Assessments of dentate gyrus function: discoveries and debates.

There has been considerable speculation regarding the function of the dentate gyrus (DG) - a subregion of the mammalian hippocampus - in learning and memory. In this Perspective article, we compare leading theories of DG function. We note that these theories all critically rely on the generation of distinct patterns of activity in the region to signal differences between experiences and to reduce interference between memories. However, these theories are divided by the roles they attribute to the DG during learning and recall and by the contributions they ascribe to specific inputs or cell types within the DG. These differences influence the information that the DG is thought to impart to downstream structures. We work towards a holistic view of the role of DG in learning and memory by first developing three critical questions to foster a dialogue between the leading theories. We then evaluate the extent to which previous studies address our questions, highlight remaining areas of conflict, and suggest future experiments to bridge these theories.

Recalibration of path integration in hippocampal place cells.

Hippocampal place cells are spatially tuned neurons that serve as elements of a 'cognitive map' in the mammalian brain1. To detect the animal's location, place cells are thought to rely upon two interacting mechanisms: sensing the position of the animal relative to familiar landmarks2,3 and measuring the distance and direction that the animal has travelled from previously occupied locations4-7. The latter mechanism-known as path integration-requires a finely tuned gain factor that relates the animal's self-movement to the updating of position on the internal cognitive map, as well as external landmarks to correct the positional error that accumulates8,9. Models of hippocampal place cells and entorhinal grid cells based on path integration treat the path-integration gain as a constant9-14, but behavioural evidence in humans suggests that the gain is modifiable15. Here we show, using physiological evidence from rat hippocampal place cells, that the path-integration gain is a highly plastic variable that can be altered by persistent conflict between self-motion cues and feedback from external landmarks. In an augmented-reality system, visual landmarks were moved in proportion to the movement of a rat on a circular track, creating continuous conflict with path integration. Sustained exposure to this cue conflict resulted in predictable and prolonged recalibration of the path-integration gain, as estimated from the place cells after the landmarks were turned off. We propose that this rapid plasticity keeps the positional update in register with the movement of the rat in the external world over behavioural timescales. These results also demonstrate that visual landmarks not only provide a signal to correct cumulative error in the path-integration system4,8,16-19, but also rapidly fine-tune the integration computation itself.

Object-place-context learning impairment correlates with spatial learning impairment in aged Long-Evans rats.

The hippocampal formation is vulnerable to the process of normal aging. In humans, the extent of this age-related deterioration varies among individuals. Long-Evans rats replicate these individual differences as they age, and therefore they serve as a valuable model system to study aging in the absence of neurodegenerative diseases. In the Morris water maze, aged memory-unimpaired (AU) rats navigate to remembered goal locations as effectively as young rats and demonstrate minimal alterations in physiological markers of synaptic plasticity, whereas aged memory-impaired (AI) rats show impairments in both spatial navigation skills and cellular and molecular markers of plasticity. The present study investigates whether another cognitive domain is affected similarly to navigation in aged Long-Evans rats. We tested the ability of young, AU, and AI animals to recognize novel object-place-context (OPC) configurations and found that performance on the novel OPC recognition paradigm was significantly correlated with performance on the Morris water maze. In the first OPC test, young and AU rats, but not AI rats, successfully recognized and preferentially explored objects in novel OPC configurations. In a second test with new OPC configurations, all age groups showed similar OPC associative recognition memory. The results demonstrated similarities in the behavioral expression of associative, episodic-like memory between young and AU rats and revealed age-related, individual differences in functional decline in both navigation and episodic-like memory abilities.

Integrating time from experience in the lateral entorhinal cortex.

The encoding of time and its binding to events are crucial for episodic memory, but how these processes are carried out in hippocampal-entorhinal circuits is unclear. Here we show in freely foraging rats that temporal information is robustly encoded across time scales from seconds to hours within the overall population state of the lateral entorhinal cortex. Similarly pronounced encoding of time was not present in the medial entorhinal cortex or in hippocampal areas CA3-CA1. When animals' experiences were constrained by behavioural tasks to become similar across repeated trials, the encoding of temporal flow across trials was reduced, whereas the encoding of time relative to the start of trials was improved. The findings suggest that populations of lateral entorhinal cortex neurons represent time inherently through the encoding of experience. This representation of episodic time may be integrated with spatial inputs from the medial entorhinal cortex in the hippocampus, allowing the hippocampus to store a unified representation of what, where and when.

Superficial-layer versus deep-layer lateral entorhinal cortex: Coding of allocentric space, egocentric space, speed, boundaries, and corners.

Entorhinal cortex is the major gateway between the neocortex and the hippocampus and thus plays an essential role in subserving episodic memory and spatial navigation. It can be divided into the medial entorhinal cortex (MEC) and the lateral entorhinal cortex (LEC), which are commonly theorized to be critical for spatial (context) and non-spatial (content) inputs, respectively. Consistent with this theory, LEC neurons are found to carry little information about allocentric self-location, even in cue-rich environments, but they exhibit egocentric spatial information about external items in the environment. The superficial and deep layers of LEC are believed to mediate the input to and output from the hippocampus, respectively. As earlier studies mainly examined the spatial firing properties of superficial-layer LEC neurons, here we characterized the deep-layer LEC neurons and made direct comparisons with their superficial counterparts in single unit recordings from behaving rats. Because deep-layer LEC cells received inputs from hippocampal regions, which have strong selectivity for self-location, we hypothesized that deep-layer LEC neurons would be more informative about allocentric position than superficial-layer LEC neurons. We found that deep-layer LEC cells showed only slightly more allocentric spatial information and higher spatial consistency than superficial-layer LEC cells. Egocentric coding properties were comparable between these two subregions. In addition, LEC neurons demonstrated preferential firing at lower speeds, as well as at the boundary or corners of the environment. These results suggest that allocentric spatial outputs from the hippocampus are transformed in deep-layer LEC into the egocentric coding dimensions of LEC, rather than maintaining the allocentric spatial tuning of the CA1 place fields.

Heterogeneity of Age-Related Neural Hyperactivity along the CA3 Transverse Axis.

Age-related memory deficits are correlated with neural hyperactivity in the CA3 region of the hippocampus. Abnormal CA3 hyperactivity in aged rats has been proposed to contribute to an imbalance between pattern separation and pattern completion, resulting in overly rigid representations. Recent evidence of functional heterogeneity along the CA3 transverse axis suggests that proximal CA3 supports pattern separation while distal CA3 supports pattern completion. It is not known whether age-related CA3 hyperactivity is uniformly represented along the CA3 transverse axis. We examined the firing rates of CA3 neurons from young and aged, male, Long-Evans rats along the CA3 transverse axis. Consistent with prior studies, young CA3 cells showed an increasing gradient in mean firing rate from proximal to distal CA3. However, aged CA3 cells showed an opposite, decreasing trend, in that CA3 cells in aged rats were hyperactive in proximal CA3, but possibly hypoactive in distal CA3, compared with young (Y) rats. We suggest that, in combination with altered inputs from the entorhinal cortex and dentate gyrus (DG), the proximal CA3 region of aged rats may switch from its normal function that reflects the pattern separation output of the DG and instead performs a computation that reflects an abnormal bias toward pattern completion. In parallel, distal CA3 of aged rats may create weaker attractor basins that promote abnormal, bistable representations under certain conditions.SIGNIFICANCE STATEMENT Prior work suggested that age-related CA3 hyperactivity enhances pattern completion, resulting in rigid representations. Implicit in prior studies is the notion that hyperactivity is present throughout a functionally homogeneous CA3 network. However, more recent work has demonstrated functional heterogeneity along the CA3 transverse axis, in that proximal CA3 is involved in pattern separation and distal CA3 is involved in pattern completion. Here, we show that age-related hyperactivity is present only in proximal CA3, with potential hypoactivity in distal CA3. This result provides new insight in the role of CA3 in age-related memory impairments, suggesting that the rigid representations in aging result primarily from dysfunction of computational circuits involving the dentate gyrus (DG) and proximal CA3.

Dentate Gyrus Mossy Cells Share a Role in Pattern Separation with Dentate Granule Cells and Proximal CA3 Pyramidal Cells.

The complementary processes of pattern completion and pattern separation are thought to be essential for successful memory storage and recall. The dentate gyrus (DG) and proximal CA3 (pCA3) regions have been implicated in pattern separation, in part through extracellular recording studies of these areas. However, the DG contains two types of excitatory cells: granule cells of the granule layer and mossy cells of the hilus. Little is known about the firing properties of mossy cells in freely moving animals, and it is unclear how their activity may contribute to the mnemonic functions of the hippocampus. Furthermore, tetrodes in the dentate granule layer and pCA3 pyramidal layer can also record mossy cells, thus introducing ambiguity into the identification of cell types recorded. Using a random forests classifier, we classified cells recorded in DG (Neunuebel and Knierim, 2014) and pCA3 (Lee et al., 2015) of 16 male rats and separately examined the responses of granule cells, mossy cells, and pCA3 pyramidal cells in a local/global cue mismatch task. All three cell types displayed low correlations between the population representations of the rat's position in the standard and cue-mismatch sessions. These results suggest that all three excitatory cell types within the DG/pCA3 circuit may act as a single functional unit to support pattern separation.SIGNIFICANCE STATEMENT Mossy cells in the dentate gyrus (DG) are an integral component of the DG/pCA3 circuit. While the role of granule cells in the circuitry and computations of the hippocampus has been a focus of study for decades, the contributions of mossy cells have been largely overlooked. Recent studies have revealed the spatial firing properties of mossy cells in awake behaving animals, but how the activity of these highly active cells contributes to the mnemonic functions of the DG is uncertain. We separately analyzed mossy cells, granule cells, and pCA3 cells and found that all three cell types respond similarly to a local/global cue mismatch, suggesting that they form a single functional unit supporting pattern separation.

Attractor dynamics of spatially correlated neural activity in the limbic system.

Attractor networks are a popular computational construct used to model different brain systems. These networks allow elegant computations that are thought to represent a number of aspects of brain function. Although there is good reason to believe that the brain displays attractor dynamics, it has proven difficult to test experimentally whether any particular attractor architecture resides in any particular brain circuit. We review models and experimental evidence for three systems in the rat brain that are presumed to be components of the rat's navigational and memory system. Head-direction cells have been modeled as a ring attractor, grid cells as a plane attractor, and place cells both as a plane attractor and as a point attractor. Whereas the models have proven to be extremely useful conceptual tools, the experimental evidence in their favor, although intriguing, is still mostly circumstantial.

Origin and role of path integration in the cognitive representations of the hippocampus: computational insights into open questions.

Path integration is a straightforward concept with varied connotations that are important to different disciplines concerned with navigation, such as ethology, cognitive science, robotics and neuroscience. In studying the hippocampal formation, it is fruitful to think of path integration as a computation that transforms a sense of motion into a sense of location, continuously integrated with landmark perception. Here, we review experimental evidence that path integration is intimately involved in fundamental properties of place cells and other spatial cells that are thought to support a cognitive abstraction of space in this brain system. We discuss hypotheses about the anatomical and computational origin of path integration in the well-characterized circuits of the rodent limbic system. We highlight how computational frameworks for map-building in robotics and cognitive science alike suggest an essential role for path integration in the creation of a new map in unfamiliar territory, and how this very role can help us make sense of differences in neurophysiological data from novel versus familiar and small versus large environments. Similar computational principles could be at work when the hippocampus builds certain non-spatial representations, such as time intervals or trajectories defined in a sensory stimulus space.

Framing spatial cognition: neural representations of proximal and distal frames of reference and their roles in navigation.

The most common behavioral test of hippocampus-dependent, spatial learning and memory is the Morris water task, and the most commonly studied behavioral correlate of hippocampal neurons is the spatial specificity of place cells. Despite decades of intensive research, it is not completely understood how animals solve the water task and how place cells generate their spatially specific firing fields. Based on early work, it has become the accepted wisdom in the general neuroscience community that distal spatial cues are the primary sources of information used by animals to solve the water task (and similar spatial tasks) and by place cells to generate their spatial specificity. More recent research, along with earlier studies that were overshadowed by the emphasis on distal cues, put this common view into question by demonstrating primary influences of local cues and local boundaries on spatial behavior and place-cell firing. This paper first reviews the historical underpinnings of the "standard" view from a behavioral perspective, and then reviews newer results demonstrating that an animal's behavior in such spatial tasks is more strongly controlled by a local-apparatus frame of reference than by distal landmarks. The paper then reviews similar findings from the literature on the neurophysiological correlates of place cells and other spatially correlated cells from related brain areas. A model is proposed by which distal cues primarily set the orientation of the animal's internal spatial coordinate system, via the head direction cell system, whereas local cues and apparatus boundaries primarily set the translation and scale of that coordinate system.

Tracking the flow of hippocampal computation: Pattern separation, pattern completion, and attractor dynamics.

Classic computational theories of the mnemonic functions of the hippocampus ascribe the processes of pattern separation to the dentate gyrus (DG) and pattern completion to the CA3 region. Until the last decade, the large majority of single-unit studies of the hippocampus in behaving animals were from the CA1 region. The lack of data from the DG, CA3, and the entorhinal inputs to the hippocampus severely hampered the ability to test these theories with neurophysiological techniques. The past ten years have seen a major increase in the recordings from the CA3 region and the medial entorhinal cortex (MEC), with an increasing (but still limited) number of experiments from the lateral entorhinal cortex (LEC) and DG. This paper reviews a series of studies in a local-global cue mismatch (double-rotation) experiment in which recordings were made from cells in the anterior thalamus, MEC, LEC, DG, CA3, and CA1 regions. Compared to the standard cue environment, the change in the DG representation of the cue-mismatch environment was greater than the changes in its entorhinal inputs, providing support for the theory of pattern separation in the DG. In contrast, the change in the CA3 representation of the cue-mismatch environment was less than the changes in its entorhinal and DG inputs, providing support for a pattern completion/error correction function of CA3. The results are interpreted in terms of continuous attractor network models of the hippocampus and the relationship of these models to pattern separation and pattern completion theories. Whereas DG may perform an automatic pattern separation function, the attractor dynamics of CA3 allow it to perform a pattern separation or pattern completion function, depending on the nature of its inputs and the relative strength of the internal attractor dynamics.

From the GPS to HM: Place cells, grid cells, and memory.

A longstanding debate in hippocampus research has revolved around how to reconcile spatial mapping functions of the hippocampus with the global amnesia produced by hippocampal damage in humans. Is the hippocampus primarily a cognitive map used to support spatial learning, or does it support more general types of learning necessary for declarative memory? In recent years, a general consensus has emerged that the hippocampus receives both spatial and nonspatial inputs from the entorhinal cortex. The hippocampus creates representations of experience in a particular spatial and temporal context. This process allows the individual components of experience to be stored in such a way that they can be retrieved together as a conscious recollection.

Conflicts between local and global spatial frameworks dissociate neural representations of the lateral and medial entorhinal cortex.

Manipulation of spatial reference frames is a common experimental tool to investigate the nature of hippocampal information coding and to investigate high-order processes, such as cognitive coordination. However, it is unknown how the hippocampus afferents represent the local and global reference frames of an environment. To address these issues, single units were recorded in freely moving rats with multi-tetrode arrays targeting the superficial layers of the lateral entorhinal cortex (LEC) and medial entorhinal cortex (MEC), the two primary cortical inputs to the hippocampus. Rats ran clockwise laps around a circular track partitioned into quadrants covered by different textures (the local reference frame). The track was centered in a circular environment with distinct landmarks on the walls (the global reference frame). Here we demonstrate a novel dissociation between MEC and LEC in that the global frame controlled the MEC representation and the local frame controlled the LEC representation when the reference frames were rotated in equal, but opposite, directions. Consideration of the functional anatomy of the hippocampal circuit and popular models of attractor dynamics in CA3 suggests a mechanistic explanation of previous data showing a dissociation between the CA3 and CA1 regions in their responses to this local-global conflict. Furthermore, these results are consistent with a model of the LEC providing the hippocampus with the external sensory content of an experience and the MEC providing the spatial context, which combine to form conjunctive codes in the hippocampus that form the basis of episodic memory.

Continuous Bump Attractor Networks Require Explicit Error Coding for Gain Recalibration.

Representations of continuous variables are crucial to create internal models of the external world. A prevailing model of how the brain maintains these representations is given by continuous bump attractor networks (CBANs) in a broad range of brain functions across different areas, such as spatial navigation in hippocampal/entorhinal circuits and working memory in prefrontal cortex. Through recurrent connections, a CBAN maintains a persistent activity bump, whose peak location can vary along a neural space, corresponding to different values of a continuous variable. To track the value of a continuous variable changing over time, a CBAN updates the location of its activity bump based on inputs that encode the changes in the continuous variable (e.g., movement velocity in the case of spatial navigation)-a process akin to mathematical integration. This integration process is not perfect and accumulates error over time. For error correction, CBANs can use additional inputs providing ground-truth information about the continuous variable's correct value (e.g., visual landmarks for spatial navigation). These inputs enable the network dynamics to automatically correct any representation error. Recent experimental work on hippocampal place cells has shown that, beyond correcting errors, ground-truth inputs also fine-tune the gain of the integration process, a crucial factor that links the change in the continuous variable to the updating of the activity bump's location. However, existing CBAN models lack this plasticity, offering no insights into the neural mechanisms and representations involved in the recalibration of the integration gain. In this paper, we explore this gap by using a ring attractor network, a specific type of CBAN, to model the experimental conditions that demonstrated gain recalibration in hippocampal place cells. Our analysis reveals the necessary conditions for neural mechanisms behind gain recalibration within a CBAN. Unlike error correction, which occurs through network dynamics based on ground-truth inputs, gain recalibration requires an additional neural signal that explicitly encodes the error in the network's representation via a rate code. Finally, we propose a modified ring attractor network as an example CBAN model that verifies our theoretical findings. Combining an error-rate code with Hebbian synaptic plasticity, this model achieves recalibration of integration gain in a CBAN, ensuring accurate representation for continuous variables.

Multiplexing of temporal and spatial information in the lateral entorhinal cortex.

Episodic memory involves the processing of spatial and temporal aspects of personal experiences. The lateral entorhinal cortex (LEC) plays an essential role in subserving memory. However, the specific mechanism by which LEC integrates spatial and temporal information remains elusive. Here, we recorded LEC neurons while rats performed foraging and shuttling behaviors on one-dimensional, linear or circular tracks. Unlike open-field foraging tasks, many LEC cells displayed spatial firing fields in these tasks and demonstrated selectivity for traveling directions. Furthermore, some LEC neurons displayed changes in the firing rates of their spatial rate maps during a session, a phenomenon referred to as rate remapping. Importantly, this temporal modulation was consistent across sessions, even when the spatial environment was altered. Notably, the strength of temporal modulation was found to be greater in LEC compared to other brain regions, such as the medial entorhinal cortex (MEC), CA1, and CA3. Thus, the spatial rate mapping observed in LEC neurons may serve as a coding mechanism for temporal context, allowing for flexible multiplexing of spatial and temporal information.

Continuous Bump Attractor Networks Require Explicit Error Coding for Gain Recalibration.

Representations of continuous variables are crucial to create internal models of the external world. A prevailing model of how the brain maintains these representations is given by continuous bump attractor networks (CBANs) in a broad range of brain functions across different areas, such as spatial navigation in hippocampal/entorhinal circuits and working memory in prefrontal cortex. Through recurrent connections, a CBAN maintains a persistent activity bump, whose peak location can vary along a neural space, corresponding to different values of a continuous variable. To track the value of a continuous variable changing over time, a CBAN updates the location of its activity bump based on inputs that encode the changes in the continuous variable (e.g., movement velocity in the case of spatial navigation)-a process akin to mathematical integration. This integration process is not perfect and accumulates error over time. For error correction, CBANs can use additional inputs providing ground-truth information about the continuous variable's correct value (e.g., visual landmarks for spatial navigation). These inputs enable the network dynamics to automatically correct any representation error. Recent experimental work on hippocampal place cells has shown that, beyond correcting errors, ground-truth inputs also fine-tune the gain of the integration process, a crucial factor that links the change in the continuous variable to the updating of the activity bump's location. However, existing CBAN models lack this plasticity, offering no insights into the neural mechanisms and representations involved in the recalibration of the integration gain. In this paper, we explore this gap by using a ring attractor network, a specific type of CBAN, to model the experimental conditions that demonstrated gain recalibration in hippocampal place cells. Our analysis reveals the necessary conditions for neural mechanisms behind gain recalibration within a CBAN. Unlike error correction, which occurs through network dynamics based on ground-truth inputs, gain recalibration requires an additional neural signal that explicitly encodes the error in the network's representation via a rate code. Finally, we propose a modified ring attractor network as an example CBAN model that verifies our theoretical findings. Combining an error-rate code with Hebbian synaptic plasticity, this model achieves recalibration of integration gain in a CBAN, ensuring accurate representation for continuous variables.

Control and recalibration of path integration in place cells using optic flow.

Hippocampal place cells are influenced by both self-motion (idiothetic) signals and external sensory landmarks as an animal navigates its environment. To continuously update a position signal on an internal 'cognitive map', the hippocampal system integrates self-motion signals over time, a process that relies on a finely calibrated path integration gain that relates movement in physical space to movement on the cognitive map. It is unclear whether idiothetic cues alone, such as optic flow, exert sufficient influence on the cognitive map to enable recalibration of path integration, or if polarizing position information provided by landmarks is essential for this recalibration. Here, we demonstrate both recalibration of path integration gain and systematic control of place fields by pure optic flow information in freely moving rats. These findings demonstrate that the brain continuously rebalances the influence of conflicting idiothetic cues to fine-tune the neural dynamics of path integration, and that this recalibration process does not require a top-down, unambiguous position signal from landmarks.

Spatial firing correlates of physiologically distinct cell types of the rat dentate gyrus.

The dentate gyrus (DG) occupies a key position in information flow through the hippocampus. Its principal cell, the granule cell, has spatially selective place fields. However, the behavioral correlates of cells located in the hilus of the rat dentate gyrus are unknown. We report here that cells below the granule layer show spatially selective firing that consists of multiple subfields. Other cells recorded from the DG had single place fields. Compared with cells with multiple fields, cells with single fields fired at lower rates during sleep were less bursty, and were more likely to be recorded simultaneously with large populations of neurons that were active during sleep and silent during behavior. We propose that cells with single fields are likely to be mature granule cells that use sparse encoding to potentially disambiguate input patterns. Furthermore, we hypothesize that cells with multiple fields might be cells of the hilus or newborn granule cells. These data are the first demonstration, based on physiological criteria, that single- and multiple-field cells constitute at least two distinct cell classes in the DG. Because of the heterogeneity of firing correlates and cell types in the DG, understanding which cell types correspond to which firing patterns, and how these correlates change with behavioral state and between different environments, are critical questions for testing long-standing computational theories that the DG performs a pattern separation function using a very sparse coding strategy.

Influence of local objects on hippocampal representations: Landmark vectors and memory.

The hippocampus is thought to represent nonspatial information in the context of spatial information. An animal can derive both spatial information as well as nonspatial information from the objects (landmarks) it encounters as it moves around in an environment. In this article, correlates of both object-derived spatial as well as nonspatial information in the hippocampus of rats foraging in the presence of objects are demonstrated. A new form of CA1 place cells, called landmark-vector cells, that encode spatial locations as a vector relationship to local landmarks is described. Such landmark vector relationships can be dynamically encoded. Of the 26 CA1 neurons that developed new fields in the course of a day's recording sessions, in eight cases, the new fields were located at a similar distance and direction from a landmark as the initial field was located relative to a different landmark. In addition, object-location memory in the hippocampus is also described. When objects were removed from an environment or moved to new locations, a small number of neurons in CA1 and CA3 increased firing at the locations where the objects used to be. In some neurons, this increase occurred only in one location, indicating object + place conjunctive memory; in other neurons, the increase in firing was seen at multiple locations where an object used to be. Taken together, these results demonstrate that the spatially restricted firing of hippocampal neurons encode multiple types of information regarding the relationship between an animal's location and the location of objects in its environment.