# Automated Measurement of Grid Cell Firing Characteristics
## 3 Software Performance  
To analyze the field detection performance of GridMet, we tested the software with 30 grid cell recordings from previous work [10] using a non-field filtering threshold value of 31% which was earlier identified as top-performing [7]. The criteria used to identify the correct detection of fields included visual inspection of original autocorrelogram plots. These field detection tests only used the central seven fields in the plots. The software was required to detect those seven fields and not incorrectly include a field farther from the plot center instead of any of those fields. GridMet was also required in order to not merge multiple distinct fields into one. In some cases, hard-to-distinguish merging that caused minor differences in results was not counted as an issue. Additional criteria are included in online documentation. Field detection worked correctly on 90% of cells; three cells had minor issues (described below as limitations) with only small effects on results. The plots of all autocorrelograms and field detection used in these tests are available in online documentation along with annotations regarding which cells were identified as having correct detection. To facilitate and encourage reproducibility, the recording dataset underlying the plots is included with the GridMet software when it is downloaded. We also compared the grid field spacing estimates of GridMet and Opexebo using the same set of 30 cells. In general, the two software programs returned similar estimates, but four cells yielded particularly different results. Those cells all had an approximately 29 cm difference in the estimated mean of the field spacing. Manual inspection revealed that GridMet more accurately quantified the field spacings of these recorded cells. Each of the cells caused Opexebo to misestimate the spacing of a pair of fields, which skewed the results. Overall, the ability to better handle these cells indicates that GridMet compares  
Figure 4: Field orientation. (**A**,**B**) Marker C is the centroid position for the grid field located nearest the center of the plot. Marker N is the centroid that is closest to the most-center field’s centroid. favorably to Opexebo in terms of field spacing measurement robustness with respect to cell diversity (see online documentation for further details). Orientation measurements were not compared because GridMet and Opexebo adopt different definitions of the angles to be extracted, complicating the interpretation of any possible discrepancies. The execution speed of GridMet was evaluated on a desktop computer running Ubuntu Linux 21.10 on an Intel I9-13900 KS 24-core processor with 128 GB of RAM and a WD_BLACK NVMe SSD SN850 hard drive. Running the software, including generating statistics and plots, took 3 s when launched from MATLAB and 11 s when launched in the runtime file version. In this test, results were generated from an autocorrelogram automatically created from a plain text file of a heat map of a cell's firing rates. The results of the field detection revealed some limitations in the software. One of the cells with field detection issues (id: n4) had 2 out of 35 pixels representing its field area present in another field's area. This error appears to have been due to the close proximity of the involved fields, with possible overlap occurring in their autocorrelogram. However, the observed error in this case only had a very small effect on the metrics reported. The other two cells with detection issues (ids: n11, n35) similarly had especially close fields, with possibly overlapping autocorrelogram data. In two instances for each of these two cells, GridMet incorrectly merged a pair of distinct fields into a single field. This indicates a limitation in field detection when fields are in very close proximity. This only had a small effect on statistics in this case. Future work might address these limitations.