Table 1.
Classification criteria used in rodent place-cell studies.
Table 2.
Summary of classification methods and modulation types used to identify place-related neuronal activity in human single-neuron studies.
Fig 1.
Overview of place cells and detection methods in rats and humans.
a) Simulated place cells with varying spatial tuning properties on a linear track. b) Place cell detection pipeline commonly used in rats (Spatial Information) and in human (ANOVA). c) Examples of rat (top) and human (bottom) place cells, illustrating a range of tuning profiles from more prominent spatial selectivity (left) to weaker or more diffuse spatial modulation (right).
Fig 2.
Place Cell Identification Methods in Rats.
a) Task Schematic. Rats traverse bi-direnctionally along a 250 cm linear track to obtain water rewards located at each end (Directions A and B). b) Proportion of neurons identified as significant by ANOVA permutation testing across brain regions. CA1 and CA3: Hippocampus, DG: Dentate Gyrus, EC: Entorhinal Cortex. c) Proportion of neurons exceeding the SI threshold of 0.25 (light red) and those confirmed by SI permutation testing (dark red) across brain regions. d) Proportion of neurons classified as significant as a function of increasing ANOVA F-statistics. e) Same as in d), plotted against Spatial Information Scores. f) Agreement between permutation-based SI classification and ANOVA classification as a function of increasing F-statistics. g) Same as f., plotted against spatial information scores. Dashed lines in d. and f. mark the observed threshold for ANOVA (F = 2.0); dashed lines in e. and g. indicate spatial information threshold (SI = 0.25). h) Top: Comparison between SI permutation testing (p < 0.05) and ANOVA permutation testing (p < 0.05). Bottom: Comparison between SI thresholding (SI > 0.25) and ANOVA classification. Percentages indicate the proportion of neurons in each classification category.
Fig 3.
Place Cell Identification Methods in Humans.
a) Task Schematic. Subjects are moved along a linear track (40 virtual units) to learn and recall the location of four objects. b) Proportion of neurons identified as significant by ANOVA permutation testing across brain regions. H: Hippocampus, A: Amygdala, EC: Entorhinal Cortex, C: Cingulate. c) Proportion of neurons exceeding the SI threshold of 0.25 (light red) and those confirmed by SI permutation testing (dark red) across brain regions. d) Proportion of neurons classified as significant as a function of increasing ANOVA F-statistic. e) Same as in d), plotted against Spatial Information Scores. f. Agreement between permutation-based SI classification and ANOVA classification as a function of increasing F-statistic. g) Same as f., plotted against spatial information scores. Dashed lines in d. and e. mark the observed threshold for ANOVA (F = 1.6); dashed lines in f. and g. indicate the spatial information threshold (SI = 0.25). h) Top: Comparison between SI permutation testing (p < 0.05) and ANOVA permutation testing (p < 0.05). Bottom: Comparison between SI thresholding (SI > 0.25) and ANOVA classification. Percentages indicate the proportion of neurons in each classification category.
Fig 4.
Statistical distribution of place cell measures in rats and humans.
a) Rats: ANOVA F-statistics 0.14–400.20; spatial information 0.01–4.54. b) Humans: ANOVA F-statistics 0.43–3.12; spatial information 0.01–3.64. Neurons are plotted by spatial information (x-axis) and ANOVA F-statistic (y-axis). Each point represents a single neuron. Dashed lines indicate thresholds (SI = 0.25; F = 2.0 for rats, 1.6 for humans). Marginal histograms show metric distributions and neuron counts by category. Bottom, Example firing rate maps for gray (low SI, low ANOVA), blue (high ANOVA), and red (high SI).
Fig 5.
a) Place Field Components. Simulated place fields are generated by combining three components: Peak - Gaussian-shaped firing rate profile, Baseline - constant firing rate across space, and Noise - random fluctuations in firing rate. The final place field (right) is produced by summing these components. b) Field Properties. Place field tuning parameters are varied across neurons to model population-level diversity. Peak - modulation of peak firing rate, Base - adjustment of background firing rate, and Width - spread of the place field, define each neuron’s trial-averaged spatial tuning profile. In contrast, Noise - random fluctuations added to the firing rate, varies across trials, introducing realistic trial-to-trial variability in field expression. c) Trial-Level Field Consistency. To assess within-neuron reliability, we compute two metrics: Place Field Consistency (left) quantifies the spatial stability of peak firing across trials, while Presence Ratio (right) measures the fraction of trials in which a detectable place field is expressed, reflecting tuning persistence over time.
Fig 6.
Impact of place field features on spatial information and ANOVA statistics.
a) Simulated firing rate maps illustrating variation across six place field parameters: peak firing rate, width, baseline firing rate, noise, place field consistency, and presence ratio. b) Spatial information (SI, red) and ANOVA F-statistics (blue) as a function of each place field parameter. c) Joint distributions of SI (y-axis) and ANOVA F-statistics (x-axis), with grayscale indicating values of the corresponding parameter.
Fig 7.
Estimated tuning features reveal distinct structures captured by SI and ANOVA.
Principal component analysis (PCA) was applied to tuning features derived from hippocampal neurons, including: even–odd correlation, peak-to-average ratio, place field width, presence ratio, place field consistency, number of place fields, average firing rate, and peak firing rate. a) Top: Rat neurons projected onto the first two principal components, colored by spatial information (SI). Bottom: Example rat neurons with high, median, and low SI, shown with corresponding firing rate maps. b) Top: Same PCA projection as in a, but colored by ANOVA F-statistic. Bottom: Example rat neurons with high, median, and low F-statistics, shown with corresponding firing rate maps. (c–e) Principal component analysis (PCA) projections and loading vectors of place cell features for rats (c), humans (d), and simulations (e). Points represent neurons projected into the first two principal components. Arrows indicate the loading vectors for each feature, with their direction and length representing the contribution and magnitude of each feature to the first two principal components.