Grid and Nongrid Cells in Medial Entorhinal Cortex Represent Spatial Location and Environmental Features with Complementary Coding Schemes

Abstract

The medial entorhinal cortex (mEC) has been identified as a hub for spatial information processing by the discovery of grid, border, and head-direction cells. Here we find that in addition to these well-characterized classes, nearly all of the remaining two-thirds of mEC cells can be categorized as spatially selective. We refer to these cells as nongrid spatial cells and confirmed that their spatial firing patterns were unrelated to running speed and highly reproducible within the same environment. However, in response to manipulations of environmental features, such as box shape or box color, nongrid spatial cells completely reorganized their spatial firing patterns. At the same time, grid cells retained their spatial alignment and predominantly responded with redistributed firing rates across their grid fields. Thus, mEC contains a joint representation of both spatial and environmental feature content, with specialized cell types showing different types of integrated coding of multimodal information.

Keywords: border cells; entorhinal cortex; grid cells; hippocampus; memory; nongrid cells; place cell; remapping; spatial navigation.

Figure 1. Approximately 95 % of cells in mEC superficial layers were identified as spatial

(A) Each cell’s spatial information (top) and within-session spatial correlation (bottom) is plotted against the respective 95^th percentile value from the same cell’s shuffled data (‘cutoff’). Approximately 95 % of mEC cells were above the cutoffs for both spatial information and spatial correlation. (B) Of the spatial cells, 17.7 % had significant grid scores and 9.3 % had significant border scores. The remaining 67.5 % did not have either of these specialized firing patterns and are thus referred to as ‘non-grid spatial cells’. (C) Rate maps of four cells that were identified as non-grid spatial. Firing rates are color-coded according to the scale bar on the right. For each rate map, the peak rate is noted above and the spatial information (SI) value is noted below. In the box marked by the stippled line, rate maps from the four cells are shown separately for the first and second half of the 10-min behavioral session. The within-session correlation (WSC) between the two half-session rate maps is noted below each pair of rate maps. See Figure S1 for histology and cluster quality metrics and Figure S2 for comparison to previously used classification criteria for mEC cells. Table S1 shows that all cell types were identified in similar proportions across animals.

Figure 2. For manipulations of box shape or box color, grid cell maps showed only minor shifts in their spatial location, but substantial redistributions of firing rates

(A) Schematic of the experimental paradigms. (B) Firing rate maps of an example grid cell that was recorded across all conditions. The peak rate of the cell across each set of four sessions is indicated to the right. (C) To measure the spatial stability between sessions, spatial cross-correlations were computed for the rate maps of each grid cell. For the example cell in (B), spatial cross-correlation maps are shown across matching conditions (left column) and across different shapes (right, top), colors (right, middle), and locations (right, bottom). Cross-correlation matrices are scaled from the minimum to the maximum correlation coefficient (noted to the right, blue to red). The displacement of the central peak from the origin measures the extent of the shift in the grid pattern. (D) Displacement from the origin when manipulating box shape, color, or location. For each comparison, the shift between repetitions of matching conditions was subtracted from the shift between sessions in different conditions (Δ displacement). Grid cells exhibited a significant displacement for shape, color, and location changes (Wilcoxon signed-rank matching vs different shape: z = 3.8, p < 0.001; different color: z = 3.3, p < 0.01; different location: z = 2.5, p < 0.05), although the median increase was only 1.3 cm for the shape change and 2.8 cm for the color change. (E) To further evaluate the overall firing profile of grid fields, we considered coordinated rate changes across grid fields and, for each grid cell, generated firing rate vectors (RVs) in which each element is the mean rate of a grid field. Rate maps and the resulting RVs (row vectors and bar graphs) are shown for two example grid cells in the shape-change and color-change paradigms. The average Spearman correlation of RVs across matching and different conditions is noted for both examples. Note that the vector is similar between matching conditions but changes across different conditions. (F) For grid cells with at least three fields (shape: n = 27; color: n = 14), rate vector correlation coefficients between matching environments and different environments are shown, along with the mean ± SEM. In addition, the Δ between the correlation coefficient in matching and different environments is plotted. RVs are more correlated between matching boxes compared to boxes with different shapes or different colors (Wilcoxon signed-rank matching vs different shape: z = −2.4, p < 0.05; different color: z = −2.0, p < 0.05). (G) Cumulative density functions (CDFs) of the spatial correlation between pairs of grid cell rate maps. Pairs were from recordings of the same cell in either matching or in different environments, and cell identity was permutated to generate chance correlations. Compared to correlations between rate maps in matching environments, spatial correlations decreased in response to manipulations of environment shape or color (Wilcoxon signed-rank matching vs different shape: z = −4.7, p < 0.001; different color: z = −3.6, p < 0.001). When comparing across different locations, spatial correlations were not different from shuffled values (Mann-Whitney U different location vs shuffled: z = −1.4, n.s.). * p < 0.05, ** p < 0.01, *** p < 0.001. See Figure S3 for corresponding data from individual animals and Figure S4 for corresponding hippocampal CA1 place cell data.

Figure 3. For manipulations of box shape or box color, non-grid spatial cells exhibited profound changes in their spatial firing patterns

(A) Schematic of the experimental paradigms (redrawn from Figure 2). (B) Firing rate maps of an example non-grid spatial cell across all three conditions. (C) Spatial cross-correlation maps for the example cell in (B) indicate that the predominant firing has shifted to a different location. (D) Displacement of the cross-correlation central peak for manipulations of box shape, color, or location (Δ displacement between different and matching conditions). Manipulations of box shape or box color resulted in pronounced shifts in the predominant firing location (matching vs different shape: median displacement increase of 13.1 cm, Wilcoxon signed-rank, z = 8.1, p < 0.001; different color: median increase of 15.3 cm, z = 9.1, p < 0.001), but which was less than the more complete reorganization of the spatial maps across different locations (matching vs different location: median increase of 28.9 cm, z = 5.6, p < 0.001). (E) CDFs of the spatial correlation between pairs of rate maps. Pairs were from recordings of the same cell in either matching or in different environments, and cell identity was permutated to generate chance correlations. Spatial correlations decreased in response to manipulations of environment shape or color (Wilcoxon signed-rank matching vs different shape: z = −8.7, p < 0.001; different color: z = −10.0, p < 0.001) but less than across different locations where correspondence between maps was not different from shuffled values (Mann-Whitney U different location vs shuffled: z = 1.0, n.s.). (F) Comparison of the median cross-correlation displacement (left) and the median spatial correlation (right) between grid cells and non-grid spatial cells. Horizontal lines: medians of all cells; circles: medians of individual animals for matching (M, filled) and different conditions (D, open). *** p < 0.001. See Figure S4 for corresponding hippocampal place cell data.

Figure 4. Analysis of only simultaneous recordings of grid and non-grid spatial cells confirmed the results from the full dataset

Across all sessions, 38 of 48 grid cells were recorded with non-grid spatial cells (NGS) and 109 of 236 NGS cells were recorded with grid cells. (A) Rate maps of simultaneously recorded cell ensembles during manipulations of environment shape (left), color (middle), and location (right). Different ensembles are presented for each of the three manipulations. (B) Cross-correlation displacement (top row) and spatial correlation (bottom row) of subsets of grid and NGS cells across matching or different box shapes (left), colors (middle), and locations (right). Data are first presented for all recorded grid and NGS cells (All, same data as in Figs. 2 and 3) and then divided into subsets: grid cells with at least one NGS cell (w/NGS), NGS cells with at least one grid cell (w/Gr), and either cell type recorded simultaneously without cells from the opposing category (w/out). For each subset, the number of cells is noted in parenthesis. See Table S2 for additional quantification of simultaneous recordings of grid and non-grid spatial cells.