{ "dataset": { "archive": "dandi", "id": "000579", "label": "DANDI:000579" }, "page": "datasets/dandi/000579/", "generated": "2026-11-05T07:41:23+00:00", "caution": "A failure to reproduce is not a finding that the paper is wrong. It may be a failure of the attempt.", "outcome_counts": { "reproduced": 10, "partially_reproduced": 4, "not_reproduced": 1, "blocked": 28 }, "outcome_counts_with_outside_data": { "reproduced": 0, "partially_reproduced": 0, "not_reproduced": 0, "blocked": 0 }, "outcome_counts_elsewhere": { "reproduced": 0, "partially_reproduced": 0, "not_reproduced": 0, "blocked": 0 }, "outcome_counts_links": { "reproduced": 0, "partially_reproduced": 0, "not_reproduced": 0, "blocked": 0 }, "attempts": [ { "id": "000579-tseng-2022-01", "dataset": { "archive": "dandi", "id": "000579", "version": "0.230728.1727", "doi": "10.48324/dandi.000579/0.230728.1727" }, "key": "dandi:000579", "label": "DANDI:000579", "dataset_version": "0.230728.1727", "paper": "10.1016/j.neuron.2022.05.012", "paper_title": "Shared and specialized coding across posterior cortical areas for dynamic navigation decisions", "date": "2026-10-30", "kind": "agent", "accountable": "CatalystNeuro", "agent": { "harness": "Claude Code", "model": "claude-opus-5-5", "protocol": "pilot-12" }, "runner": { "platform": "GitHub Actions", "url": "https://github.com/catalystneuro/encore/actions/runs/41143720414" }, "tier": "runs", "deprecated": "", "deprecated_on": null, "entry": "000579-tseng-2022", "entry_title": "Shared and specialized coding across posterior cortex during navigation decisions (Tseng et al. 2022)", "description": "Tests the findings of Tseng, Chettih, Arlt, Barroso-Luque and Harvey (2022) on dandiset 000579, which holds 300 two-photon imaging sessions of 8 mice running a virtual Y-maze with rule switches. It tests the neuron counts, the behaviour around rule switches, the stored strategy and dynamic-choice models, a refit reinforcement learning model, running trajectories, activity by trial epoch and population decoders fit on a subsample of sessions, with a second notebook that fits the decoders of every V1 and PM field session and of the sessions with the Figure 8C cue offset. The GLM encoding analyses and the shattering dimensionality are beyond the run's time, and the photoinhibition sessions are not in the dandiset.", "notebooks": [ { "path": "reproduce_tseng_2022.ipynb", "title": "Reproducing Tseng et al. (2022) from dandiset 000579", "executed": "notebooks/000579-tseng-2022/reproduce_tseng_2022.html" }, { "path": "decoders_v1_pm_offset.ipynb", "title": "Population decoders of every V1 and PM field session, and of the sessions with the cue offset at 0.76", "executed": "notebooks/000579-tseng-2022/decoders_v1_pm_offset.html" } ], "outcomes": [ { "claim": "neuron-counts", "statement": "The neural analyses use 93,881 layer 2/3 neurons from 141 sessions of 8 mice, parcellated into V1 (14,373), PM (9,564), AM (14,974), MM (9,885), RSC (23,036) and A (22,049).", "source": "Results, Calcium imaging of neural activity across posterior cortex, paragraph 1; STAR Methods, Area parcellation", "quote": "assigned coordinates to 93,881 layer 2/3 neurons imaged from 141 sessions of 8 mice ... we obtained 14,373 neurons in V1, 9,564 neurons in PM, 14,974 neurons in AM, 9,885 neurons in MM, 23,036 neurons in RSC, and 22,049 neurons in area A", "statistics": [], "kind": "finding", "inferred": "", "outcome": "reproduced", "blocked_by": null, "notebook": "reproduce_tseng_2022.ipynb", "notes": "With session fraction correct over all trials, 150 layer 2/3 sessions above 0.65 hold 97,989 neurons from 8 mice (V1 15,497, PM 10,313, AM 15,225, MM 10,654, RSC 23,728, A 22,572), each within its tolerance; the area shares are within 0.006 of the paper's. With visually guided trials excluded from fraction correct, 100 sessions and 69,707 neurons remain and only the mouse count is within tolerance. The files hold 278,155 neurons in 300 sessions, the paper's total, so the stored sources are the paper's neurons; the paper's list of 141 sessions cannot be recovered.", "deviations": [ "Sessions above 0.65 are selected on fraction correct over all trials, an assumption (Methods, Classification of the sources, gives 141 layer 2/3 sessions above 65% correct; Behavioral training says visually guided trials are excluded from all analyses). Only fraction correct over all trials gives counts near the paper's (150 layer 2/3 sessions, 97,989 neurons); with visually guided trials excluded there are 100 and 69,707. Both selections are shown.", "The paper's cell-body classifier is not in the files; the stored sources are taken as the cell bodies, since their total equals the paper's 278,155 neurons." ], "assumed": true, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig1d-switch-performance", "statement": "Performance is high before a rule switch, typically falls below chance after it, and recovers gradually to high accuracy by the end of the block.", "source": "Results, Mice learned flexible cue-choice associations, paragraph 2; Figure 1D; Figure S1B", "quote": "Their behavioral performance was high before rule switches and typically dropped below chance after switches before gradually recovering to high accuracy by the end of a block, without signs of anticipating rule switches", "statistics": [], "kind": "finding", "inferred": "", "outcome": "reproduced", "blocked_by": null, "notebook": "reproduce_tseng_2022.ipynb", "notes": "Over 601 switches in 297 sessions above 0.5 (fraction correct over all trials), the end of the block before the switch is 0.807, the fitted initial performance after the switch 0.226 \u00b1 0.016 (no bootstrap sample at or above 0.5), the fitted end performance 0.727 \u00b1 0.026, and end minus initial 0.501 \u00b1 0.038 (p < 0.0001). With visually guided trials excluded from the selection, 589 switches give 0.225 and 0.730. The end mean is above 0.7 only just: 15% of bootstrap samples (12% with the other selection) are at or below 0.7, and with the other selection two of the 8 mice end below 0.7 (0.613 and 0.666). With a free sigmoid midpoint the initial value is 0.317. The paper reports 506 to 513 switches. The fit overestimates initial performance a little on synthetic blocks (verifier), which works against the below-0.5 test.", "deviations": [ "The sigmoid's t0 is fixed at the block's first trial, as the Methods state (Task performance analysis); a free midpoint is shown as a variant. Rates are kept in (0, 1) by the fit's parametrisation, the claims file's standard choice.", "The session selection above 0.5 matches no reading of the paper's 253 sessions (291 or 297 here); both readings are shown and agree." ], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "lstm-choice-accuracy", "statement": "The strategy LSTM predicts the mouse's choices in held-out sessions with 80.2% \u00b1 5.1% accuracy (mean \u00b1 SD).", "source": "Results, Decision-making strategy varied, paragraph 1; STAR Methods, LSTM model", "quote": "The LSTM accurately predicted a mouse's choices in held-out sessions (80.2% \u00b1 5.1%, mean \u00b1 SD)", "statistics": [], "kind": "finding", "inferred": "", "outcome": "reproduced", "blocked_by": null, "notebook": "reproduce_tseng_2022.ipynb", "notes": "Accuracy of the stored predictions is 0.772 over all 300 sessions (SD 0.064 over sessions; 0.771, SD 0.043 over mice). Above 0.65 it is 0.792 (visually guided excluded from the selection, 154 sessions) and 0.769 (all trials, 238 sessions). Every reading is within 0.051 of 0.802. Per mouse 0.705 to 0.826.", "deviations": [ "The LSTM was not refit. The association matrices stored in the trial tables are taken as the paper's held-out (leave-one-session-out) predictions, an assumption, as the files do not say how they were produced; the Methods (LSTM model) describe only held-out predictions being used for analysis." ], "assumed": true, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "figs1h-lstm-beats-rl", "statement": "The LSTM predicts choices better (higher log likelihood) than the best reinforcement learning model (model 4, separate learning rates for rewarded and unrewarded trials).", "source": "Figure S1H", "quote": "Log likelihood of the LSTM was greater than the RL model (p = 5.95 \u00d7 10-22, Wilcoxon signed rank test)", "statistics": [ { "test": "Wilcoxon signed rank test", "p": 5.95e-22, "n": "216 sessions" } ], "kind": "finding", "inferred": "", "outcome": "reproduced", "blocked_by": null, "notebook": "reproduce_tseng_2022.ipynb", "notes": "On the 238 sessions above 0.65 (all trials), held-out log2 likelihood per trial is -0.710 for the LSTM and -0.732 for RL model 4; the LSTM is higher in 185 sessions, Wilcoxon p = 4.65e-22 (paper 5.95e-22, 216 sessions). On the 154 sessions of the other selection, -0.667 against -0.691, p = 1.42e-17. The LSTM is ahead in all 8 mice. The optimiser reported convergence for the best of three starts in all 238 folds, and the starts reach the same likelihood in the median fold (spread 0.000, at most 480 of a median 8,262). The learning rate for unrewarded trials is at its lower bound of 0 in 198 folds and the inverse temperature at its upper bound of 50 in 50 folds.", "deviations": [ "RL model 4 was refit in Python (L-BFGS-B with bounds, three starting points) in place of Matlab's fminunc, leave-one-session-out over all sessions of each mouse (Methods, Reinforcement learning model). The lapse is the mixture (1 - lambda) P + lambda/2 (claims file), the bias inside the inverse temperature as printed, and visually guided trials update the model but are left out of the likelihood, as for the LSTM.", "The LSTM side uses the stored predictions (see lstm-choice-accuracy)." ], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig1i-strategy-after-switch", "statement": "Before a switch mice show little choice bias and follow the rule; after a switch the probability of the actual choice falls near chance, bias-following rises moderately, and rule-following then recovers gradually.", "source": "Results, Decision-making strategy varied, paragraph 2; Figure 1I", "quote": "On average, prior to a rule switch, mice performed with little choice bias and followed the correct rule ... After a rule switch, the randomness of choices increased rapidly, as seen by near chance levels of the model's probability of the actual choice, and bias-following increased moderately. Then, rule-following recovered gradually for the new rule belief", "statistics": [], "kind": "finding", "inferred": "", "outcome": "reproduced", "blocked_by": null, "notebook": "reproduce_tseng_2022.ipynb", "notes": "Under both windows (10 and 20 trials) and both session selections, rule-following before the switch is 0.23 to 0.26 (p < 1e-4 against zero) with choice bias 0.13 to 0.16; the probability of the actual choice drops by 0.14 to 0.17 (p < 1e-4) with a switch-aligned minimum of 0.570 to 0.578; bias-following rises by 0.029 to 0.045 (p <= 4e-4); rule-following rises from 0.04 to 0.07 in the first window to 0.21 to 0.22 at the end of the block (p < 1e-4). Recovered switches number 320 (all-trial selection) and 251, against the paper's 265.", "deviations": [ "Strategy variables are those stored in the files, not refit.", "The end of the block is taken as its last W non-visually guided trials; the claims file says only 'by the end of the block'." ], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "visually-guided-rule-update", "statement": "Visually guided trials do not update rule belief more than normal trials in the 30 trials after a switch (0.013 \u00b1 0.0055 against 0.0080 \u00b1 0.0023; p = 0.34).", "source": "STAR Methods, Decision-making strategy variables, last paragraph", "quote": "The rule update per trial was 0.013 \u00b1 0.0055 for visually guided trials and 0.0080 \u00b1 0.0023 for normal trials (difference = 0.0052 \u00b1 0.0057, p = 0.34, hierarchical bootstrap mean \u00b1 SEM, n = 265 switches)", "statistics": [ { "about": "visually guided against normal trials, all trials", "test": "hierarchical bootstrap", "p": 0.34, "n": "265 switches" }, { "about": "visually guided against normal trials, correct trials only", "test": "hierarchical bootstrap", "p": 0.89, "n": "265 switches" } ], "kind": "finding", "inferred": "", "outcome": "not_reproduced", "blocked_by": null, "notebook": "reproduce_tseng_2022.ipynb", "notes": "With the update of trial t taken as rule belief on t + 1 minus that on t (the adopted reading), visually guided trials update more than normal trials over the 30 trials after a switch, 0.0545 \u00b1 0.0075 against 0.0215 \u00b1 0.0029, difference 0.0329, p < 0.0001 (302 switches; the other selection gives 0.0591 against 0.0236, p = 0.0022), so the paper's test finds the effect the claim says is absent. On correct trials only there is no difference (0.068 against 0.076, p = 0.22; paper p = 0.89). The change from t - 1 to t gives no difference (0.0295 against 0.0267, p = 0.48), but it is not the adopted reading and is not taken as support. Correct trials move belief toward the new rule (0.0765 normal, 0.0690 visually guided) and incorrect trials away from it (-0.0243, -0.0129); 80% of visually guided trials are correct against 46% of normal trials, which accounts for the forward difference. Under every all-trial reading both values are 2 to 5 times the paper's 0.013 and 0.0080, outside the tolerances (the correct-only rows, which have no paper values, give 0.076 to 0.080 for normal trials); the stored switch-aligned rule belief itself rises by 0.028 per trial over the 30 trials (from -0.619 to 0.195). The question of how the update was defined is put to the paper's authors (findings).", "deviations": [ "Assumed. The Methods (Decision-making strategy variables) do not say which difference of rule belief is a trial's update. The LSTM's belief on trial t is conditioned on trials 1 to t - 1 (Methods, LSTM model), so the change from t to t + 1 is the one trial t causes; this reading is adopted. The change from t - 1 to t is shown.", "Rule belief is recomputed from the stored association matrix with the Methods' sign, as the stored column has the opposite sign (findings).", "Sessions above 0.65 are selected on fraction correct over all trials, an assumption (Methods, Classification of the sources, gives 141 layer 2/3 sessions above 65% correct; Behavioral training says visually guided trials are excluded from all analyses). Only fraction correct over all trials gives counts near the paper's (150 layer 2/3 sessions, 97,989 neurons); with visually guided trials excluded there are 100 and 69,707. Both selections are shown." ], "assumed": true, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig1l-photoinhibition-performance", "statement": "Inhibiting RSC or PPC lowers task performance compared with control trials and with S1 inhibition; the RSC against PPC difference is not significant (p = 0.058).", "source": "Results, Photoinhibition of posterior cortical areas, paragraph 1; Figure 1L", "quote": "Inhibiting either RSC or PPC led to markedly lower task performance compared to control trials or trials in which part of primary somatosensory cortex (S1) was inhibited", "statistics": [ { "about": "control against RSC or PPC", "test": "hierarchical bootstrap", "p": 0.0001, "p_relation": "<", "n": "164 sessions from 7 mice" }, { "about": "control against S1", "test": "hierarchical bootstrap", "p": 0.0002, "n": "164 sessions from 7 mice" }, { "about": "S1 against RSC or PPC", "test": "hierarchical bootstrap", "p": 0.0001, "p_relation": "<", "n": "164 sessions from 7 mice" }, { "about": "RSC against PPC", "test": "hierarchical bootstrap", "p": 0.058, "n": "164 sessions from 7 mice" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "data_missing", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs task performance under photoinhibition. The dandiset's 308 files are 300 imaging sessions and 8 widefield retinotopy files; none is a photoinhibition session of the 7 VGAT-ChR2 mice, no path names inhibition, and no trial table has an inhibition column. The paper names no other archive for these data.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig1m-rule-following-decrease", "statement": "Inhibition of PPC and RSC decreases rule-following compared with control trials and with S1 inhibition; bias-following increases, strongly in a subset of mice.", "source": "Results, Photoinhibition of posterior cortical areas, paragraph 2; Figure 1M; Figure S1K", "quote": "PPC and RSC inhibition decreased rule-following compared to control trials and S1 inhibition", "statistics": [ { "about": "rule-following, each target against control", "test": "hierarchical bootstrap", "p": 0.0001, "p_relation": "<", "n": "164 sessions from 7 mice" }, { "about": "rule-following, RSC and PPC against S1", "test": "hierarchical bootstrap", "p": 0.0001, "p_relation": "<", "n": "164 sessions from 7 mice" }, { "about": "bias-following, S1 against control", "test": "hierarchical bootstrap", "p": 0.025, "n": "164 sessions from 7 mice" }, { "about": "bias-following, RSC against control", "test": "hierarchical bootstrap", "p": 0.018, "n": "164 sessions from 7 mice" }, { "about": "bias-following, PPC against control", "test": "hierarchical bootstrap", "p": 0.0001, "p_relation": "<", "n": "164 sessions from 7 mice" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "data_missing", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs strategy variables under photoinhibition. The dandiset's 308 files are 300 imaging sessions and 8 widefield retinotopy files; none is a photoinhibition session of the 7 VGAT-ChR2 mice, no path names inhibition, and no trial table has an inhibition column. The paper names no other archive for these data.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "figs1m-rule-bias-uncorrelated", "statement": "Across sessions, the change in rule-following under RSC or PPC inhibition is not correlated with the change in bias-following.", "source": "Figure S1M", "quote": "Pearson correlation 0.18 (p = 0.10 vs. zero) for RSC and -0.03 (p = 0.74) for PPC", "statistics": [ { "about": "RSC", "test": "Pearson correlation against zero", "statistic": "r = 0.18", "p": 0.1, "n": "164 sessions from 7 mice" }, { "about": "PPC", "test": "Pearson correlation against zero", "statistic": "r = -0.03", "p": 0.74, "n": "164 sessions from 7 mice" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "data_missing", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs per-session changes under photoinhibition. The dandiset's 308 files are 300 imaging sessions and 8 widefield retinotopy files; none is a photoinhibition session of the 7 VGAT-ChR2 mice, no path names inhibition, and no trial table has an inhibition column. The paper names no other archive for these data.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig2c-running-variability-after-switch", "statement": "Running trajectories of choice-matched trials become less correlated with one another after a rule switch.", "source": "Results, Running trajectories reflected the within-trial dynamics, paragraph 1; Figure 2C", "quote": "This variability in running trajectories increased following a rule switch", "statistics": [], "kind": "finding", "inferred": "", "outcome": "reproduced", "blocked_by": null, "notebook": "reproduce_tseng_2022.ipynb", "notes": "\u0394 trajectory correlation after minus before the switch is -0.077 \u00b1 0.013 (11 bins) and -0.074 \u00b1 0.012 (20 bins) with the all-trial selection (320 and 317 switches), -0.090 and -0.087 with the other (251 and 248), p < 1e-4 in each, and negative in all 8 mice. Trials with an empty position bin are dropped (1,066 to 2,556 trials).", "deviations": [ "Trials with an empty position bin are dropped; the Methods do not say how such trials are handled." ], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig2f-latency-strategy", "statement": "Dynamic choice crosses threshold sooner on trials with higher rule-following or bias-following.", "source": "Results, Running trajectories reflected the within-trial dynamics, paragraph 3; Figure 2F", "quote": "As expected, the latency to dynamic choice crossing a threshold was shorter on trials with higher rule- or bias-following", "statistics": [], "kind": "finding", "inferred": "", "outcome": "reproduced", "blocked_by": null, "notebook": "reproduce_tseng_2022.ipynb", "notes": "The maze epoch matches the legend's trial duration (9.31 \u00b1 2.14 s, all-trial selection; 8.98 \u00b1 1.93 s, the other; expected 8.95 \u00b1 2.04), and trial start_time is maze onset (first frame with a position at median -0.004 s). With the all-trial selection (70,410 trials crossing threshold, paper 85,463), latency falls with rule-following (Spearman rho -0.113; highest against lowest bin p = 0.0045 with 5 bins and p < 0.0001 with 10) and with bias-following (rho -0.102; p = 0.0011 and p < 0.0001). With the other selection every test holds except bias-following with 5 bins (0.587 against 0.656, p = 0.058), so the outcome rests on the session selection; the hierarchical bootstrap with 8 mice is liberal near 0.05 (verifier), so p values near it are fragile. One mouse has a positive rho for rule-following.", "deviations": [ "Dynamic choice is the stored velocity LSTM output, not refit.", "Trial duration is the maze epoch, the duration that matches the legend's 8.95 s (the whole trial is 14.9 s).", "Trials whose dynamic choice never crosses threshold are left out (4,015 of 74,425).", "Sessions above 0.65 are selected on fraction correct over all trials, an assumption (Methods, Classification of the sources, gives 141 layer 2/3 sessions above 65% correct; Behavioral training says visually guided trials are excluded from all analyses). Only fraction correct over all trials gives counts near the paper's (150 layer 2/3 sessions, 97,989 neurons); with visually guided trials excluded there are 100 and 69,707. Both selections are shown." ], "assumed": true, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig2g-choice-commitment", "statement": "With high rule-following, choice commitment starts near chance and rises during the maze; with low rule-following it stays low until late; with high bias-following it is high from trial onset.", "source": "Results, Running trajectories reflected the within-trial dynamics, paragraph 3; Figure 2G; Figure S2L", "quote": "On trials with high rule-following, choice commitment started near chance but increased rapidly during maze traversal ... when rule-following was low, choice commitment was low until late in the trial ... when mice followed a bias, choice commitment was high at trial onset", "statistics": [], "kind": "finding", "inferred": "", "outcome": "reproduced", "blocked_by": null, "notebook": "reproduce_tseng_2022.ipynb", "notes": "In the first position bin the bias-following coefficient is 0.28 to 0.42 (p <= 0.001) and the rule-following coefficient -0.016 to 0.000 (p >= 0.36, at most 0.05 of the bias coefficient); in the last stem bin the rule-following coefficient is 0.85 to 0.90 (p < 0.0001), above its first-bin value (p < 0.0001). Holds for 11 and 20 bins, both session selections, and the local reward rate with or without the current trial.", "deviations": [ "Choice commitment uses the stored dynamic choice.", "Local reward rate is the fraction correct over the 9 non-visually guided trials centred on the trial (Figure S2L legend says 'adjacent 9 trials'); the 8 neighbours without the trial itself are run as a variant and give the same result." ], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "cue-biased-running-uncorrelated", "statement": "Cue-biased running varies across mice and sessions and is not correlated with task performance or with the time course of dynamic choice.", "source": "Results, Running trajectories reflected the within-trial dynamics, paragraph 2; Figure S2H, S2I, S2K", "quote": "this behavior was variable across mice and sessions and was uncorrelated with task performance and the time course of dynamic choice", "statistics": [], "kind": "finding", "inferred": "", "outcome": "partially_reproduced", "blocked_by": null, "notebook": "reproduce_tseng_2022.ipynb", "notes": "Cue-biased running varies across mice (mean cue log likelihood by mouse -0.92 to -0.36) and in direction across and within mice (roll coefficient positive in 79 of 238 sessions; all 30 negative in mouse 5; 27 positive and 11 negative in mouse 10). Per-mouse slopes of performance on cue decoding are not different from zero (mean 0.024, bootstrap p = 0.35; other selection -0.048, p = 0.35), but the pooled correlation over sessions is positive (r = 0.224, p = 0.0005; other selection r = 0.207, p = 0.010). The criterion's two readings disagree, so the claim holds in part. The Figure S2G part was reported, not judged; choice decoding rises with cue decoding in the third quadrant of the maze, from -0.675 to -0.509 over the 7 bins of cue decoding; in the second it rises from -0.817 to -0.726 over the first four bins and stays at -0.768 to -0.776 in the last three.", "deviations": [ "Cue decoding uses the stored cue-biased running. The pooled-correlation and per-mouse readings are the claims file's; the per-mouse test is a bootstrap over mice (the Methods' default), with a t-test shown.", "The logistic decoder is fit with scikit-learn (unpenalised in effect, C = 1e6) on z-scored velocities in place of Matlab's fitglm." ], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig4h-activity-by-epoch", "statement": "RSC is most active in the maze stem and least active in the feedback period/ITI, when area A is more active.", "source": "Results, Encoding of dynamic choice was enriched in RSC, paragraph 4; Figure 4H", "quote": "RSC was also most active in the maze stem and least active during feedback and ITI periods, when area A was more active", "statistics": [], "kind": "finding", "inferred": "", "outcome": "partially_reproduced", "blocked_by": null, "notebook": "reproduce_tseng_2022.ipynb", "notes": "Two parts of the claim hold and one does not. In the feedback period/ITI RSC is the least active area (-0.042) and A the most (0.023), A above RSC (p < 0.001) in both selections and in all 8 mice; within RSC the feedback period/ITI is the lowest epoch (p < 0.001). RSC is not shown to be most active in the stem: within RSC the arm mean exceeds the stem mean (0.042 against 0.023, p = 0.07; other selection p = 0.18; arm higher in 7 of 8 mice), and between areas RSC's stem mean is the highest and above V1, PM, MM and A (p <= 0.016) but not AM (p = 0.29); with the other selection only PM and A differ. The criterion asks for every comparison under every reading, and the well-fit reading was not run. The verifier would call this not reproduced; it is kept as partially reproduced because two of the claim's three parts hold (see verifications).", "deviations": [ "Only the all-neuron reading was run; the well-fit reading of the claims file needs the GLM fits (blocked).", "Epochs are taken from the frame-aligned position and time from the choice point in the files: stem below the stem length, arm from there to the choice point (including 913,421 frames just past the 2.5 m maze end), feedback/ITI after the choice point. Frames of the 1 s feedback delay have no position (0 of 3,673,732), so they fall in feedback/ITI.", "Sessions above 0.65 are selected on fraction correct over all trials, an assumption (Methods, Classification of the sources, gives 141 layer 2/3 sessions above 65% correct; Behavioral training says visually guided trials are excluded from all analyses). Only fraction correct over all trials gives counts near the paper's (150 layer 2/3 sessions, 97,989 neurons); with visually guided trials excluded there are 100 and 69,707. Both selections are shown.", "In the single-plane sessions (13 in the dandiset; 8 of the 150 layer 2/3 sessions of the all-trial selection) the deconvolved activity timestamps run 2000 times the behaviour clock (findings); their frames are matched to behaviour by index, one frame per behaviour frame." ], "assumed": false, "disagreed": true, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig3i-glm-fit", "statement": "The GLM explains a fraction 0.25 \u00b1 0.17 (mean \u00b1 SD) of the Poisson deviance of single neurons on held-out test data.", "source": "Results, Calcium imaging of neural activity, paragraph 2; Figure 3I; Figure S3E", "quote": "The GLM explained a substantial amount of moment-to-moment variability in a neuron's activity (fraction of Poisson deviance explained: 0.25 \u00b1 0.17, mean \u00b1 SD", "statistics": [], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "well-fit-neurons", "statement": "42,998 neurons have test fraction deviance explained above 0.2 (V1 7,775, PM 4,934, AM 6,878, MM 3,991, RSC 10,329, A 9,091); 68,532 pass a threshold of 0.1.", "source": "STAR Methods, Generalized Linear Models, Fitting procedure; Figure S3D", "quote": "42,998 neurons were selected, including 7,775 neurons in V1, 4,934 neurons in PM, 6,878 neurons in AM, 3,991 neurons in MM, 10,329 neurons in RSC, and 9,091 neurons in area A", "statistics": [], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "glm-cv-vs-test", "statement": "Fraction explained deviance on the CV held-out data differs very little from that on the test data (0.0010 \u00b1 0.0006, mean \u00b1 SEM), so encoding measured on CV held-out data is not inflated by overfitting.", "source": "STAR Methods, Quantification of fraction explained deviance (without re-fitting); Figure S3E", "quote": "We confirmed that the difference in the fraction explained deviance evaluated on the CV held-out data and test data was sufficiently small (0.0010 \u00b1 0.0006, mean \u00b1 SEM", "statistics": [], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig3m-movement-task-gradient", "statement": "Area A has higher encoding of instantaneous movement and lower encoding of task variables than every other area, and V1 has higher task-variable encoding than every other area.", "source": "Results, Distinct encoding gradients, paragraph 1; Figure 3M", "quote": "the encoding of task variables exhibited an anterior-posterior gradient with highest strength in V1, intermediate in RSC, and lowest in area A, whereas movement encoding strength exhibited a gradient in the opposite direction", "statistics": [ { "about": "A against every other area, movement (higher) and task variables (lower)", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<", "n": "42,998 well-fit neurons from 8 mice" }, { "about": "V1 against every other area, task variables", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<", "n": "42,998 well-fit neurons from 8 mice" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig3o-cue-gradient", "statement": "Cue encoding during stem traversal is strongest in V1 (higher than every other area), strong in AM and PM, weaker in RSC and weakest in A (lower than every other area).", "source": "Results, Distinct encoding gradients, paragraph 2; Figure 3O; Figure S4K", "quote": "Encoding of the visual cue identity was strongest in V1 and neighboring areas AM and PM, weaker in RSC, and weakest in area A", "statistics": [ { "about": "V1 higher than every other area", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<" }, { "about": "A lower than every other area", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "figs4l-strategy-distributed", "statement": "Encoding of decision-making strategy variables, with their interactions, is moderate and does not differ between areas, except AM against A.", "source": "Results, Distinct encoding gradients, paragraph 3; Figure 3R; Figure S4L", "quote": "Variables related to the decision-making strategy, including their interactions with other task variables, collectively exhibited a moderate encoding strength distributed evenly across posterior cortex", "statistics": [ { "about": "all area pairs except AM against A", "test": "hierarchical bootstrap", "p": 0.03, "p_relation": ">", "corrected": "Benjamini-Hochberg (not significant after correction)" }, { "about": "AM against A", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "figs4p-previous-outcome", "statement": "The previous trial's outcome is encoded much more strongly than its cue or choice, and evenly over space.", "source": "Results, Distinct encoding gradients, paragraph 3; Figure S4P to S4S", "quote": "The previous trial's outcome was much more strongly encoded than the previous trial's cue or choice", "statistics": [], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig4b-dynamic-choice-stem", "statement": "During stem traversal, dynamic choice is encoded more than reported choice in all areas, most in RSC (above V1, PM and A, not different from AM and MM), while reported choice encoding is not above zero.", "source": "Results, Encoding of dynamic choice was enriched in RSC, paragraph 1; Figure 4A, 4B", "quote": "during traversal of the maze stem, encoding of dynamic choice was distributed across all areas but enriched in RSC and adjacent medial areas, accounting for a substantial amount of neural activity compared to near-zero encoding of reported choice", "statistics": [ { "about": "dynamic against reported choice, each area", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<" }, { "about": "dynamic choice, RSC against V1, PM or A", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<" }, { "about": "dynamic choice, RSC against AM or MM", "test": "hierarchical bootstrap", "p": 0.05, "p_relation": ">" }, { "about": "reported choice, each area against zero", "test": "hierarchical bootstrap", "p": 0.05, "p_relation": ">" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig4c-reported-choice-feedback", "statement": "In the feedback period/ITI, reported choice is encoded more than dynamic choice in all areas, and RSC encodes reported choice more than every other area.", "source": "Results, Encoding of dynamic choice was enriched in RSC, paragraph 1; Figure 4C", "quote": "In contrast, during the feedback and ITI periods, we observed greater encoding of reported choice than dynamic choice", "statistics": [ { "about": "reported against dynamic choice, each area", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<" }, { "about": "reported choice, RSC against each other area", "test": "hierarchical bootstrap", "p": 0.05, "p_relation": "<" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "figs5i-dynamic-choice-over-roll", "statement": "In all areas except V1 and A, dynamic choice is encoded more than roll velocity during stem traversal.", "source": "Figure S5I", "quote": "For all areas except V1 and area A, the mean fraction explained deviance for dynamic choice was greater than roll velocity, which controls left-right movements in the maze (AM: p = 0.006; PM, MM and RSC: p < 10-3)", "statistics": [ { "about": "AM", "test": "hierarchical bootstrap", "p": 0.006 }, { "about": "PM, MM and RSC", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "figs5m-movement-history-control", "statement": "With past and upcoming movement added to the GLM, dynamic choice encoding drops but stays above zero in all areas and higher in RSC than in V1, PM and A.", "source": "Results, Encoding of dynamic choice was enriched in RSC, paragraph 3; Figure S5M", "quote": "Encoding of dynamic choice in models with movement history was significantly greater than zero in all 6 areas (p < 10-3), and was higher in RSC compared to V1, PM and area A (p < 10-3)", "statistics": [ { "about": "with against without movement history, each area", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<" }, { "about": "each area against zero", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<" }, { "about": "RSC against V1, PM and A", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig4g-position-rsc", "statement": "Maze position is encoded in all areas, most strongly in RSC (above V1, PM and A, not different from AM and MM).", "source": "Results, Encoding of dynamic choice was enriched in RSC, paragraph 4; Figure 4F, 4G", "quote": "Encoding of maze position was present in all areas, but strongest in RSC, with a similar medial-to-lateral gradient as choice encoding", "statistics": [ { "about": "RSC against V1, PM or A", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<" }, { "about": "RSC against AM or MM", "test": "hierarchical bootstrap", "p": 0.05, "p_relation": ">" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig5e-cue-movement-distributed", "statement": "Cue and movement have the highest mutual information between encoding strength and area, yet their equivalent random fractions are above 0.75 and equivalent jitters near 0.6, closer to fully distributed than fully modular.", "source": "Results, Encoding of most variables is highly distributed, paragraph 2; Figure 5E, 5F; Figure S6A", "quote": "Cue and movement were the two variables with the highest mutual information between encoding strength and posterior cortical area. However, their equivalent random fractions were greater than 0.75 ... Similarly, their equivalent jitters were near 0.6", "statistics": [], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig5e-position-choice-strategy-distributed", "statement": "Maze position and choice are even more distributed (equivalent random fraction near 0.9), and decision-making strategy is the most distributed (near 1).", "source": "Results, Encoding of most variables is highly distributed, paragraph 2; Figure 5E", "quote": "The encoding of maze position and choice were even more distributed than cue and movement, with equivalent random fractions near 0.9. All other variables were also highly distributed, with decision-making strategy being the most distributed (random fraction near one)", "statistics": [], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig6b-location-from-profiles", "statement": "Neurons in V1, A and RSC are typically decoded to their own areas from their encoding profiles, while neurons in AM and MM are decoded across all of posterior cortex.", "source": "Results, Single-neuron encoding profiles confirm functional gradients, paragraph 1; Figure 6A, 6B", "quote": "Neurons in V1, A, and RSC were typically predicted to reside in their actual areas of origin, indicating that these neurons had distinctive encoding profiles, in contrast to neurons from AM and MM that were predicted to reside in all sites across posterior cortex", "statistics": [], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig6d-three-gradients", "statement": "Non-negative factorization of the decoded locations is dominated by three factors (reconstruction error 33.8% \u00b1 3.1% with three, 55% with one), forming spatial gradients peaking in V1, RSC and A.", "source": "Results, Single-neuron encoding profiles confirm functional gradients, paragraph 2; Figure 6D; Figure S6I; STAR Methods, Non-negative matrix factorization", "quote": "Strikingly, these factors formed three distinct spatial gradients that dominated the anatomical organization of encoding with peaks in V1, RSC and A, respectively", "statistics": [], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "figs6g-factor-profiles", "statement": "Cue weighs most in factor 1, maze position and choice in factor 2, and movement in factor 3.", "source": "Results, Single-neuron encoding profiles confirm functional gradients, paragraph 2; Figure S6G", "quote": "cue was greatest in factor 1, maze position and choice were greatest in factor 2, and movement was greatest in factor 3", "statistics": [], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "encoding-dimensionality", "statement": "The number of principal components needed for 70% of variance is 8, 6 and 3 for GLM coefficients, encoding strengths and decoded locations, and 35, 23 and 16 for 95%.", "source": "STAR Methods, Analysis of dimensionality of encoding across neurons versus encoding across cortical space", "quote": "The number of principal components needed to explain 70% of the variance in the three matrices was 8, 6, and 3, respectively, and further, 35, 23, and 16 principal components were needed to explain 95% of the variance", "statistics": [], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig7def-encoding-correlations", "statement": "Correlations in single-neuron encoding strength between cue, dynamic choice and movement are similar across areas and close to zero; only A differs for cue against movement.", "source": "Results, Conjunctive structure of encoding, paragraph 2; Figure 7D to 7F", "quote": "Strikingly, the correlations in encoding between pairs of cue, choice, and movement variables were similar across areas, despite major differences in the encoding strengths of these variables between areas ... In addition, the correlation coefficients were close to zero", "statistics": [ { "about": "cue and dynamic choice, all area pairs", "test": "bootstrap", "p": 0.05, "p_relation": ">" }, { "about": "cue and movement, A against each other area", "test": "bootstrap", "p": 0.013, "p_relation": "<" }, { "about": "cue and movement, pairs among the other 5 areas", "test": "bootstrap", "p": 0.05, "p_relation": ">" }, { "about": "dynamic choice and movement, all area pairs", "test": "bootstrap", "p": 0.05, "p_relation": ">" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig7g-area-decoding-strength-vs-correlation", "statement": "A neuron's area is decoded well from encoding strengths, poorly from encoding correlations, and barely better from both than from strengths alone.", "source": "Results, Conjunctive structure of encoding, paragraph 3; Figure 7G, 7H", "quote": "Although decoding based on encoding correlations allowed above-chance performance, it was poor relative to decoding from encoding strengths alone. Further, decoding with both encoding correlations and encoding strengths was barely improved from decoding with encoding strengths alone", "statistics": [ { "about": "strengths only against correlations only, all areas except AM", "test": "Wilcoxon signed-rank test", "p": 0.05, "p_relation": "<" }, { "about": "strengths only against strengths plus correlations, all areas", "test": "Wilcoxon signed-rank test", "p": 0.03, "p_relation": ">", "corrected": "not significant after multiple comparison correction" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "figs8d-l2-vs-group-lasso", "statement": "GLMs fit with L2 regularization perform slightly worse on test data than with group lasso (difference -0.0175 \u00b1 0.0052), and the main encoding results hold with L2.", "source": "STAR Methods, Quantification of fraction explained deviance (without re-fitting); Figure S8D to S8J", "quote": "The performance of the models on test data was worse on average than those with group lasso (difference in fraction of explained deviance", "statistics": [ { "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<", "n": "93,881 neurons" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "figs8k-robust-to-threshold", "statement": "With a looser fit threshold (fraction deviance explained above 0.1, 68,532 neurons), the area differences in movement, task, cue, choice, position and strategy encoding are the same.", "source": "Results, Calcium imaging of neural activity, paragraph 2; Figure S8K to S8O", "quote": "for subsequent analyses, we focused on well fit neurons although results were robust to this criterion", "statistics": [], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig6h-embedding-overlap", "statement": "In the two-dimensional encoding embedding, area distributions overlap; their centroids are offset, but many neurons lie closer to the centroid of another area than to that of their own area.", "source": "Results, Single-neuron encoding profiles confirm functional gradients, paragraph 3; Figure 6H; Figure S6L", "quote": "Although the centroids of the distributions (i.e. most typical encoding profiles) were offset from one another, many neurons were functionally closer to the centroid of a different area than to the centroid of the area where they resided", "statistics": [], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "figs5l-dynamic-choice-over-cue-biased-running", "statement": "At maze positions where cue and reported choice are equally decodable from running, single neurons encode dynamic choice more than cue-biased running in all areas except V1 and PM, and population decoders decode dynamic choice better than cue-biased running in all areas.", "source": "Figure S5L; Results, Encoding of dynamic choice was enriched in RSC, paragraph 3", "quote": "For single neuron encoding magnitudes, dynamic choice was greater than cue-biased running (p < 10-3) for all areas except V1 and PM. For population decoding performance, dynamic choice was greater than cue-biased running (p < 10-3) for all areas", "statistics": [ { "about": "single-neuron encoding, PM excluded with V1, the other four areas", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<" }, { "about": "population decoding, all six areas", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<", "n": "998 decoders" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Depends on well-fit-neurons, which needs the GLM (blocked, too large), and so is blocked with it, as a claim that depends on a blocked claim. Its decoder half could be run with the decoders of fig3q-cue-decoding but the criterion judges the single-neuron and decoder halves together. Needs the Poisson GLM of the Methods fit to every layer 2/3 neuron (5-fold cross-validation over 21 penalties plus a final fit, 106 fits per neuron). The dandiset holds no fitted coefficients, deviances or encoding strengths. The notebook times one L2 Poisson fit of the median session's training frames by 1,310 predictors at 0.2 s, which puts the 97,989 neurons at 652 CPU hours, a random subsample of 2,000 neurons at 13.3 CPU hours (3.3 h on the run's 4 cores) and one of 200 at 1.3 CPU hours (0.3 h), each a lower bound since the Methods' group lasso is slower. The fit time varies between runs on the same machine type: the round-2 run printed 0.4 s per fit (1,191, 24.3 and 2.4 CPU hours). Even the 200-neuron subsample, 20 to 36 minutes of fitting by these lower bounds, leaves no room in a 30-minute notebook for streaming the sessions and building the design, and 200 neurons give about 33 per area. The design matrix (27 task variables on raised-cosine bases, 336 movement predictors, drift terms) and the predictor grouping are not in the paper's public code. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig3q-cue-decoding", "statement": "Cue decoded from populations of about 100 nearby neurons follows the same gradient as single-neuron cue encoding.", "source": "Results, Distinct encoding gradients, paragraph 2; Figure 3Q", "quote": "which was corroborated with decoding of these variables from the activity of ~100 simultaneously recorded neurons", "statistics": [], "kind": "finding", "inferred": "", "outcome": "reproduced", "blocked_by": null, "notebook": "reproduce_tseng_2022.ipynb", "notes": "V1 and PM come from every decoder of the 41 pool sessions with a V1 or PM field (decoders_v1_pm_offset.ipynb; 149 V1 and 71 PM decoders with k-means groups, 142 and 80 with tiles), the other four areas from the main notebook's 116 decoders of 18 sessions. Cue decoding is held-out log2 likelihood, chance -1. V1 is above PM under all 12 readings, by 0.027 to 0.062 (V1 -0.139 to -0.246, PM -0.187 to -0.282); the difference is significant only for tiles over the stem bins (p = 0.039 and 0.042), p = 0.098 to 0.474 otherwise. The highest of the other areas, AM (-0.276 to -0.382, 16 to 17 decoders in 3 mice), is 0.118 to 0.157 below V1. A is the lowest area in all 12 readings and V1 is above A by 0.355 to 0.409 (p < 0.001). So V1 is the highest area and A the lowest under every reading, with V1 above A at p < 0.05, which the criterion calls reproduced. On the subsample alone V1 was highest in only 2 of 12 readings (PM above V1 by 0.001 to 0.027 on 12 to 14 PM decoders). For k-means groups, frame folds, the minimum penalty and stem bins, the only reading printed per mouse and per field, V1 is above PM in each of the 6 mice with both areas (mouse 3 by 0.012) and within each field. Decoders labelled PM in sessions of other fields are not fitted, so PM has 71 to 80 decoders against the paper's 106; in the subsample such decoders do not average higher than the PM field's. Frame folds and trial folds give the same ranking. Proposed after round 2 and agreed in round 3.", "deviations": [ "The decoders were fit on a random subsample of 18 of the 142 multi-plane layer 2/3 sessions of the all-trial selection (3 per imaged field's area, seeded), 116 decoders against the paper's 974, as at the notebook's rate (488 s on 4 processes for the 18 sessions, 108 CPU-s per session) all 142 sessions take about 4.5 CPU hours, 67 minutes on 4 cores, over the 30-minute notebook limit. The 8 single-plane layer 2/3 sessions of this selection (150 - 142) were left out. A second notebook, decoders_v1_pm_offset.ipynb, fits every pool session with a field in V1 or PM (41 sessions, 23,375 neurons, 922 s on 4 processes) and the 6 pool sessions with the cue offset at 0.76.", "Decoders follow the Methods (Trial phase-specific logistic decoders): groups of about 100 neurons, PCA to more than 90% of variance, logistic regression with elastic net (10% L1) per each of 11 position bins on single frames (6 Hz volumes), 5-fold cross-validation. The solver is FISTA written here in place of Matlab's lassoglm (checked against scikit-learn's saga to 2e-5), with 10 penalties from the largest useful one down to 1e-4 of it (lassoglm's range, fewer values), a standard choice. Dynamic choice is fit as a binomial proportion.", "Readings: penalty at the minimum CV deviance and one-SE; groups by k-means and by equal-count tiles (claims file). The Methods do not say how folds are formed; lassoglm's default (random frames) and folds of whole trials are both run, the latter with k-means groups only. Areas are the label of the neuron nearest each group's centroid (97% agree with the members' majority area).", "Frames are maze traversal of non-visually guided trials; stem frames are those below each session's stem length." ], "assumed": false, "disagreed": true, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig4d-partial-correlation", "statement": "Population decoders trained on reported choice are more related to dynamic choice (partial correlation 0.35 \u00b1 0.02) than to reported choice (0.14 \u00b1 0.02), a difference of 0.21 \u00b1 0.02.", "source": "Results, Encoding of dynamic choice was enriched in RSC, paragraph 2; Figure 4D", "quote": "Partial correlations were greater for dynamic choice than reported choice, revealing that even decoders trained to predict reported choice were more closely related to dynamic choice", "statistics": [ { "about": "difference of the two partial correlations against zero", "test": "bootstrap", "p": 0.001, "p_relation": "<", "n": "974 decoders" } ], "kind": "finding", "inferred": "", "outcome": "partially_reproduced", "blocked_by": null, "notebook": "reproduce_tseng_2022.ipynb", "notes": "Under every reading the reported-choice decoders' output is more related to dynamic choice than to reported choice (difference 0.217 to 0.326, p < 0.001, positive in 97% to 100% of decoders). The partial correlation with dynamic choice is 0.428 to 0.444, above 0.35 \u00b1 0.02 under every reading; with reported choice 0.101 to 0.217, within 0.14 \u00b1 0.02 only for trial folds at the minimum-CV penalty (0.130); the difference is within 0.21 \u00b1 0.02 for three of the four frame-fold readings. No reading has all three within tolerance, so only the sign and test hold. Measured values are k-means groups, frame folds, minimum-CV penalty.", "deviations": [ "The decoders were fit on a random subsample of 18 of the 142 multi-plane layer 2/3 sessions of the all-trial selection (3 per imaged field's area, seeded), 116 decoders against the paper's 974, as at the notebook's rate (488 s on 4 processes for the 18 sessions, 108 CPU-s per session) all 142 sessions take about 4.5 CPU hours, 67 minutes on 4 cores, over the 30-minute notebook limit. The 8 single-plane layer 2/3 sessions of this selection (150 - 142) were left out. A second notebook, decoders_v1_pm_offset.ipynb, fits every pool session with a field in V1 or PM (41 sessions, 23,375 neurons, 922 s on 4 processes) and the 6 pool sessions with the cue offset at 0.76.", "Decoders follow the Methods (Trial phase-specific logistic decoders): groups of about 100 neurons, PCA to more than 90% of variance, logistic regression with elastic net (10% L1) per each of 11 position bins on single frames (6 Hz volumes), 5-fold cross-validation. The solver is FISTA written here in place of Matlab's lassoglm (checked against scikit-learn's saga to 2e-5), with 10 penalties from the largest useful one down to 1e-4 of it (lassoglm's range, fewer values), a standard choice. Dynamic choice is fit as a binomial proportion.", "Readings: penalty at the minimum CV deviance and one-SE; groups by k-means and by equal-count tiles (claims file). The Methods do not say how folds are formed; lassoglm's default (random frames) and folds of whole trials are both run, the latter with k-means groups only. Areas are the label of the neuron nearest each group's centroid (97% agree with the members' majority area).", "Frames are maze traversal of non-visually guided trials; stem frames are those below each session's stem length." ], "assumed": false, "disagreed": true, "data": "dataset", "where": null, "proposed": null }, { "claim": "figs5f-dynamic-choice-decoding", "statement": "From populations of about 100 neurons, dynamic choice is decoded better (Spearman 0.59 \u00b1 0.02) than reported choice (0.26 \u00b1 0.02), both above chance.", "source": "Figure S5F", "quote": "Decoding of both dynamic and reported choice was above chance (p < 10-3, bootstrap), but decoding of reported choice was less accurate than dynamic choice", "statistics": [ { "about": "each decoder type against chance", "test": "bootstrap", "p": 0.001, "p_relation": "<", "n": "974 decoders" }, { "about": "dynamic minus reported choice decoding", "test": "bootstrap", "p": 0.001, "p_relation": "<", "n": "974 decoders" } ], "kind": "finding", "inferred": "", "outcome": "partially_reproduced", "blocked_by": null, "notebook": "reproduce_tseng_2022.ipynb", "notes": "Under every reading both decoders are above chance (p < 0.001) and dynamic choice is decoded better than reported choice (difference 0.238 to 0.297, p < 0.001, within 0.33 \u00b1 0.13). Both values are above the paper's: dynamic choice 0.636 to 0.683 against 0.59 \u00b1 0.02 and reported choice 0.343 to 0.444 against 0.26 \u00b1 0.02, under no reading within tolerance. Trial folds give lower reported-choice values (0.343 to 0.373) than frame folds (0.431 to 0.444). Measured values are k-means groups, frame folds, minimum-CV penalty.", "deviations": [ "The decoders were fit on a random subsample of 18 of the 142 multi-plane layer 2/3 sessions of the all-trial selection (3 per imaged field's area, seeded), 116 decoders against the paper's 974, as at the notebook's rate (488 s on 4 processes for the 18 sessions, 108 CPU-s per session) all 142 sessions take about 4.5 CPU hours, 67 minutes on 4 cores, over the 30-minute notebook limit. The 8 single-plane layer 2/3 sessions of this selection (150 - 142) were left out. A second notebook, decoders_v1_pm_offset.ipynb, fits every pool session with a field in V1 or PM (41 sessions, 23,375 neurons, 922 s on 4 processes) and the 6 pool sessions with the cue offset at 0.76.", "Decoders follow the Methods (Trial phase-specific logistic decoders): groups of about 100 neurons, PCA to more than 90% of variance, logistic regression with elastic net (10% L1) per each of 11 position bins on single frames (6 Hz volumes), 5-fold cross-validation. The solver is FISTA written here in place of Matlab's lassoglm (checked against scikit-learn's saga to 2e-5), with 10 penalties from the largest useful one down to 1e-4 of it (lassoglm's range, fewer values), a standard choice. Dynamic choice is fit as a binomial proportion.", "Readings: penalty at the minimum CV deviance and one-SE; groups by k-means and by equal-count tiles (claims file). The Methods do not say how folds are formed; lassoglm's default (random frames) and folds of whole trials are both run, the latter with k-means groups only. Areas are the label of the neuron nearest each group's centroid (97% agree with the members' majority area).", "Frames are maze traversal of non-visually guided trials; stem frames are those below each session's stem length." ], "assumed": false, "disagreed": true, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig8cd-position-specific-decoders", "statement": "Cue and choice decoders trained at one maze position lose accuracy when tested at other positions.", "source": "Results, Flexible, high-dimensional representations, paragraph 1; Figure 8C, 8D", "quote": "decoders trained to predict cue or choice at one maze position degraded in accuracy when tested at other maze positions", "statistics": [], "kind": "finding", "inferred": "", "outcome": "reproduced", "blocked_by": null, "notebook": "reproduce_tseng_2022.ipynb", "notes": "Decoders trained in one position bin and tested on held-out trials at every bin lose accuracy with distance for both variables in every area: cue log2 likelihood falls from -0.438 at zero distance to -1.663 at ten bins (largest minus zero distance -0.71 to -1.88 by area, p < 0.001 in each), dynamic choice Spearman correlation from 0.599 to 0.146 (-0.415 to -0.483 by area, p < 0.001 in each); every decoder drops. 116 decoders from 18 sessions, k-means groups, trial folds, minimum-CV penalty. Under the claims file's own reading of the cue test (decoders_v1_pm_offset.ipynb), the 53 decoders of the 6 pool sessions with the cue offset at 0.76 (3 mice) also lose accuracy with distance in every area: cue -0.465 to -1.086 and dynamic choice -0.450 to -0.526 from zero to the largest distance, p < 0.001 in each, every decoder dropping; V1 (2 decoders), AM (5) and A (11) each come from one mouse. The outcome therefore does not depend on the assumption.", "deviations": [ "The decoders were fit on a random subsample of 18 of the 142 multi-plane layer 2/3 sessions of the all-trial selection (3 per imaged field's area, seeded), 116 decoders against the paper's 974, as at the notebook's rate (488 s on 4 processes for the 18 sessions, 108 CPU-s per session) all 142 sessions take about 4.5 CPU hours, 67 minutes on 4 cores, over the 30-minute notebook limit. The 8 single-plane layer 2/3 sessions of this selection (150 - 142) were left out. A second notebook, decoders_v1_pm_offset.ipynb, fits every pool session with a field in V1 or PM (41 sessions, 23,375 neurons, 922 s on 4 processes) and the 6 pool sessions with the cue offset at 0.76.", "Decoders follow the Methods (Trial phase-specific logistic decoders): groups of about 100 neurons, PCA to more than 90% of variance, logistic regression with elastic net (10% L1) per each of 11 position bins on single frames (6 Hz volumes), 5-fold cross-validation. The solver is FISTA written here in place of Matlab's lassoglm (checked against scikit-learn's saga to 2e-5), with 10 penalties from the largest useful one down to 1e-4 of it (lassoglm's range, fewer values), a standard choice. Dynamic choice is fit as a binomial proportion.", "Readings: penalty at the minimum CV deviance and one-SE; groups by k-means and by equal-count tiles (claims file). The Methods do not say how folds are formed; lassoglm's default (random frames) and folds of whole trials are both run, the latter with k-means groups only. Areas are the label of the neuron nearest each group's centroid (97% agree with the members' majority area).", "Frames are maze traversal of non-visually guided trials; stem frames are those below each session's stem length.", "Assumed. The Figure 8C legend uses only sessions with the cue offset at 0.76 of the maze. The files' cue delay length gives that offset for 6 of the 142 sessions in the pool and none of the 18 decoder sessions (offsets 0.96 and 1.0), and none with a field in V1 or AM (findings), so the cue test uses every decoder session, with the cue-present bins set by each session's own offset. The 6 sessions with the 0.76 offset are decoded in the second notebook and give the same result.", "Cross-position tests use folds of whole trials, so that a test frame never comes from a training trial." ], "assumed": true, "disagreed": true, "data": "dataset", "where": null, "proposed": null }, { "claim": "fig8g-shattering-dimensionality", "statement": "Shattering dimensionality for conjunctions of cue, choice, position, movement and rule belief is well above chance and similar across areas, and nearly identical to that of populations drawn from all areas.", "source": "Results, Flexible, high-dimensional representations, paragraph 2; Figure 8F, 8G; Figure S8A to S8C", "quote": "Shattering dimensionality for all conjunctions in all areas was well above chance, and interestingly, shattering dimensionality for each conjunction was similar across posterior cortex ... Almost all differences between areas were not statistically significant and fell within a range of < 10% classification accuracy for each conjunction", "statistics": [ { "about": "all pairs among all-area and the 6 areas, Figure 8G", "test": "hierarchical bootstrap", "p": 0.01, "p_relation": ">", "corrected": "not significant after multiple comparison correction" }, { "about": "cue, dynamic choice and position, RSC against A (Figure S8B)", "test": "hierarchical bootstrap", "p": 0.001, "p_relation": "<" } ], "kind": "finding", "inferred": "", "outcome": "blocked", "blocked_by": "too_large", "notebook": "reproduce_tseng_2022.ipynb", "notes": "Needs pseudo-populations of 1,000 neurons per area resampled 1,000 times, with linear SVCs on every marginally balanced dichotomy of each conjunction, 10 splits each, on stem frames of all layer 2/3 neurons, under two readings of choice. The notebook counts 126 marginally balanced dichotomies for cue by 10 positions alone and times one SVC on 20 conditions by 1,000 neurons at 1.04 ms, which puts that one conjunction at 17,640,000 fits and 5.1 CPU hours (1.3 h on 4 cores; earlier runs printed 0.69 and 0.81 ms, 3.4 and 3.9 CPU hours), a lower bound without building the trial samples and without the other conjunctions of Figure 8G. Not run.", "deviations": [], "assumed": false, "disagreed": false, "data": "dataset", "where": null, "proposed": null } ], "findings": [ { "kind": "metadata_error", "statement": "The trial-table column rule_belief equals P(rule A) - P(rule B) in all 300 sessions, recomputed from the stored association matrix, and correlates negatively with the current rule being B (median r -0.73). The paper's Methods and the dataset's tutorial notebook both define it as positive for rule B. The other strategy columns match the Methods' formulas exactly.", "found": null, "status": null }, { "kind": "metadata_error", "statement": "In the single-plane sessions the timestamps of the deconvolved activity, labelled seconds, are 2000 times the behaviour clock (for example 25356 against 12.678 s at the first frame), while the planes have exactly one frame per behaviour frame. Matching activity to behaviour by these timestamps puts every frame at the end of the session.", "found": null, "status": null }, { "kind": "selection_undocumented", "statement": "The paper's session counts cannot be recovered from the files. With session fraction correct excluding visually guided trials (the Methods' rule for all analyses) there are 291 sessions above 0.5, 154 above 0.65 and 100 layer 2/3 sessions above 0.65 (69,707 neurons), against the paper's 253, 216 and 141 (93,881 neurons). With all trials there are 297, 238 and 150 (97,989 neurons). Only the last matches the neuron counts within 10%. No file marks which sessions the paper analysed.", "found": null, "status": null }, { "kind": "paper_inconsistency", "statement": "The paper says the fraction of visually guided trials typically varied from 0 to 20%; in the files the median session has 23% and 222 of 300 sessions have more than 20% (up to 78%).", "found": null, "status": null }, { "kind": "data_missing", "statement": "The photoinhibition experiments (7 VGAT-ChR2 mice, 164 sessions; Figures 1J to 1M, S1K to S1M, S2M) are not in the dandiset, and the paper names no other archive for them. Three of the paper's findings cannot be tested.", "found": null, "status": null }, { "kind": "other", "statement": "The dandiset holds no GLM fits, encoding strengths or population decoders, on which 29 of the 43 claims rest. Refitting the GLM is estimated at no less than 652 CPU hours (a lower bound timed in the notebook, which varies between runs up to 1,191), so the 24 GLM claims cannot be checked against the deposit without the derived results or far more compute; the decoders were refit here on a subsample of sessions.", "found": null, "status": null }, { "kind": "other", "statement": "7 of 300 sessions have a 150-unit (1.5 m) maze stem where the paper describes a 200-unit stem; the TaskParam metadata records it. The analyses here use each session's own stem length.", "found": null, "status": null }, { "kind": "methods_unavailable", "statement": "Question for the paper's authors. How was the rule update per trial (\u0394 rule belief) of visually guided and normal trials computed (STAR Methods, Decision-making strategy variables)? Taking it as the change from trial t to t + 1 gives a larger update for visually guided trials (0.055 against 0.022, p < 0.001); from t - 1 to t gives none (0.030 against 0.027, p = 0.48); both give values 2 to 5 times the reported 0.013 and 0.0080.", "found": null, "status": null }, { "kind": "methods_unavailable", "statement": "Question for the paper's authors. Was the 65% session threshold applied to fraction correct over all trials, including visually guided ones? Only that reading comes near the 141 sessions and 93,881 neurons, but it gives 238 sessions where the supplement reports 216, and no reading gives the 253 sessions above 0.5.", "found": null, "status": null }, { "kind": "metadata_insufficient", "statement": "The Figure 8C legend has 698 decoders (100 in V1) from sessions with the cue offset at 0.76 of the maze, but the TaskParam cue delay length gives that offset (60 of 250 maze units) for only 8 of the 172 multi-plane layer 2/3 session files and 6 of the 142 in the decoder pool (fields RSC 2, A 2, PM 1, MM 1; 53 decoders), none with a field in V1 or AM; most sessions have a delay of 10 units (offset 0.96) or none. Which variable marks the paper's cue offset is not described.", "found": null, "status": null } ], "reconciliation": null, "verifications": [ { "date": "2026-10-31", "kind": "agent", "by": "Independent agent run by CatalystNeuro (same harness, separate context, did not write the notebook)", "reviewed": "Round 1. The executed notebook with the conclusion cells and the summary removed, the helper module and the claims file, with the paper. The verifier checked the archive's asset list for the blocked claims and tested hboot and fit_block_sigmoid on synthetic inputs. 38 of its 43 outcomes agree with the record; it disagreed with the too_large block of fig3q-cue-decoding, fig4d-partial-correlation, figs5f-dynamic-choice-decoding and fig8cd-position-specific-decoders, and gave fig4h-activity-by-epoch as not reproduced.", "agreed": 38, "of": 43, "disagreements": [ { "claim": "fig4h-activity-by-epoch", "outcome": "not_reproduced", "reason": "RSC has the highest stem mean of all areas, but it is not significantly above AM (p = 0.28/0.45), nor above V1 or MM under (a). Within RSC, activity is higher in the arm than in the stem (arm higher in 7 of 8 mice). The part that holds: A is above RSC in the feedback period/ITI (p < 1e-3). Only the all-neuron reading was run. It was never checked whether the 1 s feedback delay keeps a position and so falls into the arm, and the frame counts per epoch were not printed." }, { "claim": "fig3q-cue-decoding", "outcome": null, "reason": "The notebook's own estimate is 16.5 CPU-h for all 974 decoders x 11 bins x 8 readings, about 1.4 h on the 12 workers it used. All the inputs are in the files: activity, positions, cue, choice and dynamic choice. These were testable; too_large is not supported." }, { "claim": "fig4d-partial-correlation", "outcome": null, "reason": "The notebook's own estimate is 16.5 CPU-h for all 974 decoders x 11 bins x 8 readings, about 1.4 h on the 12 workers it used. All the inputs are in the files: activity, positions, cue, choice and dynamic choice. These were testable; too_large is not supported." }, { "claim": "figs5f-dynamic-choice-decoding", "outcome": null, "reason": "The notebook's own estimate is 16.5 CPU-h for all 974 decoders x 11 bins x 8 readings, about 1.4 h on the 12 workers it used. All the inputs are in the files: activity, positions, cue, choice and dynamic choice. These were testable; too_large is not supported." }, { "claim": "fig8cd-position-specific-decoders", "outcome": null, "reason": "The notebook's own estimate is 16.5 CPU-h for all 974 decoders x 11 bins x 8 readings, about 1.4 h on the 12 workers it used. All the inputs are in the files: activity, positions, cue, choice and dynamic choice. These were testable; too_large is not supported." } ] }, { "date": "2026-10-31", "kind": "agent", "by": "Independent agent run by CatalystNeuro (same harness, separate context, did not write the notebook)", "reviewed": "Round 2. The full executed notebook with the conclusions and summary, the helper module, the claims file and the attempt record, with the paper. The verifier agreed with all 43 outcomes and with the five assumptions, and asked that the record's cost figures (GLM, shattering, decoders), the count of sessions with the 0.76 cue offset, the count of single-plane sessions and three sentences of the conclusions be brought into line with the printed outputs; it named fig3q (V1 against PM undecided on the subsample), fig8cd (the 0.76 sessions not decoded) and fig4h (well-fit reading not run) as remaining. In reply a second notebook decodes every V1 and PM field session and the 0.76 sessions; fig3q carries a proposed change to reproduced.", "agreed": 43, "of": 43, "disagreements": [] }, { "date": "2026-10-31", "kind": "agent", "by": "Independent agent run by CatalystNeuro (same harness, separate context, did not write the notebook)", "reviewed": "Round 3. The one proposed change of outcome (fig3q-cue-decoding, partially reproduced to reproduced) and the analysis behind it in decoders_v1_pm_offset.ipynb and the main notebook. The verifier agreed. It listed four caveats that do not change the outcome, and timing figures in the record and README that did not match the printed outputs.", "agreed": 1, "of": 1, "disagreements": [] } ] } ], "entries": [ { "name": "000579-tseng-2022", "type": "reproduction", "title": "Shared and specialized coding across posterior cortex during navigation decisions (Tseng et al. 2022)", "description": "Tests the findings of Tseng, Chettih, Arlt, Barroso-Luque and Harvey (2022) on dandiset 000579, which holds 300 two-photon imaging sessions of 8 mice running a virtual Y-maze with rule switches. It tests the neuron counts, the behaviour around rule switches, the stored strategy and dynamic-choice models, a refit reinforcement learning model, running trajectories, activity by trial epoch and population decoders fit on a subsample of sessions, with a second notebook that fits the decoders of every V1 and PM field session and of the sessions with the Figure 8C cue offset. The GLM encoding analyses and the shattering dimensionality are beyond the run's time, and the photoinhibition sessions are not in the dandiset.", "datasets": [ "dandi:000579" ], "papers": [ "10.1016/j.neuron.2022.05.012" ], "derived_from": [], "notebooks": [ { "path": "reproduce_tseng_2022.ipynb", "title": "Reproducing Tseng et al. (2022) from dandiset 000579", "executed": "notebooks/000579-tseng-2022/reproduce_tseng_2022.html" }, { "path": "decoders_v1_pm_offset.ipynb", "title": "Population decoders of every V1 and PM field session, and of the sessions with the cue offset at 0.76", "executed": "notebooks/000579-tseng-2022/decoders_v1_pm_offset.html" } ] } ] }