The hippocampal indexing theory and episodic memory: Updating the index

Elements of episodic memory: insights from artificial agents

Many recent artificial intelligence (AI) systems take inspiration from biological episodic memory. Here, we ask how these 'episodic-inspired' AI systems might inform our understanding of biological episodic memory. We discuss work showing that these systems implement some key features of episodic memory while differing in important respects and appear to enjoy behavioural advantages in the domains of strategic decision-making, fast learning, navigation, exploration and acting over temporal distance. We propose that these systems could be used to evaluate competing theories of episodic memory's operations and function. However, further work is needed to validate them as models of episodic memory and isolate the contributions of their memory systems to their behaviour. More immediately, we propose that these systems have a role to play in directing episodic memory research by highlighting novel or neglected hypotheses as pursuit-worthy. In this vein, we propose that the evidence reviewed here highlights two pursuit-worthy hypotheses about episodic memory's function: that it plays a role in planning that is independent of future-oriented simulation, and that it is adaptive in virtue of its contributions to fast learning in novel, sparse-reward environments. This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.

Episodic recombination and the role of time in mental travel

Mental time travel is often presented as a singular mechanism, but theoretical and empirical considerations suggest that it is composed of component processes. What are these components? Three hypotheses about the major components of mental time travel are commonly considered: (i) remembering and imagining might, respectively, rely on different processes, (ii) past- and future-directed forms of mental time travel might, respectively, rely on different processes, and (iii) the creation of episodic representations and the determination of their temporal orientation might, respectively, rely on different processes. Here, we flesh out the last of these proposals. First, we argue for 'representational continuism': the view that different forms of mental travel are continuous with regard to their core representational contents. Next, we propose an updated account of episodic recombination (the mechanism generating these episodic contents) and review evidence in its support. On this view, episodic recombination is a natural kind best viewed as a form of compositional computation. Finally, we argue that episodic recombination should be distinguished from mechanisms determining the temporal orientation of episodic representations. Thus, we suggest that mental travel is a singular capacity, while mental time travel has at least two major components: episodic representations and their temporal orientation. This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.

Distinct mechanisms and functions of episodic memory

The concept of episodic memory (EM) faces significant challenges by two claims: EM might not be a distinct memory system, and EM might be an epiphenomenon of a more general capacity for mental time travel (MTT). Nevertheless, the observations leading to these arguments do not preclude the existence of a mechanically and functionally distinct EM system. First, modular systems, like cognition, can have distinct subsystems that may not be distinguishable in the system's final output. EM could be such a subsystem, even though its effects may be difficult to distinguish from those of other subsystems. Second, EM could have a distinct and consistent low-level function, which is used in diverse high-level functions such as MTT. This article introduces the scenario construction framework, proposing that EM crucially rests on memory traces containing the gist of an episodic experience. During retrieval, EM traces trigger the reconstruction of semantic representations, which were active during the remembered episode, and are further enriched with semantic information, to generate a scenario of the past experience. This conceptualization of EM is consistent with studies on the neural basis of EM and resolves the two challenges while retaining the key properties associated with EM. This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.

Barcode activity in a recurrent network model of the hippocampus enables efficient memory binding

Forming an episodic memory requires binding together disparate elements that co-occur in a single experience. One model of this process is that neurons representing different components of a memory bind to an "index" - a subset of neurons unique to that memory. Evidence for this model has recently been found in chickadees, which use hippocampal memory to store and recall locations of cached food. Chickadee hippocampus produces sparse, high-dimensional patterns ("barcodes") that uniquely specify each caching event. Unexpectedly, the same neurons that participate in barcodes also exhibit conventional place tuning. It is unknown how barcode activity is generated, and what role it plays in memory formation and retrieval. It is also unclear how a memory index (e.g. barcodes) could function in the same neural population that represents memory content (e.g. place). Here, we design a biologically plausible model that generates barcodes and uses them to bind experiential content. Our model generates barcodes from place inputs through the chaotic dynamics of a recurrent neural network and uses Hebbian plasticity to store barcodes as attractor states. The model matches experimental observations that memory indices (barcodes) and content signals (place tuning) are randomly intermixed in the activity of single neurons. We demonstrate that barcodes reduce memory interference between correlated experiences. We also show that place tuning plays a complementary role to barcodes, enabling flexible, contextually-appropriate memory retrieval. Finally, our model is compatible with previous models of the hippocampus as generating a predictive map. Distinct predictive and indexing functions of the network are achieved via an adjustment of global recurrent gain. Our results suggest how the hippocampus may use barcodes to resolve fundamental tensions between memory specificity (pattern separation) and flexible recall (pattern completion) in general memory systems.

Concept and location neurons in the human brain provide the 'what' and 'where' in memory formation

Our brains create new memories by capturing the 'who/what', 'where' and 'when' of everyday experiences. On a neuronal level, mechanisms facilitating a successful transfer into episodic memory are still unclear. We investigated this by measuring single neuron activity in the human medial temporal lobe during encoding of item-location associations. While previous research has found predictive effects in population activity in human MTL structures, we could attribute such effects to two specialized sub-groups of neurons: concept cells in the hippocampus, amygdala and entorhinal cortex (EC), and a second group of parahippocampal location-selective neurons. In both item- and location-selective populations, firing rates were significantly higher during successfully encoded trials. These findings are in line with theories of hippocampal indexing, since selective index neurons may act as pointers to neocortical representations. Overall, activation of distinct populations of neurons could directly support the connection of the 'what' and 'where' of episodic memory.

Reconstructing Spatiotemporal Trajectories of Visual Object Memories in the Human Brain

How the human brain reconstructs, step-by-step, the core elements of past experiences is still unclear. Here, we map the spatiotemporal trajectories along which visual object memories are reconstructed during associative recall. Specifically, we inquire whether retrieval reinstates feature representations in a copy-like but reversed direction with respect to the initial perceptual experience, or alternatively, this reconstruction involves format transformations and regions beyond initial perception. Participants from two cohorts studied new associations between verbs and randomly paired object images, and subsequently recalled the objects when presented with the corresponding verb cue. We first analyze multivariate fMRI patterns to map where in the brain high- and low-level object features can be decoded during perception and retrieval, showing that retrieval is dominated by conceptual features, represented in comparatively late visual and parietal areas. A separately acquired EEG dataset is then used to track the temporal evolution of the reactivated patterns using similarity-based EEG-fMRI fusion. This fusion suggests that memory reconstruction proceeds from anterior frontotemporal to posterior occipital and parietal regions, in line with a conceptual-to-perceptual gradient but only partly following the same trajectories as during perception. Specifically, a linear regression statistically confirms that the sequential activation of ventral visual stream regions is reversed between image perception and retrieval. The fusion analysis also suggests an information relay to frontoparietal areas late during retrieval. Together, the results shed light onto the temporal dynamics of memory recall and the transformations that the information undergoes between the initial experience and its later reconstruction from memory.

Synaptic rearrangement of NMDA receptors controls memory engram formation and malleability in the cortex

Initially hippocampal dependent, memory representations rely on a broadly distributed cortical network as they mature over time. How these cortical engrams acquire stability during systems-level memory consolidation without compromising their dynamic nature remains unclear. We identified a highly responsive "consolidation switch" in the synaptic composition of N-methyl-d-aspartate receptors (NMDARs), which dictates the progressive embedding and persistence of enduring memories in the rat cortex. Cortical GluN2B subunit-containing NMDARs were preferentially recruited upon encoding of associative olfactory memory to support neuronal allocation of memory engrams. As consolidation proceeds, a learning-induced redistribution of GluN2B subunit-containing NMDARs outward synapses increased synaptic GluN2A subunit contribution and enabled stabilization of remote memories. In contrast, synaptic reincorporation of GluN2B subunits occurred during subsequent forgetting. By manipulating the surface distribution of GluN2A and GluN2B subunit-containing NMDARs at cortical synapses, we uncovered that the rearrangement of GluN2B-containing NMDARs constitutes an essential tuning mechanism that determines the fate of cortical memory engrams and controls their malleability.

Brain-consistent architecture for imagination

Background

Imagination represents a pivotal capability of human intelligence. To develop human-like artificial intelligence, uncovering the computational architecture pertinent to imaginative capabilities through reverse engineering the brain's computational functions is essential. The existing Structure-Constrained Interface Decomposition (SCID) method, leverages the anatomical structure of the brain to extract computational architecture. However, its efficacy is limited to narrow brain regions, making it unsuitable for realizing the function of imagination, which involves diverse brain areas such as the neocortex, basal ganglia, thalamus, and hippocampus.

Objective

In this study, we proposed the Function-Oriented SCID method, an advancement over the existing SCID method, comprising four steps designed for reverse engineering broader brain areas. This method was applied to the brain's imaginative capabilities to design a hypothetical computational architecture. The implementation began with defining the human imaginative ability that we aspire to simulate. Subsequently, six critical requirements necessary for actualizing the defined imagination were identified. Constraints were established considering the unique representational capacity and the singularity of the neocortex's modes, a distributed memory structure responsible for executing imaginative functions. In line with these constraints, we developed five distinct functions to fulfill the requirements. We allocated specific components for each function, followed by an architectural proposal aligning each component with a corresponding brain organ.

Results

In the proposed architecture, the distributed memory component, associated with the neocortex, realizes the representation and execution function; the imaginary zone maker component, associated with the claustrum, accomplishes the dynamic-zone partitioning function; the routing conductor component, linked with the complex of thalamus and basal ganglia, performs the manipulation function; the mode memory component, related to the specific agranular neocortical area executes the mode maintenance function; and the recorder component, affiliated with the hippocampal formation, handles the history management function. Thus, we have provided a fundamental cognitive architecture of the brain that comprehensively covers the brain's imaginative capacities.

Effect of levels-of-processing on rates of forgetting

The levels-of-processing (LOP) framework, proposing that deep processing yields superior retention, has provided an important paradigm for memory research and a practical means of improving learning. However, the available levels-of-processing literature focuses on immediate memory performance. It is assumed within the LOP framework that deep processing will lead to slower forgetting than will shallow processing. However, it is unclear whether, or how, the initial level of processing affects the forgetting slopes over longer retention intervals. The present three experiments were designed to explore whether items encoded at qualitatively different LOP are forgotten at different rates. In the first two experiments, depth of processing was manipulated within-participants at encoding under deep and shallow conditions (semantic vs. rhyme judgement in Experiment 1; semantic vs. consonant-vowel pattern decision in Experiment 2). Recognition accuracy (d prime) was measured between-participants immediately after learning and at 30-min, 2-h, and 24-h delays. The third experiment employed a between-participants design, contrasting the rates of forgetting following semantic and phonological (rhyme) processing at immediate, 30-min, 2-h, and 6-h delays. Results from the three experiments consistently demonstrated a large effect size of levels of processing on immediate performance and a medium-to-large level effect size on delayed recognition, but crucially no LOP × delay group interaction. Analysis of the retention curves revealed no significant differences between the slopes of forgetting for deep and shallow processing. These results suggest that the rates of forgetting are independent of the qualitatively distinct encoding operations manipulated by levels of processing.

The homogenous hippocampus: How hippocampal cells process available and potential goals

We present here a view of the firing patterns of hippocampal cells that is contrary, both functionally and anatomically, to conventional wisdom. We argue that the hippocampus responds to efference copies of goals encoded elsewhere; and that it uses these to detect and resolve conflict or interference between goals in general. While goals can involve space, hippocampal cells do not encode spatial (or other special types of) memory, as such. We also argue that the transverse circuits of the hippocampus operate in an essentially homogeneous way along its length. The apparently different functions of different parts (e.g. memory retrieval versus anxiety) result from the different (situational/motivational) inputs on which those parts perform the same fundamental computational operations. On this view, the key role of the hippocampus is the iterative adjustment, via Papez-like circuits, of synaptic weights in cell assemblies elsewhere.

Remapping revisited: how the hippocampus represents different spaces

The representation of distinct spaces by hippocampal place cells has been linked to changes in their place fields (the locations in the environment where the place cells discharge strongly), a phenomenon that has been termed 'remapping'. Remapping has been assumed to be accompanied by the reorganization of subsecond cofiring relationships among the place cells, potentially maximizing hippocampal information coding capacity. However, several observations challenge this standard view. For example, place cells exhibit mixed selectivity, encode non-positional variables, can have multiple place fields and exhibit unreliable discharge in fixed environments. Furthermore, recent evidence suggests that, when measured at subsecond timescales, the moment-to-moment cofiring of a pair of cells in one environment is remarkably similar in another environment, despite remapping. Here, I propose that remapping is a misnomer for the changes in place fields across environments and suggest instead that internally organized manifold representations of hippocampal activity are actively registered to different environments to enable navigation, promote memory and organize knowledge.

A sparse quantized hopfield network for online-continual memory

An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-independent, identically distributed way. Further, synaptic plasticity in the brain depends only on information local to synapses. Deep networks, on the other hand, typically use non-local learning algorithms and are trained in an offline, non-noisy, independent, identically distributed setting. Understanding how neural networks learn under the same constraints as the brain is an open problem for neuroscience and neuromorphic computing. A standard approach to this problem has yet to be established. In this paper, we propose that discrete graphical models that learn via an online maximum a posteriori learning algorithm could provide such an approach. We implement this kind of model in a neural network called the Sparse Quantized Hopfield Network. We show our model outperforms state-of-the-art neural networks on associative memory tasks, outperforms these networks in online, continual settings, learns efficiently with noisy inputs, and is better than baselines on an episodic memory task.

Contemporary neurocognitive models of memory: A descriptive comparative analysis

The great complexity involved in the study of memory has given rise to numerous hypotheses and models associated with various phenomena at different levels of analysis. This has allowed us to delve deeper in our knowledge about memory but has also made it difficult to synthesize and integrate data from different lines of research. In this context, this work presents a descriptive comparative analysis of contemporary models that address the structure and function of multiple memory systems. The main goal is to outline a panoramic view of the key elements that constitute these models in order to visualize both the current state of research and possible future directions. The elements that stand out from different levels of analysis are distributed neural networks, hierarchical organization, predictive coding, homeostasis, and evolutionary perspective.

Feedback scales the spatial tuning of cortical responses during visual memory

Perception, working memory, and long-term memory each evoke neural responses in visual cortex, suggesting that memory uses encoding mechanisms shared with perception. While previous research has largely focused on how perception and memory are similar, we hypothesized that responses in visual cortex would differ depending on the origins of the inputs. Using fMRI, we quantified spatial tuning in visual cortex while participants (both sexes) viewed, maintained in working memory, or retrieved from long-term memory a peripheral target. In each of these conditions, BOLD responses were spatially tuned and were aligned with the target's polar angle in all measured visual field maps including V1. As expected given the increasing sizes of receptive fields, polar angle tuning during perception increased in width systematically up the visual hierarchy from V1 to V2, V3, hV4, and beyond. In stark contrast, the widths of tuned responses were broad across the visual hierarchy during working memory and long-term memory, matched to the widths in perception in later visual field maps but much broader in V1. This pattern is consistent with the idea that mnemonic responses in V1 stem from top-down sources. Moreover, these tuned responses when biased (clockwise or counterclockwise of target) predicted matched biases in memory, suggesting that the readout of maintained and reinstated mnemonic responses influences memory guided behavior. We conclude that feedback constrains spatial tuning during memory, where earlier visual maps inherit broader tuning from later maps thereby impacting the precision of memory.

Barcoding of episodic memories in the hippocampus of a food-caching bird

The hippocampus is critical for episodic memory. Although hippocampal activity represents place and other behaviorally relevant variables, it is unclear how it encodes numerous memories of specific events in life. To study episodic coding, we leveraged the specialized behavior of chickadees-food-caching birds that form memories at well-defined moments in time whenever they cache food for subsequent retrieval. Our recordings during caching revealed very sparse, transient barcode-like patterns of firing across hippocampal neurons. Each "barcode" uniquely represented a caching event and transiently reactivated during the retrieval of that specific cache. Barcodes co-occurred with the conventional activity of place cells but were uncorrelated even for nearby cache locations that had similar place codes. We propose that animals recall episodic memories by reactivating hippocampal barcodes. Similarly to computer hash codes, these patterns assign unique identifiers to different events and could be a mechanism for rapid formation and storage of many non-interfering memories.

Beyond hippocampus: Thalamic and prefrontal contributions to an evolving memory

The hippocampus has long been at the center of memory research, and rightfully so. However, with emerging technological capabilities, we can increasingly appreciate memory as a more dynamic and brain-wide process. In this perspective, our goal is to begin developing models to understand the gradual evolution, reorganization, and stabilization of memories across the brain after their initial formation in the hippocampus. By synthesizing studies across the rodent and human literature, we suggest that as memory representations initially form in hippocampus, parallel traces emerge in frontal cortex that cue memory recall, and as they mature, with sustained support initially from limbic then diencephalic then cortical circuits, they become progressively independent of hippocampus and dependent on a mature cortical representation. A key feature of this model is that, as time progresses, memory representations are passed on to distinct circuits with progressively longer time constants, providing the opportunity to filter, forget, update, or reorganize memories in the process of committing to long-term storage.

Overnight neuronal plasticity and adaptation to emotional distress

Expressions such as 'sleep on it' refer to the resolution of distressing experiences across a night of sound sleep. Sleep is an active state during which the brain reorganizes the synaptic connections that form memories. This Perspective proposes a model of how sleep modifies emotional memory traces. Sleep-dependent reorganization occurs through neurophysiological events in neurochemical contexts that determine the fates of synapses to grow, to survive or to be pruned. We discuss how low levels of acetylcholine during non-rapid eye movement sleep and low levels of noradrenaline during rapid eye movement sleep provide a unique window of opportunity for plasticity in neuronal representations of emotional memories that resolves the associated distress. We integrate sleep-facilitated adaptation over three levels: experience and behaviour, neuronal circuits, and synaptic events. The model generates testable hypotheses for how failed sleep-dependent adaptation to emotional distress is key to mental disorders, notably disorders of anxiety, depression and post-traumatic stress with the common aetiology of insomnia.

Mystery of the memory engram: History, current knowledge, and unanswered questions

The quest to understand the memory engram has intrigued humans for centuries. Recent technological advances, including genetic labelling, imaging, optogenetic and chemogenetic techniques, have propelled the field of memory research forward. These tools have enabled researchers to create and erase memory components. While these innovative techniques have yielded invaluable insights, they often focus on specific elements of the memory trace. Genetic labelling may rely on a particular immediate early gene as a marker of activity, optogenetics may activate or inhibit one specific type of neuron, and imaging may capture activity snapshots in a given brain region at specific times. Yet, memories are multifaceted, involving diverse arrays of neuronal subpopulations, circuits, and regions that work in concert to create, store, and retrieve information. Consideration of contributions of both excitatory and inhibitory neurons, micro and macro circuits across brain regions, the dynamic nature of active ensembles, and representational drift is crucial for a comprehensive understanding of the complex nature of memory.

Differential effects of bilateral hippocampal CA3 damage on the implicit learning and recognition of complex event sequences

Learning regularities in the environment is a fundament of human cognition, which is supported by a network of brain regions that include the hippocampus. In two experiments, we assessed the effects of selective bilateral damage to human hippocampal subregion CA3, which was associated with autobiographical episodic amnesia extending ~50 years prior to the damage, on the ability to recognize complex, deterministic event sequences presented either in a spatial or a non-spatial configuration. In contrast to findings from related paradigms, modalities, and homologue species, hippocampal damage did not preclude recognition memory for an event sequence studied and tested at four spatial locations, whereas recognition memory for an event sequence presented at a single location was at chance. In two additional experiments, recognition memory for novel single-items was intact, whereas the ability to recognize novel single-items in a different location from that presented at study was at chance. The results are at variance with a general role of the hippocampus in the learning and recognition of complex event sequences based on non-adjacent spatial and temporal dependencies. We discuss the impact of the results on established theoretical accounts of the hippocampal contributions to implicit sequence learning and episodic memory.

Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus

Pattern separation is a valuable computational function performed by neuronal circuits, such as the dentate gyrus, where dissimilarity between inputs is increased, reducing noise and increasing the storage capacity of downstream networks. Pattern separation is studied from both in vivo experimental and computational perspectives and, a number of different measures (such as orthogonalisation, decorrelation, or spike train distance) have been applied to quantify the process of pattern separation. However, these are known to give conclusions that can differ qualitatively depending on the choice of measure and the parameters used to calculate it. We here demonstrate that arbitrarily increasing sparsity, a noticeable feature of dentate granule cell firing and one that is believed to be key to pattern separation, typically leads to improved classical measures for pattern separation even, inappropriately, up to the point where almost all information about the inputs is lost. Standard measures therefore both cannot differentiate between pattern separation and pattern destruction, and give results that may depend on arbitrary parameter choices. We propose that techniques from information theory, in particular mutual information, transfer entropy, and redundancy, should be applied to penalise the potential for lost information (often due to increased sparsity) that is neglected by existing measures. We compare five commonly-used measures of pattern separation with three novel techniques based on information theory, showing that the latter can be applied in a principled way and provide a robust and reliable measure for comparing the pattern separation performance of different neurons and networks. We demonstrate our new measures on detailed compartmental models of individual dentate granule cells and a dentate microcircuit, and show how structural changes associated with epilepsy affect pattern separation performance. We also demonstrate how our measures of pattern separation can predict pattern completion accuracy. Overall, our measures solve a widely acknowledged problem in assessing the pattern separation of neural circuits such as the dentate gyrus, as well as the cerebellum and mushroom body. Finally we provide a publicly available toolbox allowing for easy analysis of pattern separation in spike train ensembles.

Distinguishing examples while building concepts in hippocampal and artificial networks

The hippocampal subfield CA3 is thought to function as an auto-associative network that stores experiences as memories. Information from these experiences arrives directly from the entorhinal cortex as well as indirectly through the dentate gyrus, which performs sparsification and decorrelation. The computational purpose for these dual input pathways has not been firmly established. We model CA3 as a Hopfield-like network that stores both dense, correlated encodings and sparse, decorrelated encodings. As more memories are stored, the former merge along shared features while the latter remain distinct. We verify our model's prediction in rat CA3 place cells, which exhibit more distinct tuning during theta phases with sparser activity. Finally, we find that neural networks trained in multitask learning benefit from a loss term that promotes both correlated and decorrelated representations. Thus, the complementary encodings we have found in CA3 can provide broad computational advantages for solving complex tasks.

The Nonclassic Psychedelic Ibogaine Disrupts Cognitive Maps

Background

The ability of psychedelic compounds to profoundly alter mental function has been long known, but the underlying changes in cellular-level information encoding remain poorly understood.

Methods

We used two-photon microscopy to record from the retrosplenial cortex in head-fixed mice running on a treadmill before and after injection of the nonclassic psychedelic ibogaine (40 mg/kg intraperitoneally).

Results

We found that the cognitive map, formed by the representation of position encoded by ensembles of individual neurons in the retrosplenial cortex, was destabilized by ibogaine when mice had to infer position between tactile landmarks. This corresponded with increased neural activity rates, loss of correlation structure, and increased responses to cues. Ibogaine had surprisingly little effect on the size-frequency distribution of network activity events, suggesting that signal propagation within the retrosplenial cortex was largely unaffected.

Conclusions

Taken together, these data support proposals that compounds with psychedelic properties disrupt representations that are important for constraining neocortical activity, thereby increasing the entropy of neural signaling. Furthermore, the loss of expected position encoding between landmarks recapitulated effects of hippocampal impairment, suggesting that disruption of cognitive maps or other hippocampal processing may be a contributing mechanism of discoordinated neocortical activity in psychedelic states.

Memory Storage in Distributed Engram Cell Ensembles

One of the most fascinating aspects of the brain is its ability to acquire new information from experience and retain it over time as memory. The search for physical correlates of memory, the memory engram, has been a longstanding priority in modern neurobiology. Advanced genetic approaches have led to the localization of engram cells in a few brain regions, including the hippocampus and cortex. Additionally, engram cells exhibit learning-induced, persistent modifications and have at least two states, active and silent. However, it has been hypothesized that engrams for a specific memory are distributed among multiple brain regions that are functionally connected, referred to as a unified engram complex. Recent tissue-clearing techniques have permitted high-throughput analyses of intact brain samples, which have been used to obtain a map of the engram complex for a contextual fear memory. Careful examination of these engram complex maps has revealed a potentially underappreciated contribution of subcortical regions, specifically thalamic nuclei, to memory function. These more holistic studies support the unified engram complex hypothesis for memory storage and have important implications for understanding dysfunctional engrams in the context of human disease.

Theta mediated dynamics of human hippocampal-neocortical learning systems in memory formation and retrieval

Episodic memory arises as a function of dynamic interactions between the hippocampus and the neocortex, yet the mechanisms have remained elusive. Here, using human intracranial recordings during a mnemonic discrimination task, we report that 4-5 Hz (theta) power is differentially recruited during discrimination vs. overgeneralization, and its phase supports hippocampal-neocortical when memories are being formed and correctly retrieved. Interactions were largely bidirectional, with small but significant net directional biases; a hippocampus-to-neocortex bias during acquisition of new information that was subsequently correctly discriminated, and a neocortex-to-hippocampus bias during accurate discrimination of new stimuli from similar previously learned stimuli. The 4-5 Hz rhythm may facilitate the initial stages of information acquisition by neocortex during learning and the recall of stored information from cortex during retrieval. Future work should further probe these dynamics across different types of tasks and stimuli and computational models may need to be expanded accordingly to accommodate these findings.

Sharpening the blades of the dentate gyrus: how adult-born neurons differentially modulate diverse aspects of hippocampal learning and memory

For decades, the mammalian hippocampus has been the focus of cellular, anatomical, behavioral, and computational studies aimed at understanding the fundamental mechanisms underlying cognition. Long recognized as the brain's seat for learning and memory, a wealth of knowledge has been accumulated on how the hippocampus processes sensory input, builds complex associations between objects, events, and space, and stores this information in the form of memories to be retrieved later in life. However, despite major efforts, our understanding of hippocampal cognitive function remains fragmentary, and models trying to explain it are continually revisited. Here, we review the literature across all above-mentioned domains and offer a new perspective by bringing attention to the most distinctive, and generally neglected, feature of the mammalian hippocampal formation, namely, the structural separability of the two blades of the dentate gyrus into "supra-pyramidal" and "infra-pyramidal". Next, we discuss recent reports supporting differential effects of adult neurogenesis in the regulation of mature granule cell activity in these two blades. We propose a model for how differences in connectivity and adult neurogenesis in the two blades can potentially provide a substrate for subtly different cognitive functions.

Spiking neural network-based computational modeling of episodic memory

In this research article, a spiking neural network-based simulation of the hippocampus is performed to model the functionalities of episodic memory. The purpose of the simulation is to find a computational model through the biological architecture of the hippocampus and correct values for their architectural biological parameters to support the episodic memory functionalities. The episodic store of the model is represented by the collection of events, where each event is further subdivided into coactive activities of experience. The model has tried to mimic the three functionalities of episodic memory, which are pattern separation, pattern association, and their recallings. In pattern separation model used the dentate biological connectivity to generate almost different output patterns corresponding to similar input patterns to reduce interference between two similar memory traces so that ambiguity can be reduced during recalling. In pattern association, an STDP based event encoding and forgetting mechanism are used to mimic the encoding function of the CA3 region in which the coactive activities get associated with each other. A decoder is proposed based on CA1, which can answer the stored event related queries. Along with these functionalities model also supports recalling and encoding based forgetting. Experimental work is performed on the model for the given set of events to check for the pattern separation efficiency, pattern completion efficiency and to check the capability of decoding the answer. An empirical analysis of the results is done and compared with the SMRITI model of episodic memory.

Hippocampal neurons code individual episodic memories in humans

The hippocampus is an essential hub for episodic memory processing. However, how human hippocampal single neurons code multi-element associations remains unknown. In particular, it is debated whether each hippocampal neuron represents an invariant element within an episode or whether single neurons bind together all the elements of a discrete episodic memory. Here we provide evidence for the latter hypothesis. Using single-neuron recordings from a total of 30 participants, we show that individual neurons, which we term episode-specific neurons, code discrete episodic memories using either a rate code or a temporal firing code. These neurons were observed exclusively in the hippocampus. Importantly, these episode-specific neurons do not reflect the coding of a particular element in the episode (that is, concept or time). Instead, they code for the conjunction of the different elements that make up the episode.

Theta mediated dynamics of human hippocampal-neocortical learning systems in memory formation and retrieval

Episodic memory arises as a function of dynamic interactions between the hippocampus and the neocortex, yet the mechanisms have remained elusive. Here, using human intracranial recordings during a mnemonic discrimination task, we report that 4-5 Hz (theta) power is differentially recruited during discrimination vs. overgeneralization, and its phase supports hippocampal-neocortical when memories are being formed and correctly retrieved. Interactions were largely bidirectional, with small but significant net directional biases; a hippocampus-to-neocortex bias during acquisition of new information that was subsequently correctly discriminated, and a neocortex-to-hippocampus bias during accurate discrimination of new stimuli from similar previously learned stimuli. The 4-5 Hz rhythm may facilitate the initial stages of information acquisition by neocortex during learning and the recall of stored information from cortex during retrieval. Future work should further probe these dynamics across different types of tasks and stimuli and computational models may need to be expanded accordingly to accommodate these findings.

Hippocampal orchestration of associative and sequential memory networks for episodic retrieval

Episodic memory involves the recollection of contextual details replayed mentally across time. Here, we propose the association-sequence network (ASN) model, characterizing complementary cortico-hippocampal networks underlying the retrieval of simultaneously associated and sequentially ordered events. Participants viewed objects, presented singly or in pairs, and later reported whether two objects were shown simultaneously, consecutively, or farther apart in time. Behavioral results and hippocampal activation reveal a correlation between the two sequential conditions but not the simultaneous condition, despite the temporal proximity of consecutive pairs. We also find that anterior hippocampal activity is modulated by temporal distance. Distinct cortical networks are engaged during simultaneous and sequential memory (prefrontal cortex and angular gyrus for association; supplementary motor cortex and precuneus for sequence); notably, these regions show differential connectivity with the hippocampus. The ASN model provides a comprehensive framework for how we reconstruct memories that are both rich in associative detail and temporally dynamic in nature.

Tissue clearing applications in memory engram research

A memory engram is thought to be the physical substrate of the memory trace within the brain, which is generally depicted as a neuronal ensemble activated by learning to fire together during encoding and retrieval. It has been postulated that engram cell ensembles are functionally interconnected across multiple brain regions to store a single memory as an "engram complex", but visualizing this engram complex across the whole brain has for long been hindered by technical limitations. With the recent development of tissue clearing techniques, advanced light-sheet microscopy, and automated 3D image analysis, it has now become possible to generate a brain-wide map of engram cells and thereby to visualize the "engram complex". In this review, we first provide a comprehensive summary of brain-wide engram mapping studies to date. We then compile a guide on implementing the optimal tissue clearing technique for engram tagging approaches, paying particular attention to visualize engram reactivation as a critical mnemonic property, for which whole-brain multiplexed immunostaining becomes a challenging prerequisite. Finally, we highlight the potential of tissue clearing to simultaneously shed light on both the circuit connectivity and molecular underpinnings of engram cells in a single snapshot. In doing so, novel brain regions and circuits can be identified for subsequent functional manipulation, thus providing an opportunity to robustly examine the "engram complex" underlying memory storage.

Engram neurons: Encoding, consolidation, retrieval, and forgetting of memory

Tremendous strides have been made in our understanding of the neurobiological substrates of memory - the so-called memory "engram". Here, we integrate recent progress in the engram field to illustrate how engram neurons transform across the "lifespan" of a memory - from initial memory encoding, to consolidation and retrieval, and ultimately to forgetting. To do so, we first describe how cell-intrinsic properties shape the initial emergence of the engram at memory encoding. Second, we highlight how these encoding neurons preferentially participate in synaptic- and systems-level consolidation of memory. Third, we describe how these changes during encoding and consolidation guide neural reactivation during retrieval, and facilitate memory recall. Fourth, we describe neurobiological mechanisms of forgetting, and how these mechanisms can counteract engram properties established during memory encoding, consolidation, and retrieval. Motivated by recent experimental results across these four sections, we conclude by proposing some conceptual extensions to the traditional view of the engram, including broadening the view of cell-type participation within engrams and across memory stages. In collection, our review synthesizes general principles of the engram across memory stages, and describes future avenues to further understand the dynamic engram.

The Computational and Neural Bases of Context-Dependent Learning

Flexible behavior requires the creation, updating, and expression of memories to depend on context. While the neural underpinnings of each of these processes have been intensively studied, recent advances in computational modeling revealed a key challenge in context-dependent learning that had been largely ignored previously: Under naturalistic conditions, context is typically uncertain, necessitating contextual inference. We review a theoretical approach to formalizing context-dependent learning in the face of contextual uncertainty and the core computations it requires. We show how this approach begins to organize a large body of disparate experimental observations, from multiple levels of brain organization (including circuits, systems, and behavior) and multiple brain regions (most prominently the prefrontal cortex, the hippocampus, and motor cortices), into a coherent framework. We argue that contextual inference may also be key to understanding continual learning in the brain. This theory-driven perspective places contextual inference as a core component of learning.

Fear memory recall involves hippocampal somatostatin interneurons

Fear-related memory traces are encoded by sparse populations of hippocampal principal neurons that are recruited based on their inhibitory-excitatory balance during memory formation. Later, the reactivation of the same principal neurons can recall the memory. The details of this mechanism are still unclear. Here, we investigated whether disinhibition could play a major role in this process. Using optogenetic behavioral experiments, we found that when fear was associated with the inhibition of mouse hippocampal somatostatin positive interneurons, the re-inhibition of the same interneurons could recall fear memory. Pontine nucleus incertus neurons selectively inhibit hippocampal somatostatin cells. We also found that when fear was associated with the activity of these incertus neurons or fibers, the reactivation of the same incertus neurons or fibers could also recall fear memory. These incertus neurons showed correlated activity with hippocampal principal neurons during memory recall and were strongly innervated by memory-related neocortical centers, from which the inputs could also control hippocampal disinhibition in vivo. Nonselective inhibition of these mouse hippocampal somatostatin or incertus neurons impaired memory recall. Our data suggest a novel disinhibition-based memory mechanism in the hippocampus that is supported by local somatostatin interneurons and their pontine brainstem inputs.

Pattern Completion and Rate Remapping in Retrosplenial Cortex

Principles governing the encoding, storage, and updating of memories in cortical networks are poorly understood. In retrosplenial cortex (RSC), cells respond to the animal's position as it navigates a real or virtual (VR) linear track. Position correlated cells (PCCs) in RSC require an intact hippocampus to form. To examine whether PCCs undergo pattern completion and remapping like hippocampal cells, neuronal activity in RSC or CA1 was recorded using two-photon calcium imaging in mice running on VR tracks. RSC and CA1 PCC activity underwent global and rate remapping depending on the degree of change to familiar environments. The formation of position correlated fields in both regions required stability across laps; however, once formed, PCCs became robust to object destabilization, indicating pattern completion of the previously formed memory. Thus, memory and remapping properties were conserved between RSC and CA1, suggesting that these functional properties are transmitted to cortex to support memory functions.

Consolidation of cellular memory representations in superficial neocortex

Systems-level memory consolidation, a key concept in memory research, involves the conversion of memories that depend on the hippocampus for their formation into efficient hippocampus-independent forms, presumably encoded by cortico-cortical connections. Yet, little is understood about the nature of consolidated neural codes at the cellular ensemble level. Mice require an intact hippocampus for "virtual" spatial learning and to develop neocortical representations of the corresponding experiences. We find that, whereas a novel virtual environment is neither learned nor represented in superficial cortex following severe damage to hippocampus, pre-operatively learned memories and their corresponding sparse and widespread neural ensemble representations in cortical layers II-III are preserved, a sine qua non of memory consolidation. These findings provide a new window for future study of the cellular mechanisms of memory consolidation.

Replay and compositional computation

Replay in the brain has been viewed as rehearsal or, more recently, as sampling from a transition model. Here, we propose a new hypothesis: that replay is able to implement a form of compositional computation where entities are assembled into relationally bound structures to derive qualitatively new knowledge. This idea builds on recent advances in neuroscience, which indicate that the hippocampus flexibly binds objects to generalizable roles and that replay strings these role-bound objects into compound statements. We suggest experiments to test our hypothesis, and we end by noting the implications for AI systems which lack the human ability to radically generalize past experience to solve new problems.

The Tensor Brain: A Unified Theory of Perception, Memory, and Semantic Decoding

We present a unified computational theory of an agent's perception and memory. In our model, both perception and memory are realized by different operational modes of the oscillating interactions between a symbolic index layer and a subsymbolic representation layer. The two layers form a bilayer tensor network (BTN). The index layer encodes indices for concepts, predicates, and episodic instances. The representation layer broadcasts information and reflects the cognitive brain state; it is our model of what authors have called the "mental canvas" or the "global workspace." As a bridge between perceptual input and the index layer, the representation layer enables the grounding of indices by their subsymbolic embeddings, which are implemented as connection weights linking both layers. The propagation of activation to earlier perceptual processing layers in the brain can lead to embodiments of indices. Perception and memories first create subsymbolic representations, which are subsequently decoded semantically to produce sequences of activated indices that form symbolic triple statements. The brain is a sampling engine: only activated indices are communicated to the remaining parts of the brain. Triple statements are dynamically embedded in the representation layer and embodied in earlier processing layers: the brain speaks to itself. Although memory appears to be about the past, its main purpose is to support the agent in the present and the future. Recent episodic memory provides the agent with a sense of the here and now. Remote episodic memory retrieves relevant past experiences to provide information about possible future scenarios. This aids the agent in decision making. "Future" episodic memory, based on expected future events, guides planning and action. Semantic memory retrieves specific information, which is not delivered by current perception, and defines priors for future observations. We argue that it is important for the agent to encode individual entities, not just classes and attributes. Perception is learning: episodic memories are constantly being formed, and we demonstrate that a form of self-supervised learning can acquire new concepts and refine existing ones. We test our model on a standard benchmark data set, which we expanded to contain richer representations for attributes, classes, and individuals. Our key hypothesis is that obtaining a better understanding of perception and memory is a crucial prerequisite to comprehending human-level intelligence.

Unraveling the mysteries of dendritic spine dynamics: Five key principles shaping memory and cognition

Recent research extends our understanding of brain processes beyond just action potentials and chemical transmissions within neural circuits, emphasizing the mechanical forces generated by excitatory synapses on dendritic spines to modulate presynaptic function. From in vivo and in vitro studies, we outline five central principles of synaptic mechanics in brain function: P1: Stability - Underpinning the integral relationship between the structure and function of the spine synapses. P2: Extrinsic dynamics - Highlighting synapse-selective structural plasticity which plays a crucial role in Hebbian associative learning, distinct from pathway-selective long-term potentiation (LTP) and depression (LTD). P3: Neuromodulation - Analyzing the role of G-protein-coupled receptors, particularly dopamine receptors, in time-sensitive modulation of associative learning frameworks such as Pavlovian classical conditioning and Thorndike's reinforcement learning (RL). P4: Instability - Addressing the intrinsic dynamics crucial to memory management during continual learning, spotlighting their role in "spine dysgenesis" associated with mental disorders. P5: Mechanics - Exploring how synaptic mechanics influence both sides of synapses to establish structural traces of short- and long-term memory, thereby aiding the integration of mental functions. We also delve into the historical background and foresee impending challenges.

Has the concept of systems consolidation outlived its usefulness? Identification and evaluation of premises underlying systems consolidation

Systems consolidation has mostly been treated as a neural construct defined by the time-dependent change in memory representation from the hippocampus (HPC) to other structures, primarily the neocortex. Here, we identify and evaluate the explicit and implicit premises that underlie traditional or standard models and theories of systems consolidation based on evidence from research on humans and other animals. We use the principle that changes in neural representation over time and experience are accompanied by corresponding changes in psychological representations, and vice versa, to argue that each of the premises underlying traditional or standard models and theories of systems consolidation is found wanting. One solution is to modify or abandon the premises or theories and models. This is reflected in moderated models of systems consolidation that emphasize the early role of the HPC in training neocortical memories until they stabilize. The fault, however, may lie in the very concept of systems consolidation and its defining feature. We propose that the concept be replaced by one of memory systems reorganization, which does not carry the theoretical baggage of systems consolidation and is flexible enough to capture the dynamic nature of memory from inception to very long-term retention and retrieval at a psychological and neural level. The term "memory system reorganization" implies that memory traces are not fixed, even after they are presumably consolidated. Memories can continue to change as a result of experience and interactions among memory systems across the lifetime. As will become clear, hippocampal training of neocortical memories is only one type of such interaction, and not always the most important one, even at inception. We end by suggesting some principles of memory reorganization that can help guide research on dynamic memory processes that capture corresponding changes in memory at the psychological and neural levels.

The dorsal hippocampus' role in context-based timing in rodents

To act proactively, we must predict when future events will occur. Individuals generate temporal predictions using cues that indicate an event will happen after a certain duration elapses. Neural models of timing focus on how the brain represents these cue-duration associations. However, these models often overlook the fact that situational factors frequently modulate temporal expectations. For example, in realistic environments, the intervals associated with different cues will often covary due to a common underlying cause. According to the 'common cause hypothesis,' observers anticipate this covariance such that, when one cue's interval changes, temporal expectations for other cues shift in the same direction. Furthermore, as conditions will often differ across environments, the same cue can mean different things in different contexts. Therefore, updates to temporal expectations should be context-specific. Behavioral work supports these predictions, yet their underlying neural mechanisms are unclear. Here, we asked whether the dorsal hippocampus mediates context-based timing, given its broad role in context-conditioning. Specifically, we trained rats with either hippocampal or sham lesions that two cues predicted reward after either a short or long duration elapsed (e.g., tone-8 s/light-16 s). Then, we moved rats to a new context and extended the long cue's interval (e.g., light-32 s). This caused rats to respond later to the short cue, despite never being trained to do so. Importantly, when returned to the initial training context, sham rats shifted back toward both cues' original intervals. In contrast, lesion rats continued to respond at the long cue's newer interval. Surprisingly, they still showed contextual modulation for the short cue, responding earlier like shams. These data suggest the hippocampus only mediates context-based timing if a cue is explicitly paired and/or rewarded across distinct contexts. Furthermore, as lesions did not impact timing measures at baseline or acquisition for the long cue's new interval, our data suggests that the hippocampus only modulates timing when context is relevant.

Stronger memory representation after memory reinstatement during retrieval in the human hippocampus

Memory retrieval allows us to reinstate previously encoded information but is also considered to contribute to memory enhancement. Retrieval-induced enhancement may involve processing to strengthen memory traces, but neural processing beyond reinstatement during retrieval remains elusive. Here, we show that hippocampal processing, different from memory reinstatement, exists during retrieval in the human brain. By tracking changes in the response patterns in the selected hippocampal and cortical regions over time during retrieval based on functional MRI, we found that the representation of associative memory in CA3/DG became stronger even after cortical memory reinstatement, while CA1 showed significant memory representation at retrieval onset with the cortical reinstatement, but not afterwards. This tendency was not observed in the condition without active retrieval. Moreover, subsequent long-term memory performance depended on the delayed CA3/DG representation during retrieval. These findings suggest that CA3/DG contributes to neural processing beyond memory reinstatement during retrieval, which may lead to memory enhancement.

A Model of Semantic Completion in Generative Episodic Memory

Many studies have suggested that episodic memory is a generative process, but most computational models adopt a storage view. In this article, we present a model of the generative aspects of episodic memory. It is based on the central hypothesis that the hippocampus stores and retrieves selected aspects of an episode as a memory trace, which is necessarily incomplete. At recall, the neocortex reasonably fills in the missing parts based on general semantic information in a process we call semantic completion. The model combines two neural network architectures known from machine learning, the vector-quantized variational autoencoder (VQ-VAE) and the pixel convolutional neural network (PixelCNN). As episodes, we use images of digits and fashion items (MNIST) augmented by different backgrounds representing context. The model is able to complete missing parts of a memory trace in a semantically plausible way up to the point where it can generate plausible images from scratch, and it generalizes well to images not trained on. Compression as well as semantic completion contribute to a strong reduction in memory requirements and robustness to noise. Finally, we also model an episodic memory experiment and can reproduce that semantically congruent contexts are always recalled better than incongruent ones, high attention levels improve memory accuracy in both cases, and contexts that are not remembered correctly are more often remembered semantically congruently than completely wrong. This model contributes to a deeper understanding of the interplay between episodic memory and semantic information in the generative process of recalling the past.

Real-time visualization of mRNA synthesis during memory formation in live mice

Arc is one of the genes that are rapidly transcribed by neuronal activity and thus used as a marker for memory trace or engram cells. However, the dynamics of engram cell populations is not well-known because of the difficulty in monitoring the rapid and transient gene expression in live animals. Using a mouse model in which endogenous Arc messenger RNA (mRNA) is fluorescently labeled, we demonstrate that Arc-expressing neuronal populations have distinct dynamics in different brain regions and that only a small subpopulation that consistently expresses Arc during both memory encoding and retrieval exhibits context-specific calcium activity. This live-animal RNA-imaging technique will offer a powerful tool for connecting gene expression to neuronal activity patterns and to behavior.

Memories are thought to be encoded in populations of neurons called memory trace or engram cells. However, little is known about the dynamics of these cells because of the difficulty in real-time monitoring of them over long periods of time in vivo. To overcome this limitation, we present a genetically encoded RNA indicator (GERI) mouse for intravital chronic imaging of endogenous Arc messenger RNA (mRNA)—a popular marker for memory trace cells. We used our GERI to identify Arc-positive neurons in real time without the delay associated with reporter protein expression in conventional approaches. We found that the Arc-positive neuronal populations rapidly turned over within 2 d in the hippocampal CA1 region, whereas ∼4% of neurons in the retrosplenial cortex consistently expressed Arc following contextual fear conditioning and repeated memory retrievals. Dual imaging of GERI and a calcium indicator in CA1 of mice navigating a virtual reality environment revealed that only the population of neurons expressing Arc during both encoding and retrieval exhibited relatively high calcium activity in a context-specific manner. This in vivo RNA-imaging approach opens the possibility of unraveling the dynamics of the neuronal population underlying various learning and memory processes.

A way forward for design and analysis of neuroimaging studies of memory consolidation

Several novel ideas and suggestions were made in response to our discussion paper (Tallman et al., this issue). Careful consideration of the content and context of memory while accounting for the neuroanatomy and functional specialization of the hippocampus may reveal more consistent patterns in fMRI studies of memory consolidation. Below we address these ideas as well as issues that arise when interpreting the fMRI signal in memory consolidation studies. In addition, we describe new analyses suggested by the commentators that clarify our findings with respect to current theories.

Hippocampal Estrogen Signaling Mediates Sex Differences in Retroactive Interference

Despite being a crucial physiological function of the brain, the mechanisms underlying forgetting are still poorly understood. Estrogens play a critical role in different brain functions, including memory. However, the effects of sex hormones on forgetting vulnerabilitymediated by retroactive interference (RI), a phenomenon in which newly acquired information interferes with the retrieval of already stored information, are still poorly understood. The aim of our study was to characterize the sex differences in interference-mediated forgetting and identify the underlying molecular mechanisms. We found that adult male C57bl/6 mice showed a higher susceptibility to RI-dependent memory loss than females. The preference index (PI) in the NOR paradigm was 52.7 ± 5.9% in males and 62.3 ± 13.0% in females. The resistance to RI in female mice was mediated by estrogen signaling involving estrogen receptor α activation in the dorsal hippocampus. Accordingly, following RI, females showed higher phosphorylation levels (+30%) of extracellular signal-regulated kinase1/2 (ERK1/2) in the hippocampus. Pharmacological inhibition of ERK1/2 made female mice prone to RI. The PI was 70.6 ± 11.0% in vehicle-injected mice and 47.4 ± 10.8% following PD98059 administration. Collectively, our data suggest that hippocampal estrogen α receptor-ERK1/2 signaling is critically involved in a pattern separation mechanism that inhibits object-related RI in female mice.

Brain-wide mapping reveals that engrams for a single memory are distributed across multiple brain regions

Neuronal ensembles that hold specific memory (memory engrams) have been identified in the hippocampus, amygdala, or cortex. However, it has been hypothesized that engrams of a specific memory are distributed among multiple brain regions that are functionally connected, referred to as a unified engram complex. Here, we report a partial map of the engram complex for contextual fear conditioning memory by characterizing encoding activated neuronal ensembles in 247 regions using tissue phenotyping in mice. The mapping was aided by an engram index, which identified 117 cFos+ brain regions holding engrams with high probability, and brain-wide reactivation of these neuronal ensembles by recall. Optogenetic manipulation experiments revealed engram ensembles, many of which were functionally connected to hippocampal or amygdala engrams. Simultaneous chemogenetic reactivation of multiple engram ensembles conferred a greater level of memory recall than reactivation of a single engram ensemble, reflecting the natural memory recall process. Overall, our study supports the unified engram complex hypothesis for memory storage.

Acquiring new memories in neocortex of hippocampal-lesioned mice

The hippocampus interacts with the neocortical network for memory retrieval and consolidation. Here, we found the lateral entorhinal cortex (LEC) modulates learning-induced cortical long-range gamma synchrony (20–40 Hz) in a hippocampal-dependent manner. The long-range gamma synchrony, which was coupled to the theta (7–10 Hz) rhythm and enhanced upon learning and recall, was mediated by inter-cortical projections from layer 5 neurons of the LEC to layer 2 neurons of the sensory and association cortices. Artificially induced cortical gamma synchrony across cortical areas improved memory encoding in hippocampal lesioned mice for originally hippocampal-dependent tasks. Mechanistically, we found that activities of cortical c-Fos labeled neurons, which showed egocentric map properties, were modulated by LEC-mediated gamma synchrony during memory recall, implicating a role of cortical synchrony to generate an integrative memory representation from disperse features. Our findings reveal the hippocampal mediated organization of cortical memories and suggest brain-machine interface approaches to improve cognitive function.

Hippocampal lesioned mice form new memories. Here, the authors show the lateral entorhinal cortex modulates learning-induced cortical long-range gamma synchrony in a hippocampal-dependent manner and artificially induced cortical gamma synchrony across cortical areas improved memory encoding in hippocampal lesioned mice.

Brain activity patterns underlying memory confidence

The primary aim of this review is to examine the brain activity patterns that are related to subjectively perceived memory confidence. We focus on the main brain regions involved in episodic memory: the medial temporal lobe (MTL), prefrontal cortex (PFC), and posterior parietal cortex (PPC), and relate activity in their subregions to memory confidence. How this brain activity in both the encoding and retrieval phase is related to (subsequent) memory confidence ratings will be discussed. Specifically, encoding related activity in MTL regions and ventrolateral PFC mainly shows a positive linear increase with subsequent memory confidence, while dorsolateral and ventromedial PFC activity show mixed patterns. In addition, encoding-related PPC activity seems to only have indirect effects on memory confidence ratings. Activity during retrieval in both the hippocampus and parahippocampal cortex increases with memory confidence, especially during high-confident recognition. Retrieval-related activity in the PFC and PPC show mixed relationships with memory confidence, likely related to post-retrieval monitoring and attentional processes, respectively. In this review, these MTL, PFC, and PPC activity patterns are examined in detail and related to their functional roles in memory processes. This insight into brain activity that underlies memory confidence is important for our understanding of brain-behaviour relations and memory-guided decision making.

De novo inter-regional coactivations of preconfigured local ensembles support memory

Neuronal ensembles in the amygdala, ventral hippocampus, and prefrontal cortex are involved in fear memory; however, how inter-regional ensemble interactions support memory remains elusive. Using multi-regional large-scale electrophysiology in the aforementioned structures of fear-conditioned rats, we found that the local ensembles activated during fear memory acquisition are inter-regionally coactivated during the subsequent sleep period, which relied on brief bouts of fast network oscillations. During memory retrieval, the coactivations reappeared, together with fast oscillations. Coactivation-participating-ensembles were configured prior to memory acquisition in the amygdala and prefrontal cortex but developed through experience in the hippocampus. Our findings suggest that elements of a given memory are instantly encoded within various brain regions in a preconfigured manner, whereas hippocampal ensembles and the network for inter-regional integration of the distributed information develop in an experience-dependent manner to form a new memory, which is consistent with the hippocampal memory index hypothesis.

The authors show that fear-memory-related cell-ensembles in the amygdala, hippocampus, and prefrontal cortex are inter-regionally co-activated in post-learning sleep. The co-activations are hosted by fast network oscillations and re-appear during recall.