CA3 retrieves coherent representations from degraded input: direct evidence for CA3 pattern completion and dentate gyrus pattern separation

Abstract

Theories of associative memory suggest that successful memory storage and recall depend on a balance between two complementary processes: pattern separation (to minimize interference) and pattern completion (to retrieve a memory when presented with partial or degraded input cues). Putative attractor circuitry in the hippocampal CA3 region is thought to be the final arbiter between these two processes. Here we present direct, quantitative evidence that CA3 produces an output pattern closer to the originally stored representation than its degraded input patterns from the dentate gyrus (DG). We simultaneously recorded activity from CA3 and DG of behaving rats when local and global reference frames were placed in conflict. CA3 showed a coherent population response to the conflict (pattern completion), even though its DG inputs were severely disrupted (pattern separation). The results thus confirm the hallmark predictions of a longstanding computational model of hippocampal memory processing.

Figure 1

Basic properties of CA3 and DG neural firing and experimental procedure. (A–B) Recording location examples show tetrodes targeting CA3 (A) and DG (B). Because the transverse axis of the hippocampus is angled relative to the midline, the DG tetrodes targeted sites medial and posterior to the regions sampled by CA3 tetrodes. Scale bar equals 500 μm and arrows indicate the end of the tetrode tracks. Other tracks are visible that ended in adjacent sections. (C) One day of the experimental protocol consisted of three standard sessions interleaved with two cue-mismatch sessions. The mismatch angles depicted are 180° and 45°. (D–E) Putative cell types from CA3 (D) and DG (E) were differentiated by the mean firing rates (Hz; abscissa) and spike widths (ms; ordinate) of all well-isolated cells recorded in the first standard session of the day. For CA3 cells, two distinct groups were observed (putative principal cells with a mean firing rate < 10 Hz and putative interneurons with a mean firing rate ≥10 Hz). Three groups of cells were apparent in DG: (a) < 2 Hz, (b) 2–10 Hz, and (c) > 10 Hz. (F–G) The distribution of spatial information scores (Skaggs et al., 1996) from CA3 (F) was significantly higher than for DG (G) (Mann-Whitney U-test, Z = 3.1, p < 0.03). See Figure S6 for the information score distribution of the DG neurons that fired < 2 Hz.

Figure 2

CA3 cellular responses. Example spike (red points) and trajectory (grey line) plots of CA3 cells. Values in the center of mismatch sessions indicate the total mismatch angle. The grey and black lines show the amount of the local and global cue rotations, respectively. Boxes enclosing rate maps from Std1 and Mis sessions indicate cells categorized as clockwise (navy blue), counterclockwise (cyan), appear (green), disappear (orange), or ambiguous (maroon). A plot of the rotation correlation analysis between the Std1 and Mis sessions (red line) is shown to the right of each set of rate maps. Peak correlations above 0.6 (green line) located in the black or grey box indicated that the fields rotated CW or CCW, respectively. Asterisks indicate that the standard sessions for cell were the same sessions.

Figure 3

DG cellular responses. The figure format is identical to the CA3 cellular responses seen in figure 2. See also Figures S1 and S2.

Figure 4

Population responses to cue-mismatch manipulations. Spatial correlation matrices were produced by correlating the normalized firing rate vectors for a standard session with those of the following mismatch or standard session (Figure S4). CA3 representations maintained coherence in all mismatch sessions (column 2), indicated by the bands of high correlation (white) shifting below the identity line (red dash), despite the decorrelated DG representations found in the input (columns 4 and 6). See also Figures S4, S5, and S6.

Figure 5

Quantifying input and output representations. (A) Polar plots were created from the spatial correlation matrices to represent the population activity between standard-1 vs. standard-2 (grey) and standard vs. mismatch (color) sessions. Each polar plot was created by calculating the average correlation along each diagonal of the corresponding correlation matrix to convert the 2D matrix into a 1D polar plot. The grey and black tick marks labeled “L” and “G” indicate the rotation angles of the local and global cue sets, respectively. The black dots indicate the angle at which the population correlations for the Std-Mis comparisons were maximum. For CA3, the maximum correlations closely followed the rotation of the local cues. (B) Mean vectors were calculated to quantify the coherence of the representations between sessions. Error bars represent the 95% confidence interval calculated with a bootstrap analysis. Collapsed across mismatch angles, CA3 had significantly larger mean vectors than its DG input. With respect to individual mismatch angles, the CA3 mean vectors were significantly larger than the DG mean vectors for the 45°. 90°, and 135° angles. ***, p < .001; **, p < .002.

Figure 6

Analysis of Individual Cell Rotation Amounts. Each dot indicates the amount that the cell’s spatial firing rotated between the standard and mismatch sessions (CA3, red; DG, blue). The arrows at the centers of the polar plots denote the mean vector. The mean vector length for CA3 cells was significant for all mismatch angles, whereas the mean vector length for the DG was variable (Rayleigh test; CA3, all angles, p < 0.001; DG, 45° and 90°, p < 0.04). CA3 followed the local cues for all mismatch angles and DG followed the local cues for 3 out of the 4 mismatch angles (45°, 135°, and 180°). ***, p < 0.001; *, p < 0.04.

Figure 7

Ensemble coherence. (A) Examples of simultaneous recordings from each region. Data within a circular plot were recorded simultaneously, but different plots come from different data sets. (B) Histograms show the number of cells in each simultaneously recorded data set for the four regions. On average, CA3 had more cells per data set than DG. (C) Simulations showed that small sample sizes could artificially increase the size of the mean vector. Data points (i.e., rotation angles) were randomly selected with replacement from a uniform distribution of orientations (1–360°) to calculate the expected value of the mean vector based on the samples coming from a random distribution. Simulations were run 1000 times for each of 10 sample sizes (n = 2–11 samples) and the average length of all 1000 mean vectors (average MV length) was plotted as a function of sample size. The mean vector length was largest for ensembles with two cells and decreased nonlinearly as the sample size increased. To account for the effect of sample size on the real data, the mean vector length of an ensemble was considered significant at α = 0.05 when it was greater than 950 of the 1000 mean vector lengths from the simulated data run with an equal number of cells in the sample. Error bars show SEM. (D) The proportion of significant mean vector lengths for each region. CA3 showed more significant clustering than its DG input (CA3 [11/40], DG [1/29]; χ² (1) = 6.77, p < 0.01).