Prediction strength modulates responses in human area CA1 to sequence violations

Mismatch novelty exploration training shifts VPAC1 receptor-mediated modulation of hippocampal synaptic plasticity by endogenous VIP in male rats

Novelty influences hippocampal-dependent memory through metaplasticity. Mismatch novelty detection activates the human hippocampal CA1 area and enhances rat hippocampal-dependent learning and exploration. Remarkably, mismatch novelty training (NT) also enhances rodent hippocampal synaptic plasticity while inhibition of VIP interneurons promotes rodent exploration. Since VIP, acting on VPAC1 receptors (Rs), restrains hippocampal LTP and depotentiation by modulating disinhibition, we now investigated the impact of NT on VPAC1 modulation of hippocampal synaptic plasticity in male Wistar rats. NT enhanced both CA1 hippocampal LTP and depotentiation unlike exploring an empty holeboard (HT) or a fixed configuration of objects (FT). Blocking VIP VPAC1Rs with PG 97269 (100 nM) enhanced both LTP and depotentiation in naïve animals, but this effect was less effective in NT rats. Altered endogenous VIP modulation of LTP was absent in animals exposed to the empty environment (HT). HT and FT animals showed mildly enhanced synaptic VPAC1R levels, but neither VIP nor VPAC1R levels were altered in NT animals. Conversely, NT enhanced the GluA1/GluA2 AMPAR ratio and gephyrin synaptic content but not PSD-95 excitatory synaptic marker. In conclusion, NT influences hippocampal synaptic plasticity by reshaping brain circuits modulating disinhibition and its control by VIP-expressing hippocampal interneurons while upregulation of VIP VPAC1Rs is associated with the maintenance of VIP control of LTP in FT and HT animals. This suggests VIP receptor ligands may be relevant to co-adjuvate cognitive recovery therapies in aging or epilepsy, where LTP/LTD imbalance occurs.

Prediction error and event segmentation in episodic memory

Organizing the continuous flow of experiences into meaningful events is a crucial prerequisite for episodic memory. Prediction error and event segmentation both play important roles in supporting the genesis of meaningful mnemonic representations of events. We review theoretical contributions discussing the relationship between prediction error and event segmentation, as well as literature on episodic memory related to prediction error and event segmentation. We discuss the extent of overlap of mechanisms underlying memory emergence through prediction error and event segmentation, with a specific focus on attention and working memory. Finally, we identify areas in research that are currently developing and suggest future directions. We provide an overview of mechanisms underlying memory formation through predictions, violations of predictions, and event segmentation.

False memories for ending of events

Memories are not perfect recordings of the past and can be subject to systematic biases. Memory distortions are often caused by our experience of what typically happens in a given situation. However, it is unclear whether memory for events is biased by the knowledge that events usually have a predictable structure (a beginning, middle, and an end). Using video clips of everyday situations, we tested how interrupting events at unexpected time points affects memory of how those events ended. In four free recall experiments (1, 2, 4, and 5), we found that interrupting clips just before a salient piece of action was completed, resulted in the false recall of details about how the clip might have ended. We refer to this as "event extension." On the other hand, interrupting clips just after one scene had ended and a new scene started, resulted in omissions of details about the true ending of the clip (Experiments 4 and 5). We found that these effects were present, albeit attenuated, when testing memory shortly after watching the video clips compared to a week later (Experiments 5a and 5b). The event extension effect was not present when memory was tested with a recognition paradigm (Experiment 3). Overall, we conclude that when people watch videos that violate their expectations of typical event structure, they show a bias to later recall the videos as if they had ended at a predictable event boundary, exhibiting event extension or the omission of details depending on where the original video was interrupted. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Dual memory model for experience-once task-incremental lifelong learning

Experience replay (ER) is a widely-adopted neuroscience-inspired method to perform lifelong learning. Nonetheless, existing ER-based approaches consider very coarse memory modules with simple memory and rehearsal mechanisms that cannot fully exploit the potential of memory replay. Evidence from neuroscience has provided fine-grained memory and rehearsal mechanisms, such as the dual-store memory system consisting of PFC-HC circuits. However, the computational abstraction of these processes is still very challenging. To address these problems, we introduce the Dual-Memory (Dual-MEM) model emulating the memorization, consolidation, and rehearsal process in the PFC-HC dual-store memory circuit. Dual-MEM maintains an incrementally updated short-term memory to benefit current-task learning. At the end of the current task, short-term memories will be consolidated into long-term ones for future rehearsal to alleviate forgetting. For the Dual-MEM optimization, we propose two learning policies that emulate different memory retrieval strategies: Direct Retrieval Learning and Mixup Retrieval Learning. Extensive evaluations on eight benchmarks demonstrate that Dual-MEM delivers compelling performance while maintaining high learning and memory utilization efficiencies under the challenging experience-once setting.

Adult age differences in event memory updating: The roles of prior-event retrieval and prediction

Remembering past events can lead to predictions of what is to come and to experiencing prediction errors when things change. Previous research has shown enhanced memory updating for ongoing events that are inconsistent with predictions based on past experiences. According to the Event Memory Retrieval and Comparison (EMRC) Theory, such memory updating depends on the encoding of configural representations that bind retrieved features of the previous event, changed features, and the relationship between the two. We investigated potential age-related differences in these mechanisms by showing older and younger adults two movies of everyday activities. Activities in the second movie either repeated from the first movie or included changed endings. During the second movie, before activities ended, participants were instructed to predict the upcoming action based on the first movie. One week later, participants were instructed to recall activity endings from the second movie. For younger adults, having predicted endings consistent with the first movie before seeing changed endings was subsequently associated with better recall of these changed endings and recollection that activities had changed. Conversely, for older adults, making such predictions prior to changes was associated with intruding details from the first movie endings and was less strongly associated with change recollection. Consistent with EMRC, these findings suggest that retrieval of relevant experiences when events change can trigger prediction errors that prompt associative encoding of existing memories and current perceptions. These mechanisms were less efficient in older adults, which may account for their poorer event memory updating than younger adults. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The effect of prediction error on episodic memory encoding is modulated by the outcome of the predictions

Expectations can lead to prediction errors of varying degrees depending on the extent to which the information encountered in the environment conforms with prior knowledge. While there is strong evidence on the computationally specific effects of such prediction errors on learning, relatively less evidence is available regarding their effects on episodic memory. Here, we had participants work on a task in which they learned context/object-category associations of different strengths based on the outcomes of their predictions. We then used a reinforcement learning model to derive subject-specific trial-to-trial estimates of prediction error at encoding and link it to subsequent recognition memory. Results showed that model-derived prediction errors at encoding influenced subsequent memory as a function of the outcome of participants' predictions (correct vs. incorrect). When participants correctly predicted the object category, stronger prediction errors (as a consequence of weak expectations) led to enhanced memory. In contrast, when participants incorrectly predicted the object category, stronger prediction errors (as a consequence of strong expectations) led to impaired memory. These results highlight the important moderating role of choice outcome that may be related to interactions between the hippocampal and striatal dopaminergic systems.

Contextual incongruency triggers memory reinstatement and the disruption of neural stability

Schemas, or internal representation models of the environment, are thought to be central in organising our everyday life behaviour by giving stability and predictiveness to the structure of the world. However, when an element from an unfolding event mismatches the schema-derived expectations, the coherent narrative is interrupted and an update to the current event model representation is required. Here, we asked whether the perceived incongruence of an item from an unfolding event and its impact on memory relied on the disruption of neural stability patterns preceded by the neural reactivation of the memory representations of the just-encoded event. Our study includes data from two different experiments whereby human participants (N = 33, 26 females and N = 18, 16 females, respectively) encoded images of objects preceded by trial-unique sequences of events depicting daily routine. We found that neural stability patterns gradually increased throughout the ongoing exposure to a schema-consistent episode, which was corroborated by the re-analysis of data from two other experiments, and that the brain stability pattern was interrupted when the encoding of an object of the event was incongruent with the ongoing schema. We found that the decrease in neural stability for low-congruence items was seen at ∼1000 ms from object encoding onset and that it was preceded by an enhanced N400 ERP and an increased degree of neural reactivation of the just-encoded episode. Current results offer new insights into the neural mechanisms and their temporal orchestration that are engaged during online encoding of schema-consistent episodic narratives and the detection of incongruencies.

Hippocampal representations switch from errors to predictions during acquisition of predictive associations

We constantly exploit the statistical regularities in our environment to help guide our perception. The hippocampus has been suggested to play a pivotal role in both learning environmental statistics, as well as exploiting them to generate perceptual predictions. However, it is unclear how the hippocampus balances encoding new predictive associations with the retrieval of existing ones. Here, we present the results of two high resolution human fMRI studies (N = 24 for both experiments) directly investigating this. Participants were exposed to auditory cues that predicted the identity of an upcoming visual shape (with 75% validity). Using multivoxel decoding analysis, we find that the hippocampus initially preferentially represents unexpected shapes (i.e., those that violate the cue regularities), but later switches to representing the cue-predicted shape regardless of which was actually presented. These findings demonstrate that the hippocampus is involved both acquiring and exploiting predictive associations, and is dominated by either errors or predictions depending on whether learning is ongoing or complete.

Hippocampal Contribution to Probabilistic Feedback Learning: Modeling Observation- and Reinforcement-based Processes

Simple probabilistic reinforcement learning is recognized as a striatum-based learning system, but in recent years, has also been associated with hippocampal involvement. This study examined whether such involvement may be attributed to observation-based learning (OL) processes, running in parallel to striatum-based reinforcement learning. A computational model of OL, mirroring classic models of reinforcement-based learning (RL), was constructed and applied to the neuroimaging data set of Palombo, Hayes, Reid, and Verfaellie (2019). Hippocampal contributions to value-based learning: Converging evidence from fMRI and amnesia. Cognitive, Affective & Behavioral Neuroscience, 19(3), 523-536. Results suggested that OL processes may indeed take place concomitantly to reinforcement learning and involve activation of the hippocampus and central orbitofrontal cortex. However, rather than independent mechanisms running in parallel, the brain correlates of the OL and RL prediction errors indicated collaboration between systems, with direct implication of the hippocampus in computations of the discrepancy between the expected and actual reinforcing values of actions. These findings are consistent with previous accounts of a role for the hippocampus in encoding the strength of observed stimulus-outcome associations, with updating of such associations through striatal reinforcement-based computations. In addition, enhanced negative RL prediction error signaling was found in the anterior insula with greater use of OL over RL processes. This result may suggest an additional mode of collaboration between the OL and RL systems, implicating the error monitoring network.

Understanding Everyday Events: Predictive-Looking Errors Drive Memory Updating

Memory-guided predictions can improve event comprehension by guiding attention and the eyes to the location where an actor is about to perform an action. But when events change, viewers may experience predictive-looking errors and need to update their memories. In two experiments (Ns = 38 and 98), we examined the consequences of mnemonic predictive-looking errors for comprehending and remembering event changes. University students watched movies of everyday activities with actions that were repeated exactly and actions that were repeated with changed features-for example, an actor reached for a paper towel on one occasion and a dish towel on the next. Memory guidance led to predictive-looking errors that were associated with better memory for subsequently changed event features. These results indicate that retrieving recent event features can guide predictions during unfolding events and that error signals derived from mismatches between mnemonic predictions and actual events contribute to new learning.

A neural network model of when to retrieve and encode episodic memories

Recent human behavioral and neuroimaging results suggest that people are selective in when they encode and retrieve episodic memories. To explain these findings, we trained a memory-augmented neural network to use its episodic memory to support prediction of upcoming states in an environment where past situations sometimes reoccur. We found that the network learned to retrieve selectively as a function of several factors, including its uncertainty about the upcoming state. Additionally, we found that selectively encoding episodic memories at the end of an event (but not mid-event) led to better subsequent prediction performance. In all of these cases, the benefits of selective retrieval and encoding can be explained in terms of reducing the risk of retrieving irrelevant memories. Overall, these modeling results provide a resource-rational account of why episodic retrieval and encoding should be selective and lead to several testable predictions.

Uncertainty alters the balance between incremental learning and episodic memory

A key question in decision-making is how humans arbitrate between competing learning and memory systems to maximize reward. We address this question by probing the balance between the effects, on choice, of incremental trial-and-error learning versus episodic memories of individual events. Although a rich literature has studied incremental learning in isolation, the role of episodic memory in decision-making has only recently drawn focus, and little research disentangles their separate contributions. We hypothesized that the brain arbitrates rationally between these two systems, relying on each in circumstances to which it is most suited, as indicated by uncertainty. We tested this hypothesis by directly contrasting contributions of episodic and incremental influence to decisions, while manipulating the relative uncertainty of incremental learning using a well-established manipulation of reward volatility. Across two large, independent samples of young adults, participants traded these influences off rationally, depending more on episodic information when incremental summaries were more uncertain. These results support the proposal that the brain optimizes the balance between different forms of learning and memory according to their relative uncertainties and elucidate the circumstances under which episodic memory informs decisions.

Prediction errors disrupt hippocampal representations and update episodic memories

The brain supports adaptive behavior by generating predictions, learning from errors, and updating memories to incorporate new information. Prediction error, or surprise, triggers learning when reality contradicts expectations. Prior studies have shown that the hippocampus signals prediction errors, but the hypothesized link to memory updating has not been demonstrated. In a human functional MRI study, we elicited mnemonic prediction errors by interrupting familiar narrative videos immediately before the expected endings. We found that prediction errors reversed the relationship between univariate hippocampal activation and memory: greater hippocampal activation predicted memory preservation after expected endings, but memory updating after surprising endings. In contrast to previous studies, we show that univariate activation was insufficient for understanding hippocampal prediction error signals. We explain this surprising finding by tracking both the evolution of hippocampal activation patterns and the connectivity between the hippocampus and neuromodulatory regions. We found that hippocampal activation patterns stabilized as each narrative episode unfolded, suggesting sustained episodic representations. Prediction errors disrupted these sustained representations and the degree of disruption predicted memory updating. The relationship between hippocampal activation and subsequent memory depended on concurrent basal forebrain activation, supporting the idea that cholinergic modulation regulates attention and memory. We conclude that prediction errors create conditions that favor memory updating, prompting the hippocampus to abandon ongoing predictions and make memories malleable.

Mnemonic prediction errors promote detailed memories

When our experience violates our predictions, it is adaptive to update our knowledge to promote a more accurate representation of the world and facilitate future predictions. Theoretical models propose that these mnemonic prediction errors should be encoded into a distinct memory trace to prevent interference with previous, conflicting memories. We investigated this proposal by repeatedly exposing participants to pairs of sequentially presented objects (A → B), thus evoking expectations. Then, we violated participants' expectations by replacing the second object in the pairs with a novel object (A → C). The following item memory test required participants to discriminate between identical old items and similar lures, thus testing detailed and distinctive item memory representations. In two experiments, mnemonic prediction errors enhanced item memory: Participants correctly identified more old items as old when those items violated expectations during learning, compared with items that did not violate expectations. This memory enhancement for C items was only observed when participants later showed intact memory for the related A → B pairs, suggesting that strong predictions are required to facilitate memory for violations. Following up on this, a third experiment reduced prediction strength prior to violation and subsequently eliminated the memory advantage of violations. Interestingly, mnemonic prediction errors did not increase gist-based mistakes of identifying old items as similar lures or identifying similar lures as old. Enhanced item memory in the absence of gist-based mistakes suggests that violations enhanced memory for items' details, which could be mediated via distinct memory traces. Together, these results advance our knowledge of how mnemonic prediction errors promote memory formation.

Hippocampal Representations of Event Structure and Temporal Context during Episodic Temporal Order Memory

The hippocampus plays an important role in representing spatial locations and sequences and in transforming representations. How these representational structures and operations support memory for the temporal order of random items is still poorly understood. We addressed this question by leveraging the method of loci, a powerful mnemonic strategy for temporal order memory that particularly recruits hippocampus-dependent computations of spatial locations and associations. Applying representational similarity analysis to functional magnetic resonance imaging activation patterns revealed that hippocampal subfields contained representations of multiple features of sequence structure, including spatial locations, location distance, and sequence boundaries, as well as episodic-like temporal context. Critically, the hippocampal CA1 exhibited spatial transformation of representational patterns, showing lower pattern similarity for items in same locations than closely matched different locations during retrieval, whereas the CA23DG exhibited sequential transformation of representational patterns, showing lower pattern similarity for items in near locations than in far locations during encoding. These transformations enabled the encoding of multiple items in the same location and disambiguation of adjacent items. Our results suggest that the hippocampus can flexibly reconfigure multiplexed event structure representations to support accurate temporal order memory.

The Prefrontal-Hippocampal Comparator: Volition and Episodic Memory

This review describes recent research that is relevant to the prefrontal-hippocampal comparator model with the following conclusions: 1. Hippocampal area CA1 serves, at least in part, as an associative match-mismatch comparator. 2. Voluntary movement strengthens episodic memories for goal-directed behavior. 3. Hippocampal theta power serves as a prediction error signal during hippocampal dependent tasks. 4. The self-referential component of episodic memory in humans is mediated by the corollary discharge (the efference copy of the action plan developed by prefrontal cortex and transmitted to hippocampus where it is stored as a working memory; CA1 uses this efference copy to compare the expected consequences of action to the actual consequences of action). 5. Impairments in the production or transmission of this corollary discharge may contribute to some of the symptoms of schizophrenia. Unresolved issues and suggestions for future research are discussed.

Expectation-driven novelty effects in episodic memory

Novel and unexpected stimuli are often prioritised in memory, given their inherent salience. Nevertheless, not all forms of novelty show such an enhancement effect. Here, we discuss the role expectation plays in modulating the way novelty affects memory processes, circuits, and subsequent performance. We first review independent effects of expectation on memory, and then consider how different types of novelty are characterised by expectation. We argue that different types of novelty defined by expectation implicate differential neurotransmission in memory formation brain regions and may also result in the creation of different types of memory. Contextual novelty, which is unexpected by definition, is often associated with better recollection, supported by dopaminergic-hippocampal interactions. On the other hand, expected stimulus novelty is supported by engagement of medial temporal cortices, as well as the hippocampus, through cholinergic modulation. Furthermore, when expected stimulus novelty results in enhanced memory, it is predominantly driven by familiarity. The literature reviewed here highlights the complexity of novelty-sensitive memory systems, the distinction between types of novelty, and how they are differentially affected by expectancy.

A predictive account of how novelty influences declarative memory

A rich body of studies in the human and non-human literature has examined the question how novelty influences memory. For a variety of different stimuli, ranging from simple objects and words to vastly complex scenarios, the literature reports that novelty improves memory in some cases, but impairs memory in other cases. In recent attempts to reconcile these conflicting findings, novelty has been divided into different subtypes, such as relative versus absolute novelty, or stimulus versus contextual novelty. Nevertheless, a single overarching theory of novelty and memory has been difficult to attain, probably due to the complexities in the interactions among stimuli, environmental factors (e.g., spatial and temporal context) and level of prior knowledge (but see Duszkiewicz et al., 2019; Kafkas & Montaldi, 2018b; Schomaker & Meeter, 2015). Here we describe how a predictive coding framework might be able to shed new light on different types of novelty and how they affect declarative memory in humans. More precisely, we consider how prior expectations modulate the influence of novelty on encoding episodes into memory, e.g., in terms of surprise, and how novelty/surprise affect memory for surrounding information. By reviewing a range of behavioural findings and their possible underlying neurobiological mechanisms, we highlight where a predictive coding framework succeeds and where it appears to struggle.

Simulations reveal how M-currents and memory-based inputs from CA3 enable single neuron mismatch detection for EC3 inputs to the CA1 subfield of hippocampus

Significant evidence has accumulated to support the hypothesis that hippocampal region CA1 operates as an associative mismatch detector (e.g., Hasselmo ME, Schnell E, Barkai E. J Neurosci 15: 5249-5262, 1995; Duncan K, Curtis C, Davachi L. J Neurosci 29: 131-139, 2009; Kumaran D, Maguire EA. J Neurosci 27: 8517-8524, 2007; Lisman JE, Grace AA. Neuron 46: 703-713, 2005; Lisman JE, Otmakhova NA. Hippocampus 11: 551-568 2001; Lörincz A, Buzsáki G. Ann N Y Acad Sci 911: 83-111, 2000; Meeter M, Murre JMJ, Talamini LM. Hippocampus 14: 722-741, 2004; Schiffer AM, Ahlheim C, Wurm MF, Schubotz RI. PLoS One 7: e36445, 2012; Vinogradova OS. Hippocampus 11: 578-598 2001). CA1 compares predictive synaptic signals from CA3 with synaptic signals from EC3, which reflect actual sensory inputs. The new CA1 pyramidal model presented here shows that the distal-proximal segregation of synaptic inputs from EC3 versus CA3, along with other biophysical features, enable such pyramids to serve as comparators that switch output encoding from a brief burst, for a match, to prolonged tonic spiking, for a mismatch. By including often-overlooked features of CA1 pyramidal neurons, this new model allows simulation of pharmacological effects that can eliminate either the match (phasic mode) response or the mismatch (tonic mode) response. These simulations reveal that dysfunctions can arise from either too much or too little ACh stimulation of the muscarinic receptors that control KCNQ channels. Additionally, a dysfunction caused by administration of an N-methyl-d-aspartate antagonist could be rescued by simultaneous administration of a KCNQ channel agonist, such as retigabine.NEW & NOTEWORTHY Hippocampal region CA1 operates as an associative mismatch detector, comparing predictive signals from CA3 with signals from EC3 reflecting sensory inputs. This new CA1 pyramidal model shows that biophysical features enable these comparators to switch output between brief bursts for matches and tonic spiking for mismatches. This suggests that cognitive learning models (e.g., predictive coding) may require much less match/mismatch circuitry than commonly assumed. Additional simulations illuminate deficits seen in psychiatric disorders and drug-induced states.

Mnemonic prediction errors bias hippocampal states

When our experience violates our predictions, it is adaptive to upregulate encoding of novel information, while down-weighting retrieval of erroneous memory predictions to promote an updated representation of the world. We asked whether mnemonic prediction errors promote hippocampal encoding versus retrieval states, as marked by distinct network connectivity between hippocampal subfields. During fMRI scanning, participants were cued to internally retrieve well-learned complex room-images and were then presented with either an identical or a modified image (0-4 changes). In the left hemisphere, we find that CA1-entorhinal connectivity increases, and CA1-CA3 connectivity decreases, with the number of changes. Further, in the left CA1, the similarity between activity patterns during cued-retrieval of the learned room and during the image is lower when the image includes changes, consistent with a prediction error signal in CA1. Our findings provide a mechanism by which mnemonic prediction errors may drive memory updating—by biasing hippocampal states.

When our expectations are violated, it is adaptive to update our internal models to improve predictions in the future. Here, the authors show that during mnemonic violations, hippocampal networks are biased towards an encoding state and away from a retrieval state to potentially update these predictions.

Neural Signatures of Learning Novel Object-Scene Associations

Objects are perceived within rich visual contexts, and statistical associations may be exploited to facilitate their rapid recognition. Recent work using natural scene-object associations suggests that scenes can prime the visual form of associated objects, but it remains unknown whether this relies on an extended learning process. We asked participants to learn categorically structured associations between novel objects and scenes in a paired associate memory task while ERPs were recorded. In the test phase, scenes were first presented (2500 msec), followed by objects that matched or mismatched the scene; degree of contextual mismatch was manipulated along visual and categorical dimensions. Matching objects elicited a reduced N300 response, suggesting visuostructural priming based on recently formed associations. Amplitude of an extended positivity (onset ∼200 msec) was sensitive to visual distance between the presented object and the contextually associated target object, most likely indexing visual template matching. Results suggest recent associative memories may be rapidly recruited to facilitate object recognition in a top-down fashion, with clinical implications for populations with impairments in hippocampal-dependent memory and executive function.

Acquisition of temporal order requires an intact CA3 commissural/associational (C/A) feedback system in mice

Episodic memory, an essential element of orderly thinking, requires the organization of serial events into narratives about the identity of cues along with their locations and temporal order (what, where, and when). The hippocampus plays a central role in the acquisition and retrieval of episodes with two of its subsystems being separately linked to what and where information. The substrates for the third element are poorly understood. Here we report that in hippocampal slices field CA3 maintains self-sustained activity for remarkable periods following a brief input and that this effect is extremely sensitive to minor network perturbations. Using behavioral tests, that do not involve training or explicit rewards, we show that partial silencing of the CA3 commissural/associational network in mice blocks acquisition of temporal order, but not the identity or location, of odors. These results suggest a solution to the question of how hippocampus adds time to episodic memories.

Medial Orbitofrontal Cortex, Dorsolateral Prefrontal Cortex, and Hippocampus Differentially Represent the Event Saliency

Two primary functions attributed to the hippocampus and prefrontal cortex (PFC) network are retaining the temporal and spatial associations of events and detecting deviant events. It is unclear, however, how these two functions converge into one mechanism. Here, we tested whether increased activity with perceiving salient events is a deviant detection signal or contains information about the event associations by reflecting the magnitude of deviance (i.e., event saliency). We also tested how the deviant detection signal is affected by the degree of anticipation. We studied regional neural activity when people watched a movie that had varying saliency of a novel or an anticipated flow of salient events. Using intracranial electroencephalography from 10 patients, we observed that high-frequency activity (50-150 Hz) in the hippocampus, dorsolateral PFC, and medial OFC tracked event saliency. We also observed that medial OFC activity was stronger when the salient events were anticipated than when they were novel. These results suggest that dorsolateral PFC and medial OFC, as well as the hippocampus, signify the saliency magnitude of events, reflecting the hierarchical structure of event associations.

Transcending time in the brain: How event memories are constructed from experience

Our daily lives unfold continuously, yet when we reflect on the past, we remember those experiences as distinct and cohesive events. To understand this phenomenon, early investigations focused on how and when individuals perceive natural breakpoints, or boundaries, in ongoing experience. More recent research has examined how these boundaries modulate brain mechanisms that support long-term episodic memory. This work has revealed that a complex interplay between hippocampus and prefrontal cortex promotes the integration and separation of sequential information to help organize our experiences into mnemonic events. Here, we discuss how both temporal stability and change in one's thoughts, goals, and surroundings may provide scaffolding for these neural processes to link and separate memories across time. When learning novel or familiar sequences of information, dynamic hippocampal processes may work both independently from and in concert with other brain regions to bind sequential representations together in memory. The formation and storage of discrete episodic memories may occur both proactively as an experience unfolds. They may also occur retroactively, either during a context shift or when reactivation mechanisms bring the past into the present to allow integration. We also describe conditions and factors that shape the construction and integration of event memories across different timescales. Together these findings shed new light on how the brain transcends time to transform everyday experiences into meaningful memory representations.

A translational perspective on neural circuits of fear extinction: Current promises and challenges

Fear extinction is the well-known process of fear reduction through repeated re-exposure to a feared stimulus without the aversive outcome. The last two decades have witnessed a surge of interest in extinction learning. First, extinction learning is observed across species, and especially research on rodents has made great strides in characterising the physical substrate underlying extinction learning. Second, extinction learning is considered of great clinical significance since it constitutes a crucial component of exposure treatment. While effective in reducing fear responding in the short term, extinction learning can lose its grip, resulting in a return of fear (i.e., laboratory model for relapse of anxiety symptoms in patients). Optimization of extinction learning is, therefore, the subject of intense investigation. It is thought that the success of extinction learning is, at least partly, determined by the mismatch between what is expected and what actually happens (prediction error). However, while much of our knowledge about the neural circuitry of extinction learning and factors that contribute to successful extinction learning comes from animal models, translating these findings to humans has been challenging for a number of reasons. Here, we present an overview of what is known about the animal circuitry underlying extinction of fear, and the role of prediction error. In addition, we conducted a systematic literature search to evaluate the degree to which state-of-the-art neuroimaging methods have contributed to translating these findings to humans. Results show substantial overlap between networks in animals and humans at a macroscale, but current imaging techniques preclude comparisons at a smaller scale, especially in sub-cortical areas that are functionally heterogeneous. Moreover, human neuroimaging shows the involvement of numerous areas that are not typically studied in animals. Results obtained in research aimed to map the extinction circuit are largely dependent on the methods employed, not only across species, but also across human neuroimaging studies. Directions for future research are discussed.

Low-Frequency Oscillatory Correlates of Auditory Predictive Processing in Cortical-Subcortical Networks: A MEG-Study

Emerging evidence supports the role of neural oscillations as a mechanism for predictive information processing across large-scale networks. However, the oscillatory signatures underlying auditory mismatch detection and information flow between brain regions remain unclear. To address this issue, we examined the contribution of oscillatory activity at theta/alpha-bands (4-8/8-13 Hz) and assessed directed connectivity in magnetoencephalographic data while 17 human participants were presented with sound sequences containing predictable repetitions and order manipulations that elicited prediction-error responses. We characterized the spectro-temporal properties of neural generators using a minimum-norm approach and assessed directed connectivity using Granger Causality analysis. Mismatching sequences elicited increased theta power and phase-locking in auditory, hippocampal and prefrontal cortices, suggesting that theta-band oscillations underlie prediction-error generation in cortical-subcortical networks. Furthermore, enhanced feedforward theta/alpha-band connectivity was observed in auditory-prefrontal networks during mismatching sequences, while increased feedback connectivity in the alpha-band was observed between hippocampus and auditory regions during predictable sounds. Our findings highlight the involvement of hippocampal theta/alpha-band oscillations towards auditory prediction-error generation and suggest a spectral dissociation between inter-areal feedforward vs. feedback signalling, thus providing novel insights into the oscillatory mechanisms underlying auditory predictive processing.

Associative Prediction of Visual Shape in the Hippocampus

Perception can be cast as a process of inference, in which bottom-up signals are combined with top-down predictions in sensory systems. In line with this, neural activity in sensory cortex is strongly modulated by prior expectations. Such top-down predictions often arise from cross-modal associations, such as when a sound (e.g., bell or bark) leads to an expectation of the visual appearance of the corresponding object (e.g., bicycle or dog). We hypothesized that the hippocampus, which rapidly learns arbitrary relationships between stimuli over space and time, may be involved in forming such associative predictions. We exposed male and female human participants to auditory cues predicting visual shapes, while measuring high-resolution fMRI signals in visual cortex and the hippocampus. Using multivariate reconstruction methods, we discovered a dissociation between these regions: representations in visual cortex were dominated by whichever shape was presented, whereas representations in the hippocampus reflected only which shape was predicted by the cue. The strength of hippocampal predictions correlated across participants with the amount of expectation-related facilitation in visual cortex. These findings help bridge the gap between memory and sensory systems in the human brain.SIGNIFICANCE STATEMENT The way we perceive the world is to a great extent determined by our prior knowledge. Despite this intimate link between perception and memory, these two aspects of cognition have mostly been studied in isolation. Here we investigate their interaction by asking how memory systems that encode and retrieve associations can inform perception. We find that upon hearing a familiar auditory cue, the hippocampus represents visual information that had previously co-occurred with the cue, even when this expectation differs from what is currently visible. Furthermore, the strength of this hippocampal expectation correlates with facilitation of perceptual processing in visual cortex. These findings help bridge the gap between memory and sensory systems in the human brain.

Knowledge acquisition is governed by striatal prediction errors

Discrepancies between expectations and outcomes, or prediction errors, are central to trial-and-error learning based on reward and punishment, and their neurobiological basis is well characterized. It is not known, however, whether the same principles apply to declarative memory systems, such as those supporting semantic learning. Here, we demonstrate with fMRI that the brain parametrically encodes the degree to which new factual information violates expectations based on prior knowledge and beliefs-most prominently in the ventral striatum, and cortical regions supporting declarative memory encoding. These semantic prediction errors determine the extent to which information is incorporated into long-term memory, such that learning is superior when incoming information counters strong incorrect recollections, thereby eliciting large prediction errors. Paradoxically, by the same account, strong accurate recollections are more amenable to being supplanted by misinformation, engendering false memories. These findings highlight a commonality in brain mechanisms and computational rules that govern declarative and nondeclarative learning, traditionally deemed dissociable.

CA1 and CA3 differentially support spontaneous retrieval of episodic contexts within human hippocampal subfields

The hippocampus plays a critical role in spatial and episodic memory. Mechanistic models predict that hippocampal subfields have computational specializations that differentially support memory. However, there is little empirical evidence suggesting differences between the subfields, particularly in humans. To clarify how hippocampal subfields support human spatial and episodic memory, we developed a virtual reality paradigm where participants passively navigated through houses (spatial contexts) across a series of videos (episodic contexts). We then used multivariate analyses of high-resolution fMRI data to identify neural representations of contextual information during recollection. Multi-voxel pattern similarity analyses revealed that CA1 represented objects that shared an episodic context as more similar than those from different episodic contexts. CA23DG showed the opposite pattern, differentiating between objects encountered in the same episodic context. The complementary characteristics of these subfields explain how we can parse our experiences into cohesive episodes while retaining the specific details that support vivid recollection.

Computational studies have hinted that hippocampal subfields represent information differently. Here, the authors show that when retrieving items that share an episodic context, subfield CA1 represent similarities between items whereas CA2/3/dentate gyrus represents item-unique features.

Dissociable profiles of generalization/discrimination in the human hippocampus during associative retrieval

When encountering stimuli that vary slightly from previous experiences, neural signals within the CA₃ and dentate gyrus (CA₃ DG) hippocampal subfields are thought to facilitate mnemonic discrimination, whereas CA₁ may be less sensitive to minor stimulus changes, allowing for generalization across similar events. Studies have also posited a critical role for CA₁ in the comparison of events to memory-derived expectations, but the degree to which these processes are impacted by explicit retrieval demands is yet unclear. To evaluate extant accounts of hippocampal subfield function, we acquired high-resolution fMRI data as participants performed a task in which famous names were used to cue the retrieval of previously paired images. Although both left CA₃ DG and CA₁ showed match enhancement effects, responding more to original paired images (targets) than to never-before-seen images (novels), the sensitivity of these subfields to stimulus changes and task demands diverged. CA₃ DG showed a goal-independent, yet highly specific, preference for previously encountered stimuli, responding equally strongly to targets and mispaired associates, while showing equally weak responses to close lures and novels. In contrast, recognition signals in CA₁ were goal-dependent (i.e., not evoked by mispaired associates), yet accommodating of subtle stimulus differences, such that close lures evoked comparable activity as targets. © 2016 Wiley Periodicals, Inc.

Hippocampal Mismatch Signals Are Modulated by the Strength of Neural Predictions and Their Similarity to Outcomes

The hippocampus is thought to compare predicted events with current perceptual input, generating a mismatch signal when predictions are violated. However, most prior studies have only inferred when predictions occur without measuring them directly. Moreover, an important but unresolved question is whether hippocampal mismatch signals are modulated by the degree to which predictions differ from outcomes. Here, we conducted a human fMRI study in which subjects repeatedly studied various word-picture pairs, learning to predict particular pictures (outcomes) from the words (cues). After initial learning, a subset of cues was paired with a novel, unexpected outcome, whereas other cues continued to predict the same outcome. Critically, when outcomes changed, the new outcome was either "near" to the predicted outcome (same visual category as the predicted picture) or "far" from the predicted outcome (different visual category). Using multivoxel pattern analysis, we indexed cue-evoked reactivation (prediction) within neocortical areas and related these trial-by-trial measures of prediction strength to univariate hippocampal responses to the outcomes. We found that prediction strength positively modulated hippocampal responses to unexpected outcomes, particularly when unexpected outcomes were close, but not identical, to the prediction. Hippocampal responses to unexpected outcomes were also associated with a tradeoff in performance during a subsequent memory test: relatively faster retrieval of new (updated) associations, but relatively slower retrieval of the original (older) associations. Together, these results indicate that hippocampal mismatch signals reflect a comparison between active predictions and current outcomes and that these signals are most robust when predictions are similar, but not identical, to outcomes.

SIGNIFICANCE STATEMENT

Although the hippocampus is widely thought to signal "mismatches" between memory-based predictions and outcomes, previous research has not linked hippocampal mismatch signals directly to neural measures of prediction strength. Here, we show that hippocampal mismatch signals increase as a function of the strength of predictions in neocortical regions. This increase in hippocampal mismatch signals was particularly robust when outcomes were similar, but not identical, to predictions. These results indicate that hippocampal mismatch signals are driven by both the active generation of predictions and the similarity between predictions and outcomes.

The hippocampus: a special place for time

Many findings have demonstrated that memories of past events are temporally organized. It is well known that the hippocampus is critical for such episodic memories, but, until recently, little was known about the temporal organization of mnemonic representations in the hippocampus. Recent developments in human and animal research have revealed important insights into the role of the hippocampus in learning and retrieving sequences of events. Here, we review these findings, including lesion and single-unit recording studies in rodents, functional magnetic resonance imaging studies in humans, and computational models that link findings from these studies to the anatomy of the hippocampal circuit. The findings converge toward the idea that the hippocampus is essential for learning sequences of events, allowing the brain to distinguish between memories for conceptually similar but temporally distinct episodes, and to associate representations of temporally contiguous, but otherwise unrelated experiences.