta_squared(manova.mv_test())
  304. print("Partial eta-squared for each effect:", eta_squared)
  306. # %% [markdown]
  307. # # figure F
  309. # %%
  311. fig,[ax,ax2] = plt.subplots(1, 2,figsize=(10, 4))
  312. all_chunk_reverse_start_mean, all_chunk_forward_start_mean, all_chunk_reverse_end_mean, all_chunk_forward_end_mean = extract_start_end_points(pd.DataFrame(ED7_data_dict['rem_master']['start_end']))
  313. plot_start_end_times(all_chunk_reverse_start_mean,all_chunk_forward_start_mean,all_chunk_reverse_end_mean,all_chunk_forward_end_mean,ax,ax2,'REM','#32495C')
  315. fig,[ax,ax2] = plt.subplots(1, 2,figsize=(10, 4))
  316. all_chunk_reverse_start_mean, all_chunk_forward_start_mean, all_chunk_reverse_end_mean, all_chunk_forward_end_mean = extract_start_end_points(pd.DataFrame(ED7_data_dict['nrem_master']['start_end']))
  317. plot_start_end_times(all_chunk_reverse_start_mean,all_chunk_forward_start_mean,all_chunk_reverse_end_mean,all_chunk_forward_end_mean,ax,ax2,'NREM','#69BD45')
  319. plt.show()
  320. #### STATS
  322. nrem_all_chunk_reverse_start_mean, nrem_all_chunk_forward_start_mean, nrem_all_chunk_reverse_end_mean, nrem_all_chunk_forward_end_mean = extract_start_end_points(pd.DataFrame(ED7_data_dict['nrem_master']['start_end']))
  323. rem_all_chunk_reverse_start_mean, rem_all_chunk_forward_start_mean, rem_all_chunk_reverse_end_mean, rem_all_chunk_forward_end_mean = extract_start_end_points(pd.DataFrame(ED7_data_dict['rem_master']['start_end']))
  325. group1 = [x for x in nrem_all_chunk_reverse_start_mean if not np.isnan(x)]
  326. group2 = [x for x in nrem_all_chunk_forward_start_mean if not np.isnan(x)]
  327. group3 = [x for x in nrem_all_chunk_reverse_end_mean if not np.isnan(x)]
  328. group4 = [x for x in nrem_all_chunk_forward_end_mean if not np.isnan(x)]
  329. group5 = [x for x in rem_all_chunk_reverse_start_mean if not np.isnan(x)]
  330. group6 = [x for x in rem_all_chunk_forward_start_mean if not np.isnan(x)]
  331. group7 = [x for x in rem_all_chunk_reverse_end_mean if not np.isnan(x)]
  332. group8 = [x for x in rem_all_chunk_forward_end_mean if not np.isnan(x)]
  334. for group in [group1, group2, group3, group4, group5, group6, group7, group8]:
  335. print(scipy.stats.shapiro(group))
  337. df_nrem = pd.DataFrame({
  338. 'group': ['late'] * len(nrem_all_chunk_forward_start_mean),
  339. 'forward_start': nrem_all_chunk_forward_start_mean,
  340. 'forward_end': nrem_all_chunk_forward_end_mean,
  341. 'reverse_start': nrem_all_chunk_reverse_start_mean,
  342. 'reverse_end': nrem_all_chunk_reverse_end_mean,
  343. })
  344. df_rem = pd.DataFrame({
  345. 'group': ['early'] * len(rem_all_chunk_forward_start_mean),
  346. 'forward_start': rem_all_chunk_forward_start_mean,
  347. 'forward_end': rem_all_chunk_forward_end_mean,
  348. 'reverse_start': rem_all_chunk_reverse_start_mean,
  349. 'reverse_end': rem_all_chunk_reverse_end_mean,
  350. })
  352. print('_____________________________________________________________________________________________')
  353. # 1. Concatenate and clean
  354. df = pd.concat([df_nrem, df_rem], ignore_index=True)
  355. df_clean = df.dropna(subset=['forward_start','forward_end','reverse_start','reverse_end']).reset_index(drop=True)
  356. # 2. Build your feature matrix
  357. X = df_clean[['forward_start','forward_end','reverse_start','reverse_end']].values
  358. # 3. Compute Euclidean distances
  359. dist_array = squareform(pdist(X, metric='euclidean'))
  360. # 4. Create DistanceMatrix with simple integer-string IDs (0,1,2,…)
  361. ids = [str(i) for i in range(len(df_clean))]
  362. dm = DistanceMatrix(dist_array, ids=ids)
  363. # 5. Extract grouping as a plain list (length N)
  364. grouping = df_clean['group'].tolist()
  365. # 6. Run PERMANOVA
  366. result = permanova(distance_matrix=dm,
  367. grouping=grouping,
  368. permutations=999)
  369. print(result)
  370. #Compute R² manually (effect size)
  371. # ss_total = sum of squared distances / n
  372. D = dm.data # <-- this is the fix
  373. n = len(grouping)
  374. ss_total = np.sum(D**2) / n
  375. # ss_between: sum of squared group means
  376. group_labels = np.array(grouping)
  377. unique_groups = np.unique(group_labels)
  378. ss_between = 0
  379. for g in unique_groups:
  380. idx = np.where(group_labels == g)[0]
  381. Di = D[np.ix_(idx, idx)]
  382. ss_between += len(idx) * (Di.mean() ** 2)
  383. r2 = ss_between / ss_total
  384. print(f"PERMANOVA effect size R²: {r2:.5f}")
  389. # %% [markdown]
  390. # # figure G
  392. # %%
  393. rem_counts = collapse(convert_word_keys_to_numeric(ED7_data_dict['rem_master']['coactive_freqs']))
  394. nrem_counts = collapse(convert_word_keys_to_numeric(ED7_data_dict['nrem_master']['coactive_freqs']))
  396. colors = {"nrem": "#727272", "rem": "#74C269"}
  397. offset = 0.2
  400. # PLOT
  401. # ============================================================
  402. fig, (ax1, ax2) = plt.subplots(
  403. 1, 2, figsize=(4, 5),
  404. gridspec_kw={'width_ratios': [2, 10]}
  405. )
  407. # Left panel: coactive = 1 (median)
  408. for counts, color, off in [
  409. (rem_counts, colors["rem"], offset),
  410. (nrem_counts, colors["nrem"], -offset)
  411. ]:
  412. plot_group(ax1, counts, ['1'], off, color, np.median)
  414. ax1.set_xlim(0.5, 1.5)
  415. ax1.set_ylim(0, 1)
  416. ax1.set_ylabel('relative frequency')
  419. # Right panel: coactive >= 2 (mean)
  420. keys = [str(i) for i in range(2, 7)]
  422. for counts, color, off in [
  423. (rem_counts, colors["rem"], offset),
  424. (nrem_counts, colors["nrem"], -offset)
  425. ]:
  426. plot_group(ax2, counts, keys, off, color, np.mean)
  428. ax2.set_xlabel('number of coactive events')
  429. ax2.set_ylim(0, 0.4)
  431. plt.tight_layout()
  432. plt.show()
  435. # ============================================================
  436. # STATS
  437. # ============================================================
  438. permanova_coactive_freqs(convert_word_keys_to_numeric(ED7_data_dict['nrem_master']['coactive_freqs']), convert_word_keys_to_numeric(ED7_data_dict['rem_master']['coactive_freqs']))
  442. # %% [markdown]
  443. # # figure H
  445. # %%
  446. # PLOT
  447. plot_ordered_misrodered(ED7_data_dict['nrem_master']['ordered_misordered'],'nrem','ordered/misordered proportion')
  448. plot_ordered_misrodered(ED7_data_dict['rem_master']['ordered_misordered'],'rem','ordered/misordered proportion')
  450. # STATS
  451. list1 = [float(item[0]) for item in ED7_data_dict['nrem_master']['ordered_misordered']]
  452. list2 = [float(item[1]) for item in ED7_data_dict['nrem_master']['ordered_misordered']]
  453. list1= [x for x in list1 if not np.isnan(x)]
  454. list2= [x for x in list2 if not np.isnan(x)]
  455. permutation_test(list1, list2)
  456. print(f'cohens d = {cohens_d(list1, list2)}')
  458. print('------------------')
  459. list3 = [float(item[0]) for item in ED7_data_dict['rem_master']['ordered_misordered']]
  460. list4 = [float(item[1]) for item in ED7_data_dict['rem_master']['ordered_misordered']]
  461. list3= [x for x in list3 if not np.isnan(x)]
  462. list4= [x for x in list4 if not np.isnan(x)]
  463. permutation_test(list3, list4)
  464. print(f'cohens d = {cohens_d(list3, list4)}')
  466. # test for normality
  467. print('------------------')
  468. print(scipy.stats.shapiro(list1))
  469. print(scipy.stats.shapiro(list2))
  470. print(scipy.stats.shapiro(list3))
  471. print(scipy.stats.shapiro(list4))
  472. print('------------------')
  473. # parmanova between groups
  474. permanova_ordered_misordered(ED7_data_dict['nrem_master']['ordered_misordered'], ED7_data_dict['rem_master']['ordered_misordered'])
  477. # %% [markdown]
  478. # # figure I
  480. # %%
  481. plot_ordered_misrodered(ED7_data_dict['nrem_master']['task_nontask'],'nrem','task related/unrelated proportion')
  482. plot_ordered_misrodered(ED7_data_dict['rem_master']['task_nontask'],'rem','task related/unrelated proportion')
  484. # STATS
  485. list1 = [float(item[0]) for item in ED7_data_dict['nrem_master']['task_nontask']]
  486. list2 = [float(item[1]) for item in ED7_data_dict['nrem_master']['task_nontask']]
  487. list1= [x for x in list1 if not np.isnan(x)]
  488. list2= [x for x in list2 if not np.isnan(x)]
  489. permutation_test(list1, list2)
  490. print(f'cohens d = {cohens_d(list1, list2)}')
  492. print('------------------')
  493. list3 = [float(item[0]) for item in ED7_data_dict['rem_master']['task_nontask']]
  494. list4 = [float(item[1]) for item in ED7_data_dict['rem_master']['task_nontask']]
  495. list3= [x for x in list3 if not np.isnan(x)]
  496. list4= [x for x in list4 if not np.isnan(x)]
  497. permutation_test(list3, list4)
  498. print(f'cohens d = {cohens_d(list3, list4)}')
  500. # test for normality
  501. print(scipy.stats.shapiro(list1))
  502. print(scipy.stats.shapiro(list2))
  503. print(scipy.stats.shapiro(list3))
  504. print(scipy.stats.shapiro(list4))
  506. #permanova for nrem vs rem
  507. permanova_ordered_misordered(ED7_data_dict['rem_master']['task_nontask'], ED7_data_dict['rem_master']['task_nontask'])
  510. # %% [markdown]
  511. # # figure J
  513. # %%
  514. awake_seqbyseq_neuron_involvements = ED7_data_dict['neuron_involvement_master']['awake_seqbyseq_neuron_involvements']
  516. fig, ax = plt.subplots(1, 1,figsize=(5, 5))
  517. nrem_awake_sleep_relationship = return_binned_neuron_awake_sleep_rel(awake_seqbyseq_neuron_involvements,ED7_data_dict['nrem_master']['sleep_seqbyseq_neuron_involvements'])
  518. plot_awake_sleep_relationship(nrem_awake_sleep_relationship,'#69BD45',ax)
  519. rem_awake_sleep_relationship = return_binned_neuron_awake_sleep_rel(awake_seqbyseq_neuron_involvements,ED7_data_dict['rem_master']['sleep_seqbyseq_neuron_involvements'])
  520. plot_awake_sleep_relationship(rem_awake_sleep_relationship,'#32495C',ax)
  521. ax.set_title('nrem vs rem')
  523. ## STATS
  524. m =manova_groups_neuron_involvement(nrem_awake_sleep_relationship,rem_awake_sleep_relationship)
  525. eta_squared = compute_partial_eta_squared(m)
  526. print("Partial eta-squared for each effect:", eta_squared)
  531. # %%
  534. # %%
  537. # %%

ED5_plots.ipynb at commit 2e1ef18, under MIT · at the source

Overview

Authors: Emmett J. Thompson1, Lars B. Rollik1, Benjamin Waked1, Georgina Mills1, Sthitapranjya Pati1, Jasvin Kaur1, Ben Geva1, Haoyu Li1, Rodrigo Carrasco-Davis2, Tom George1, Clementine Domine2, William Dorrell2, Marcus Stephenson-Jones1
ORCID iDs: Emmett J. Thompson, Lars B. Rollik, Marcus Stephenson-Jones
  1. Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London,London, UK
  2. Gatsby Computational Neuroscience Unit, University College London,London, UK
Institutions: University College London (United Kingdom)
Journal: Nature neuroscience, volume 29, issue 9, pages 2237-2248
Dates: received 16 March 2026; accepted 9 June 2026; published online 23 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41593-026-02362-5 · PMID 42493550 · PMCID PMC13533836 · OpenAlex W7170145842
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Preprocessing, Evoked potentials, Machine learning, Single-unit activity, calcium imaging
Keywords: Consolidation, Hippocampus
MeSH: Hippocampus*, Memory*, Memory Consolidation*, Sleep*, Animals, Male, Mice, Mice, Inbred C57BL (* major topic)
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) (557533); Wellcome Trust (Wellcome) (219627/Z/19/Z)
Citations: not cited yet (Europe PMC); 73 references in the paper

Abstract

Sleep is crucial for consolidating all forms of memory and a core mechanism underlying this process is offline replay. Current models propose that replay originates in the hippocampus and triggers reactivation across cortical and subcortical networks. However, conflicting evidence about the role of the hippocampus in offline consolidation of nondeclarative memories raises the question of whether hippocampal replay drives their consolidation. Here we show that replay occurs in the dorsal striatum during offline consolidation of a procedural memory in mice, independently of the hippocampus, and that its content predicts subsequent performance improvements. Neural sequences linked to salient behavioral events were prioritized for replay, with positive and negative behavioral outcomes having opposing effects on individual replay events. All features of replay persisted despite complete bilateral hippocampal lesions. These findings demonstrate that procedural replay occurs independently of the hippocampus, indicating that replay-driven memory consolidation can operate through parallel, independent mechanisms.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 19 matches between paragraphs and lines of code.

SainsburyWellcomeCentre/BakingTray

License: LGPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 50eb5ca97a7477d768e10154413a4bf8103e2c59, 7 September 2026
Languages: MATLAB (332)
Size: 435 files, 332 scripts
Software Heritage: not archived
Found in: the text, “Tissue processing and image analysis”
Holds: README, license file, tests
Not found: CITATION.cff, environment file, continuous integration, documentation
Tools: Image Processing Toolbox (15 files), Statistics and Machine Learning Toolbox (2 files)
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  • 27 September 2026: the link answers
334 files
At the source: github.com/SainsburyWellcomeCentre/BakingTray

Zenodo 363160

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lindermanlab/ppseq.jl

License: MIT
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Languages: Julia (20), Jupyter (3)
Size: 30 files, 23 scripts
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At the source: github.com/lindermanlab/PPSeq.jl/pull/15

stephensonjoneslab/thompson_et_al_2026

License: MIT
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Evidence: files inventoried
Commit: 2e1ef18164faa4129eec848de30814e796d2712f, 8 May 2026
Languages: Jupyter (23), Python (20)
Size: 73 files, 43 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (environment.yaml, requirements.txt), 23 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (23 files), Matplotlib (22 files), pandas (22 files), SciPy (22 files), h5py (18 files), statsmodels (18 files), seaborn (15 files), Pingouin (6 files), Pillow (4 files), scikit-learn (2 files), scikit-posthocs (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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45 files
At the source: github.com/StephensonJonesLab/Thompson_et_al_2026/tree/V1.02

Code availability

The analysis code is publicly available at https://github.com/StephensonJonesLab/Thompson_et_al_2026/tree/V1.02 and https://github.com/lindermanlab/PPSeq.jl/pull/15.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 398 scripts, each with its path and the digest of its content;
  • 19 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

The data that support the findings of this study are available via Zenodo at https://doi.org/10.5281/zenodo.20055819 (ref. 73). In addition, Allen Brain Atlas connectivity (https://connectivity.brain-map.org/) data were used to demarcate the corticostriatal projection patterns from prelimbic and cingulate cortex (experiment nos.: 157711748 and 112514202) and motor cortex (experiment nos.: 180720175 and 180709942). Source data are provided with this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 2 keywords, 8 MeSH terms, 2 funders, 73 references.

Cite

This paper

Thompson, E. J., Rollik, L. B., Waked, B., Mills, G., Pati, S., Kaur, J., Geva, B., Li, H., Carrasco-Davis, R., George, T., Domine, C., Dorrell, W., & Stephenson-Jones, M. (2026). Replay of procedural memory is independent of the hippocampus. Nature neuroscience, 29(9), 2237-2248. https://doi.org/10.1038/s41593-026-02362-5

BibTeX

@article{thompson2026replay,
author = {Thompson, Emmett J. and Rollik, Lars B. and Waked, Benjamin and Mills, Georgina and Pati, Sthitapranjya and Kaur, Jasvin and Geva, Ben and Li, Haoyu and Carrasco-Davis, Rodrigo and George, Tom and Domine, Clementine and Dorrell, William and Stephenson-Jones, Marcus},
title = {{Replay of procedural memory is independent of the hippocampus}},
journal = {Nature neuroscience},
year = {2026},
month = jul,
volume = {29},
number = {9},
pages = {2237--2248},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02362-5},
url = {https://doi.org/10.1038/s41593-026-02362-5},
pmid = {42493550},
pmcid = {PMC13533836}
}

RIS

TY - JOUR
AU - Thompson, Emmett J.
AU - Rollik, Lars B.
AU - Waked, Benjamin
AU - Mills, Georgina
AU - Pati, Sthitapranjya
AU - Kaur, Jasvin
AU - Geva, Ben
AU - Li, Haoyu
AU - Carrasco-Davis, Rodrigo
AU - George, Tom
AU - Domine, Clementine
AU - Dorrell, William
AU - Stephenson-Jones, Marcus
TI - Replay of procedural memory is independent of the hippocampus
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/07/23
VL - 29
IS - 9
SP - 2237
EP - 2248
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02362-5
UR - https://doi.org/10.1038/s41593-026-02362-5
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Replay of procedural memory is independent of the hippocampus",
"container-title": "Nature neuroscience",
"author": [
{
"family": "Thompson",
"given": "Emmett J."
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