Progressive Transformation of Hippocampal Neuronal Representations in “Morphed” Environments

A theory of hippocampal function: New developments

We develop further here the only quantitative theory of the storage of information in the hippocampal episodic memory system and its recall back to the neocortex. The theory is upgraded to account for a revolution in understanding of spatial representations in the primate, including human, hippocampus, that go beyond the place where the individual is located, to the location being viewed in a scene. This is fundamental to much primate episodic memory and navigation: functions supported in humans by pathways that build 'where' spatial view representations by feature combinations in a ventromedial visual cortical stream, separate from those for 'what' object and face information to the inferior temporal visual cortex, and for reward information from the orbitofrontal cortex. Key new computational developments include the capacity of the CA3 attractor network for storing whole charts of space; how the correlations inherent in self-organizing continuous spatial representations impact the storage capacity; how the CA3 network can combine continuous spatial and discrete object and reward representations; the roles of the rewards that reach the hippocampus in the later consolidation into long-term memory in part via cholinergic pathways from the orbitofrontal cortex; and new ways of analysing neocortical information storage using Potts networks.

Hippocampal-dependent navigation in head-fixed mice using a floating real-world environment

Head-fixation of mice enables high-resolution monitoring of neuronal activity coupled with precise control of environmental stimuli. Virtual reality can be used to emulate the visual experience of movement during head fixation, but a low inertia floating real-world environment (mobile homecage, MHC) has the potential to engage more sensory modalities and provide a richer experimental environment for complex behavioral tasks. However, it is not known whether mice react to this adapted environment in a similar manner to real environments, or whether the MHC can be used to implement validated, maze-based behavioral tasks. Here, we show that hippocampal place cell representations are intact in the MHC and that the system allows relatively long (20 min) whole-cell patch clamp recordings from dorsal CA1 pyramidal neurons, revealing sub-threshold membrane potential dynamics. Furthermore, mice learn the location of a liquid reward within an adapted T-maze guided by 2-dimensional spatial navigation cues and relearn the location when spatial contingencies are reversed. Bilateral infusions of scopolamine show that this learning is hippocampus-dependent and requires intact cholinergic signalling. Therefore, we characterize the MHC system as an experimental tool to study sub-threshold membrane potential dynamics that underpin complex navigation behaviors.

Understanding How Acute Alcohol Impacts Neural Encoding in the Rodent Brain

Alcohol impacts neural circuitry throughout the brain and has wide-ranging effects on the biophysical properties of neurons in these circuits. Articulating how these wide-ranging effects might eventually result in altered computational properties has the potential to provide a tractable working model of how alcohol alters neural encoding. This chapter reviews what is currently known about how acute alcohol influences neural activity in cortical, hippocampal, and dopaminergic circuits as these have been the primary focus of understanding how alcohol alters neural computation. While other neural systems have been the focus of exhaustive work on this topic, these brain regions are the ones where in vivo neural recordings are available, thus optimally suited to make the link between changes in neural activity and behavior. Rodent models have been key in developing an understanding of how alcohol impacts the function of these circuits, and this chapter therefore focuses on work from mice and rats. While progress has been made, it is critical to understand the challenges and caveats associated with experimental procedures, especially when performed in vivo, which are designed to answer this question and if/how to translate these data to humans. The hypothesis is discussed that alcohol impairs the ability of neural circuits to acquire states of neural activity that are transiently elevated and characterized by increased complexity. It is hypothesized that these changes are distinct from the traditional view of alcohol being a depressant of neural activity in the forebrain.

Increased flexibility of CA3 memory representations following environmental enrichment

Environmental enrichment (EE) improves memory, particularly the ability to discriminate similar past experiences.1,2,3,4,5,6 The hippocampus supports this ability via pattern separation, the encoding of similar events using dissimilar memory representations.7 This is carried out in the dentate gyrus (DG) and CA3 subfields.8,9,10,11,12 Upregulation of adult neurogenesis in the DG improves memory through enhanced pattern separation.1,2,3,4,5,6,11,13,14,15,16 Adult-born granule cells (abGCs) in DG are suggested to contribute to pattern separation by driving inhibition in regions such as CA3,13,14,15,16,17,18 leading to sparser, nonoverlapping representations of similar events (although a role for abGCs in driving excitation in the hippocampus has also been reported16). Place cells in the hippocampus contribute to pattern separation by remapping to spatial and contextual alterations to the environment.19,20,21,22,23,24,25,26,27 How spatial responses in CA3 are affected by EE and input from increased numbers of abGCs in DG is, however, unknown. Here, we investigate the neural mechanisms facilitating improved memory following EE using associative recognition memory tasks that model the automatic and integrative nature of episodic memory. We find that EE-dependent improvements in difficult discriminations are related to increased neurogenesis and sparser memory representations across the hippocampus. Additionally, we report for the first time that EE changes how CA3 place cells discriminate similar contexts. CA3 place cells of enriched rats show greater spatial tuning, increased firing rates, and enhanced remapping to contextual changes. These findings point to more precise and flexible CA3 memory representations in enriched rats, which provides a putative mechanism for EE-dependent improvements in fine memory discrimination.

Getting LOST: A conceptual framework for supporting and enhancing spatial navigation in aging

Spatial navigation is more difficult and effortful for older than younger individuals, a shift which occurs for a variety of neurological, physical, and cognitive reasons associated with aging. Despite a large body of evidence documenting age-related deficits in spatial navigation, comparatively less research addresses how to facilitate more effective navigation behavior for older adults. Since navigation challenges arise for a variety of reasons in old age, a one-size-fits-all solution is unlikely to work. Here, we introduce a framework for the variety of spatial navigation challenges faced in aging, which we call LOST-Location, Orientation, Spatial mapping, and Transit. The LOST framework builds on evidence from the cognitive neuroscience of spatial navigation, which reveals distinct components underpinning human wayfinding. We evaluate research on navigational aids-devices and depictions-which help people find their way around; and we reflect on how navigation aids solve (or fail to solve) specific wayfinding difficulties faced by older adults. In summary, we emphasize a bespoke approach to improving spatial navigation in aging, which focuses on tailoring navigation solutions to specific navigation challenges. Our hope is that by providing precise support to older navigators, navigation opportunities can facilitate independence and exploration, while minimizing the danger of becoming lost. We conclude by delineating critical knowledge gaps in how to improve older adults' spatial navigation capacities that the novel LOST framework could guide to address. This article is categorized under: Psychology > Development and Aging Neuroscience > Cognition Neuroscience > Behavior.

Contextual memory engrams, and the neuromodulatory influence of the locus coeruleus

Here, we review the basis of contextual memory at a conceptual and cellular level. We begin with an overview of the philosophical foundations of traversing space, followed by theories covering the material bases of contextual representations in the hippocampus (engrams), exploring functional characteristics of the cells and subfields within. Next, we explore various methodological approaches for investigating contextual memory engrams, emphasizing plasticity mechanisms. This leads us to discuss the role of neuromodulatory inputs in governing these dynamic changes. We then outline a recent hypothesis involving noradrenergic and dopaminergic projections from the locus coeruleus (LC) to different subregions of the hippocampus, in sculpting contextual representations, giving a brief description of the neuroanatomical and physiological properties of the LC. Finally, we examine how activity in the LC influences contextual memory processes through synaptic plasticity mechanisms to alter hippocampal engrams. Overall, we find that phasic activation of the LC plays an important role in promoting new learning and altering mnemonic processes at the behavioral and cellular level through the neuromodulatory influence of NE/DA in the hippocampus. These findings may provide insight into mechanisms of hippocampal remapping and memory updating, memory processes that are potentially dysregulated in certain psychiatric and neurodegenerative disorders.

Hippocampal sequences span experience relative to rewards

Hippocampal place cells fire in sequences that span spatial environments and non-spatial modalities, suggesting that hippocampal activity can anchor to the most behaviorally salient aspects of experience. As reward is a highly salient event, we hypothesized that sequences of hippocampal activity can anchor to rewards. To test this, we performed two-photon imaging of hippocampal CA1 neurons as mice navigated virtual environments with changing hidden reward locations. When the reward moved, the firing fields of a subpopulation of cells moved to the same relative position with respect to reward, constructing a sequence of reward-relative cells that spanned the entire task structure. The density of these reward-relative sequences increased with task experience as additional neurons were recruited to the reward-relative population. Conversely, a largely separate subpopulation maintained a spatially-based place code. These findings thus reveal separate hippocampal ensembles can flexibly encode multiple behaviorally salient reference frames, reflecting the structure of the experience.

Efficient, continual, and generalized learning in the brain - neural mechanism of Mental Schema 2.0

There has been tremendous progress in artificial neural networks (ANNs) over the past decade; however, the gap between ANNs and the biological brain as a learning device remains large. With the goal of closing this gap, this paper reviews learning mechanisms in the brain by focusing on three important issues in ANN research: efficiency, continuity, and generalization. We first discuss the method by which the brain utilizes a variety of self-organizing mechanisms to maximize learning efficiency, with a focus on the role of spontaneous activity of the brain in shaping synaptic connections to facilitate spatiotemporal learning and numerical processing. Then, we examined the neuronal mechanisms that enable lifelong continual learning, with a focus on memory replay during sleep and its implementation in brain-inspired ANNs. Finally, we explored the method by which the brain generalizes learned knowledge in new situations, particularly from the mathematical generalization perspective of topology. Besides a systematic comparison in learning mechanisms between the brain and ANNs, we propose "Mental Schema 2.0," a new computational property underlying the brain's unique learning ability that can be implemented in ANNs.

Estimating neuronal firing density: A quantitative analysis of firing rate map algorithms

The analysis of neurons that exhibit receptive fields dependent on an organism's spatial location, such as grid, place or boundary cells typically begins by mapping their activity in space using firing rate maps. However, mapping approaches are varied and depend on multiple tuning parameters that are usually chosen qualitatively by the experimenter and thus vary significantly across studies. Small changes in parameters such as these can impact results significantly, yet, to date a quantitative investigation of firing rate maps has not been attempted. Using simulated datasets, we examined how tuning parameters, recording duration and firing field size affect the accuracy of spatial maps generated using the most widely used approaches. For each approach we found a clear subset of parameters which yielded low-error firing rate maps and isolated the parameters yielding 1) the least error possible and 2) the Pareto-optimal parameter set which balanced error, computation time, place field detection accuracy and the extrapolation of missing values. Smoothed bivariate histograms and averaged shifted histograms were consistently associated with the fastest computation times while still providing accurate maps. Adaptive smoothing and binning approaches were found to compensate for low positional sampling the most effectively. Kernel smoothed density estimation also compensated for low sampling well and resulted in accurate maps, but it was also among the slowest methods tested. Overall, the bivariate histogram, coupled with spatial smoothing, is likely the most desirable method in the majority of cases.

Beyond Correlation: Optimal Transport Metrics For Characterizing Representational Stability and Remapping in Neurons Encoding Spatial Memory

Spatial representations in the entorhinal cortex (EC) and hippocampus (HPC) are fundamental to cognitive functions like navigation and memory. These representations, embodied in spatial field maps, dynamically remap in response to environmental changes. However, current methods, such as Pearson's correlation coefficient, struggle to capture the complexity of these remapping events, especially when fields do not overlap, or transformations are non-linear. This limitation hinders our understanding and quantification of remapping, a key aspect of spatial memory function. To address this, we propose a family of metrics based on the Earth Mover's Distance (EMD) as a versatile framework for characterizing remapping. Applied to both normalized and unnormalized distributions, the EMD provides a granular, noise-resistant, and rate-robust description of remapping. This approach enables the identification of specific cell types and the characterization of remapping in various scenarios, including disease models. Furthermore, the EMD's properties can be manipulated to identify spatially tuned cell types and to explore remapping as it relates to alternate information forms such as spatiotemporal coding. By employing approximations of the EMD, we present a feasible, lightweight approach that complements traditional methods. Our findings underscore the potential of the EMD as a powerful tool for enhancing our understanding of remapping in the brain and its implications for spatial navigation, memory studies and beyond.

Entorhinal cortex glutamatergic and GABAergic projections bidirectionally control discrimination and generalization of hippocampal representations

Discrimination and generalization are crucial brain-wide functions for memory and object recognition that utilize pattern separation and completion computations. Circuit mechanisms supporting these operations remain enigmatic. We show lateral entorhinal cortex glutamatergic (LEC GLU ) and GABAergic (LEC GABA ) projections are essential for object recognition memory. Silencing LEC GLU during in vivo two-photon imaging increased the population of active CA3 pyramidal cells but decreased activity rates, suggesting a sparse coding function through local inhibition. Silencing LEC GLU also decreased place cell remapping between different environments validating this circuit drives pattern separation and context discrimination. Optogenetic circuit mapping confirmed that LEC GLU drives dominant feedforward inhibition to prevent CA3 somatic and dendritic spikes. However, conjunctively active LEC GABA suppresses this local inhibition to disinhibit CA3 pyramidal neuron soma and selectively boost integrative output of LEC and CA3 recurrent network. LEC GABA thus promotes pattern completion and context generalization. Indeed, without this disinhibitory input, CA3 place maps show decreased similarity between contexts. Our findings provide circuit mechanisms whereby long-range glutamatergic and GABAergic cortico-hippocampal inputs bidirectionally modulate pattern separation and completion, providing neuronal representations with a dynamic range for context discrimination and generalization.

A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields

Hippocampus place cell discharge is temporally unreliable across seconds and days, and place fields are multimodal, suggesting an "ensemble cofiring" spatial coding hypothesis with manifold dynamics that does not require reliable spatial tuning, in contrast to hypotheses based on place field (spatial tuning) stability. We imaged mouse CA1 (cornu ammonis 1) ensembles in two environments across three weeks to evaluate these coding hypotheses. While place fields "remap," being more distinct between than within environments, coactivity relationships generally change less. Decoding location and environment from 1-s ensemble location-specific activity is effective and improves with experience. Decoding environment from cell-pair coactivity relationships is also effective and improves with experience, even after removing place tuning. Discriminating environments from 1-s ensemble coactivity relies crucially on the cells with the most anti-coactive cell-pair relationships because activity is internally organized on a low-dimensional manifold of non-linear coactivity relationships that intermittently reregisters to environments according to the anti-cofiring subpopulation activity.

A map of spatial navigation for neuroscience

Spatial navigation has received much attention from neuroscientists, leading to the identification of key brain areas and the discovery of numerous spatially selective cells. Despite this progress, our understanding of how the pieces fit together to drive behavior is generally lacking. We argue that this is partly caused by insufficient communication between behavioral and neuroscientific researchers. This has led the latter to under-appreciate the relevance and complexity of spatial behavior, and to focus too narrowly on characterizing neural representations of space-disconnected from the computations these representations are meant to enable. We therefore propose a taxonomy of navigation processes in mammals that can serve as a common framework for structuring and facilitating interdisciplinary research in the field. Using the taxonomy as a guide, we review behavioral and neural studies of spatial navigation. In doing so, we validate the taxonomy and showcase its usefulness in identifying potential issues with common experimental approaches, designing experiments that adequately target particular behaviors, correctly interpreting neural activity, and pointing to new avenues of research.

Object-centered population coding in CA1 of the hippocampus

Objects and landmarks are crucial for guiding navigation and must be integrated into the cognitive map of space. Studies of object coding in the hippocampus have primarily focused on activity of single cells. Here, we record simultaneously from large numbers of hippocampal CA1 neurons to determine how the presence of a salient object in the environment alters single-neuron and neural-population activity of the area. The majority of the cells showed some change in their spatial firing patterns when the object was introduced. At the neural-population level, these changes were systematically organized according to the animal's distance from the object. This organization was widely distributed across the cell sample, suggesting that some features of cognitive maps-including object representation-are best understood as emergent properties of neural populations.

Active experience, not time, determines within-day representational drift in dorsal CA1

Memories of past events can be recalled long after the event, indicating stability. But new experiences are also integrated into existing memories, indicating plasticity. In the hippocampus, spatial representations are known to remain stable but have also been shown to drift over long periods of time. We hypothesized that experience, more than the passage of time, is the driving force behind representational drift. We compared the within-day stability of place cells' representations in dorsal CA1 of the hippocampus of mice traversing two similar, familiar tracks for different durations. We found that the more time the animals spent actively traversing the environment, the greater the representational drift, regardless of the total elapsed time between visits. Our results suggest that spatial representation is a dynamic process, related to the ongoing experiences within a specific context, and is related to memory update rather than to passive forgetting.

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.

Entorhinal cortex dysfunction in Alzheimer's disease

The entorhinal cortex (EC) is the brain region that often exhibits the earliest histological alterations in Alzheimer's disease (AD), including the formation of neurofibrillary tangles and cell death. Recently, brain imaging studies from preclinical AD patients and electrophysiological recordings from AD animal models have shown that impaired neuronal activity in the EC precedes neurodegeneration. This implies that memory impairments and spatial navigation deficits at the initial stage of AD are likely caused by activity dysfunction rather than by cell death. This review focuses on recent findings on EC dysfunction in AD, and discusses the potential pathways for mitigating AD progression by protecting the EC.

Spatial memory deficits in Alzheimer's disease and their connection to cognitive maps' formation by place cells and grid cells

Whenever we navigate through different contexts, we build a cognitive map: an internal representation of the territory. Spatial navigation is a complex skill that involves multiple types of information processing and integration. Place cells and grid cells, collectively with other hippocampal and medial entorhinal cortex neurons (MEC), form a neural network whose activity is critical for the representation of self-position and orientation along with spatial memory retrieval. Furthermore, this activity generates new representations adapting to changes in the environment. Though there is a normal decline in spatial memory related to aging, this is dramatically increased in pathological conditions such as Alzheimer's disease (AD). AD is a multi-factorial neurodegenerative disorder affecting mainly the hippocampus-entorhinal cortex (HP-EC) circuit. Consequently, the initial stages of the disease have disorientation and wandering behavior as two of its hallmarks. Recent electrophysiological studies have linked spatial memory deficits to difficulties in spatial information encoding. Here we will discuss map impairment and remapping disruption in the HP-EC network, as a possible circuit mechanism involved in the spatial memory and navigation deficits observed in AD, pointing out the benefits of virtual reality as a tool for early diagnosis and rehabilitation.

Contextual inference in learning and memory

Context is widely regarded as a major determinant of learning and memory across numerous domains, including classical and instrumental conditioning, episodic memory, economic decision-making, and motor learning. However, studies across these domains remain disconnected due to the lack of a unifying framework formalizing the concept of context and its role in learning. Here, we develop a unified vernacular allowing direct comparisons between different domains of contextual learning. This leads to a Bayesian model positing that context is unobserved and needs to be inferred. Contextual inference then controls the creation, expression, and updating of memories. This theoretical approach reveals two distinct components that underlie adaptation, proper and apparent learning, respectively referring to the creation and updating of memories versus time-varying adjustments in their expression. We review a number of extensions of the basic Bayesian model that allow it to account for increasingly complex forms of contextual learning.

Subtle visual change in a virtual environment induces heterogeneous remapping systematically in CA1, but not CA3

Environmental change may lead to new memories or modify old ones, but the underlying neural mechanisms are largely unclear. We recorded hippocampal place cells simultaneously from CA1 and CA3 in a virtual reality environment. Compared with CA1, place cells in CA3 are more tolerant of individual landmark changes but undergo orthogonal changes to code distinctively different environments. As visual noise (virtual fog) is introduced to a visually enriched environment, place cells in CA1 split into two subpopulations: in one, place cells maintain their field locations while changing their firing rates to reflect sensory changes; in the other, place cells exhibit global remapping in response to the contextual change. In contrast, place cells in CA3 exhibit mainly rate remapping under the same conditions. Our results suggest that CA1 may simultaneously represent heterogeneous maps of the same environment when subtle visual noise induces both sensory and contextual changes.

Task-selective place cells show behaviorally driven dynamics during learning and stability during memory recall

Decades of work propose that hippocampal activity supports internal representation of learned experiences and contexts, allowing individuals to form long-term memories and quickly adapt behavior to changing environments. However, recent studies insinuate hippocampal representations can drift over time, raising the question: how could the hippocampus hold stable memories when activity of its neuronal maps fluctuates? We hypothesized that task-dependent hippocampal maps set by learning rules and structured attention stabilize as a function of behavioral performance. To test this, we imaged hippocampal CA1 pyramidal neurons during learning and memory recall phases of a new task where mice use odor cues to navigate between two reward zones. Across learning, both orthogonal and overlapping task-dependent place maps form rapidly, discriminating trial context with strong correlation to behavioral performance. Once formed, task-selective place maps show increased long-term stability during memory recall phases. We conclude that memory demand and attention stabilize hippocampal activity to maintain contextually rich spatial representations.

Neural circuit dynamics of drug-context associative learning in the mouse hippocampus

The environmental context associated with previous drug consumption is a potent trigger for drug relapse. However, the mechanism by which neural representations of context are modified to incorporate information associated with drugs of abuse remains unknown. Using longitudinal calcium imaging in freely behaving mice, we find that unlike the associative learning of natural reward, drug-context associations for psychostimulants and opioids are encoded in a specific subset of hippocampal neurons. After drug conditioning, these neurons weakened their spatial coding for the non-drug paired context, resulting in an orthogonal representation for the drug versus non-drug context that was predictive of drug-seeking behavior. Furthermore, these neurons were selected based on drug-spatial experience and were exclusively tuned to animals' allocentric position. Together, this work reveals how drugs of abuse alter the hippocampal circuit to encode drug-context associations and points to the possibility of targeting drug-associated memory in the hippocampus.

Artificial intelligence insights into hippocampal processing

Advances in artificial intelligence, machine learning, and deep neural networks have led to new discoveries in human and animal learning and intelligence. A recent artificial intelligence agent in the DeepMind family, muZero, can complete a variety of tasks with limited information about the world in which it is operating and with high uncertainty about features of current and future space. To perform, muZero uses only three functions that are general yet specific enough to allow learning across a variety of tasks without overgeneralization across different contexts. Similarly, humans and animals are able to learn and improve in complex environments while transferring learning from other contexts and without overgeneralizing. In particular, the mammalian extrahippocampal system (eHPCS) can guide spatial decision making while simultaneously encoding and processing spatial and contextual information. Like muZero, the eHPCS is also able to adjust contextual representations depending on the degree and significance of environmental changes and environmental cues. In this opinion, we will argue that the muZero functions parallel those of the hippocampal system. We will show that the different components of the muZero model provide a framework for thinking about generalizable learning in the eHPCS, and that the evaluation of how transitions in cell representations occur between similar and distinct contexts can be informed by advances in artificial intelligence agents such as muZero. We additionally explain how advances in AI agents will provide frameworks and predictions by which to investigate the expected link between state changes and neuronal firing. Specifically, we will discuss testable predictions about the eHPCS, including the functions of replay and remapping, informed by the mechanisms behind muZero learning. We conclude with additional ways in which agents such as muZero can aid in illuminating prospective questions about neural functioning, as well as how these agents may shed light on potential expected answers.

Neuronal signature of spatial decision-making during navigation by freely moving rats by using calcium imaging

A challenge in spatial memory is understanding how place cell firing contributes to decision-making in navigation. A spatial recency task was created in which freely moving rats first became familiar with a spatial context over several days and thereafter were required to encode and then selectively recall one of three specific locations within it that was chosen to be rewarded that day. Calcium imaging was used to record from more than 1,000 cells in area CA1 of the hippocampus of five rats during the exploration, sample, and choice phases of the daily task. The key finding was that neural activity in the startbox rose steadily in the short period prior to entry to the arena and that this selective population cell firing was predictive of the daily changing goal on correct trials but not on trials in which the animals made errors. Single-cell and population activity measures converged on the idea that prospective coding of neural activity can be involved in navigational decision-making.

Complementary task representations in hippocampus and prefrontal cortex for generalizing the structure of problems

Humans and other animals effortlessly generalize prior knowledge to solve novel problems, by abstracting common structure and mapping it onto new sensorimotor specifics. To investigate how the brain achieves this, in this study, we trained mice on a series of reversal learning problems that shared the same structure but had different physical implementations. Performance improved across problems, indicating transfer of knowledge. Neurons in medial prefrontal cortex (mPFC) maintained similar representations across problems despite their different sensorimotor correlates, whereas hippocampal (dCA1) representations were more strongly influenced by the specifics of each problem. This was true for both representations of the events that comprised each trial and those that integrated choices and outcomes over multiple trials to guide an animal's decisions. These data suggest that prefrontal cortex and hippocampus play complementary roles in generalization of knowledge: PFC abstracts the common structure among related problems, and hippocampus maps this structure onto the specifics of the current situation.

Task specificity in mouse parietal cortex

Parietal cortex is implicated in a variety of behavioral processes, but it is unknown whether and how its individual neurons participate in multiple tasks. We trained head-fixed mice to perform two visual decision tasks involving a steering wheel or a virtual T-maze and recorded from the same parietal neurons during these two tasks. Neurons that were active during the T-maze task were typically inactive during the steering-wheel task and vice versa. Recording from the same neurons in the same apparatus without task stimuli yielded the same specificity as in the task, suggesting that task specificity depends on physical context. To confirm this, we trained some mice in a third task combining the steering wheel context with the visual environment of the T-maze. This hybrid task engaged the same neurons as those engaged in the steering-wheel task. Thus, participation by neurons in mouse parietal cortex is task specific, and this specificity is determined by physical context.

Grid cell remapping under three-dimensional object and social landmarks detected by implantable microelectrode arrays for the medial entorhinal cortex

Grid cells with stable hexagonal firing patterns in the medial entorhinal cortex (MEC) carry the vital function of serving as a metric for the surrounding environment. Whether this mechanism processes only spatial information or involves nonspatial information remains elusive. Here, we fabricated an MEC-shaped microelectrode array (MEA) to detect the variation in neural spikes and local field potentials of the MEC when rats forage in a square enclosure with a planar, three-dimensional object and social landmarks in sequence. The results showed that grid cells exhibited rate remapping under social conditions in which spike firing fields closer to the social landmark had a higher firing rate. Furthermore, global remapping showed that hexagonal firing patterns were rotated and scaled when the planar landmark was replaced with object and social landmarks. In addition, when grid cells were activated, the local field potentials were dominated by the theta band (5-8 Hz), and spike phase locking was observed at troughs of theta oscillations. Our results suggest the pattern separation mechanism of grid cells in which the spatial firing structure and firing rate respond to spatial and social information, respectively, which may provide new insights into how the brain creates a cognitive map.

Long-term transverse imaging of the hippocampus with glass microperiscopes

The hippocampus consists of a stereotyped neuronal circuit repeated along the septal-temporal axis. This transverse circuit contains distinct subfields with stereotyped connectivity that support crucial cognitive processes, including episodic and spatial memory. However, comprehensive measurements across the transverse hippocampal circuit in vivo are intractable with existing techniques. Here, we developed an approach for two-photon imaging of the transverse hippocampal plane in awake mice via implanted glass microperiscopes, allowing optical access to the major hippocampal subfields and to the dendritic arbor of pyramidal neurons. Using this approach, we tracked dendritic morphological dynamics on CA1 apical dendrites and characterized spine turnover. We then used calcium imaging to quantify the prevalence of place and speed cells across subfields. Finally, we measured the anatomical distribution of spatial information, finding a non-uniform distribution of spatial selectivity along the DG-to-CA1 axis. This approach extends the existing toolbox for structural and functional measurements of hippocampal circuitry.

Unilateral Perforant Path Transection Does Not Alter Lateral Entorhinal Cortical or Hippocampal CA3 Arc Expression

It is well established that degradation of perforant path fibers is associated with age-related cognitive dysfunction and CA3 hyperactivity. Whether this fiber loss triggers a cascade of other functional changes within the hippocampus circuit has not been causatively established, however. Thus, the current study evaluated the effect of perforant path fiber loss on neuronal activity in CA3 and layer II of the lateral entorhinal cortex (LEC) in relation to mnemonic similarity task performance. Expression of the immediate early gene Arc was quantified in rats that received a unilateral right hemisphere transection of the perforant path or sham surgery that cut the cortex but left the fibers intact. Behavior-related expression of Arc mRNA was measured to test the hypothesis that fiber loss leads to elevated activation of CA3 and LEC neurons, as previously observed in aged rats that were impaired on the mnemonic similarity task. Transection of perforant path fibers, which has previously been shown to lead to a decline in mnemonic similarity task performance, did not alter Arc expression. Arc expression in CA3, however, was correlated with task performance on the more difficult discrimination trials across both surgical groups. These observations further support a link between CA3 activity and mnemonic similarity task performance but suggest the reduced input from the entorhinal cortex to the hippocampus, as observed in old age, does not causatively elevate CA3 activity.

Neural kernels for recursive support vector regression as a model for episodic memory

Retrieval of episodic memories requires intrinsic reactivation of neuronal activity patterns. The content of the memories is thereby assumed to be stored in synaptic connections. This paper proposes a theory in which these are the synaptic connections that specifically convey the temporal order information contained in the sequences of a neuronal reservoir to the sensory-motor cortical areas that give rise to the subjective impression of retrieval of sensory motor events. The theory is based on a novel recursive version of support vector regression that allows for efficient continuous learning that is only limited by the representational capacity of the reservoir. The paper argues that hippocampal theta sequences are a potential neural substrate underlying this reservoir. The theory is consistent with confabulations and post hoc alterations of existing memories.

Sound Localization of World and Head-Centered Space in Ferrets

The location of sounds can be described in multiple coordinate systems that are defined relative to ourselves, or the world around us. Evidence from neural recordings in animals point toward the existence of both head-centered and world-centered representations of sound location in the brain; however, it is unclear whether such neural representations have perceptual correlates in the sound localization abilities of nonhuman listeners. Here, we establish novel behavioral tests to determine the coordinate systems in which ferrets can localize sounds. We found that ferrets could learn to discriminate between sound locations that were fixed in either world-centered or head-centered space, across wide variations in sound location in the alternative coordinate system. Using probe sounds to assess broader generalization of spatial hearing, we demonstrated that in both head and world-centered tasks, animals used continuous maps of auditory space to guide behavior. Single trial responses of individual animals were sufficiently informative that we could then model sound localization using speaker position in specific coordinate systems and accurately predict ferrets' actions in held-out data. Our results demonstrate that ferrets, an animal model in which neurons are known to be tuned to sound location in egocentric and allocentric reference frames, can also localize sounds in multiple head and world-centered spaces.SIGNIFICANCE STATEMENT Humans can describe the location of sounds either relative to themselves, or in the world, independent of their momentary position. These different spaces are also represented in the activity of neurons in animals, but it is not clear whether nonhuman listeners also perceive both head and world-centered sound location. Here, we designed behavioral tasks in which ferrets discriminated between sounds using their position in the world, or relative to the head. Subjects learnt to solve both problems and generalized sound location in each space when presented with infrequent probe sounds. These findings reveal a perceptual correlate of neural sensitivity previously observed in the ferret brain and establish that, like humans, ferrets can access an auditory map of their local environment.

Response Flexibility: The Role of the Lateral Habenula

The ability to make appropriate decisions that result in an optimal outcome is critical for survival. This process involves assessing the environment as well as integrating prior knowledge about the environment with information about one's current internal state. There are many neural structures that play critical roles in mediating these processes, but it is not yet known how such information coalesces to influence behavioral output. The lateral habenula (LHb) has often been cited as a structure critical for adaptive and flexible responding when environmental contexts and internal state changes. A challenge, however, has been understanding how LHb promotes response flexibility. In this review, we hypothesize that the LHb enables flexible responding following the integration of context memory and internal state information by signaling downstream brainstem structures known to drive hippocampal theta. In this way, animals respond more flexibly in a task situation not because the LHb selects a particular action, but rather because LHb enhances a hippocampal neural state that is often associated with greater attention, arousal, and exploration. In freely navigating animals, these are essential conditions that are needed to discover and implement appropriate alternative choices and behaviors. As a corollary to our hypothesis, we describe short- and intermediate-term functions of the LHb. Finally, we discuss the effects on the behavior of LHb dysfunction in short- and intermediate-timescales, and then suggest that new therapies may act on the LHb to alleviate the behavioral impairments following long-term LHb disruption.

Flexible rerouting of hippocampal replay sequences around changing barriers in the absence of global place field remapping

Flexibility is a hallmark of memories that depend on the hippocampus. For navigating animals, flexibility is necessitated by environmental changes such as blocked paths and extinguished food sources. To better understand the neural basis of this flexibility, we recorded hippocampal replays in a spatial memory task where barriers as well as goals were moved between sessions to see whether replays could adapt to new spatial and reward contingencies. Strikingly, replays consistently depicted new goal-directed trajectories around each new barrier configuration and largely avoided barrier violations. Barrier-respecting replays were learned rapidly and did not rely on place cell remapping. These data distinguish sharply between place field responses, which were largely stable and remained tied to sensory cues, and replays, which changed flexibly to reflect the learned contingencies in the environment and suggest sequenced activations such as replay to be an important link between the hippocampus and flexible memory.

Characterization of exploratory patterns and hippocampal-prefrontal network oscillations during the emergence of free exploration

During free exploration, the emergence of patterned and sequential behavioral responses to an unknown environment reflects exploration traits and adaptation. However, the behavioral dynamics and neural substrates underlying the exploratory behavior remain poorly understood. We developed computational tools to quantify the exploratory behavior and performed in vivo electrophysiological recordings in a large arena in which mice made sequential excursions into unknown territory. Occupancy entropy was calculated to characterize the cumulative and moment-to-moment behavioral dynamics in explored and unexplored territories. Local field potential analysis revealed that the theta activity in the dorsal hippocampus (dHPC) was highly correlated with the occupancy entropy. Individual dHPC and prefrontal cortex (PFC) oscillatory activities could classify various aspects of free exploration. Initiation of exploration was accompanied by a coordinated decrease and increase in theta activity in PFC and dHPC, respectively. Our results indicate that dHPC and PFC work synergistically in shaping free exploration by modulating exploratory traits during emergence and visits to an unknown environment.

A model of naturalistic decision making in preference tests

Decisions as to whether to continue with an ongoing activity or to switch to an alternative are a constant in an animal’s natural world, and in particular underlie foraging behavior and performance in food preference tests. Stimuli experienced by the animal both impact the choice and are themselves impacted by the choice, in a dynamic back and forth. Here, we present model neural circuits, based on spiking neurons, in which the choice to switch away from ongoing behavior instantiates this back and forth, arising as a state transition in neural activity. We analyze two classes of circuit, which differ in whether state transitions result from a loss of hedonic input from the stimulus (an “entice to stay” model) or from aversive stimulus-input (a “repel to leave” model). In both classes of model, we find that the mean time spent sampling a stimulus decreases with increasing value of the alternative stimulus, a fact that we linked to the inclusion of depressing synapses in our model. The competitive interaction is much greater in “entice to stay” model networks, which has qualitative features of the marginal value theorem, and thereby provides a framework for optimal foraging behavior. We offer suggestions as to how our models could be discriminatively tested through the analysis of electrophysiological and behavioral data.

Many decisions are of the ilk of whether to continue sampling a stimulus or to switch to an alternative, a key feature of foraging behavior. We produce two classes of model for such stay-switch decisions, which differ in how decisions to switch stimuli can arise. In an “entice-to-stay” model, a reduction in the necessary positive stimulus input causes switching decisions. In a “repel-to-leave” model, a rise in aversive stimulus input produces a switch decision. We find that in tasks where the sampling of one stimulus follows another, adaptive biological processes arising from a highly hedonic stimulus can reduce the time spent at the following stimulus, by up to ten-fold in the “entice-to-stay” models. Along with potentially observable behavioral differences that could distinguish the classes of networks, we also found signatures in neural activity, such as oscillation of neural firing rates and a rapid change in rates preceding the time of choice to leave a stimulus. In summary, our model findings lead to testable predictions and suggest a neural circuit-based framework for explaining foraging choices.

Graded remapping of hippocampal ensembles under sensory conflicts

Hippocampal place cells are thought to constitute a cognitive map of space derived from multimodal sensory inputs. Alteration of allocentric (visual) cues in a fixed environment is known to induce modulations of place cell activity to varying degrees from rate changes to global remapping. To determine how hippocampal ensembles combine multimodal sensory cues, we examine hippocampal CA1 remapping in Mongolian gerbils in a 1D virtual reality experiment, during which self-motion cues (locomotor, vestibular, and optic flow information) and allocentric visual cues are altered. We observe that self-motion cues are over-represented, but responsiveness to allocentric visual cues, although task-irrelevant, elicits both rate and global remapping in the hippocampal ensemble. We propose that remapping can be reconciled by considering global, partial, and rate remapping on a continuous scale on which the graded change of activity in the entire CA1 population can be interpreted as the expectancy about the animal's spatial environment.

Dentate granule cells encode auditory decisions after reinforcement learning in rats

Auditory-cued goal-oriented behaviors requires the participation of cortical and subcortical brain areas, but how neural circuits associate sensory-based decisions with goal locations through learning remains poorly understood. The hippocampus is critical for spatial coding, suggesting its possible involvement in transforming sensory inputs to the goal-oriented decisions. Here, we developed an auditory discrimination task in which rats learned to navigate to goal locations based on the frequencies of auditory stimuli. Using in vivo calcium imaging in freely behaving rats over the course of learning, we found that dentate granule cells became more active, spatially tuned, and responsive to task-related variables as learning progressed. Furthermore, only after task learning, the activity of dentate granule cell ensembles represented the navigation path and predicts auditory decisions as early as when rats began to approach the goals. Finally, chemogenetic silencing of dentate gyrus suppressed task learning. Our results demonstrate that dentate granule cells gain task-relevant firing pattern through reinforcement learning and could be a potential link of sensory decisions to spatial navigation.

Epistemic Autonomy: Self-supervised Learning in the Mammalian Hippocampus

Biological cognition is based on the ability to autonomously acquire knowledge, or epistemic autonomy. Such self-supervision is largely absent in artificial neural networks (ANN) because they depend on externally set learning criteria. Yet training ANN using error backpropagation has created the current revolution in artificial intelligence, raising the question of whether the epistemic autonomy displayed in biological cognition can be achieved with error backpropagation-based learning. We present evidence suggesting that the entorhinal-hippocampal complex combines epistemic autonomy with error backpropagation. Specifically, we propose that the hippocampus minimizes the error between its input and output signals through a modulatory counter-current inhibitory network. We further discuss the computational emulation of this principle and analyze it in the context of autonomous cognitive systems.

Hippocampal sub-networks exhibit distinct spatial representation deficits in Alzheimer's disease model mice

Not much is known about how the dentate gyrus (DG) and hippocampal CA3 networks, critical for memory and spatial processing, malfunction in Alzheimer's disease (AD). While studies of associative memory deficits in AD have focused mainly on behavior, here, we directly measured neurophysiological network dysfunction. We asked what the pattern of deterioration of different networks is during disease progression. We investigated how the associative memory-processing capabilities in different hippocampal subfields are affected by familial AD (fAD) mutations leading to amyloid-β dyshomeostasis. Specifically, we focused on the DG and CA3, which are known to be involved in pattern completion and separation and are susceptible to pathological alterations in AD. To identify AD-related deficits in neural-ensemble dynamics, we recorded single-unit activity in wild-type (WT) and fAD model mice (APPSwe+PSEN1/ΔE9) in a novel tactile morph task, which utilizes the extremely developed somatosensory modality of mice. As expected from the sub-network regional specialization, we found that tactile changes induced lower rate map correlations in the DG than in CA3 of WT mice. This reflects DG pattern separation and CA3 pattern completion. In contrast, in fAD model mice, we observed pattern separation deficits in the DG and pattern completion deficits in CA3. This demonstration of region-dependent impairments in fAD model mice contributes to understanding of brain networks deterioration during fAD progression. Furthermore, it implies that the deterioration cannot be studied generally throughout the hippocampus but must be researched at a finer resolution of microcircuits. This opens novel systems-level approaches for analyzing AD-related neural network deficits.

Experience-dependent contextual codes in the hippocampus

The hippocampus contains neural representations capable of supporting declarative memory. Hippocampal place cells are one such representation, firing in one or few locations in a given environment. Between environments, place cell firing fields remap (turning on/off or moving to a new location) to provide a population-wide code for distinct contexts. However, the manner by which contextual features combine to drive hippocampal remapping remains a matter of debate. Using large-scale in vivo two-photon intracellular calcium recordings in mice during virtual navigation, we show that remapping in the hippocampal region CA1 is driven by prior experience regarding the frequency of certain contexts and that remapping approximates an optimal estimate of the identity of the current context. A simple associative-learning mechanism reproduces these results. Together, our findings demonstrate that place cell remapping allows an animal to simultaneously identify its physical location and optimally estimate the identity of the environment.

Distinct Mechanisms of Over-Representation of Landmarks and Rewards in the Hippocampus

In the hippocampus, locations associated with salient features are represented by a disproportionately large number of neurons, but the cellular and molecular mechanisms underlying this over-representation remain elusive. Using longitudinal calcium imaging in mice learning to navigate in virtual reality, we find that the over-representation of reward and landmark locations are mediated by persistent and separable subsets of neurons, with distinct time courses of emergence and differing underlying molecular mechanisms. Strikingly, we find that in mice lacking Shank2, an autism spectrum disorder (ASD)-linked gene encoding an excitatory postsynaptic scaffold protein, the learning-induced over-representation of landmarks was absent whereas the over-representation of rewards was substantially increased, as was goal-directed behavior. These findings demonstrate that multiple hippocampal coding processes for unique types of salient features are distinguished by a Shank2-dependent mechanism and suggest that abnormally distorted hippocampal salience mapping may underlie cognitive and behavioral abnormalities in a subset of ASDs.

Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps

Cognitive maps are mental representations of spatial and conceptual relationships in an environment, and are critical for flexible behavior. To form these abstract maps, the hippocampus has to learn to separate or merge aliased observations appropriately in different contexts in a manner that enables generalization and efficient planning. Here we propose a specific higher-order graph structure, clone-structured cognitive graph (CSCG), which forms clones of an observation for different contexts as a representation that addresses these problems. CSCGs can be learned efficiently using a probabilistic sequence model that is inherently robust to uncertainty. We show that CSCGs can explain a variety of cognitive map phenomena such as discovering spatial relations from aliased sensations, transitive inference between disjoint episodes, and formation of transferable schemas. Learning different clones for different contexts explains the emergence of splitter cells observed in maze navigation and event-specific responses in lap-running experiments. Moreover, learning and inference dynamics of CSCGs offer a coherent explanation for disparate place cell remapping phenomena. By lifting aliased observations into a hidden space, CSCGs reveal latent modularity useful for hierarchical abstraction and planning. Altogether, CSCG provides a simple unifying framework for understanding hippocampal function, and could be a pathway for forming relational abstractions in artificial intelligence.

Ion-channel regulation of response decorrelation in a heterogeneous multi-scale model of the dentate gyrus

Heterogeneities in biological neural circuits manifest in afferent connectivity as well as in local-circuit components such as neuronal excitability, neural structure and local synaptic strengths. The expression of adult neurogenesis in the dentate gyrus (DG) amplifies local-circuit heterogeneities and guides heterogeneities in afferent connectivity. How do neurons and their networks endowed with these distinct forms of heterogeneities respond to perturbations to individual ion channels, which are known to change under several physiological and pathophysiological conditions? We sequentially traversed the ion channels-neurons-network scales and assessed the impact of eliminating individual ion channels on conductance-based neuronal and network models endowed with disparate local-circuit and afferent heterogeneities. We found that many ion channels differentially contributed to specific neuronal or network measurements, and the elimination of any given ion channel altered several functional measurements. We then quantified the impact of ion-channel elimination on response decorrelation, a well-established metric to assess the ability of neurons in a network to convey complementary information, in DG networks endowed with different forms of heterogeneities. Notably, we found that networks constructed with structurally immature neurons exhibited functional robustness, manifesting as minimal changes in response decorrelation in the face of ion-channel elimination. Importantly, the average change in output correlation was dependent on the eliminated ion channel but invariant to input correlation. Our analyses suggest that neurogenesis-driven structural heterogeneities could assist the DG network in providing functional resilience to molecular perturbations.

•

Perturbations at one scale result in a cascading impact on physiology across scales.

•

Heterogeneous multi-scale models used to assess the impact of ion-channel deletion.

•

Mapping of structural components to functional outcomes is many-to-many.

•

Differential & variable impact of ion channel deletion on response decorrelation.

•

Neurogenesis-induced structural heterogeneity confers resilience to perturbations.

The Continuity of Context: A Role for the Hippocampus

Tracking moment-to-moment change in input and detecting change sufficient to require altering behavior is crucial to survival. Here, we discuss how the brain evaluates change over time, focusing on the hippocampus and its role in tracking context. We leverage the anatomy and physiology of the hippocampal longitudinal axis, re-entrant loops, and amorphous networks to account for stimulus equivalence and the updating of an organism's sense of its context. Place cells have a central role in tracking contextual continuities and discontinuities across multiple scales, a capacity beyond current models of pattern separation and completion. This perspective highlights the critical role of the hippocampus in both spatial cognition and episodic memory: tracking change and detecting boundaries separating one context, or episode, from another.

Scalable representation of time in the hippocampus

Hippocampal "time cells" encode specific moments of temporally organized experiences that may support hippocampal functions for episodic memory. However, little is known about the reorganization of the temporal representation of time cells during changes in temporal structures of episodes. We investigated CA1 neuronal activity during temporal bisection tasks, in which the sets of time intervals to be discriminated were designed to be extended or contracted across the blocks of trials. Assemblies of neurons encoded elapsed time during the interval, and the representation was scaled when the set of interval times was varied. Theta phase precession and theta sequences of time cells were also scalable, and the fine temporal relationships were preserved between pairs in theta cycles. Moreover, theta sequences reflected the rats' decisions on the basis of their time estimation. These findings demonstrate that scalable features of time cells may support the capability of flexible temporal representation for memory formation.

Subcircuits of Deep and Superficial CA1 Place Cells Support Efficient Spatial Coding across Heterogeneous Environments

The hippocampus is thought to guide navigation by forming a cognitive map of space. Different environments differ in geometry and the availability of cues that can be used for navigation. Although several spatial coding mechanisms are known to coexist in the hippocampus, how they are influenced by various environmental features is not well understood. To address this issue, we examined the spatial coding characteristics of hippocampal neurons in mice and rats navigating in different environments. We found that CA1 place cells located in the superficial sublayer were more active in cue-poor environments and preferentially used a firing rate code driven by intra-hippocampal inputs. In contrast, place cells located in the deep sublayer were more active in cue-rich environments and used a phase code driven by entorhinal inputs. Switching between these two spatial coding modes was supported by the interaction between excitatory gamma inputs and local inhibition.

From Topological Analyses to Functional Modeling: The Case of Hippocampus

Topological data analyses are widely used for describing and conceptualizing large volumes of neurobiological data, e.g., for quantifying spiking outputs of large neuronal ensembles and thus understanding the functions of the corresponding networks. Below we discuss an approach in which convergent topological analyses produce insights into how information may be processed in mammalian hippocampus—a brain part that plays a key role in learning and memory. The resulting functional model provides a unifying framework for integrating spiking data at different timescales and following the course of spatial learning at different levels of spatiotemporal granularity. This approach allows accounting for contributions from various physiological phenomena into spatial cognition—the neuronal spiking statistics, the effects of spiking synchronization by different brain waves, the roles played by synaptic efficacies and so forth. In particular, it is possible to demonstrate that networks with plastic and transient synaptic architectures can encode stable cognitive maps, revealing the characteristic timescales of memory processing.

The brain in motion: How ensemble fluidity drives memory-updating and flexibility

While memories are often thought of as flashbacks to a previous experience, they do not simply conserve veridical representations of the past but must continually integrate new information to ensure survival in dynamic environments. Therefore, 'drift' in neural firing patterns, typically construed as disruptive 'instability' or an undesirable consequence of noise, may actually be useful for updating memories. In our view, continual modifications in memory representations reconcile classical theories of stable memory traces with neural drift. Here we review how memory representations are updated through dynamic recruitment of neuronal ensembles on the basis of excitability and functional connectivity at the time of learning. Overall, we emphasize the importance of considering memories not as static entities, but instead as flexible network states that reactivate and evolve across time and experience.

Long-Term Characterization of Hippocampal Remapping during Contextual Fear Acquisition and Extinction

Hippocampal CA1 place cell spatial maps are known to alter their firing properties in response to contextual fear conditioning, a process called "remapping." In the present study, we use chronic calcium imaging to examine remapping during fear retrieval and extinction of an inhibitory avoidance task in mice of both sexes over an extended period of time and with thousands of neurons. We demonstrate that hippocampal ensembles encode space at a finer scale following fear memory acquisition. This effect is strongest near the shock grid. We also characterize the long-term effects of shock on place cell ensemble stability, demonstrating that shock delivery induces several days of high fear and low between-session place field stability, followed by a new, stable spatial representation that appears after fear extinction. Finally, we identify a novel group of CA1 neurons that robustly encode freeze behavior independently from spatial location. Thus, following fear acquisition, hippocampal CA1 place cells sharpen their spatial tuning and dynamically change spatial encoding stability throughout fear learning and extinction.SIGNIFICANCE STATEMENT The hippocampus contains place cells that encode an animal's location. This spatial code updates, or remaps, in response to environmental change. It is known that contextual fear can induce such remapping; in the present study, we use chronic calcium imaging to examine inhibitory avoidance-induced remapping over an extended period of time and with thousands of neurons and demonstrate that hippocampal ensembles encode space at a finer scale following electric shock, an effect which is enhanced by threat proximity. We also identify a novel group of freeze behavior-activated neurons. These results suggest that, more than merely shuffling their spatial code following threat exposure, place cells enhance their spatial coding with the possible benefit of improved threat localization.