DANDI:000943

⊘Blocked31

• ⊘BlockedResults, paragraph 1; Supplementary file 1· Too large to analyse

  Of 1881 MEC neurons recorded in nine mice, 103 were classified as grid cells from their open arena firing, 11.4 +- 17.4 grid cells per mouse, 0.4 to 9.5% of each mouse's neurons.

  we identified 103/1881 neurons as grid cells (11.4±17.4 grid cells/mouse, range 0.4–9.5%, Supplementary file 1 )

  Results, paragraph 1; Supplementary file 1

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. Grid cells are then classified from each unit's open arena rate map with 1000 shuffles per unit, which also needs the units. Even with units, the open arena rate maps would rest on position columns whose meaning is inferred from their ranges (the nine columns are undescribed), and 18 of the 41 open arena sessions of M1, one of the paper's nine mice, span only 67.5 to 89.8 cm against the 100 x 100 cm arena the files describe, so a re-sort could still meet a metadata_insufficient block.

• ⊘BlockedSupplementary file 1· Too large to analyse

  Per mouse, the numbers of cells, sessions, trials per session and grid cells are as in Supplementary file 1, in total 1881 cells and 215 sessions, with most grid cells from M11 (53) and M14 (26).

  Not quoted: Numbers from the table in Supplementary file 1; no sentence of the text states the per-mouse values.

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It also depends on grid-cell-count. The parts that need no units were compared in the data check, not as a test of the claim. The dandiset has 242 VR sessions on the 200 cm track with at least 10 trials for the nine mice (paper 215), 27 more, outside the claim's tolerance of 21.5; per mouse M1 11 (11), M3 30 (26), M6 21 (21), M7 28 (23), M10 25 (16), M11 35 (35), M12 28 (21), M13 27 (25), M14 37 (37), so five of nine are within 10% of the table and M3, M7, M10 and M12 exceed it by 15%, 22%, 56% and 33%. The mean over the nine mice of each mouse's mean trials per session is 183.9 (paper 179.1). The paper counts only sessions with kept units, which the dandiset cannot show, so the excess sessions may be sessions without kept units. Cells and grid cells per mouse need the units.

• ⊘BlockedResults, paragraph 1· Too large to analyse

  Grid cells had field sizes of 7.5 +- 3.3 cm and grid spacing of 72.5 +- 13.7 cm.

  These grid cells had field sizes of 7.5±3.3 cm and grid spacing of 72.5±13.7 cm

  Results, paragraph 1

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It also depends on grid-cell-count.

• ⊘BlockedResults, Grid cells exhibit either task-anchored or task-independent firing, paragraph 2; Figure 2D-E· Too large to analyse

  At the session level, 68 of 103 grid cells had periodogram peaks within 0.05 of an integer spatial frequency (task-anchored), 32 had peaks elsewhere (task-independent) and 3 had no peak above the false alarm threshold (aperiodic), over 61 sessions with 233 +- 135 trials per session.

  We found that 68 of 103 grid cells had peaks in their periodograms within 5% of an integer multiple of the spatial frequency of the track repetition ... By contrast, 32 of 103 grid cells had periodograms with peaks at frequencies outside 5% ... were rare (3/103)

  Results, Grid cells exhibit either task-anchored or task-independent firing, paragraph 2; Figure 2D-E

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedResults, Grid cells exhibit either task-anchored or task-independent firing, paragraph 2; Figure 2E· Too large to analyse

  Among non-grid cells, the proportion with session-level task-anchored firing was similar to that of grid cells, task-independent periodic firing was rarer and aperiodic firing was more common.

  When we carried out similar analyses for non-grid cells, we found that the proportion of neurons with task-anchored firing was similar, but task-independent periodic firing was rarer, while aperiodic firing neurons were more common

  Results, Grid cells exhibit either task-anchored or task-independent firing, paragraph 2; Figure 2E

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedFigure 2F; Results, Grid cells exhibit either task-anchored or task-independent firing, paragraph 3· Too large to analyse

  Session-level task-anchored grid cells had higher spatial information scores on the track than task-independent grid cells.

  These differences manifest as substantially higher spatial information scores, but similar average firing rates, for grid cells with session-level task-anchored firing compared with task-independent firing

  Figure 2F; Results, Grid cells exhibit either task-anchored or task-independent firing, paragraph 3

  What the paper reports

  • effect of session-level class (TAG, TIG, AG): linear mixed model on log spatial information, ANOVA against null model, Χ2 = 9.54, DF = 2, p = 0.008 (n = 103 grid cells)
  • TAG against TIG: pairwise comparison of estimated marginal means, T = 2.815, DF = 73.6, p = 0.02

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedFigure 2F; Results, Grid cells exhibit either task-anchored or task-independent firing, paragraph 3· Too large to analyse

  Mean firing rates on the track did not differ between session-level task-anchored and task-independent grid cells.

  These differences manifest as substantially higher spatial information scores, but similar average firing rates

  Figure 2F; Results, Grid cells exhibit either task-anchored or task-independent firing, paragraph 3

  What the paper reports

  • TAG against TIG: pairwise comparison of estimated marginal means, T = 0.562, DF = 4.12, p = 0.8

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedFigure 2F; Results, Grid cells exhibit either task-anchored or task-independent firing, paragraph 3· Too large to analyse

  Periodogram peak power and peak width did not differ between task-anchored and task-independent grid cells.

  Features of the periodogram such as peak power and peak width did not differ between task-anchored grid cells and task-independent grid cells

  Figure 2F; Results, Grid cells exhibit either task-anchored or task-independent firing, paragraph 3

  What the paper reports

  • peak power, TAG against TIG: pairwise comparison of estimated marginal means, T = 2.033, DF = 19.0, p = 0.13
  • peak width, TAG against TIG: pairwise comparison of estimated marginal means, T = –1.029, DF = 3.27, p = 0.61

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedFigure 2F; Results, Grid cells exhibit either task-anchored or task-independent firing, paragraph 3· Too large to analyse

  Aperiodic grid cells had much lower mean firing rates than task-anchored or task-independent grid cells, suggesting their lack of periodicity reflects inactivity.

  The aperiodic grid cells had much lower mean firing rates than the task-anchored grid or task-independent grid cells suggesting that their apparent lack of periodicity reflects inactivity

  Figure 2F; Results, Grid cells exhibit either task-anchored or task-independent firing, paragraph 3

  What the paper reports

  • effect of session-level class on log firing rate: linear mixed model, ANOVA against null model, Χ2 = 10.215, DF = 2, p = 0.006
  • TAG against AG: pairwise comparison of estimated marginal means, T = 2.906, DF = 42.07, p = 0.02
  • TIG against AG: pairwise comparison of estimated marginal means, T = 2.588, DF = 67.29, p = 0.03 (n = 3 aperiodic grid cells)

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedResults, Grid cells switch between task-anchored and task-independent firing, paragraph 2; Figure 3F· Too large to analyse

  For 26.2% of grid cells (27/103) the trial-level classification was consistently task-anchored or task-independent on more than 85% of trials, whereas for 73.8% (76/103) no single coding scheme accounted for more than 85% of trials.

  For 26.2% of grid cells (27/103) the classification was consistently (>85% of trials) task-anchored or task-independent ( Figure 3F ), whereas for 73.8% of grid cells (76/103) no single coding scheme accounted for more than 85% of trials

  Results, Grid cells switch between task-anchored and task-independent firing, paragraph 2; Figure 3F

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedResults, Grid cells switch between task-anchored and task-independent firing, paragraph 2; Figure 3F· Too large to analyse

  Grid cells classified as task-anchored at the session level were biased towards high proportions of task-anchored trials, and session-level task-independent cells towards high proportions of task-independent trials, though many varied between trials.

  Grid cells identified as having task-anchored firing in our initial session-level analysis ( Figure 2 ) were biassed towards high proportions of task-anchored trials

  Results, Grid cells switch between task-anchored and task-independent firing, paragraph 2; Figure 3F

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedFigure 3G; Results, Grid cells switch between task-anchored and task-independent firing, paragraph 3· Too large to analyse

  Within grid cells, spatial information was higher on trials classified as task-anchored than on trials classified as task-independent.

  This difference was associated with substantially lower spatial information scores for trials with task-independent firing, while mean firing rates were similar

  Figure 3G; Results, Grid cells switch between task-anchored and task-independent firing, paragraph 3

  What the paper reports

  • linear mixed model on log spatial information, ANOVA against null model, Χ2 = 30.98, DF = 1, p < 10⁻⁷

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedFigure 3G· Too large to analyse

  Mean firing rates of grid cells were similar on task-anchored and task-independent trials.

  whereas the average firing rate was similar between task-anchored and task-independent trials (right)

  Figure 3G

  What the paper reports

  • linear mixed model on log firing rate, ANOVA against null model, Χ2 = 0.022, DF = 1, p = 0.88

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedFigure 3-figure supplement 4A-B; Results, Grid cells switch between task-anchored and task-independent firing, paragraph 2· Too large to analyse

  Grouping grid cells by a stable mode over trials (more than 85%), stable task-anchored cells had higher spatial information than stable task-independent cells while mean firing rates did not differ, as with the session-level grouping.

  Conclusions from this analysis are similar to those of Figure 2F

  Figure 3-figure supplement 4A-B; Results, Grid cells switch between task-anchored and task-independent firing, paragraph 2

  What the paper reports

  • spatial information, effect of group: linear mixed model on log spatial information, ANOVA against null model, Χ2 = 21.47, DF = 2, p < 10⁻⁴
  • spatial information, TA against TI: pairwise comparison of estimated marginal means, T = 4.229, DF = 89.9, p = 2 × 10⁻⁴
  • firing rate, TA against TI: pairwise comparison of estimated marginal means, T = –0.532, DF = 21.4, p = 0.86

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedFigure 3-figure supplement 5B; Results, Grid cells switch between task-anchored and task-independent firing, paragraph 2· Too large to analyse

  Grid cells spent more of a session in task-independent periodic firing than non-grid cells; the distributions of task-anchored, task-independent and aperiodic epochs differed between grid and non-grid cells.

  For non-grid cells, consistent with session-level analyses, we again found that a sub-population showed task-anchored firing at a trial level, while task-independent periodic firing was substantially less common than in the grid cell population

  Figure 3-figure supplement 5B; Results, Grid cells switch between task-anchored and task-independent firing, paragraph 2

  What the paper reports

  • task-anchored epochs: Kolmogorov-Smirnov test, KS = 0.14, p = 0.035 (n = 103 grid cells, 1778 non-grid cells)
  • task-independent epochs: Kolmogorov-Smirnov test, KS = 0.42, p < 10⁻¹⁵
  • aperiodic epochs: Kolmogorov-Smirnov test, KS = 0.38, p < 10⁻¹²

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedFigure 3-figure supplement 6B; Results, Grid cells switch between task-anchored and task-independent firing, paragraph 3· Too large to analyse

  Periods of task-anchored or task-independent firing extended in blocks across multiple trials, with a distribution of block lengths that differed from that of trial-shuffled data.

  among all grid cells periods of task-anchored or task-independent firing extended in blocks across multiple trials, with the distribution of block lengths substantially different to that generated by shuffled data

  Figure 3-figure supplement 6B; Results, Grid cells switch between task-anchored and task-independent firing, paragraph 3

  What the paper reports

  • Kolmogorov-Smirnov test, KS = 0.066, p = 3 × 10⁻⁴ (n = 1765 blocks (data), 2359 blocks (shuffled))

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedFigure 3-figure supplement 7B· Too large to analyse

  The percentage of beaconed trials in a session did not affect how often grid cells showed task-anchored coding.

  The percentage of beaconed trials did not have a significant effect on the occurrence of task-anchored coding

  Figure 3-figure supplement 7B

  What the paper reports

  • ANOVA comparison of binomial GLMMs with and without % beaconed trials, Χ2 = 0.032, DF = 1, p = 0.95

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedFigure 4D-E; Results, Grid cells switch between task-anchored and task-independent firing, paragraph 4· Too large to analyse

  Simultaneously recorded grid cells agreed strongly in their trial-by-trial firing mode, with agreement greater between grid cell pairs than between pairs involving non-grid cells, whose agreement was more variable and lower on average.

  Agreement was greater between grid cell pairs than between pairs involving non-grid cells

  Figure 4D-E; Results, Grid cells switch between task-anchored and task-independent firing, paragraph 4

  What the paper reports

  • G-G against NG-NG: pairwise comparison of estimated marginal means, T = –10.455, DF = 10,720, p < 10⁻⁴
  • G-G against G-NG: pairwise comparison of estimated marginal means, T = –8.415, DF = 10,710, p < 10⁻⁴
  • NG-NG against G-NG: pairwise comparison of estimated marginal means, T = –6.853, DF = 10,352, p < 10⁻⁴

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedFigure 4E legend· Too large to analyse

  For grid-grid, non-grid-non-grid and grid-non-grid pairs, agreement in firing mode differed from that of shuffled trial classifications.

  Agreement in the firing mode between each combination of grid (G) and non-grid (NG) cells and corresponding scores for the shuffled data (lower), and the difference between the shuffled and actual scores

  Figure 4E legend

  What the paper reports

  • G-G, data against shuffled: generalised linear mixed model (beta), ANOVA, Χ2 = 58.89, DF = 1, p < 10⁻¹³
  • NG-NG, data against shuffled: generalised linear mixed model (beta), ANOVA, Χ2 = 20.42, DF = 1, p < 10⁻⁵
  • G-NG, data against shuffled: generalised linear mixed model (beta), ANOVA, Χ2 = 30.37, DF = 1, p < 10⁻⁷

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedFigure 4F; Results, Grid cells switch between task-anchored and task-independent firing, paragraph 4· Too large to analyse

  The drop in spatial information when the grid population was task-independent, relative to when it was task-anchored, was greater for grid cells than for non-grid cells, which kept similar spatial information.

  Non-grid cells also differed from the grid cell population in that their activity maintained similar spatial information between trials when grid cells were task-anchored versus when they were task-independent

  Figure 4F; Results, Grid cells switch between task-anchored and task-independent firing, paragraph 4

  What the paper reports

  • grid against non-grid difference: linear mixed model, ANOVA against null model, Χ2 = 21.1, DF = 1, p < 10⁻⁵
  • grid cells against zero: estimated marginal mean against zero, T = 2.723, DF = 11.87, p = 0.018
  • non-grid cells against zero: estimated marginal mean against zero, T = –1.278, DF = 2.58, p = 0.3

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track.

• ⊘BlockedFigure 6B; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 2· Too large to analyse

  In sessions with stable grid codes, the proportion of hit trials on cued (beaconed) trials was similar whether grid firing was task-anchored or task-independent.

  On cued trials the spatial organisation of stopping behaviour ( Figures 5C vs D and 6A ) and the proportion of hit trials ( Figure 6B ) was similar for both groups

  Figure 6B; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 2

  What the paper reports

  • binomial GLMM, ANOVA against null model, Χ2 = 2.70, DF = 1, p = 0.1

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track. The behaviour the claim also needs (trial types, positions, and stops from speed below 4.7 cm/s) is in the VR position series; reward times are not stored, but a hit is defined by a stop in the reward zone, so the behaviour side could be computed. The trial-level grid classes cannot.

• ⊘BlockedFigure 6B; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 2· Too large to analyse

  In sessions with stable grid codes, the proportion of hit trials on non-beaconed and probe trials was substantially reduced when grid firing was task-independent.

  while the proportion of hit trials was substantially reduced ( Figure 6B )

  Figure 6B; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 2

  What the paper reports

  • non-beaconed trials: binomial GLMM, ANOVA against null model, Χ2 = 5.24, DF = 1, p = 0.022
  • probe trials: binomial GLMM, ANOVA against null model, Χ2 = 4.55, DF = 1, p = 0.033

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track. The behaviour the claim also needs (trial types, positions, and stops from speed below 4.7 cm/s) is in the VR position series; reward times are not stored, but a hit is defined by a stop in the reward zone, so the behaviour side could be computed. The trial-level grid classes cannot.

• ⊘BlockedFigure 6A; Figure 5C-D; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 2· Too large to analyse

  In sessions with stable grid codes, stopping on non-beaconed and probe trials was spatially organised around the reward zone when grid activity was task-anchored, but largely not when it was task-independent; on cued trials it was similar in both.

  on non-beaconed and probe trials stopping behaviour was clearly spatial when grid activity was task-anchored, but spatial organisation was largely absent when grid activity was task-independent

  Figure 6A; Figure 5C-D; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 2

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track. The behaviour the claim also needs (trial types, positions, and stops from speed below 4.7 cm/s) is in the VR position series; reward times are not stored, but a hit is defined by a stop in the reward zone, so the behaviour side could be computed. The trial-level grid classes cannot.

• ⊘BlockedFigure 6D; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 3· Too large to analyse

  Over all sessions, at the trial level, the proportion of hits on beaconed trials was similar whether grid cells were task-anchored or task-independent.

  On cued trials the spatial organisation of stopping behaviour ( Figures 5E–F – 6C ), and the proportion of hit trials ( Figure 6D ), was similar irrespective of whether grid cell fields were task-anchored or task-independent

  Figure 6D; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 3

  What the paper reports

  • binomial GLMM, ANOVA against null model, Χ2 = 2.84, DF = 1, p = 0.09

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track. The behaviour the claim also needs (trial types, positions, and stops from speed below 4.7 cm/s) is in the VR position series; reward times are not stored, but a hit is defined by a stop in the reward zone, so the behaviour side could be computed. The trial-level grid classes cannot.

• ⊘BlockedFigure 6D; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 3· Too large to analyse

  Over all sessions, at the trial level, the proportion of hits on non-beaconed trials was reduced when grid firing was task-independent.

  and the proportion of successful trials was reduced compared with when grid firing was task-anchored ( Figure 6D )

  Figure 6D; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 3

  What the paper reports

  • binomial GLMM, ANOVA against null model, Χ2 = 10.14, DF = 1, p = 0.001

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track. The behaviour the claim also needs (trial types, positions, and stops from speed below 4.7 cm/s) is in the VR position series; reward times are not stored, but a hit is defined by a stop in the reward zone, so the behaviour side could be computed. The trial-level grid classes cannot.

• ⊘BlockedFigure 6D; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 3· Too large to analyse

  On probe trials the proportion of hits was lower when grid firing was task-independent, though the paper's test for probe trials was not significant.

  On average, spatial stopping behaviour was reduced on non-beaconed and probe trials during which grid firing was task-independent ( Figure 6C ), and the proportion of successful trials was reduced

  Figure 6D; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 3

  What the paper reports

  • binomial GLMM, ANOVA against null model, Χ2 = 1.70, DF = 1, p = 0.19

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track. The behaviour the claim also needs (trial types, positions, and stops from speed below 4.7 cm/s) is in the VR position series; reward times are not stored, but a hit is defined by a stop in the reward zone, so the behaviour side could be computed. The trial-level grid classes cannot.

• ⊘BlockedFigure 6C; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 3· Too large to analyse

  Over all sessions, spatial stopping on non-beaconed and probe trials was reduced on trials when grid firing was task-independent, while on cued trials it was similar.

  On average, spatial stopping behaviour was reduced on non-beaconed and probe trials during which grid firing was task-independent ( Figure 6C )

  Figure 6C; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 3

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track. The behaviour the claim also needs (trial types, positions, and stops from speed below 4.7 cm/s) is in the VR position series; reward times are not stored, but a hit is defined by a stop in the reward zone, so the behaviour side could be computed. The trial-level grid classes cannot.

• ⊘BlockedFigure 6E; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 4· Too large to analyse

  Task-anchored firing occurred on a greater proportion of non-beaconed and probe hit trials than task-independent firing, but on a smaller proportion of run trials.

  task-anchored firing occurred on a greater proportion of non-beaconed and probe trials that were hits compared with task-independent firing, but on a smaller proportion of run trials

  Figure 6E; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 4

  What the paper reports

  • beaconed trials: binomial GLMM, ANOVA against null model, Χ2 = 4.49, DF = 2, p = 0.11
  • non-beaconed trials: binomial GLMM, ANOVA against null model, Χ2 = 7.75, DF = 2, p = 0.02
  • probe trials: binomial GLMM, ANOVA against null model, Χ2 = 5.40, DF = 2, p = 0.07

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track. The behaviour the claim also needs (trial types, positions, and stops from speed below 4.7 cm/s) is in the VR position series; reward times are not stored, but a hit is defined by a stop in the reward zone, so the behaviour side could be computed. The trial-level grid classes cannot.

• ⊘BlockedFigure 6F; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 4· Too large to analyse

  The odds ratio for reward on task-anchored relative to task-independent trials was close to 1 when cues were available but much larger without cues.

  the odds ratio for receipt of reward on task-anchored versus task-independent trials was close to 1 when cues were available, but was much larger in the absence of cues

  Figure 6F; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 4

  What the paper reports

  • beaconed trials: odds ratio from binomial GLMM, p = 0.23
  • non-beaconed trials: odds ratio from binomial GLMM, p = 0.001
  • probe trials: odds ratio from binomial GLMM, p = 0.27

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track. The behaviour the claim also needs (trial types, positions, and stops from speed below 4.7 cm/s) is in the VR position series; reward times are not stored, but a hit is defined by a stop in the reward zone, so the behaviour side could be computed. The trial-level grid classes cannot.

• ⊘BlockedFigure 6-figure supplement 1B; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 3· Too large to analyse

  The differences in outcome between task-anchored and task-independent trials were not associated with differences in running speed profiles.

  The differences in outcomes between task-anchored versus task-independent trials were not associated with differences in running speed profiles

  Figure 6-figure supplement 1B; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 3

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track. The behaviour the claim also needs (trial types, positions, and stops from speed below 4.7 cm/s) is in the VR position series; reward times are not stored, but a hit is defined by a stop in the reward zone, so the behaviour side could be computed. The trial-level grid classes cannot.

• ⊘BlockedFigure 6-figure supplement 2F-H; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 4· Too large to analyse

  Classifying trials by correlation with a template of task-anchored firing also gave similar performance on cued trials but impaired performance on uncued trials when grid cells were not task-anchored.

  also indicated a similar behavioural performance on cued trials, but impaired performance on uncued trials when grid cells were in a task-independent mode

  Figure 6-figure supplement 2F-H; Results, Task-anchored coding by grid cells is selectively associated with successful path integration-dependent reward localisation, paragraph 4

  What the paper reports

  • hits, beaconed trials: binomial GLMM, ANOVA against null model, Χ2 = 0.62, DF = 1, p = 0.43
  • hits, non-beaconed trials: binomial GLMM, ANOVA against null model, Χ2 = 6.43, DF = 1, p = 0.01
  • hits, probe trials: binomial GLMM, ANOVA against null model, Χ2 = 3.52, DF = 1, p = 0.06
  • odds ratio, beaconed trials: odds ratio from binomial GLMM, p = 0.4
  • odds ratio, non-beaconed trials: odds ratio from binomial GLMM, p = 0.002
  • odds ratio, probe trials: odds ratio from binomial GLMM, p = 0.04

  The dandiset holds no sorted units (no file has a units table or any processed data), and the claim needs single units. Making them as the paper did means streaming the raw signal of every recording day, sorting the open arena and VR recordings of a day together with MountainSort and curating the clusters by isolation, noise overlap and peak SNR (Methods, Spike sorting). Streaming alone, at the rate measured in the executed notebook (76.4 MB/s with 16 processes; it varies by several percent between runs), takes 2.3 h for the 638 GB of the recording days (478 files) of the 239 usable 200 cm VR sessions of the nine mice that have an open arena session the same day, which are a superset of the paper's 215 sessions (2.1 h when the size is scaled to 215 sessions), and 3.6 h for all 976 GB, against the protocol's limit of 60 min for a notebook (docs/protocol.md, Author, step 3); this is a lower bound, as sorting time comes on top and was not measured. The sort was not started. It depends on the grid cells of grid-cell-count and their spikes on the track. The behaviour the claim also needs (trial types, positions, and stops from speed below 4.7 cm/s) is in the VR position series; reward times are not stored, but a hit is defined by a stop in the reward zone, so the behaviour side could be computed. The trial-level grid classes cannot.