Estimating neuronal firing density: A quantitative analysis of firing rate map algorithms

• Published in: PLoS computational biology Vol. 19; no. 12; p. e1011763
• Main Author: Grieves, Roddy M.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 27.12.2023; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Biology and Life Sciences; Bivariate analysis; Computation; Computer and Information Sciences; Data visualization; Density; Estimation; Firing rate; Histograms; Mapping; Methods; Neurons; Parameters; Physical Sciences; Quantitative analysis; Research and Analysis Methods; Sampling; Smoothing; Spatial smoothing; Tuning; United Kingdom
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011763

The analysis of neurons that exhibit receptive fields dependent on an organism’s spatial location, such as grid, place or boundary cells typically begins by mapping their activity in space using firing rate maps. However, mapping approaches are varied and depend on multiple tuning parameters that are usually chosen qualitatively by the experimenter and thus vary significantly across studies. Small changes in parameters such as these can impact results significantly, yet, to date a quantitative investigation of firing rate maps has not been attempted. Using simulated datasets, we examined how tuning parameters, recording duration and firing field size affect the accuracy of spatial maps generated using the most widely used approaches. For each approach we found a clear subset of parameters which yielded low-error firing rate maps and isolated the parameters yielding 1) the least error possible and 2) the Pareto-optimal parameter set which balanced error, computation time, place field detection accuracy and the extrapolation of missing values. Smoothed bivariate histograms and averaged shifted histograms were consistently associated with the fastest computation times while still providing accurate maps. Adaptive smoothing and binning approaches were found to compensate for low positional sampling the most effectively. Kernel smoothed density estimation also compensated for low sampling well and resulted in accurate maps, but it was also among the slowest methods tested. Overall, the bivariate histogram, coupled with spatial smoothing, is likely the most desirable method in the majority of cases.