How the hippocampus preserves order: the role of prediction and context

Remembering a past experience can, surprisingly, cause forgetting. Forgetting arises when other competing traces interfere with retrieval and inhibitory control mechanisms are engaged to suppress the distraction they cause. This form of forgetting is considered to be adaptive because it reduces future interference. The effect of this proposed inhibition process on competing memories has, however, never been observed, as behavioral methods are 'blind' to retrieval dynamics and neuroimaging methods have not isolated retrieval of individual memories. We developed a canonical template tracking method to quantify the activation state of individual target memories and competitors during retrieval. This method revealed that repeatedly retrieving target memories suppressed cortical patterns unique to competitors. Pattern suppression was related to engagement of prefrontal regions that have been implicated in resolving retrieval competition and, critically, predicted later forgetting. Thus, our findings demonstrate a cortical pattern suppression mechanism through which remembering adaptively shapes which aspects of our past remain accessible.

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Retrieval Induces Adaptive Forgetting of Competing Memories via Cortical Pattern Suppression

Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis. Several recent exploratory and theory-driven methods have begun to pursue these opportunities. These methods highlight the importance of computational techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computing. Adoption of these techniques is enabling a new generation of experiments and analyses that could transform our understanding of some of the most complex-and distinctly human-signals in the brain: acts of cognition such as thoughts, intentions and memories.

/pdf/computational-approaches-to-fmri-analysis-1kewprfdvu.pdf

Computational approaches to fMRI analysis.

Statistical learning of language: Theory, validity, and predictions of a statistical learning account of language acquisition.

Attention influences what is later remembered, but little is known about how this occurs in the brain. We hypothesized that behavioral goals modulate the attentional state of the hippocampus to prioritize goal-relevant aspects of experience for encoding. Participants viewed rooms with paintings, attending to room layouts or painting styles on different trials during high-resolution functional MRI. We identified template activity patterns in each hippocampal subfield that corresponded to the attentional state induced by each task. Participants then incidentally encoded new rooms with art while attending to the layout or painting style, and memory was subsequently tested. We found that when task-relevant information was better remembered, the hippocampus was more likely to have been in the correct attentional state during encoding. This effect was specific to the hippocampus, and not found in medial temporal lobe cortex, category-selective areas of the visual system, or elsewhere in the brain. These findings provide mechanistic insight into how attention transforms percepts into memories.

/pdf/attention-promotes-episodic-encoding-by-stabilizing-3rh8k2u8gk.pdf

Attention promotes episodic encoding by stabilizing hippocampal representations

The capacity of long-term memory is thought to be virtually unlimited. However, our memory bank may need to be pruned regularly to ensure that the information most important for behavior can be stored and accessed efficiently. Using functional magnetic resonance imaging of the human brain, we report the discovery of a context-based mechanism for determining which memories to prune. Specifically, when a previously experienced context is reencountered, the brain automatically generates predictions about which items should appear in that context. If an item fails to appear when strongly expected, its representation in memory is weakened, and it is more likely to be forgotten. We find robust support for this mechanism using multivariate pattern classification and pattern similarity analyses. The results are explained by a model in which context-based predictions activate item representations just enough for them to be weakened during a misprediction. These findings reveal an ongoing and adaptive process for pruning unreliable memories.

/pdf/pruning-of-memories-by-context-based-prediction-error-r5jyeltsg5.pdf

Pruning of memories by context-based prediction error

When an item is predicted in a particular context but the prediction is violated, memory for that item is weakened (Kim et al., 2014). Here, we explore what happens when such previously mispredicted items are later reencountered. According to prior neural network simulations, this sequence of events-misprediction and subsequent restudy-should lead to differentiation of the item's neural representation from the previous context (on which the misprediction was based). Specifically, misprediction weakens connections in the representation to features shared with the previous context and restudy allows new features to be incorporated into the representation that are not shared with the previous context. This cycle of misprediction and restudy should have the net effect of moving the item's neural representation away from the neural representation of the previous context. We tested this hypothesis using human fMRI by tracking changes in item-specific BOLD activity patterns in the hippocampus, a key structure for representing memories and generating predictions. In left CA2/3/DG, we found greater neural differentiation for items that were repeatedly mispredicted and restudied compared with items from a control condition that was identical except without misprediction. We also measured prediction strength in a trial-by-trial fashion and found that greater misprediction for an item led to more differentiation, further supporting our hypothesis. Therefore, the consequences of prediction error go beyond memory weakening. If the mispredicted item is restudied, the brain adaptively differentiates its memory representation to improve the accuracy of subsequent predictions and to shield it from further weakening.SIGNIFICANCE STATEMENT Competition between overlapping memories leads to weakening of nontarget memories over time, making it easier to access target memories. However, a nontarget memory in one context might become a target memory in another context. How do such memories get restrengthened without increasing competition again? Computational models suggest that the brain handles this by reducing neural connections to the previous context and adding connections to new features that were not part of the previous context. The result is neural differentiation away from the previous context. Here, we provide support for this theory, using fMRI to track neural representations of individual memories in the hippocampus and how they change based on learning.

https://www.jneurosci.org/content/jneuro/37/8/2022.full.pdf

Neural Differentiation of Incorrectly Predicted Memories.

Remapping refers to a decorrelation of hippocampal representations of similar spatial environments. While it has been speculated that remapping may contribute to the resolution of episodic memory interference in humans, direct evidence is surprisingly limited. We tested this idea using high-resolution, pattern-based fMRI analyses. Here we show that activity patterns in human CA3/dentate gyrus exhibit an abrupt, temporally-specific decorrelation of highly similar memory representations that is precisely coupled with behavioral expressions of successful learning. The magnitude of this learning-related decorrelation was predicted by the amount of pattern overlap during initial stages of learning, with greater initial overlap leading to stronger decorrelation. Finally, we show that remapped activity patterns carry relatively more information about learned episodic associations compared to competing associations, further validating the learning-related significance of remapping. Collectively, these findings establish a critical link between hippocampal remapping and episodic memory interference and provide insight into why remapping occurs. When two memories are similar, their encoding and retrieval can be disrupted by each other. Here the authors show that memory interference is resolved through abrupt remapping of activity patterns in the human hippocampal CA3 and dentate gyrus.

/pdf/abrupt-hippocampal-remapping-signals-resolution-of-memory-3vsgzunrq0.pdf

Abrupt hippocampal remapping signals resolution of memory interference.

We frequently encounter the same item in different contexts, and when that happens, memories of earlier encounters can get reactivated. We examined how existing memories are changed as a result of such reactivation. We hypothesized that when an item's initial and subsequent neural representations overlap, this allows the initial item to become associated with novel contextual information, interfering with later retrieval of the initial context. Specifically, we predicted a negative relationship between representational similarity across repeated experiences of an item and subsequent source memory for the initial context. We tested this hypothesis in an fMRI study, in which objects were presented multiple times during different tasks. We measured the similarity of the neural patterns in lateral occipital cortex that were elicited by the first and second presentations of objects, and related this neural overlap score to subsequent source memory. Consistent with our hypothesis, greater item-specific pattern similarity was linked to worse source memory for the initial task. In contrast, greater reactivation of the initial context was associated with better source memory. Our findings suggest that the influence of novel experiences on an existing context memory depends on how reliably a shared component (i.e., item) is represented across these episodes.

/pdf/neural-overlap-in-item-representations-across-episodes-2a4u0pbmyb.pdf

Neural Overlap in Item Representations Across Episodes Impairs Context Memory

When an item is predicted in a particular context but the prediction is violated, memory for that item is weakened (Kim et al, 2014) Here we explore what happens when such previously mispredicted items are later re-encountered According to prior neural network simulations, this sequence of events - misprediction and subsequent restudy - should lead to differentiation of the item's neural representation from the previous context (on which the misprediction was based) Specifically, misprediction weakens connections in the representation to features shared with the previous context, and restudy allows new features to be incorporated into the representation that are not shared with the previous context This cycle of misprediction and restudy should have the net effect of moving the item's neural representation away from the neural representation of the previous context We tested this hypothesis using fMRI, by tracking changes in item-specific BOLD activity patterns in the hippocampus, a key structure for representing memories and generating predictions In left CA2/3/DG, we found greater neural differentiation for items that were repeatedly mispredicted and restudied compared to items from a control condition that was identical except without misprediction We also measured prediction strength in a trial-by-trial fashion and found that greater misprediction for an item led to more differentiation, further supporting our hypothesis Thus, the consequences of prediction error go beyond memory weakening: If the mispredicted item is restudied, the brain adaptively differentiates its memory representation to improve the accuracy of subsequent predictions and to shield it from further weakening

/pdf/neural-differentiation-of-incorrectly-predicted-memories-2vbyzew8fh.pdf

Ghootae Kim

Papers

Pruning of memories by context-based prediction error

Neural Differentiation of Incorrectly Predicted Memories.

Abrupt hippocampal remapping signals resolution of memory interference.

Neural Overlap in Item Representations Across Episodes Impairs Context Memory

Neural differentiation of incorrectly predicted memories