The Role of Hippocampal Replay in Memory and Planning

Magnesium hexacyanoferrate mitigates sepsis-associated encephalopathy through inhibiting microglial activation and neuronal cuproptosis

Sepsis-associated encephalopathy (SAE) is a severe neurological complication stemming from sepsis, characterized by cognitive impairment. The underlying mechanisms involve oxidative stress, neuroinflammation, and disruptions in copper/iron homeostasis. This study introduces magnesium hexacyanoferrate (MgHCF) as a novel compound and explores its therapeutic potential in SAE. Our investigation reveals that MgHCF features intriguing properties in effectively scavenging reactive oxygen species (ROS), and chelating excess copper and iron. Treatment with MgHCF significantly attenuates microglia activation, and protects neuronal cells from oxidative damage and cytotoxicity induced by activated microglia in vitro and in vivo. Furthermore, the cognitive impairment in SAE mice is effectively alleviated by MgHCF treatment, mechanically through a reduction in the copper/iron-responsive histone methylation, and neuronal cuproptosis. These findings suggest MgHCF as a promising therapeutic agent for SAE, targeting the copper/iron signaling pathway to alleviate neuroinflammation, and neuronal cuproptosis.

A memory model of rodent spatial navigation in which place cells are memories arranged in a grid and grid cells are non-spatial

A theory and neurocomputational model are presented that explain grid cell responses as the byproduct of equally dissimilar hippocampal memories. On this account, place and grid cells are best understood as the natural consequence of memory encoding and retrieval; a precise hexagonal grid is the exception rather than the rule, emerging when the animal explores a large surface that is devoid of landmarks and objects. In the proposed memory model, place cells represent memories that are conjunctions of both spatial and non-spatial attributes, and grid cells primarily represent the non-spatial attributes (e.g. sounds, surface texture, etc.) found throughout the two-dimensional recording enclosure. Place cells support memories of the locations where non-spatial attributes can be found (e.g. positions with a particular sound), which are arranged in a hexagonal lattice owing to memory encoding and consolidation processes (pattern separation) as applied to situations in which the non-spatial attributes are found at all locations of a two-dimensional surface. Grid cells exhibit their spatial firing pattern owing to feedback from hippocampal place cells (i.e. a hexagonal pattern of remembered locations for the non-spatial attribute represented by a grid cell). Model simulations explain a wide variety of results in the rodent spatial navigation literature.

Evolutionary learning in neural networks by heterosynaptic plasticity

Training biophysical neuron models provides insights into brain circuits' organization and problem-solving capabilities. Traditional training methods like backpropagation face challenges with complex models due to instability and gradient issues. We explore evolutionary algorithms (EAs) combined with heterosynaptic plasticity as a gradient-free alternative. Our EA models agents with distinct neuron information routes, evaluated via alternating gating, and guided by dopamine-driven plasticity. This model draws inspiration from various biological mechanisms, such as dopamine function, dendritic spine meta-plasticity, memory replay, and cooperative synaptic plasticity within dendritic neighborhoods. Neural networks trained with this model recapitulate brain-like dynamics during cognition. Our method effectively trains spiking and analog neural networks in both feedforward and recurrent architectures, it also achieves performance in tasks like MNIST classification and Atari games comparable to gradient-based methods. Overall, this research extends training approaches for biophysical neuron models, offering a robust alternative to traditional algorithms.

Impaired cognitive function and altered dendritic morphology of hippocampal neurons in a mouse model of fetal alcohol spectrum disorder

Prenatal ethanol exposure is a leading preventable cause of neurodevelopmental disability, clinically categorized under fetal alcohol spectrum disorders (FASD). This study explores how developmental alcohol exposure affects the dendritic morphology of hippocampal pyramidal neurons, focusing on the actin cytoskeleton's dynamics essential for neuronal structure and synaptic function. Within this context, we hypothesized that developmental alcohol exposure disrupts actin cytoskeleton dynamics, leading to cognitive deficits and dendritic remodeling in the hippocampus. Neonatal mice (C57BL/6 J) were administered ethanol (5.0 g/kg) intraperitoneally from postnatal day 2-8, establishing a third trimester-equivalent alcohol exposure FASD model. At postnatal day 28, cognitive performance was evaluated using novel location recognition (NLR), novel object recognition (NOR), and the Morris water maze (MWM). Golgi staining assessed dendritic morphology in the hippocampal CA1 region, and the ratio of polymerized (F-actin) to globular actin (G-actin) was measured using a biochemical assay. The results revealed that developmental alcohol exposure significantly impaired recognition and spatial memory, as evidenced by decreased performances in the NOR and MWM tests across both sexes. Golgi staining revealed reduced dendritic arborization complexity and spine density in the CA1 region of the hippocampal pyramidal neurons of both male and female juvenile mice. Biochemical analyses further revealed decresed hipocampal F-actin/G-actin ratios and decreased levels of polymerized F-actin in both sexes. These findings underscore the critical role of cytoskeletal integrity in cognitive development and highlight potential targets for therapeutic intervention in FASD.

Anatomically resolved oscillatory bursts reveal dynamic motifs of thalamocortical activity during naturalistic stimulus viewing

Natural vision requires circuit mechanisms which process complex spatiotemporal stimulus features in parallel. In the mammalian forebrain, one signature of circuit activation is fast oscillatory dynamics, reflected in the local field potential (LFP). Using data from the Allen Neuropixels Visual Coding project, we show that local visual features in naturalistic stimuli induce in mouse primary visual cortex (V1) retinotopically specific oscillations in various frequency bands and V1 layers. Specifically, layer 4 (L4) narrowband gamma was linked to luminance, low-gamma to optic flow, and L4/L5 epsilon oscillations to contrast. These feature-specific oscillations were associated with distinct translaminar spike-phase coupling patterns, which were conserved across a range of stimuli containing the relevant visual features, suggesting that they might constitute feature-specific circuit motifs. Our findings highlight visually induced fast oscillations as markers of dynamic circuit motifs, which may support differential and multiplexed coding of complex visual input and thalamocortical information propagation.

A cognitive map for value-guided choice in the ventromedial prefrontal cortex

The prefrontal cortex (PFC) is crucial for economic decision-making. However, how PFC value representations facilitate flexible decisions remains unknown. We reframe economic decision-making as a navigation process through a cognitive map of choice values. We found rhesus macaques represented choices as navigation trajectories in a value space using a grid-like code. This occurred in ventromedial PFC (vmPFC) local field potential theta frequency across two datasets. vmPFC neurons deployed the same grid-like code and encoded chosen value. However, both signals depended on theta phase: occurring on theta troughs but on separate theta cycles. Finally, we found sharp-wave ripples-a key signature of planning and flexible behavior-in vmPFC. Thus, vmPFC utilizes cognitive map-based computations to organize and compare values, suggesting an alternative architecture for economic choice in PFC.

Learning produces an orthogonalized state machine in the hippocampus

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, 'state cells'). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.

Awake replay: off the clock but on the job

Hippocampal replay is widely thought to support two key cognitive functions: online decision-making and offline memory consolidation. In this review, we take a closer look at the hypothesized link between awake replay and online decision-making in rodents, and find only marginal evidence in support of this role. By contrast, the consolidation view is bolstered by new computational ideas and recent data, suggesting that (i) replay performs offline fictive learning for later goal-oriented behavior; and (ii) replay tags memories prior to sleep, prioritizing them for consolidation. Based on these recent advances, we favor an updated and refined role for awake replay - that is, supporting prioritized offline learning and tagging outside the hippocampus - rather than a direct, online role in guiding behavior.

Sleep is necessary for experience-dependent sequence plasticity in mouse primary visual cortex

STUDY OBJECTIVES

Repeated exposure to familiar visual sequences drives experience-dependent and sequence-specific plasticity in mouse primary visual cortex (V1). Prior work demonstrated a critical role for sleep in consolidating a related but mechanistically distinct form of experience-dependent plasticity in V1. Here, we assessed the role of sleep in consolidation of spatiotemporal sequence learning (sequence plasticity) in mouse V1.

METHODS

Visually evoked potentials were recorded in awake, head-fixed mice viewing sequences of four visual stimuli. Each sequence was presented 200 times per session, across multiple sessions, to drive plasticity. The effects of sleep consolidation time and sleep deprivation on plasticity were assessed.

RESULTS

Sequence plasticity occurred in V1 following as little as 1 hour of ad libitum sleep and increased with longer periods of sleep. Sleep deprivation blocked sequence plasticity consolidation, which recovered following subsequent sleep.

CONCLUSIONS

Sleep is required for the consolidation of sequence plasticity in mouse V1.

Why might animals remember? A functional framework for episodic memory research in comparative psychology

One of Clayton's major contributions to our understanding of animal minds has been her work on episodic-like memory. A central reason for the success of this work was its focus on ecological validity: rather than looking for episodic memory for arbitrary stimuli in artificial contexts, focussing on contexts in which episodic memory would serve a biological function such as food caching. This review aims to deepen this insight by surveying the numerous functions that have been proposed for episodic memory, articulating a philosophically grounded framework for understanding what exactly functions are, and drawing on these to make suggestions for future directions in the comparative cognitive psychology of episodic memory. Our review suggests four key insights. First, episodic memory may have more than one function and may have different functions in different species. Second, cross-disciplinary work is key to developing a functional account of episodic memory. Third, there is scope for further theoretical elaboration of proposals relating episodic memory to food caching and, in particular, future-oriented cognition. Finally, learning-related functions suggested by AI (artificial intelligence)-based models are a fruitful avenue for future behavioural research.

Diminished State Space Theory of Human Aging

Many new technologies, such as smartphones, computers, or public-access systems (like ticket-vending machines), are a challenge for older adults. One feature that these technologies have in common is that they involve underlying, partially observable, structures (state spaces) that determine the actions that are necessary to reach a certain goal (e.g., to move from one menu to another, to change a function, or to activate a new service). In this work we provide a theoretical, neurocomputational account to explain these behavioral difficulties in older adults. Based on recent findings from age-comparative computational- and cognitive-neuroscience studies, we propose that age-related impairments in complex goal-directed behavior result from an underlying deficit in the representation of state spaces of cognitive tasks. Furthermore, we suggest that these age-related deficits in adaptive decision-making are due to impoverished neural representations in the orbitofrontal cortex and hippocampus.

The role of memory in affirming-the-consequent fallacy

People tend to recognize that a transitive relation remains true even when its order is reversed. This affirming-the-consequent fallacy is thought to be uniquely related to human intelligence. It is generally thought that this fallacy is a byproduct of explicit reasoning at the moment of recognition of the reversed order. Here, we provide evidence suggesting a reconsideration of this account using an implicit memory paradigm, which minimizes the involvement of explicit reasoning. Specifically, we tested a two-stage memory model: (1) when a sequence of events is encoded, the memory of the reversed sequence is formed, resulting in the affirming-the-consequent fallacy, and (2) the memories of the forward and reversed sequences are integrated over time, reinforcing the fallacy. Results of behavioral and functional magnetic resonance imaging experiments were consistent with this memory-based model. Our findings suggest that the affirming-the-consequent fallacy may begin unwittingly when individuals memorize a transitive relation.

Spaces and sequences in the hippocampus: a homological perspective

Topological techniques have become a popular tool for studying information flows in neural networks. In particular, simplicial homology theory is used to analyze how cognitive representations of space emerge from large conglomerates of independent neuronal contributions. Meanwhile, a growing number of studies suggest that many cognitive functions are sustained by serial patterns of activity. Here, we investigate stashes of such patterns using path homology theory-an impartial, universal approach that does not require a priori assumptions about the sequences' nature, functionality, underlying mechanisms, or other contexts. We focus on the hippocampus-a key enabler of learning and memory in mammalian brains-and quantify the ordinal arrangement of its activity similarly to how its topology has previously been studied in terms of simplicial homologies. The results reveal that the vast majority of sequences produced during spatial navigation are structurally equivalent to one another. Only a few classes of distinct sequences form an ordinal schema of serial activity that remains stable as the pool of sequences consolidates. Importantly, the structure of both maps is upheld by combinations of short sequences, suggesting that brief activity motifs dominate physiological computations. This ordinal organization emerges and stabilizes on timescales characteristic of spatial learning, displaying similar dynamics. Yet, the ordinal maps generally do not reflect topological affinities-spatial and sequential analyses address qualitatively different aspects of spike flows, representing two complementary formats of information processing.

Significance statement

This study employs path homology theory to examine serial patterns of neuronal activity in the hippocampus, a critical region for learning and memory. While the traditional, simplicial homology approaches used to model cognitive maps, path homology provides a universal framework for analyzing the ordinal arrangement of neuronal sequences without presupposing their nature or function. The findings reveal that a limited number of distinct sequence classes, supported by combinations of short activity motifs, form a stable ordinal schema over timescales corresponding to periods of spatial learning. Notably, the ordinal maps derived from these sequences do not capture topological affinities, highlighting that spatial and sequential analyses address distinct but complementary dimensions of neural information processing.

Electrophysiological insights into Alzheimer's disease: A review of human and animal studies

This review highlights the crucial role of neuroelectrophysiology in illuminating the mechanisms underlying Alzheimer's disease (AD) pathogenesis and progression, emphasizing its potential to inform the development of effective treatments. Electrophysiological techniques provide unparalleled precision in exploring the intricate networks affected by AD, offering insights into the synaptic dysfunction, network alterations, and oscillatory abnormalities that characterize the disease. We discuss a range of electrophysiological methods, from non-invasive clinical techniques like electroencephalography and magnetoencephalography to invasive recordings in animal models. By drawing on findings from these studies, we demonstrate how electrophysiological research has deepened our understanding of AD-related network disruptions, paving the way for targeted therapeutic interventions. Moreover, we underscore the potential of electrophysiological modalities to play a pivotal role in evaluating treatment efficacy. Integrating electrophysiological data with clinical neuroimaging and longitudinal studies holds promise for a more comprehensive understanding of AD, enabling early detection and the development of personalized treatment strategies. This expanded research landscape offers new avenues for unraveling the complexities of AD and advancing therapeutic approaches.

Ezrin-mediated astrocyte-synapse signaling regulates cognitive function via astrocyte morphological changes in fine processes in male mice

Astrocytes, which actively participate in cognitive processes, have a complex spongiform morphology, highlighted by extensive ramified fine processes that closely enwrap the pre- and post-synaptic compartments, forming tripartite synapses. However, the role of astrocyte morphology in cognitive processes remains incompletely understood and even controversial. The actin-binding protein Ezrin is highly expressed in astrocytes and is a key structural determinant of astrocyte morphology. Here, we found that Ezrin expression and astrocyte fine process volume in the hippocampus of male mice increased after learning but decreased after lipopolysaccharide injection and in a mouse model of postoperative cognitive dysfunction, both of which involved models with impaired cognitive function. Additionally, astrocytic Ezrin knock-out led to significantly decreased astrocytic fine process volumes, decreased astrocyte-neuron proximity, and induced anxiety-like behaviors and cognitive dysfunction. Astrocytic Ezrin deficiency in the hippocampus was achieved by using a microRNA silencing technique delivered by adeno-associated viruses. Down-regulation of Ezrin in hippocampal astrocytes led to disrupted astrocyte-synapse interactions and impaired synaptic functions, including synaptic transmission and synaptic plasticity, which could be rescued by exogenous administration of D-serine. Remarkably, decreased Ezrin expression and reduced astrocyte fine processes volumes were also observed in aged mice with decreased cognitive function. Moreover, overexpression of astrocytic Ezrin increased astrocyte fine process volumes and improved cognitive function in aged mice. Overall, our results indicate Ezrin-mediated astrocyte fine processes integrity shapes astrocyte-synapse signaling contributing to cognitive function.

Two-dimensional neural geometry underpins hierarchical organization of sequence in human working memory

Working memory (WM) is constructive in nature. Instead of passively retaining information, WM reorganizes complex sequences into hierarchically embedded chunks to overcome capacity limits and facilitate flexible behaviour. Here, to investigate the neural mechanisms underlying hierarchical reorganization in WM, we performed two electroencephalography and one magnetoencephalography experiments, wherein humans retain in WM a temporal sequence of items, that is, syllables, which are organized into chunks, that is, multisyllabic words. We demonstrate that the one-dimensional sequence is represented by two-dimensional neural representational geometry in WM arising from left prefrontal and temporoparietal regions, with separate dimensions encoding item position within a chunk and chunk position in the sequence. Critically, this two-dimensional geometry is observed consistently in different experimental settings, even during tasks not encouraging hierarchical reorganization in WM and correlates with WM behaviour. Overall, these findings strongly support that complex sequences are reorganized into factorized multidimensional neural representational geometry in WM, which also speaks to general structure-based organizational principles given WM's involvement in many cognitive functions.

The time course and organization of hippocampal replay

The mechanisms by which the brain replays neural activity sequences remain unknown. Recording from large ensembles of hippocampal place cells in freely behaving rats, we observed that replay content is strictly organized over multiple timescales and governed by self-avoidance. After movement cessation, replays avoided the animal's previous path for 3 seconds. Chains of replays avoided self-repetition over a shorter timescale. We used a continuous attractor model of neural activity to demonstrate that neuronal fatigue both generates replay sequences and produces self-avoidance over the observed timescales. In addition, replay of past experience became predominant later into the stopping period, in a manner requiring cortical input. These results indicate a mechanism for replay generation that unexpectedly constrains which sequences can be produced across time.

Dorsal hippocampus represents locations to avoid as well as locations to approach during approach-avoidance conflict

Worrying about perceived threats is a hallmark of multiple psychological disorders including anxiety. This concern about future events is particularly important when an individual is faced with an approach-avoidance conflict. Potential goals to approach are known to be represented in the dorsal hippocampus during theta cycles. Similarly, important information that is distant from the animal's position is represented during hippocampal high-synchrony events (HSEs), which coincide with sharp-wave ripples (SWRs). It is likely that potential future threats may be similarly represented. We examined how threats and rewards were represented within the hippocampus during approach-avoidance conflicts in rats faced with a predator-like robot guarding a food reward. We found decoding of the pseudo-predator's location during HSEs when hesitating in the nest and during theta prior to retreating as the rats approached the pseudo-predator. After the first attack, we observed new place fields appearing at the location of the robot (not the location the rat was when attacked). The anxiolytic diazepam reduced anxiety-like behavior and altered hippocampal local field potentials (LFPs), including reducing SWRs, suggesting that one potential mechanism of diazepam's actions may be through altered representations of imagined threat. These results suggest that hippocampal representation of potential threats could be an important mechanism that underlies worry and a potential target for anxiolytics.

Mental Time Travel: A Retrospective

Because imagination activates the same neural circuits used in understanding the present, one can access that imagination even in non-linguistic animals through decoding techniques applied to large neural ensembles. This personal retrospective traces the history of the initial discovery that hippocampal theta sequences sweep forward to goals during moments of deliberation and discusses the history that was necessary to put ourselves in the position to recognize this signal. It also discusses how that discovery fits into the larger picture of hippocampal function and the concept of cognition as computation.

Context changes retrieval of prospective outcomes during decision deliberation

Foreseeing the future outcomes is the art of decision-making. Substantial evidence shows that, during choice deliberation, the brain can retrieve prospective decision outcomes. However, decisions are seldom made in a vacuum. Context carries information that can radically affect the outcomes of a choice. Nevertheless, most investigations of retrieval processes examined decisions in isolation, disregarding the context in which they occur. Here, we studied how context shapes prospective outcome retrieval during deliberation. We designed a decision-making task where participants were presented with object-context pairs and made decisions which led to a certain outcome. We show during deliberation, likely outcomes were retrieved in transient patterns of neural activity, as early as 3 s before participants decided. The strength of prospective outcome retrieval explains participants' behavioral efficiency, but only when context affects the decision outcome. Our results suggest context imparts strong constraints on retrieval processes and how neural representations are shaped during decision-making.

Evaluating hippocampal replay without a ground truth

During rest and sleep, memory traces replay in the brain. The dialogue between brain regions during replay is thought to stabilize labile memory traces for long-term storage. However, because replay is an internally driven, spontaneous phenomenon, it does not have a ground truth - an external reference that can validate whether a memory has truly been replayed. Instead, replay detection is based on the similarity between the sequential neural activity comprising the replay event and the corresponding template of neural activity generated during active locomotion. If the statistical likelihood of observing such a match by chance is sufficiently low, the candidate replay event is inferred to be replaying that specific memory. However, without the ability to evaluate whether replay detection methods are successfully detecting true events and correctly rejecting non-events, the evaluation and comparison of different replay methods is challenging. To circumvent this problem, we present a new framework for evaluating replay, tested using hippocampal neural recordings from rats exploring two novel linear tracks. Using this two-track paradigm, our framework selects replay events based on their temporal fidelity (sequence-based detection), and evaluates the detection performance using each event's track discriminability, where sequenceless decoding across both tracks is used to quantify whether the track replaying is also the most likely track being reactivated.

Interictal spikes during spatial working memory carry helpful or distracting representations of space and have opposing impacts on performance

In temporal lobe epilepsy, interictal spikes (IS) - hypersynchronous bursts of network activity - occur at high rates in between seizures. We sought to understand the influence of IS on working memory by recording hippocampal local field potentials from epileptic mice while they performed a delayed alternation task. We found that IS disrupted performance when they were spatially non-restricted and occurred during running. In contrast, when IS were clustered at reward locations, animals performed well. A machine learning decoding approach revealed that IS at reward sites were larger than IS elsewhere on the maze, and could be classified as occurring at specific reward locations - suggesting they carry informative content for the memory task. Finally, a spiking model revealed that spatially clustered IS preserved hippocampal replay, while spatially dispersed IS disrupted replay by causing over-generalization. Together, these results show that IS can have opposing outcomes on memory.

Is episodic-like memory like episodic memory?

Episodic memory involves the conscious recollection of personally experienced events and when absent, results in profound losses to the typical human conscious experience. Over the last 2.5 decades, the debate surrounding whether episodic memory is unique to humans has seen a lot of controversy and accordingly has received significant research attention. Various behavioural paradigms have been developed to test episodic-like memory; a term designed to reflect the behavioural characteristics of episodic memory in the absence of evidence for consciously experienced recall. In this review, we first outline the most influential paradigms that have been developed to assess episodic-like memory across a variety of non-human taxa (including mammals, birds and cephalopods), namely the what-where-when memory, incidental encoding and unexpected question, and source memory paradigms. Then, we examine whether various key features of human episodic memory are conceptually represented in episodic-like memory across phylogenetically and neurologically diverse taxa, identifying similarities, differences and gaps in the literature. We conclude that the evidence is mixed, and as episodic memory encompasses a variety of cognitive structures and processes, research on episodic-like memory in non-humans should follow this multifaceted approach and assess evidence across various behavioural paradigms that each target different aspects of human episodic memory.This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.

Memory engram stability and flexibility

Many studies have shown that memories are encoded in sparse neural ensembles distributed across the brain. During the post-encoding period, often during sleep, many of the cells that were active during encoding are reactivated, supporting consolidation of this memory. During memory recall, many of the same cells that were active during encoding and reactivated during consolidation are reactivated during recall. These ensembles of cells have been referred to as the memory engram cells, stably representing a specific memory. However, recent studies question the rigidity of the "stable memory engram." Here we review the past literature of how episodic-like memories are encoded, consolidated, and recalled. We also highlight more recent studies (as well as some older literature) that suggest that these stable memories and their representations are much more dynamic and flexible than previously thought. We highlight some of these processes, including memory updating, reconsolidation, forgetting, schema learning, memory-linking, and representational drift.

Human hippocampal and entorhinal neurons encode the temporal structure of experience

Extracting the underlying temporal structure of experience is a fundamental aspect of learning and memory that allows us to predict what is likely to happen next. Current knowledge about the neural underpinnings of this cognitive process in humans stems from functional neuroimaging research1-5. As these methods lack direct access to the neuronal level, it remains unknown how this process is computed by neurons in the human brain. Here we record from single neurons in individuals who have been implanted with intracranial electrodes for clinical reasons, and show that human hippocampal and entorhinal neurons gradually modify their activity to encode the temporal structure of a complex image presentation sequence. This representation was formed rapidly, without providing specific instructions to the participants, and persisted when the prescribed experience was no longer present. Furthermore, the structure recovered from the population activity of hippocampal-entorhinal neurons closely resembled the structural graph defining the sequence, but at the same time, also reflected the probability of upcoming stimuli. Finally, learning of the sequence graph was related to spontaneous, time-compressed replay of individual neurons' activity corresponding to previously experienced graph trajectories. These findings demonstrate that neurons in the hippocampus and entorhinal cortex integrate the 'what' and 'when' information to extract durable and predictive representations of the temporal structure of human experience.

Neuroplasticity Meets Artificial Intelligence: A Hippocampus-Inspired Approach to the Stability-Plasticity Dilemma

The stability-plasticity dilemma remains a critical challenge in developing artificial intelligence (AI) systems capable of continuous learning. This perspective paper presents a novel approach by drawing inspiration from the mammalian hippocampus-cortex system. We elucidate how this biological system's ability to balance rapid learning with long-term memory retention can inspire novel AI architectures. Our analysis focuses on key mechanisms, including complementary learning systems and memory consolidation, with emphasis on recent discoveries about sharp-wave ripples and barrages of action potentials. We propose innovative AI designs incorporating dual learning rates, offline consolidation, and dynamic plasticity modulation. This interdisciplinary approach offers a framework for more adaptive AI systems while providing insights into biological learning. We present testable predictions and discuss potential implementations and implications of these biologically inspired principles. By bridging neuroscience and AI, our perspective aims to catalyze advancements in both fields, potentially revolutionizing AI capabilities while deepening our understanding of neural processes.

Cost-benefit Tradeoff Mediates the Rule- to Memory-based Processing Transition during Practice

Practice not only improves task performance but also changes task execution from rule- to memory-based processing by incorporating experiences from practice. However, how and when this change occurs is unclear. We test the hypothesis that strategy transitions in task learning can result from decision-making guided by cost-benefit analysis. Participants learn two task sequences and are then queried about the task type at a cued sequence and position. Behavioral improvement with practice can be accounted for by a computational model implementing cost-benefit analysis, and the model-predicted strategy transition points align with the observed behavioral slowing. Model comparisons using behavioral data show that strategy transitions are better explained by a cost-benefit analysis across alternative strategies rather than solely on memory strength. Model-guided fMRI findings suggest that the brain encodes a decision variable reflecting the cost-benefit analysis and that different strategy representations are double-dissociated. Further analyses reveal that strategy transitions are associated with activation patterns in the dorsolateral prefrontal cortex and increased pattern separation in the ventromedial prefrontal cortex. Together, these findings support cost-benefit analysis as a mechanism of practice-induced strategy shift.

The brain hierarchically represents the past and future during multistep anticipation

Memory for temporal structure enables both planning of future events and retrospection of past events. We investigated how the brain flexibly represents extended temporal sequences into the past and future during anticipation. Participants learned sequences of environments in immersive virtual reality. Pairs of sequences had the same environments in a different order, enabling context-specific learning. During fMRI, participants anticipated upcoming environments multiple steps into the future in a given sequence. Temporal structure was represented in the hippocampus and across higher-order visual regions (1) bidirectionally, with graded representations into the past and future and (2) hierarchically, with further events into the past and future represented in successively more anterior brain regions. In hippocampus, these bidirectional representations were context-specific, and suppression of far-away environments predicted response time costs in anticipation. Together, this work sheds light on how we flexibly represent sequential structure to enable planning over multiple timescales.

Impaired hippocampal functions underlying memory encoding and consolidation precede robust Aβ deposition in a mouse model of Alzheimer's disease

Current therapeutic strategies for Alzheimer's disease (AD) target amyloid-beta (Aβ) fibrils and high molecular weight protofibrils associated with plaques, but molecular cascades associated with AD may drive neural systems failure before Aβ plaque deposition in AD. Employing hippocampal electrophysiological recordings and dynamic calcium imaging across the sleep-wake cycle in the APP/PS1 mouse model of AD before Aβ plaques accumulated, we detected marked impairments of hippocampal systems function: In a spatial behavioral task, but not REM sleep, phase-amplitude coupling (PAC) of the hippocampal theta and gamma oscillations was impaired and place cell calcium fluctuations were hyper-synchronized with the theta oscillation. In subsequent slow wave sleep (SWS), place cell reactivation was reduced. These degraded neural functions underlying memory encoding and consolidation support targeting pathological processes of the pre-plaque phase of AD to treat and prevent hippocampal impairments.

Expectancy-related changes in firing of dopamine neurons depend on hippocampus

The orbitofrontal cortex (OFC) and hippocampus (HC) both contribute to the cognitive maps that support flexible behavior. Previously, we used the dopamine neurons to measure the functional role of OFC. We recorded midbrain dopamine neurons as rats performed an odor-based choice task, in which expected rewards were manipulated across blocks. We found that ipsilateral OFC lesions degraded dopaminergic prediction errors, consistent with reduced resolution of the task states. Here we have repeated this experiment in male rats with ipsilateral HC lesions. The results show HC also shapes the task states, however unlike OFC, which provides information local to the trial, the HC is necessary for estimating upper-level hidden states that distinguish blocks. The results contrast the roles of the OFC and HC in cognitive mapping and suggest that the dopamine neurons access rich information from distributed regions regarding the environment's structure, potentially enabling this teaching signal to support complex behaviors.

A tradeoff between efficiency and robustness in the hippocampal-neocortical memory network during human and rodent sleep

Sleep constitutes a brain state of disengagement from the external world that supports memory consolidation and restores cognitive resources. The precise mechanisms how sleep and its varied stages support information processing remain largely unknown. Synaptic scaling models imply that daytime learning accumulates neural information, which is then consolidated and downregulated during sleep. Currently, there is a lack of in-vivo data from humans and rodents that elucidate if, and how, sleep renormalizes information processing capacities. From an information-theoretical perspective, a consolidation process should entail a reduction in neural pattern variability over the course of a night. Here, in a cross-species intracranial study, we identify a tradeoff in the neural population code during sleep where information coding efficiency is higher in the neocortex than in hippocampal archicortex in humans than in rodents as well as during wakefulness compared to sleep. Critically, non-REM sleep selectively reduces information coding efficiency through pattern repetition in the neocortex in both species, indicating a transition to a more robust information coding regime. Conversely, the coding regime in the hippocampus remained consistent from wakefulness to non-REM sleep. These findings suggest that new information could be imprinted to the long-term mnemonic storage in the neocortex through pattern repetition during sleep. Lastly, our results show that task engagement increased coding efficiency, while medically-induced unconsciousness disrupted the population code. In sum, these findings suggest that neural pattern variability could constitute a fundamental principle underlying cognitive engagement and memory formation, while pattern repetition reflects robust coding, possibly underlying the consolidation process.

A circuit from lateral hypothalamic to dorsal hippocampal dentate gyrus modulates behavioral despair in mice

Behavioral despair is one of the clinical manifestations of major depressive disorder and an important cause of disability and death. However, the neural circuit mechanisms underlying behavioral despair are poorly understood. In a well-established chronic behavioral despair (CBD) mouse model, using a combination of viral tracing, in vivo fiber photometry, chemogenetic and optogenetic manipulations, in vitro electrophysiology, pharmacological profiling techniques, and behavioral tests, we investigated the neural circuit mechanisms in regulating behavioral despair. Here, we found that CBD enhanced CaMKIIα neuronal excitability in the dorsal dentate gyrus (dDG) and dDGCaMKIIα neurons involved in regulating behavioral despair in CBD mice. Besides, dDGCaMKIIα neurons received 5-HT inputs from median raphe nucleus (MRN) and were mediated by 5-HT1A receptors, whereas MRN5-HT neurons received CaMKIIα inputs from lateral hypothalamic (LH) and were mediated by AMPA receptors to regulate behavioral despair. Furthermore, fluvoxamine exerted its role in resisting behavioral despair through the LH-MRN-dDG circuit. These findings suggest that a previously unidentified circuit of LHCaMKIIα-MRN5-HT-dDGCaMKIIα mediates behavioral despair induced by CBD. Furthermore, these support the important role of AMPA receptors in MRN and 5-HT1A receptors in dDG that might be the potential targets for treatment of behavioral despair, and explain the neural circuit mechanism of fluvoxamine-resistant behavioral despair.

Inhibitory plasticity supports replay generalization in the hippocampus

Memory consolidation assimilates recent experiences into long-term memory. This process requires the replay of learned sequences, although the content of these sequences remains controversial. Recent work has shown that the statistics of replay deviate from those of experience: stimuli that are experientially salient may be either recruited or suppressed from sharp-wave ripples. In this study, we found that this phenomenon can be explained parsimoniously and biologically plausibly by a Hebbian spike-time-dependent plasticity rule at inhibitory synapses. Using models at three levels of abstraction-leaky integrate-and-fire, biophysically detailed and abstract binary-we show that this rule enables efficient generalization, and we make specific predictions about the consequences of intact and perturbed inhibitory dynamics for network dynamics and cognition. Finally, we use optogenetics to artificially implant non-generalizable representations into the network in awake behaving mice, and we find that these representations also accumulate inhibition during sharp-wave ripples, experimentally validating a major prediction of our model. Our work outlines a potential direct link between the synaptic and cognitive levels of memory consolidation, with implications for both normal learning and neurological disease.

Repetition dynamically and rapidly increases cortical, but not hippocampal, offline reactivation

No sooner is an experience over than its neural representation begins to be transformed through memory reactivation during offline periods. The lion's share of prior research has focused on understanding offline reactivation within the hippocampus. However, it is hypothesized that consolidation processes involve offline reactivation in cortical regions as well as coordinated reactivation in the hippocampus and cortex. Using fMRI, we presented novel and repeated paired associates to participants during encoding and measured offline memory reactivation for those events during an immediate post-encoding rest period. post-encoding reactivation frequency of repeated and once-presented events did not differ in the hippocampus. However, offline reactivation in widespread cortical regions and hippocampal-cortical coordinated reactivation were significantly enhanced for repeated events. These results provide evidence that repetition might facilitate the distribution of memory representations across cortical networks, a hallmark of systems-level consolidation. Interestingly, we found that offline reactivation frequency in both hippocampus and cortex explained variance in behavioral success on an immediate associative recognition test for the once-presented information, potentially indicating a role of offline reactivation in maintaining these novel, weaker, memories. Together, our findings highlight that endogenous offline reactivation can be robustly and significantly modulated by study repetition.

The brain hierarchically represents the past and future during multistep anticipation

Memory for temporal structure enables both planning of future events and retrospection of past events. We investigated how the brain flexibly represents extended temporal sequences into the past and future during anticipation. Participants learned sequences of environments in immersive virtual reality. Pairs of sequences had the same environments in a different order, enabling context-specific learning. During fMRI, participants anticipated upcoming environments multiple steps into the future in a given sequence. Temporal structure was represented in the hippocampus and across higher-order visual regions (1) bidirectionally, with graded representations into the past and future and (2) hierarchically, with further events into the past and future represented in successively more anterior brain regions. In hippocampus, these bidirectional representations were context-specific, and suppression of far-away environments predicted response time costs in anticipation. Together, this work sheds light on how we flexibly represent sequential structure to enable planning over multiple timescales.

Replay-triggered brain-wide activation in humans

The consolidation of discrete experiences into a coherent narrative shapes the cognitive map, providing structured mental representations of our experiences. In this process, past memories are reactivated and replayed in sequence, fostering hippocampal-cortical dialogue. However, brain-wide engagement coinciding with sequential reactivation (or replay) of memories remains largely unexplored. In this study, employing simultaneous EEG-fMRI, we capture both the spatial and temporal dynamics of memory replay. We find that during mental simulation, past memories are replayed in fast sequences as detected via EEG. These transient replay events are associated with heightened fMRI activity in the hippocampus and medial prefrontal cortex. Replay occurrence strengthens functional connectivity between the hippocampus and the default mode network, a set of brain regions key to representing the cognitive map. On the other hand, when subjects are at rest following learning, memory reactivation of task-related items is stronger than that of pre-learning rest, and is also associated with heightened hippocampal activation and augmented hippocampal connectivity to the entorhinal cortex. Together, our findings highlight a distributed, brain-wide engagement associated with transient memory reactivation and its sequential replay.

Space is a latent sequence: A theory of the hippocampus

Fascinating phenomena such as landmark vector cells and splitter cells are frequently discovered in the hippocampus. Without a unifying principle, each experiment seemingly uncovers new anomalies or coding types. Here, we provide a unifying principle that the mental representation of space is an emergent property of latent higher-order sequence learning. Treating space as a sequence resolves numerous phenomena and suggests that the place field mapping methodology that interprets sequential neuronal responses in Euclidean terms might itself be a source of anomalies. Our model, clone-structured causal graph (CSCG), employs higher-order graph scaffolding to learn latent representations by mapping aliased egocentric sensory inputs to unique contexts. Learning to compress sequential and episodic experiences using CSCGs yields allocentric cognitive maps that are suitable for planning, introspection, consolidation, and abstraction. By explicating the role of Euclidean place field mapping and demonstrating how latent sequential representations unify myriad observed phenomena, our work positions the hippocampus in a sequence-centric paradigm, challenging the prevailing space-centric view.

Learning, Fast and Slow: Single- and Many-Shot Learning in the Hippocampus

The hippocampus is critical for memory and spatial navigation. The ability to map novel environments, as well as more abstract conceptual relationships, is fundamental to the cognitive flexibility that humans and other animals require to survive in a dynamic world. In this review, we survey recent advances in our understanding of how this flexibility is implemented anatomically and functionally by hippocampal circuitry, during both active exploration (online) and rest (offline). We discuss the advantages and limitations of spike timing-dependent plasticity and the more recently discovered behavioral timescale synaptic plasticity in supporting distinct learning modes in the hippocampus. Finally, we suggest complementary roles for these plasticity types in explaining many-shot and single-shot learning in the hippocampus and discuss how these rules could work together to support the learning of cognitive maps.

Shared structure facilitates working memory of multiple sequences

Daily experiences often involve the processing of multiple sequences, yet storing them challenges the limited capacity of working memory (WM). To achieve efficient memory storage, relational structures shared by sequences would be leveraged to reorganize and compress information. Here, participants memorized a sequence of items with different colors and spatial locations and later reproduced the full color and location sequences one after another. Crucially, we manipulated the consistency between location and color sequence trajectories. First, sequences with consistent trajectories demonstrate improved memory performance and a trajectory correlation between reproduced color and location sequences. Second, sequences with consistent trajectories show neural reactivation of common trajectories, and display spontaneous replay of color sequences when recalling locations. Finally, neural reactivation correlates with WM behavior. Our findings suggest that a shared common structure is leveraged for the storage of multiple sequences through compressed encoding and neural replay, together facilitating efficient information organization in WM.

Self-avoidance dominates the selection of hippocampal replay

Spontaneous neural activity sequences are generated by the brain in the absence of external input 1-12 , yet how they are produced remains unknown. During immobility, hippocampal replay sequences depict spatial paths related to the animal's past experience or predicted future 13 . By recording from large ensembles of hippocampal place cells 14 in combination with optogenetic manipulation of cortical input in freely behaving rats, we show here that the selection of hippocampal replay is governed by a novel self-avoidance principle. Following movement cessation, replay of the animal's past path is strongly avoided, while replay of the future path predominates. Moreover, when the past and future paths overlap, early replays avoid both and depict entirely different trajectories. Further, replays avoid self-repetition, on a shorter timescale compared to the avoidance of previous behavioral trajectories. Eventually, several seconds into the stopping period, replay of the past trajectory dominates. This temporal organization contrasts with established and recent predictions 9,10,15,16 but is well-recapitulated by a symmetry-breaking attractor model of sequence generation in which individual neurons adapt their firing rates over time 26-35 . However, while the model is sufficient to produce avoidance of recently traversed or reactivated paths, it requires an additional excitatory input into recently activated cells to produce the later window of past-dominance. We performed optogenetic perturbations to demonstrate that this input is provided by medial entorhinal cortex, revealing its role in maintaining a memory of past experience that biases hippocampal replay. Together, these data provide specific evidence for how hippocampal replays are generated.

Selective consolidation of learning and memory via recall-gated plasticity

In a variety of species and behavioral contexts, learning and memory formation recruits two neural systems, with initial plasticity in one system being consolidated into the other over time. Moreover, consolidation is known to be selective; that is, some experiences are more likely to be consolidated into long-term memory than others. Here, we propose and analyze a model that captures common computational principles underlying such phenomena. The key component of this model is a mechanism by which a long-term learning and memory system prioritizes the storage of synaptic changes that are consistent with prior updates to the short-term system. This mechanism, which we refer to as recall-gated consolidation, has the effect of shielding long-term memory from spurious synaptic changes, enabling it to focus on reliable signals in the environment. We describe neural circuit implementations of this model for different types of learning problems, including supervised learning, reinforcement learning, and autoassociative memory storage. These implementations involve synaptic plasticity rules modulated by factors such as prediction accuracy, decision confidence, or familiarity. We then develop an analytical theory of the learning and memory performance of the model, in comparison to alternatives relying only on synapse-local consolidation mechanisms. We find that recall-gated consolidation provides significant advantages, substantially amplifying the signal-to-noise ratio with which memories can be stored in noisy environments. We show that recall-gated consolidation gives rise to a number of phenomena that are present in behavioral learning paradigms, including spaced learning effects, task-dependent rates of consolidation, and differing neural representations in short- and long-term pathways.

Prefrontal coding of learned and inferred knowledge during REM and NREM sleep

Idling brain activity has been proposed to facilitate inference, insight, and innovative problem-solving. However, it remains unclear how and when the idling brain can create novel ideas. Here, we show that cortical offline activity is both necessary and sufficient for building unlearned inferential knowledge from previously acquired information. In a transitive inference paradigm, male C57BL/6J mice gained the inference 1 day after, but not shortly after, complete training. Inhibiting the neuronal computations in the anterior cingulate cortex (ACC) during post-learning either non-rapid eye movement (NREM) or rapid eye movement (REM) sleep, but not wakefulness, disrupted the inference without affecting the learned knowledge. In vivo Ca2+ imaging suggests that NREM sleep organizes the scattered learned knowledge in a complete hierarchy, while REM sleep computes the inferential information from the organized hierarchy. Furthermore, after insufficient learning, artificial activation of medial entorhinal cortex-ACC dialog during only REM sleep created inferential knowledge. Collectively, our study provides a mechanistic insight on NREM and REM coordination in weaving inferential knowledge, thus highlighting the power of idling brain in cognitive flexibility.

Reactivation strength during cued recall is modulated by graph distance within cognitive maps

Declarative memory retrieval is thought to involve reinstatement of neuronal activity patterns elicited and encoded during a prior learning episode. Furthermore, it is suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or, alternatively, replayed sequentially as temporally separate instances. In the current study, participants learned associations between images that were embedded in a directed graph network and retained this information over a brief 8 min consolidation period. During a subsequent cued recall session, participants retrieved the learned information while undergoing magnetoencephalographic recording. Using a trained stimulus decoder, we found evidence for clustered reactivation of learned material. Reactivation strength of individual items during clustered reactivation decreased as a function of increasing graph distance, an ordering present solely for successful retrieval but not for retrieval failure. In line with previous research, we found evidence that sequential replay was dependent on retrieval performance and was most evident in low performers. The results provide evidence for distinct performance-dependent retrieval mechanisms, with graded clustered reactivation emerging as a plausible mechanism to search within abstract cognitive maps.

Paradoxical replay can protect contextual task representations from destructive interference when experience is unbalanced

Experience replay is a powerful mechanism to learn efficiently from limited experience. Despite several decades of compelling experimental results, the factors that determine which experiences are selected for replay remain unclear. A particular challenge for current theories is that on tasks that feature unbalanced experience, rats paradoxically replay the less-experienced trajectory. To understand why, we simulated a feedforward neural network with two regimes: rich learning (structured representations tailored to task demands) and lazy learning (unstructured, task-agnostic representations). Rich, but not lazy, representations degraded following unbalanced experience, an effect that could be reversed with paradoxical replay. To test if this computational principle can account for the experimental data, we examined the relationship between paradoxical replay and learned task representations in the rat hippocampus. Strikingly, we found a strong association between the richness of learned task representations and the paradoxicality of replay. Taken together, these results suggest that paradoxical replay specifically serves to protect rich representations from the destructive effects of unbalanced experience, and more generally demonstrate a novel interaction between the nature of task representations and the function of replay in artificial and biological systems.

In Search of Dispersed Memories: Generative Diffusion Models Are Associative Memory Networks

Uncovering the mechanisms behind long-term memory is one of the most fascinating open problems in neuroscience and artificial intelligence. Artificial associative memory networks have been used to formalize important aspects of biological memory. Generative diffusion models are a type of generative machine learning techniques that have shown great performance in many tasks. Similar to associative memory systems, these networks define a dynamical system that converges to a set of target states. In this work, we show that generative diffusion models can be interpreted as energy-based models and that, when trained on discrete patterns, their energy function is (asymptotically) identical to that of modern Hopfield networks. This equivalence allows us to interpret the supervised training of diffusion models as a synaptic learning process that encodes the associative dynamics of a modern Hopfield network in the weight structure of a deep neural network. Leveraging this connection, we formulate a generalized framework for understanding the formation of long-term memory, where creative generation and memory recall can be seen as parts of a unified continuum.

Altered patterning of neural activity in a tauopathy mouse model

Alzheimer's disease (AD) is a complex neurodegenerative condition that manifests at multiple levels and involves a spectrum of abnormalities ranging from the cellular to cognitive. Here, we investigate the impact of AD-related tau-pathology on hippocampal circuits in mice engaged in spatial navigation, and study changes of neuronal firing and dynamics of extracellular fields. While most studies are based on analyzing instantaneous or time-averaged characteristics of neuronal activity, we focus on intermediate timescales-spike trains and waveforms of oscillatory potentials, which we consider as single entities. We find that, in healthy mice, spike arrangements and wave patterns (series of crests or troughs) are coupled to the animal's location, speed, and acceleration. In contrast, in tau-mice, neural activity is structurally disarrayed: brainwave cadence is detached from locomotion, spatial selectivity is lost, the spike flow is scrambled. Importantly, these alterations start early and accumulate with age, which exposes progressive disinvolvement the hippocampus circuit in spatial navigation. These features highlight qualitatively different neurodynamics than the ones provided by conventional analyses, and are more salient, thus revealing a new level of the hippocampal circuit disruptions.

The Effectiveness of Mindfulness in the Treatment of Methamphetamine Addiction Symptoms: Does Neuroplasticity Play a Role?

INTRODUCTION

Methamphetamine is a highly stimulating psychoactive drug that causes life-threatening addictions and affects millions of people around the world. Its effects on the brain are complex and include disturbances in the neurotransmitter systems and neurotoxicity. There are several known treatment methods, but their effectiveness is moderate. It must be emphasised that no drugs have been approved for treatment. For this reason, there is an urgent need to develop new, effective, and safe treatments for methamphetamine. One of the potential treatments is mindfulness meditation. In recent years, this technique has been researched extensively in the context of many neurological and psychiatric disorders.

METHODS

This review explores the use of mindfulness in the treatment of methamphetamine addiction. Searches were conducted in the PubMed/Medline, Research Gate, and Cochrane databases.

RESULTS

Ten studies were identified that used mindfulness-based interventions in the treatment of methamphetamine addiction. The results show that mindfulness is an effective form of reducing hunger, risk of relapses, stress indicators, depression, and aggression, alone or in combination with transcranial direct current stimulation (tDCS). Mindfulness also improved the cognitive function in addicts. The included studies used only behavioural measures. The potential mechanisms of mindfulness in addiction were explained, and it was proposed that it can induce neuroplasticity, alleviating the symptoms of addiction.

CONCLUSIONS

Evidence from the studies suggest that mindfulness may be an effective treatment option for methamphetamine addiction, used alone or in combination with tDCS. However, further high-quality research is required to establish the role of this treatment option in this field. The use of neuroimaging and neurophysiological measures is fundamental to understand the mechanisms of mindfulness.

No Benefit in Memory Performance after Nocturnal Memory Reactivation Coupled with Theta-tACS

Targeted memory reactivation (TMR) is an effective technique to enhance sleep-associated memory consolidation. The successful reactivation of memories by external reminder cues is typically accompanied by an event-related increase in theta oscillations, preceding better memory recall after sleep. However, it remains unclear whether the increase in theta oscillations is a causal factor or an epiphenomenon of successful TMR. Here, we used transcranial alternating current stimulation (tACS) to examine the causal role of theta oscillations for TMR during non-rapid eye movement (non-REM) sleep. Thirty-seven healthy participants learned Dutch-German word pairs before sleep. During non-REM sleep, we applied either theta-tACS or control-tACS (23 Hz) in blocks (9 min) in a randomised order, according to a within-subject design. One group of participants received tACS coupled with TMR time-locked two seconds after the reminder cue (time-locked group). Another group received tACS in a continuous manner while TMR cues were presented (continuous group). Contrary to our predictions, we observed no frequency-specific benefit of theta-tACS coupled with TMR during sleep on memory performance, neither for continuous nor time-locked stimulation. In fact, both stimulation protocols blocked the TMR-induced memory benefits during sleep, resulting in no memory enhancement by TMR in both the theta and control conditions. No frequency-specific effect was found on the power analyses of the electroencephalogram. We conclude that tACS might have an unspecific blocking effect on memory benefits typically observed after TMR during non-REM sleep.

Dorsal hippocampus represents locations to avoid as well as locations to approach during approach-avoidance conflict

Worrying about perceived threats is a hallmark of multiple psychological disorders including anxiety. This concern about future events is particularly important when an individual is faced with an approach-avoidance conflict. Potential goals to approach are known to be represented in the dorsal hippocampus during theta sweeps. Similarly, important non-local information is represented during hippocampal high synchrony events (HSEs), which are correlated with sharp-wave ripples (SWRs). It is likely that potential future threats may be similarly represented. We examined how threats and rewards were represented within the hippocampus during approach-avoidance conflicts in rats faced with a predator-like robot guarding a food reward. We found representations of the pseudo-predator during HSEs when hesitating in the nest, and during theta prior to retreating as the rats approached the pseudo-predator. After the first attack, we observed new place fields appearing at the location of the robot (not the location the rat was when attacked). The anxiolytic diazepam reduced anxiety-like behavior and altered hippocampal local field potentials, including reducing SWRs, suggesting that one potential mechanism of diazepam's actions may be through altered representations of imagined threat. These results suggest that hippocampal representation of potential threats could be an important mechanism that underlies worry and a potential target for anxiolytics.