Memory Reactivation during Learning Simultaneously Promotes Dentate Gyrus/CA2,3 Pattern Differentiation and CA1 Memory Integration

Abstract

Events that overlap with previous experience may trigger reactivation of existing memories. However, such reactivation may have different representational consequences within the hippocampal circuit. Computational theories of hippocampal function suggest that dentate gyrus and CA2,3 (DG/CA2,3) are biased to differentiate highly similar memories, whereas CA1 may integrate related events by representing them with overlapping neural codes. Here, we tested whether the formation of differentiated or integrated representations in hippocampal subfields depends on the strength of memory reactivation during learning. Human participants of both sexes learned associations (AB pairs, either face-shape or scene-shape), and then underwent fMRI scanning while they encoded overlapping associations (BC shape-object pairs). Both before and after learning, participants were also scanned while viewing indirectly related elements of the overlapping memories (A and C images) in isolation. We used multivariate pattern analyses to measure reactivation of initial pair memories (A items) during overlapping pair (BC) learning, as well as learning-related representational change for indirectly related memory elements in hippocampal subfields. When prior memories were strongly reactivated during overlapping pair encoding, DG/CA2,3 and subiculum representations for indirectly related images (A and C) became less similar, consistent with pattern differentiation. Simultaneously, memory reactivation during new learning promoted integration in CA1, where representations for indirectly related memory elements became more similar after learning. Furthermore, memory reactivation and subiculum representation predicted faster and more accurate inference (AC) decisions. These data show that reactivation of related memories during new learning leads to dissociable coding strategies in hippocampal subfields, in line with computational theories.SIGNIFICANCE STATEMENT The flexibility of episodic memory allows us to remember both the details that differentiate similar events and the commonalities among them. Here, we tested how reactivation of past experience during new learning promotes formation of neural representations that might serve these two memory functions. We found that memory reactivation during learning promoted formation of differentiated representations for overlapping memories in the dentate gyrus/CA2,3 and subiculum subfields of the hippocampus, while simultaneously leading to the formation of integrated representations of related events in subfield CA1 Furthermore, memory reactivation and subiculum representation predicted success when inferring indirect relationships among events. These findings indicate that memory reactivation is an important learning signal that influences how overlapping events are represented within the hippocampal circuit.

Keywords: associative memory; episodic memory; high-resolution fMRI; hippocampal subfield; pattern separation.

Copyright © 2021 the authors.

Figures

Figure 1.

Experimental design. A, Schematic of the behavioral task. Participants were first exposed to individually presented pictures (faces, scenes, and novel objects) that would later become indirectly related through associative learning (A and C items). Then, participants learned to associate initial pairs (face-shape or scene-shape AB associations) and were scanned while learning overlapping pairs (shape-object BC associations). Participants were scanned again in a postexposure phase while they viewed the same items from preexposure (A and C items). Participants then completed an across-episode inference task. Finally, participants completed a localizer task in which they viewed individually presented faces, scenes, objects, and shapes in a blocked design. B, Visual similarity manipulation. The similarity of the shared B item across pairs was parametrically manipulated. In this example, the top shape would have been seen in the initial AB pairs, whereas the bottom row represents the different shape morphs that could be seen when learning the overlapping BC pairs. The linking B item presented during overlapping pair learning could either be an exact match to the B item presented during initial (AB) pair learning, a high similarity or low similarity morph, or new (i.e., nonoverlapping) item. C, Subjective similarity of shape stimuli used for B linking items. An independent sample of participants rated visual similarity between parent shapes and shape morphs presented side by side using a 5 point Likert scale (1 = not at all similar, 5 = very similar). *p < 0.05 (paired t tests). Error bars indicate ± SEM.

Figure 2.

Behavioral performance. A, Overlapping pair (BC) test accuracy and (B) response time (correct trials only) by learning block for each similarity condition. C, Across-episode (AC) inference accuracy and (D) response time (correct trials only) for each similarity condition. *p < 0.05 (paired t tests). Error bars indicate ± SEM. Dotted lines indicate chance performance on the 3-alternative forced choice tests.

Figure 3.

Memory reactivation during overlapping pair learning. A, Results of the searchlight analysis identifying regions where classifier evidence for A item reactivation exceeded baseline (i.e., evidence during new, nonoverlapping pairs) when participants were learning the overlapping BC pairs. B, Evidence for reactivation of A items as a function of stimulus category (face and scene) during overlapping pair learning for each of the regions identified in A. Error bars indicate ± SEM. C, Results of the searchlight analysis identifying regions where classifier evidence for A item reactivation varied with visual similarity of the linking B item (exact match > high and low similarity). One cluster in left parietal cortex overlapped with the cluster identified in the searchlight analysis comparing reactivation to baseline (A, leftmost image); the other cluster extended into occipital cortex. All searchlight clusters are displayed on the 1 mm MNI 152 anatomic template.

Figure 4.

Assessing learning-related representational change as a function of memory reactivation during learning. A, Predictions for memory formation through associative learning. Before learning, individual A and C items in the preexposure phase do not share any relationships. After learning, the representations of A and C items may shift as a function of their shared relationships with B items. We tested for two neural outcomes; in the case of differentiation, the neural patterns for indirectly related A and C items are predicted to be less similar in the postexposure phase relative to the prelearning representations. In contrast, for memory integration, the neural similarity of indirectly related A and C items is predicted to increase from prelearning to postlearning, reflecting the formation of overlapping neural codes linking elements experienced across events. B, Four searchlight contrasts were used to determine whether memory representation in hippocampal subfields varied with memory reactivation strength during learning. Two of the searchlights identified regions in which differentiation or integration occurred across all degrees of reactivation strength. Another set of searchlights identified regions in which neural coding varied as a function of reactivation. C, Learning-related representational change in hippocampus. Subregions of DG/CA2,3 and subiculum showed differentiation of the indirectly related elements of overlapping memories, but only when reactivation was stronger during learning. In contrast, a subregion of CA1 showed evidence of memory integration, but again only when reactivation was stronger during overlapping pair learning. Hippocampal regions are depicted on an open source high-resolution group T2 template created for hippocampal subfield analyses (Schlichting et al., 2019). D, Neural similarity change in the clusters identified in the searchlight analysis (C) after reverse normalization to native space, confirming the predicted pattern of results from B. Inset, The same data separately for the within-triad and across-triad similarity measures before calculating the difference scores. Because this analysis is based on voxels identified in the searchlight analysis, it is not fully independent. Error bars indicate ± SEM.

Figure 5.

Results of the multilevel response time model used to examine relationships between neural measures and AC inference performance. A, Fit of response time model to accuracy on individual AC inference trials. B, Fit of response time model to trial-level inference response times. C, We examined whether reactivation of related memories during BC study or neural similarity change (Δ) in hippocampal subfields after learning predicted trial-level variability in AC inference performance (i.e., the slope of the drift rate from the model). Negative values indicate a decrease in the neural measures predicted faster and more accurate inference, whereas positive values indicate an increase in the neural measures predicted better inference. Reactivation of related memories and representational change within subiculum predicted improved AC inference performance. Bars represent 95% HDIs of posterior parameter estimates.