Repetition of common activities within our daily lives results in highly similar and overlapping experiences. To distinguish between these experiences in memory, unique traces are formed for each experience based on the orthogonalization of overlapping features – a process referred to as ‘pattern separation’. The presence of overlapping features in two stimuli or events creates an overlap in resulting activity patterns, and thus, a redundancy between input patterns. If this redundancy is not reduced, a novel, but similar, experienced event can result in major interference and reactivation of a previously stored memory. Pattern separation reduces this redundancy by transforming similar input patterns into less similar output patterns, ultimately allowing distinct memories for overlapping events to be stored (O’Reilly and McClelland, 1994). Thus, the need to transform or differentiate input patterns to prevent interference from and reactivation of overlapping stimuli/memories defines pattern separation function. Behaviorally, this typically involves the successful discrimination of lure stimuli from targets with highly overlapping features (see Zotow et al., 2020 for an alternative method to measure pattern separation behaviorally based on the conditional probability of retrieving similar memories). It is important to note here that although the term ‘pattern separation’ has its roots in computational neuroscience, it has been used widely by different researchers to describe a range of processes (e.g., computational processes or behavioral discrimination), arguably degrading its construct validity (see Santoro, 2013). Our use of the term in the current paper (which we believe is important as it has been used in the studies we reference) focuses on the neural processes presumed to be necessary to perform behavioral discrimination and, in most cases noted, refers to the increased distinctiveness in evoked neural patterns to stimuli that are perceptually overlapping. Although the use of the term commonly refers to these behavioral/mnemonic discrimination processes, it should be highlighted that behavioral discrimination incorporates multiple cognitive processes, with pattern separation being only one. Task demands typically dictate the extent of pattern separation involvement (or differentiation of similar inputs), and whether it is truly isolated from other mnemonic and perceptual processes involved in behavioral discrimination. Thus, the proper usage of the term ‘pattern separation’ and the validity of the tasks that aim to measure it are topics of ongoing debate. Nonetheless, to summarize, the neural and behavioral discrimination of overlapping activity patterns and stimuli, respectively, reflecting successful interference resolution, define pattern separation in the current paper. While other memory processes, such as recognition and source memory, may recruit similar brain-wide regions, interactions, or mechanisms, pattern separation (which can be involved in these memory processes depending on the conditions) requires the need to resolve interference from an overlap in stimulus features and evoked neural responses (e.g., see Stevenson et al., 2020 for evidence of distinct neural mechanisms associated with pattern separation and source memory).

A large body of work, across methods, analytical techniques, and different species, has focused on hippocampal contributions to pattern separation (e.g., O’Reilly and McClelland, 1994; Treves and Rolls, 1994; Leutgeb et al., 2007; Yassa and Stark, 2011b; LaRocque et al., 2013; Neunuebel and Knierim, 2014; Kyle et al., 2015; Berron et al., 2016; Sakon and Suzuki, 2019). This research has provided convincing evidence for reduced representational overlap for more similar events or stimuli within the hippocampus compared to events with less featural overlap. These findings are consistent with theorizing that the hippocampus contains neural circuitry that promotes the allocation of dissimilar neural populations to highly similar events, thus facilitating the mnemonic discrimination of similar experiences through their reduced neural overlap. However, a careful examination of the broader literatures on episodic memory, attention, interference resolution, and perception suggests that the process of pattern separation may be a more widespread and iterative process supported by a network of brain regions. Here, we review evidence that patterns of neural activity occurring in cognitive control and sensory regions (1) are sensitive to pattern separation demands and show hippocampal-like activity in response to overlapping stimuli, (2) temporally precede pattern separation activity in the hippocampus, and thus filter or ‘separate’ the signal reaching the hippocampus, likely influencing hippocampal output, and (3) are correlated with memory performance on tasks that rely on pattern separation, demonstrating their functional relevance. Accordingly, we highlight that pattern separation is best considered the result of network interactions and not one solely supported by intra-hippocampal processes. While previous studies have not explicitly argued that pattern separation occurs solely in the hippocampus, a focus on hippocampal processes (stemming from animal electrophysiological studies) due to their potentially unique contribution in performing an extreme version of pattern separation (see ‘A unique hippocampal role in pattern separation?’), has shifted attention away from the important domain-general contribution of extra-hippocampal regions. Although this is beginning to change (e.g., Johnson et al., 2021; Nash et al., 2021), a unifying framework has yet to be put forward. We contend that a full understanding of mnemonic discrimination requires such a framework promoting an understanding of how the hippocampus interacts with and is modulated by input from brain-wide regions during pattern separation. In particular, based on the reviewed evidence, we argue that task goals, supported by cognitive control regions, may mediate pattern separation in sensory regions feeding into hippocampus (thus regulating its cortical input), or more directly target the hippocampus, biasing it toward pattern separation. Although we will discuss cognitive control and sensory function in the context of findings demonstrating activity in typically lateral frontal and occipito-temporal regions, respectively, we acknowledge the complexity of brain-cognition mappings and the dynamic brain network properties underlying cognitive functions outlined by data-driven approaches (e.g., Cocuzza et al., 2020; Keane et al., 2021; Klein, 2012; Poldrack and Yarkoni, 2016). The aim of the proposal is not to stress a potential role for these particular regions in pattern separation, but to more generally highlight extra-hippocampal contribution to the overall process, and similar to data-driven approaches, underscore the importance of widespread brain networks for a cognitive process. Finally, in light of the continued interest in the many functions of the hippocampus and recent focus on how its activity patterns reflect top-down goals (e.g., Aly and Turk-Browne, 2018; Aoki et al., 2019; Haimerl et al., 2019), it is important now more than ever to carefully examine how and when other brain regions might directly support pattern separation or coordinate and modulate hippocampal contributions to pattern separation.

Human studies

Although animal work has typically focused on the dorsal hippocampus and manipulated spatial features, neuroimaging work in humans has allowed examination of hippocampal subfields, along with the entire brain, while also using paradigms where similarity could be manipulated for objects and their features. This is particularly important as it has provided a more complete view of pattern separation functions of the hippocampal formation as they relate to complex human behaviors (e.g., mnemonic discrimination). The first study to report evidence for perceptual pattern separation in the human hippocampus used a functional magnetic resonance imaging (fMRI) adaptation paradigm during an incidental encoding memory task, known as the Mnemonic Similarity Task (incidental version with no explicit memory demands). The study showed that during performance of an ‘indoor’/‘outdoor’ judgment task on individual items, the magnitude of blood oxygenation level-dependent (BOLD) activation in a region encompassing human DG and area CA3 (lower spatial resolution in earlier fMRI studies did not allow isolation of these two subfields) was statistically indistinguishable for completely novel items and similar lures (i.e., images of novel items that are highly similar to previously presented items) (Bakker et al., 2008). That is, unlike other regions within the medial temporal lobe (MTL), DG/CA3 showed repetition suppression for identical repeats, but not lure items, suggesting that it was treating the lure items as new (see Berron et al., 2016 for evidence based on ultra-high-resolution fMRI that this effect is localized to the human DG).

Follow-up studies manipulated the degree of similarity between lure and old items and demonstrated again that DG/CA3 showed evidence of pattern separation for highly similar lures, consistent with animal studies showing that the DG responds to minimal environmental changes (Lacy et al., 2011; Yassa et al., 2011a). In particular, relative to the reduced activity in response to identical repeats (i.e., repetition suppression), DG/CA3, but not other subfields such as CA1, showed increased activity for (1) lures with high similarity, (2) lures with low similarity, and (3) new items. This increased activity was at a similar level for all three item types, such that the activation profile was best fit with a power function when input (extent of change from old item) and output (change in activation level) are mapped (see Figure 1). This power function has since been used in fMRI studies to characterize pattern separation (e.g., Motley and Kirwan, 2012).

Figure 1

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In addition to univariate analyses that only assess brain activation levels (similar to studies reporting rate remapping), recent studies have used multivariate techniques to examine the representational content in the hippocampus when presented with similar stimuli. These studies have demonstrated that, compared to extra-hippocampal or other MTL regions, the hippocampus shows lower representational overlap (i.e., distinct activity patterns) between stimuli with overlapping features, providing evidence for orthogonalized representations theorized to emerge from pattern separation (LaRocque et al., 2013; Hulbert and Norman, 2015; Kyle et al., 2015; Favila et al., 2016; Chanales et al., 2017). For example, one study demonstrated that the similarity between activation patterns for similar scenes encountered in an associative memory paradigm was reduced in the hippocampus relative to extra-hippocampal regions (Favila et al., 2016). Critically, the extent of this reduced similarity in activation patterns in the hippocampus (i.e., pattern separation) was associated with future memory and reduced behavioral interference between the scenes (see also Wanjia et al., 2021). Another study with high spatial resolution illustrated a similar effect for learned spatial environments particularly in the DG/CA3 area, consistent with the findings reported in univariate studies (Kyle et al., 2015).

Finally, in accordance with the imaging findings, several neuropsychological studies have demonstrated that patients with hippocampal damage show deficits in behavioral indices of mnemonic pattern separation, as indexed by false recognition of lures on the standard version of the Mnemonic Similarity Task, in which participants identify if presented objects are ‘old’, ‘similar’, or ‘new’ (Motley and Kirwan, 2012; Baker et al., 2016; Hanert et al., 2019a; Hanert et al., 2019b). For example, one study demonstrated that a patient with a rare, selective lesion to the DG showed similar performance to healthy control participants in identifying target items and foils but was severely impaired in correctly rejecting lure items (Baker et al., 2016). Taken together, these findings provide convincing evidence that the hippocampus, and particularly hippocampal subfield DG, supports pattern separation by assigning distinct neural codes to overlapping events, thus reducing interference and representational similarity between these events (but see Quian Quiroga, 2020 for an alternative viewpoint).

Region interactions to support pattern separation

Given evidence that hippocampal and extra-hippocampal regions are involved in dissociating overlapping stimuli, how do interactions between these different regions overall support pattern separation as proposed by the CHiPS framework? Based on work illustrating control-mediated, top-down regulation of sensory regions feeding into the hippocampus, and that the hippocampus acts as a convergence zone integrating information from multiple brain regions during memory function (e.g., Backus et al., 2016; Mišić et al., 2014), we hypothesize that frontoparietal control areas facilitate pattern separation by resolving interference from overlapping stimuli in early sensory regions, thus regulating hippocampal input. This facilitation can occur when viewing an overlapping stimulus with no explicit long-term memory demands (perceptual pattern separation resulting in the formation of a new memory), or in pattern separation tasks that demand explicit memory decisions in response to comparison to a stored memory representation (i.e., cognitive control function resolves interference and aids in the precise discrimination of overlapping activity patterns elicited by sensory input and stored representations). Following additional interference resolution in upstream MTL regions (e.g., PrC and alErC), the hippocampus, and particularly the DG, consequently forms unique memory traces of the overlapping stimuli, as indicated by prior work (e.g., Yassa and Stark, 2011b), by maximizing outlined feature differences between those stimuli to distinguish between them in memory (see Figure 2 for a schematic of the proposed CHiPS framework). Specifically, through sparse coding in the DG which links input patterns in the ErC to non-overlapping sets of neurons in CA3, the hippocampus further differentiates input patterns from overlapping stimuli/representations, allowing the storage and subsequent recall of distinct memories (e.g., Norman and O’Reilly, 2003). The degree to which this hippocampal process successfully results in the storage of non-overlapping memories should rely to an extent on the pre-existing differentiation of input patterns to the ErC driven by extra-hippocampal regions. Fully accounting for the state of MTL input, in addition to overall neocortical–hippocampal interactions, should strengthen computational frameworks of pattern separation. Thus, according to the CHiPS framework, the processes enacted by extra-hippocampal and hippocampal regions comprise a continuum in resolving interference, with the DG playing a particular role in amplifying feature differences in memory traces (e.g., see Reagh and Yassa, 2014; Lohnas et al., 2018 for evidence that the DG/hippocampus may play a unique role in influencing behavioral or mnemonic decisions in pattern separation tasks). This unique DG role might potentially reflect a ‘break point’ in the pattern separation process for consolidated memories, with memories consolidated through DG containing amplified featural differences (see ‘A unique hippocampal role in pattern separation?’).

Figure 2

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Consistent with the continuum view of resolving interference, there is evidence from visual search and selective attention tasks that frontoparietal regions encode simple feature properties and bias downstream sensory regions to represent those features based on attentional demands (e.g., Ester et al., 2016; Bichot et al., 2019). Such feature-based attention can thus initiate pattern separation by driving attention toward diagnostic features that distinguish between similar events. The mechanism by which this occurs may involve upregulation of unique features across stimuli and simultaneous downregulation of overlapping features (see Moher et al., 2014 for evidence of goal-directed inhibition of distractor features during early visual processing). Although frontoparietal regions, including lateral frontal, intraparietal sulcus, and superior parietal regions, are the likely source of this feature modulation (e.g., Ester et al., 2016; Pidgeon and Morcom, 2016), regional specificity of feature modulation in the context of pattern separation specifically (as opposed to a domain-general contribution to memory processing; e.g., Stevenson et al., 2020) requires further investigation. Functional specialization of cognitive control regions is an area of active research and debate (e.g., Corbetta and Shulman, 2002; Badre and Nee, 2018), with some evidence suggesting that, within the lateral frontal cortex, caudal regions are more sensitive than rostral regions to stimulus features (Nee and D’Esposito, 2016; Nee and D’Esposito, 2017). Regional specificity, however, can be complicated by several factors, including age of the individual and task demands, that influence which regions are activated and the level of their activation (e.g., Grady, 2012). Uncovering the regional specificity of cognitive control regions that contribute to pattern separation is a question for future research. Nevertheless, we propose that feature-based attention and interference resolution by frontoparietal regions during pattern separation can regulate the degree of representational distinctiveness, and increase the neural representation of unique features, in sensory regions feeding into upstream MTL regions and the hippocampus (hence contributing to the perceptual pattern separation signal detected in these regions in some studies; e.g., Kim et al., 2009; Bowman et al., 2019; Klippenstein et al., 2020), and thus forms the basis of the filtered or ‘separated’ input signal reaching the hippocampus. Once this signal reaches extra-hippocampal MTL cortices, such as the PrC, domain-selective (i.e., object vs. spatial) interference resolution based on modulated features takes place before the final domain-general, hippocampally based pattern separation (Reagh and Yassa, 2014; Berron et al., 2018; Reagh et al., 2018).

In support of the proposal of pattern separation via attentionally based modulation of sensory regions, one study has demonstrated that lateral frontal and occipital regions show greater functional connectivity when correctly identifying lure objects as ‘new’ (i.e., engaging in successful pattern separation), relative to falsely endorsing them as ‘old’ (Wais et al., 2017). Furthermore, fMRI and ECoG studies have shown that encoding activity in lateral frontal regions temporally precedes activity in the hippocampus, and that this temporal dynamic is associated with successful detail-oriented memory formation (Long and Kahana, 2015; Cooper and Ritchey, 2020). Additionally, recent fMRI and electroencephalogram (EEG) work has illustrated that frontally mediated reactivation and early discrimination of low-level perceptual features in sensory regions during recall/processing of stimuli is associated with the ability to correctly discriminate those stimuli from similar lures (Bone et al., 2020; Herman et al., 2020). Finally, animal studies provide convincing evidence that (1) frontal feature-coding and input to the hippocampus drives memory recall, enabling high-fidelity retrieval (e.g., to distinguish between similar memory representations; Yadav et al., 2022), and (2) inactivation of the rodent frontal cortex and disruption of hippocampal input by blocking frontal–perirhinal communication, impairs performance on hippocampal-dependent memory tasks, including mnemonic discrimination of highly overlapping stimuli (Davies et al., 2017; Hernandez et al., 2017; Jayachandran et al., 2019; Johnson et al., 2021; see ‘Cross-species and developmental considerations’). Clearly, however, more human research is needed to establish a link between frontoparietal control function and sensory representations during pattern separation tasks, and importantly, between top-down modulated sensory input and hippocampal pattern separation and representations.

An important consideration in the CHiPS framework is how interference between overlapping stimuli is detected to engage frontoparietal control regions. One possibility is that novelty detection mechanisms that occur during early stages of stimulus processing signal the need for frontoparietal regions to increase attention to unique stimulus features and resolve potential interference. These novelty computations, which result from processing of familiar stimuli more ‘fluently’ than novel stimuli, have been shown to occur in several regions including perceptual and hippocampal regions (e.g., Knight, 1996; Czigler et al., 2004; Kim et al., 2009). For example, work on macaques has demonstrated stimulus-specific reductions in neuronal firing rates in regions such as the inferior temporal cortex in response to stimulus repetition (even with many intervening stimuli) approximately 150-ms post-stimulus onset (and as early as 70 ms in other regions; see Grill-Spector et al., 2006). Thus, perceptual and hippocampal regions likely carry out the mnemonic comparator process between overlapping stimuli required for pattern separation (based on stimulus-specific novelty computations) and provide feedback to frontoparietal control regions to modulate attention to shared/unique stimulus features. This enhances the processing of lure items and initiates the process of forming non-overlapping memory traces for these items (see bidirectional arrows in Figure 2). The extent to which early mnemonic comparator processes depend on perceptual or hippocampal regions may depend on factors such as mnemonic load, with the hippocampus playing a more significant role when an incoming stimulus is compared to a stored representation over longer delays (e.g., perceptual vs. mnemonic discrimination; see Weeden et al., 2014). Thus, early feedback signals from perceptual and/or hippocampal regions can engage interference resolution function in frontoparietal regions, which in turn modulates neural representations in sensory regions and regulates hippocampal input used for memory formation.

An alternative, but not mutually exclusive, possibility of how cross-region interactions support pattern separation is through direct interactions between cognitive control regions and the hippocampus, rather than indirectly via modulation of sensory regions. In this case, attentional modulation would directly bias hippocampal function and representations (after early novelty signals from perceptual and hippocampal regions), thus enhancing pattern separation in accordance with task demands (Figure 2). Consistent with this hypothesis, multiple studies have now illustrated that hippocampal activity patterns differ between distinct task-dependent goal states, and that these representations differ even when participants are viewing the same scene but are attending to different features of those scenes (e.g., Tambini and Davachi, 2013; Aly and Turk-Browne, 2016a; Aly and Turk-Browne, 2016b; Günseli and Aly, 2020; for a review see Aly and Turk-Browne, 2018). That is, representations or activity patterns in the hippocampus change in accordance with goal states or task demands and reflect the stimuli that are being attended to (see also Carr et al., 2013). Interestingly, one study demonstrated that hippocampal patterns represent different stimulus features of the very same objects in accordance with changing task goals (a critical function for the ability to distinguish between similar items with overlapping features), and that this effect is associated with functional connectivity between the hippocampus and a set of prefrontal regions, including the dorsolateral PFC (Mack et al., 2016; see Yadav et al., 2022 for similar evidence in animals). Moreover, converging evidence from fMRI and ECoG studies has demonstrated that the dorsolateral PFC exerts top-down inhibitory control on the hippocampus during encoding or retrieval of intrusive information, providing further support for task-dependent prefrontal–hippocampal regulation (Benoit and Anderson, 2012; Benoit et al., 2015; Oehrn et al., 2018; see also Malik et al., 2022). This regulation is possibly driven by the documented direct anatomical connections from the frontal cortex to the hippocampus (see Goldman-Rakic et al., 1984; Rajasethupathy et al., 2015; Malik et al., 2022 for evidence of such connections in non-human animals), or by fronto-hippocampal projections that traverse through intermediate structures, such as the anterior cingulate cortex and thalamus (see Anderson et al., 2016 for a review).

A few studies have provided evidence of attention-based hippocampal modulation during pattern separation tasks, suggesting a possible direct influence from cognitive control regions. For example, different studies have illustrated, based on both univariate and multivariate analyses, that in the Mnemonic Similarity Task, the hippocampus shows evidence of pattern separation for similar items only when task demands require a behavioral distinction to be made between similar and old items (Hashimoto et al., 2012; Lohnas et al., 2018). That is, hippocampal pattern separation was seen when items needed to be distinguished based on (fine-grain) perceptual, but not (coarse-grain) conceptual, features (e.g., a beach ball with slightly different features than a previously presented one would, respectively, be classified as ‘new’ and ‘old’). Similarly, another study demonstrated that hippocampal activation in response to graded changes in the similarity of lures to old items is influenced by encoding instructions or top-down demands (Motley and Kirwan, 2012). Particularly, in a continuous recognition task, hippocampal activation was better fit with a power function (i.e., the typical characterization of pattern separation) under intentional instructions that required participants to distinguish between the items based on fine-grain features, relative to incidental, category classification instructions. Further suggesting that cognitive control regions modulate the hippocampus during this task (continuous recognition task with intentional instructions), a recent study illustrated that a cognitive control dorsomedial frontal region showed functional connectivity with the hippocampus during task performance and displayed an activity pattern consistent with pattern separation (Nash et al., 2021). Finally, an anatomical study demonstrated that the integrity of the fornix, which connects the hippocampus to multiple regions including inferior prefrontal regions, is associated with behavioral performance on a pattern separation, but not recognition memory, task (Bennett et al., 2015). Although the fornix is typically considered to be a major output of the hippocampus, or hippocampal efferent, studies have demonstrated that it also contains afferent fibers feeding into the hippocampus (e.g., Schmahmann and Pandya, 2009). Thus, this finding provides anatomical support for the hypothesis that cognitive control regions might influence pattern separation that occurs within the hippocampus. Although these studies provide evidence of hippocampal modulation during pattern separation tasks, the question of whether these effects are indeed due to a direct influence from cognitive control regions or are mediated by signals relayed through sensory regions (see above) requires further investigation.

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Author details

1. Tarek Amer

   Department of Psychology, University of Victoria, Victoria, Canada

   Contribution

   Conceptualization, Funding acquisition, Writing – original draft, Writing – review and editing

   For correspondence

   tarekamer@uvic.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1866-9437

2. Lila Davachi

   1. Department of Psychology, Columbia University, New York, United States
   2. Nathan Kline Research Institute, Orangeburg, United States

   Contribution

   Conceptualization, Funding acquisition, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4317-0889

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