The input-output transformation of the hippocampal granule cells: from grid cells to place fields

Formation of cognitive maps in large-scale environments by sensorimotor integration

Hippocampus in the mammalian brain supports navigation by building a cognitive map of the environment. However, only a few studies have investigated cognitive maps in large-scale arenas. To reveal the computational mechanisms underlying the formation of cognitive maps in large-scale environments, we propose a neural network model of the entorhinal-hippocampal neural circuit that integrates both spatial and non-spatial information. Spatial information is relayed from the grid units in medial entorhinal cortex (MEC) by integrating multimodal sensory-motor signals. Non-spatial, such as object, information is imparted from the visual units in lateral entorhinal cortex (LEC) by encoding visual scenes through a deep neural network. The synaptic weights from the grid units and the visual units to the place units in the hippocampus are learned by a competitive learning rule. We simulated the model in a large box maze. The place units in the model form irregularly-spaced multiple fields across the environment. When the strength of visual inputs is dominant, the responses of place units become conjunctive and egocentric. These results point to the key role of the hippocampus in balancing spatial and non-spatial information relayed via LEC and MEC.

Robust variability of grid cell properties within individual grid modules enhances encoding of local space

Although grid cells are one of the most well-studied functional classes of neurons in the mammalian brain, whether there is a single orientation and spacing value per grid module has not been carefully tested. We analyze a recent large-scale recording of medial entorhinal cortex to characterize the presence and degree of heterogeneity of grid properties within individual modules. We find evidence for small, but robust, variability and hypothesize that this property of the grid code could enhance the encoding of local spatial information. Performing analysis on synthetic populations of grid cells, where we have complete control over the amount heterogeneity in grid properties, we demonstrate that grid property variability of a similar magnitude to the analyzed data leads to significantly decreased decoding error. This holds even when restricted to activity from a single module. Our results highlight how the heterogeneity of the neural response properties may benefit coding and opens new directions for theoretical and experimental analysis of grid cells.

Robust variability of grid cell properties within individual grid modules enhances encoding of local space

Although grid cells are one of the most well studied functional classes of neurons in the mammalian brain, whether there is a single orientation and spacing value per grid module has not been carefully tested. We analyze a recent large-scale recording of medial entorhinal cortex to characterize the presence and degree of heterogeneity of grid properties within individual modules. We find evidence for small, but robust, variability and hypothesize that this property of the grid code could enhance the encoding of local spatial information. Performing analysis on synthetic populations of grid cells, where we have complete control over the amount heterogeneity in grid properties, we demonstrate that grid property variability of a similar magnitude to the analyzed data leads to significantly decreased decoding error. This holds even when restricted to activity from a single module. Our results highlight how the heterogeneity of the neural response properties may benefit coding and opens new directions for theoretical and experimental analysis of grid cells.

Mechanisms of memory-supporting neuronal dynamics in hippocampal area CA3

Hippocampal CA3 is central to memory formation and retrieval. Although various network mechanisms have been proposed, direct evidence is lacking. Using intracellular Vm recordings and optogenetic manipulations in behaving mice, we found that CA3 place-field activity is produced by a symmetric form of behavioral timescale synaptic plasticity (BTSP) at recurrent synapses among CA3 pyramidal neurons but not at synapses from the dentate gyrus (DG). Additional manipulations revealed that excitatory input from the entorhinal cortex (EC) but not the DG was required to update place cell activity based on the animal's movement. These data were captured by a computational model that used BTSP and an external updating input to produce attractor dynamics under online learning conditions. Theoretical analyses further highlight the superior memory storage capacity of such networks, especially when dealing with correlated input patterns. This evidence elucidates the cellular and circuit mechanisms of learning and memory formation in the hippocampus.

Subfield-specific interneuron circuits govern the hippocampal response to novelty in male mice

The hippocampus is the brain's center for episodic memories. Its subregions, the dentate gyrus and CA1-3, are differentially involved in memory encoding and recall. Hippocampal principal cells represent episodic features like movement, space, and context, but less is known about GABAergic interneurons. Here, we performed two-photon calcium imaging of parvalbumin- and somatostatin-expressing interneurons in the dentate gyrus and CA1-3 of male mice exploring virtual environments. Parvalbumin-interneurons increased activity with running-speed and reduced it in novel environments. Somatostatin-interneurons in CA1-3 behaved similar to parvalbumin-expressing cells, but their dentate gyrus counterparts increased activity during rest and in novel environments. Congruently, chemogenetic silencing of dentate parvalbumin-interneurons had prominent effects in familiar contexts, while silencing somatostatin-expressing cells increased similarity of granule cell representations between novel and familiar environments. Our data indicate unique roles for parvalbumin- and somatostatin-positive interneurons in the dentate gyrus that are distinct from those in CA1-3 and may support routing of novel information.

Granule cells perform frequency-dependent pattern separation in a computational model of the dentate gyrus

Mnemonic discrimination (MD) may be dependent on oscillatory perforant path input frequencies to the hippocampus in a "U"-shaped fashion, where some studies show that slow and fast input frequencies support MD, while other studies show that intermediate frequencies disrupt MD. We hypothesize that pattern separation (PS) underlies frequency-dependent MD performance. We aim to study, in a computational model of the hippocampal dentate gyrus (DG), the network and cellular mechanisms governing this putative "U"-shaped PS relationship. We implemented a biophysical model of the DG that produces the hypothesized "U"-shaped input frequency-PS relationship, and its associated oscillatory electrophysiological signatures. We subsequently evaluated the network's PS ability using an adapted spatiotemporal task. We undertook systematic lesion studies to identify the network-level mechanisms driving the "U"-shaped input frequency-PS relationship. A minimal circuit of a single granule cell (GC) stimulated with oscillatory inputs was also used to study potential cellular-level mechanisms. Lesioning synapses onto GCs did not impact the "U"-shaped input frequency-PS relationship. Furthermore, GC inhibition limits PS performance for fast frequency inputs, while enhancing PS for slow frequency inputs. GC interspike interval was found to be input frequency dependent in a "U"-shaped fashion, paralleling frequency-dependent PS observed at the network level. Additionally, GCs showed an attenuated firing response for fast frequency inputs. We conclude that independent of network-level inhibition, GCs may intrinsically be capable of producing a "U"-shaped input frequency-PS relationship. GCs may preferentially decorrelate slow and fast inputs via spike timing reorganization and high frequency filtering.

Grid cells, border cells, and discrete complex analysis

We propose a mechanism enabling the appearance of border cells-neurons firing at the boundaries of the navigated enclosures. The approach is based on the recent discovery of discrete complex analysis on a triangular lattice, which allows constructing discrete epitomes of complex-analytic functions and making use of their inherent ability to attain maximal values at the boundaries of generic lattice domains. As it turns out, certain elements of the discrete-complex framework readily appear in the oscillatory models of grid cells. We demonstrate that these models can extend further, producing cells that increase their activity toward the frontiers of the navigated environments. We also construct a network model of neurons with border-bound firing that conforms with the oscillatory models.

Grid Cells, Border Cells and Discrete Complex Analysis

We propose a mechanism enabling the appearance of border cells-neurons firing at the boundaries of the navigated enclosures. The approach is based on the recent discovery of discrete complex analysis on a triangular lattice, which allows constructing discrete epitomes of complex-analytic functions and making use of their inherent ability to attain maximal values at the boundaries of generic lattice domains. As it turns out, certain elements of the discrete-complex framework readily appear in the oscillatory models of grid cells. We demonstrate that these models can extend further, producing cells that increase their activity towards the frontiers of the navigated environments. We also construct a network model of neurons with border-bound firing that conforms with the oscillatory models.

Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons

Identifying network architecture from observed neural activities is crucial in neuroscience studies. A key requirement is knowledge of the statistical input-output relation of single neurons in vivo. By utilizing an exact analytical solution of the spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near the threshold, we construct a framework that links synaptic type, strength, and spiking nonlinearity with the statistics of neuronal population activity. The framework explains structured pairwise and higher-order interactions of neurons receiving common inputs under different architectures. We compared the theoretical predictions with the activity of monkey and mouse V1 neurons and found that excitatory inputs given to pairs explained the observed sparse activity characterized by strong negative triple-wise interactions, thereby ruling out the alternative explanation by shared inhibition. Moreover, we showed that the strong interactions are a signature of excitatory rather than inhibitory inputs whenever the spontaneous rate is low. We present a guide map of neural interactions that help researchers to specify the hidden neuronal motifs underlying observed interactions found in empirical data.

The chicken and egg problem of grid cells and place cells

Place cells and grid cells are major building blocks of the hippocampal cognitive map. The prominent forward model postulates that grid-cell modules are generated by a continuous attractor network; that a velocity signal evoked during locomotion moves entorhinal activity bumps; and that place-cell activity constitutes summation of entorhinal grid-cell modules. Experimental data support the first postulate, but not the latter two. Several families of solutions that depart from these postulates have been put forward. We suggest a modified model (spatial modulation continuous attractor network; SCAN), whereby place cells are generated from spatially selective nongrid cells. Locomotion causes these cells to move the hippocampal activity bump, leading to movement of the entorhinal manifolds. Such inversion accords with the shift of hippocampal thought from navigation to more abstract functions.

Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model

Navigation is one of the most fundamental skills of animals. During spatial navigation, grid cells in the medial entorhinal cortex process speed and direction of the animal to map the environment. Hippocampal place cells, in turn, encode place using sensory signals and reduce the accumulated error of grid cells for path integration. Although both cell types are part of the path integration system, the dynamic relationship between place and grid cells and the error reduction mechanism is yet to be understood. We implemented a realistic model of grid cells based on a continuous attractor model. The grid cell model was coupled to a place cell model to address their dynamic relationship during a simulated animal's exploration of a square arena. The grid cell model processed the animal's velocity and place field information from place cells. Place cells incorporated salient visual features and proximity information with input from grid cells to define their place fields. Grid cells had similar spatial phases but a diversity of spacings and orientations. To determine the role of place cells in error reduction for path integration, the animal's position estimates were decoded from grid cell activities with and without the place field input. We found that the accumulated error was reduced as place fields emerged during the exploration. Place fields closer to the animal's current location contributed more to the error reduction than remote place fields. Place cells' fields encoding space could function as spatial anchoring signals for precise path integration by grid cells.

Adapting hippocampus multi-scale place field distributions in cluttered environments optimizes spatial navigation and learning

Extensive studies in rodents show that place cells in the hippocampus have firing patterns that are highly correlated with the animal's location in the environment and are organized in layers of increasing field sizes or scales along its dorsoventral axis. In this study, we use a spatial cognition model to show that different field sizes could be exploited to adapt the place cell representation to different environments according to their size and complexity. Specifically, we provide an in-depth analysis of how to distribute place cell fields according to the obstacles in cluttered environments to optimize learning time and path optimality during goal-oriented spatial navigation tasks. The analysis uses a reinforcement learning (RL) model that assumes that place cells allow encoding the state. While previous studies have suggested exploiting different field sizes to represent areas requiring different spatial resolutions, our work analyzes specific distributions that adapt the representation to the environment, activating larger fields in open areas and smaller fields near goals and subgoals (e.g., obstacle corners). In addition to assessing how the multi-scale representation may be exploited in spatial navigation tasks, our analysis and results suggest place cell representations that can impact the robotics field by reducing the total number of cells for path planning without compromising the quality of the paths learned.

Recurrent Excitatory Feedback From Mossy Cells Enhances Sparsity and Pattern Separation in the Dentate Gyrus via Indirect Feedback Inhibition

It is generally appreciated that storing memories of specific events in the mammalian brain, and associating features of the environment with behavioral outcomes requires fine-tuning of the strengths of connections between neurons through synaptic plasticity. It is less understood whether the organization of neuronal circuits comprised of multiple distinct neuronal cell types provides an architectural prior that facilitates learning and memory by generating unique patterns of neuronal activity in response to different stimuli in the environment, even before plasticity and learning occur. Here we simulated a neuronal network responding to sensory stimuli, and systematically determined the effects of specific neuronal cell types and connections on three key metrics of neuronal sensory representations: sparsity, selectivity, and discriminability. We found that when the total amount of input varied considerably across stimuli, standard feedforward and feedback inhibitory circuit motifs failed to discriminate all stimuli without sacrificing sparsity or selectivity. Interestingly, networks that included dedicated excitatory feedback interneurons based on the mossy cells of the hippocampal dentate gyrus exhibited improved pattern separation, a result that depended on the indirect recruitment of feedback inhibition. These results elucidate the roles of cellular diversity and neural circuit architecture on generating neuronal representations with properties advantageous for memory storage and recall.

Parallel processing of sensory cue and spatial information in the dentate gyrus

During exploration, animals form an internal map of an environment by combining information about landmarks and the animal's movement, a process that depends on the hippocampus. The dentate gyrus (DG) is the first stage of the hippocampal circuit where self-motion ("where") and sensory cue information ("what") are integrated, but it remains unknown how DG neurons encode this information during cognitive map formation. Using two-photon calcium imaging in mice running on a treadmill along with online cue manipulation, we identify robust sensory cue responses in DG granule cells. Cue cell responses are stable, stimulus-specific, and accompanied by inhibition of nearby neurons. This demonstrates the existence of "cue cells" in addition to better characterized "place cells" in the DG. We hypothesize that the DG supports parallel channels of spatial and non-spatial information that contribute distinctly to downstream computations and affect roles of the DG in spatial navigation and episodic memory.

Distal CA1 Maintains a More Coherent Spatial Representation than Proximal CA1 When Local and Global Cues Conflict

Entorhinal cortical projections show segregation along the transverse axis of CA1, with the medial entorhinal cortex (MEC) sending denser projections to proximal CA1 (pCA1) and the lateral entorhinal cortex (LEC) sending denser projections to distal CA1 (dCA1). Previous studies have reported functional segregation along the transverse axis of CA1 correlated with the functional differences in MEC and LEC. pCA1 shows higher spatial selectivity than dCA1 in these studies. We employ a double rotation protocol, which creates an explicit conflict between the local and the global cues, to understand the differential contributions of these reference frames to the spatial code in pCA1 and dCA1 in male Long-Evans rats. We show that pCA1 and dCA1 respond differently to this local-global cue conflict. pCA1 representation splits as predicted from the strong conflicting inputs it receives from MEC and dCA3. In contrast, dCA1 rotates more in concert with the global cues. In addition, pCA1 and dCA1 display comparable levels of spatial selectivity in this study. This finding differs from the previous studies, perhaps because of richer sensory information available in our behavior arena. Together, these observations indicate that the functional segregation along proximodistal axis of CA1 is not of the amount of spatial selectivity but that of the nature of the different inputs used to create and anchor spatial representations.SIGNIFICANCE STATEMENT Subregions of the hippocampus are thought to play different roles in spatial navigation and episodic memory. It was previously thought that the distal part of area CA1 of the hippocampus carries lesser information about space than proximal CA1 (pCA1). We report that distal CA1 (dCA1) spatial representation moves more in concert with the global cues than pCA1 when the local and the global cues conflict. We also show that spatial selectivity is comparable along the proximodistal axis in this experimental protocol. Thus, different parts of the brain receiving differential outputs from pCA1 and dCA1 receive spatial information in different spatial reference frames encoded using different sets of inputs, rather than different amounts of spatial information as thought earlier.

Learning an Efficient Hippocampal Place Map from Entorhinal Inputs Using Non-Negative Sparse Coding

Cells in the entorhinal cortex (EC) contain rich spatial information and project strongly to the hippocampus where a cognitive map is supposedly created. These cells range from cells with structured spatial selectivity, such as grid cells in the medial EC (MEC) that are selective to an array of spatial locations that form a hexagonal grid, to weakly spatial cells, such as non-grid cells in the MEC and lateral EC (LEC) that contain spatial information but have no structured spatial selectivity. However, in a small environment, place cells in the hippocampus are generally selective to a single location of the environment, while granule cells in the dentate gyrus of the hippocampus have multiple discrete firing locations but lack spatial periodicity. Given the anatomic connection from the EC to the hippocampus, how the hippocampus retrieves information from upstream EC remains unclear. Here, we propose a unified learning model that can describe the spatial tuning properties of both hippocampal place cells and dentate gyrus granule cells based on non-negative sparse coding from EC inputs. Sparse coding plays an important role in many cortical areas and is proposed here to have a key role in the hippocampus. Our results show that the hexagonal patterns of MEC grid cells with various orientations, grid spacings and phases are necessary for the model to learn different place cells that efficiently tile the entire spatial environment. However, if there is a lack of diversity in any grid parameters or a lack of hippocampal cells in the network, this will lead to the emergence of hippocampal cells that have multiple firing locations. More surprisingly, the model can also learn hippocampal place cells even when weakly spatial cells, instead of grid cells, are used as the input to the hippocampus. This work suggests that sparse coding may be one of the underlying organizing principles for the navigational system of the brain.

A neurogenic microenvironment defined by excitatory-inhibitory neuronal circuits in adult dentate gyrus

Adult neurogenesis in the dentate gyrus plays a role in adaptive brain functions such as memory formation. Adding new neurons to a specific locus of a neural circuit with functional needs is an efficient way to achieve such an adaptive function. However, it is unknown whether neurogenesis is linked to local functional demands potentially specified by the activity of neuronal circuits. By examining the distribution of neurogenesis and different types of neuronal activity in the dentate gyrus of freely moving adult rats, we find that neurogenesis is positionally associated with active excitatory neurons, some of which show place-cell activity, but is positionally dissociated from a type of interneuron with high-burst tendency. Our finding suggests that the behaviorally relevant activity of excitatory-inhibitory neuronal circuits can define a microenvironment stimulating/inhibiting neurogenesis. Such local regulation of neurogenesis may contribute to strategic recruitment of new neurons to modify functionally relevant neural circuits.

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.

A computational grid-to-place-cell transformation model indicates a synaptic driver of place cell impairment in early-stage Alzheimer's Disease

Alzheimer's Disease (AD) is characterized by progressive neurodegeneration and cognitive impairment. Synaptic dysfunction is an established early symptom, which correlates strongly with cognitive decline, and is hypothesised to mediate the diverse neuronal network abnormalities observed in AD. However, how synaptic dysfunction contributes to network pathology and cognitive impairment in AD remains elusive. Here, we present a grid-cell-to-place-cell transformation model of long-term CA1 place cell dynamics to interrogate the effect of synaptic loss on network function and environmental representation. Synapse loss modelled after experimental observations in the APP/PS1 mouse model was found to induce firing rate alterations and place cell abnormalities that have previously been observed in AD mouse models, including enlarged place fields and lower across-session stability of place fields. Our results support the hypothesis that synaptic dysfunction underlies cognitive deficits, and demonstrate how impaired environmental representation may arise in the early stages of AD. We further propose that dysfunction of excitatory and inhibitory inputs to CA1 pyramidal cells may cause distinct impairments in place cell function, namely reduced stability and place map resolution.

Biomimetic FPGA-based spatial navigation model with grid cells and place cells

The mammalian spatial navigation system is characterized by an initial divergence of internal representations, with disparate classes of neurons responding to distinct features including location, speed, borders and head direction; an ensuing convergence finally enables navigation and path integration. Here, we report the algorithmic and hardware implementation of biomimetic neural structures encompassing a feed-forward trimodular, multi-layer architecture representing grid-cell, place-cell and decoding modules for navigation. The grid-cell module comprised of neurons that fired in a grid-like pattern, and was built of distinct layers that constituted the dorsoventral span of the medial entorhinal cortex. Each layer was built as an independent continuous attractor network with distinct grid-field spatial scales. The place-cell module comprised of neurons that fired at one or few spatial locations, organized into different clusters based on convergent modular inputs from different grid-cell layers, replicating the gradient in place-field size along the hippocampal dorso-ventral axis. The decoding module, a two-layer neural network that constitutes the convergence of the divergent representations in preceding modules, received inputs from the place-cell module and provided specific coordinates of the navigating object. After vital design optimizations involving all modules, we implemented the tri-modular structure on Zynq Ultrascale+ field-programmable gate array silicon chip, and demonstrated its capacity in precisely estimating the navigational trajectory with minimal overall resource consumption involving a mere 2.92% Look Up Table utilization. Our implementation of a biomimetic, digital spatial navigation system is stable, reliable, reconfigurable, real-time with execution time of about 32 s for 100k input samples (in contrast to 40 minutes on Intel Core i7-7700 CPU with 8 cores clocking at 3.60 GHz) and thus can be deployed for autonomous-robotic navigation without requiring additional sensors.

Selective Routing of Spatial Information Flow from Input to Output in Hippocampal Granule Cells

Dentate gyrus granule cells (GCs) connect the entorhinal cortex to the hippocampal CA3 region, but how they process spatial information remains enigmatic. To examine the role of GCs in spatial coding, we measured excitatory postsynaptic potentials (EPSPs) and action potentials (APs) in head-fixed mice running on a linear belt. Intracellular recording from morphologically identified GCs revealed that most cells were active, but activity level varied over a wide range. Whereas only ∼5% of GCs showed spatially tuned spiking, ∼50% received spatially tuned input. Thus, the GC population broadly encodes spatial information, but only a subset relays this information to the CA3 network. Fourier analysis indicated that GCs received conjunctive place-grid-like synaptic input, suggesting code conversion in single neurons. GC firing was correlated with dendritic complexity and intrinsic excitability, but not extrinsic excitatory input or dendritic cable properties. Thus, functional maturation may control input-output transformation and spatial code conversion.

The role of intrinsic excitability in the evolution of memory: Significance in memory allocation, consolidation, and updating

Memory is a dynamic process that is continuously regulated by both synaptic and intrinsic neural mechanisms. While numerous studies have shown that synaptic plasticity is important in various types and phases of learning and memory, neuronal intrinsic excitability has received relatively less attention, especially regarding the dynamic nature of memory. In this review, we present evidence demonstrating the importance of intrinsic excitability in memory allocation, consolidation, and updating. We also consider the intricate interaction between intrinsic excitability and synaptic plasticity in shaping memory, supporting both memory stability and flexibility.

A theory of joint attractor dynamics in the hippocampus and the entorhinal cortex accounts for artificial remapping and grid cell field-to-field variability

The representation of position in the mammalian brain is distributed across multiple neural populations. Grid cell modules in the medial entorhinal cortex (MEC) express activity patterns that span a low-dimensional manifold which remains stable across different environments. In contrast, the activity patterns of hippocampal place cells span distinct low-dimensional manifolds in different environments. It is unknown how these multiple representations of position are coordinated. Here, we develop a theory of joint attractor dynamics in the hippocampus and the MEC. We show that the system exhibits a coordinated, joint representation of position across multiple environments, consistent with global remapping in place cells and grid cells. In addition, our model accounts for recent experimental observations that lack a mechanistic explanation: variability in the firing rate of single grid cells across firing fields, and artificial remapping of place cells under depolarization, but not under hyperpolarization, of layer II stellate cells of the MEC.

A computational model for spatial cognition combining dorsal and ventral hippocampal place field maps: multiscale navigation

Classic studies have shown that place cells are organized along the dorsoventral axis of the hippocampus according to their field size, with dorsal hippocampal place cells having smaller field sizes than ventral place cells. Studies have also suggested that dorsal place cells are primarily involved in spatial navigation, while ventral place cells are primarily involved in context and emotional encoding. Additionally, recent work has shown that the entire longitudinal axis of the hippocampus may be involved in navigation. Based on the latter, in this paper we present a spatial cognition reinforcement learning model inspired by the multiscale organization of the dorsal-ventral axis of the hippocampus. The model analyzes possible benefits of a multiscale architecture in terms of the learning speed, the path optimality, and the number of cells in the context of spatial navigation. The model is evaluated in a goal-oriented task where simulated rats need to learn a path to the goal from multiple starting locations in various open-field maze configurations. The results show that smaller scales of representation are useful for improving path optimality, whereas larger scales are useful for reducing learning time and the number of cells required. The results also show that combining scales can enhance the performance of the multiscale model, with a trade-off between path optimality and learning time.

Dentate gyrus circuits for encoding, retrieval and discrimination of episodic memories

The dentate gyrus (DG) has a key role in hippocampal memory formation. Intriguingly, DG lesions impair many, but not all, hippocampus-dependent mnemonic functions, indicating that the rest of the hippocampus (CA1-CA3) can operate autonomously under certain conditions. An extensive body of theoretical work has proposed how the architectural elements and various cell types of the DG may underlie its function in cognition. Recent studies recorded and manipulated the activity of different neuron types in the DG during memory tasks and have provided exciting new insights into the mechanisms of DG computational processes, particularly for the encoding, retrieval and discrimination of similar memories. Here, we review these DG-dependent mnemonic functions in light of the new findings and explore mechanistic links between the cellular and network properties of, and the computations performed by, the DG.

Spatiotemporally random and diverse grid cell spike patterns contribute to the transformation of grid cell to place cell in a neural network model

The medial entorhinal cortex and the hippocampus are brain regions specialized in spatial information processing. While an animal navigates around an environment, grid cells in the medial entorhinal cortex spike at multiple discrete locations, forming hexagonal grid patterns, and each grid cell is spatiotemporally dynamic with a different grid size, spacing, and orientation. In contrast, place cells in the hippocampus spike when an animal is at one or more specific locations, called a “place field”. While an animal traverses through a place field, the place cell’s spike phases relative to the hippocampal theta-frequency oscillation advance in phase, known as the “spike phase precession” phenomenon and each spike encodes the specific location within the place field. Interestingly, the medial entorhinal cortical grid cells and the hippocampal place cells are only one excitatory synapse apart. However, how the spatiotemporally dynamic multi-peaked grid cell activities are transformed into hippocampal place cell activities with spike phase precession phenomenon is yet unknown. To address this question, we construct an anatomically and physiologically realistic neural network model comprised of 10,000 grid cell models, each with a spatiotemporally dynamic grid patterns and a place cell model connected by excitatory synapses. Using this neural network model, we show that grid cells’ spike activities with spatiotemporally random and diverse grid orientation, spacing, and phases as inputs to place cell are able to generate a place field with spike phase precession. These results indicate that spatiotemporally random and diverse grid cell spike activities are essential for the formation of place cell activity observed in vivo.

Functions of adult-born neurons in hippocampal memory interference and indexing

The dentate gyrus-CA3 circuit of the hippocampus is continuously modified by the integration of adult-born dentate granule cells (abDGCs). All abDGCs undergo a prolonged period of maturation, during which they exhibit heightened synaptic plasticity and refinement of electrophysiological properties and connectivity. Consistent with theoretical models and the known functions of the dentate gyrus-CA3 circuit, acute or chronic manipulations of abDGCs support a role for abDGCs in the regulation of memory interference. In this Review, we integrate insights from studies that examine the maturation of abDGCs and their integration into the circuit with network mechanisms that support memory discrimination, consolidation and clearance. We propose that adult hippocampal neurogenesis enables the generation of a library of experiences, each registered in mature abDGC physiology and connectivity. Mature abDGCs recruit inhibitory microcircuits to support pattern separation and memory indexing.

Regulation of gamma-frequency oscillation by feedforward inhibition: A computational modeling study

Throughout the brain, reciprocally connected excitatory and inhibitory neurons interact to produce gamma-frequency oscillations. The emergent gamma rhythm synchronizes local neural activity and helps to select which cells should fire in each cycle. We previously found that such excitation-inhibition microcircuits, however, have a potentially significant caveat: the frequency of the gamma oscillation and the level of selection (i.e., the percentage of cells that are allowed to fire) vary with the magnitude of the input signal. In networks with varying levels of brain activity, such a feature may produce undesirable instability on the time and spatial structure of the neural signal with a potential for adversely impacting important neural processing mechanisms. Here we propose that feedforward inhibition solves the latter instability problem of the excitation-inhibition microcircuit. Using computer simulations, we show that the feedforward inhibitory signal reduces the dependence of both the frequency of population oscillation and the level of selection on the magnitude of the input excitation. Such a mechanism can produce stable gamma oscillations with its frequency determined only by the properties of the feedforward network, as observed in the hippocampus. As feedforward and feedback inhibition motifs commonly appear together in the brain, we hypothesize that their interaction underlies a robust implementation of general computational principles of neural processing involved in several cognitive tasks, including the formation of cell assemblies and the routing of information between brain areas.

Dentate granule and mossy cells exhibit distinct spatiotemporal responses to local change in a one-dimensional landscape of visual-tactile cues

The dentate gyrus (DG) is critical for detecting changes in environments; however, how granule cells (GCs) and mossy cells (MCs), the two excitatory cell types of the DG, respond to small changes in the object layout is unclear. Here, we recorded GCs and MCs, identified by spike feature and optogenetic tagging, as mice ran on a treadmill belt enriched with visual-tactile cues. We observed that fixing a new cue on the belt induced a reconfiguration of GC and MC spatial representations via the emergence, extinction and rate alteration of firing fields. For both GCs and MCs, the response was maximal near the cue and spread over the entire belt. However, compared to the GC response, the MC response was stronger and more immediate, peaked at a slightly earlier belt position, and exhibited a transient component reminiscent of neuromodulatory activity. A competitive neural network model reproduced the GC response contingent on both the introduction of new object-vector inputs and the reconfiguration of MC activity, the former being critical for spreading the GC response in locations distant from the cue. These findings suggest that GCs operate as a competitive network and that MCs precede GCs in detecting changes and help expand the range of GC pattern separation.

Stable memory and computation in randomly rewiring neural networks

Our brains must maintain a representation of the world over a period of time much longer than the typical lifetime of the biological components producing that representation. For example, recent research suggests that dendritic spines in the adult mouse hippocampus are transient with an average lifetime of ~10 days. If this is true, and if turnover is equally likely for all spines, ~95% of excitatory synapses onto a particular neuron will turn over within 30 days; however, a neuron's receptive field can be relatively stable over this period. Here, we use computational modeling to ask how memories can persist in neural circuits such as the hippocampus and visual cortex in the face of synapse turnover. We demonstrate that Hebbian plasticity during replay of presynaptic activity patterns can integrate newly formed synapses into pre-existing memories. Furthermore, we find that Hebbian plasticity during replay is sufficient to stabilize the receptive fields of hippocampal place cells in a model of the grid-cell-to-place-cell transformation in CA1 and of orientation-selective cells in a model of the center-surround-to-simple-cell transformation in V1. Together, these data suggest that a simple plasticity rule, correlative Hebbian plasticity of synaptic strengths, is sufficient to preserve neural representations in the face of synapse turnover, even in the absence of activity-dependent structural plasticity. NEW & NOTEWORTHY Recent research suggests that synapses turn over rapidly in some brain structures; however, memories seem to persist for much longer. We show that Hebbian plasticity of synaptic strengths during reactivation events can preserve memory in computational models of hippocampal and cortical networks despite turnover of all synapses. Our results suggest that memory can be stored in the correlation structure of a network undergoing rapid synaptic remodeling.

Stability of medial entorhinal cortex representations over time

Distinct functional cell types in the medial entorhinal cortex (mEC) have been shown to represent different aspects of experiences. To further characterize mEC cell populations, we examined whether spatial representations of neurons in mEC superficial layers depended on the scale of the environment and changed over extended time periods. Accordingly, mEC cells were recorded while rats repeatedly foraged in a small or a large environment in sessions that were separated by time intervals from minutes to hours. Comparing between large and small environments, we found that the overall precision of grid and non-grid cell spatial maps was higher in smaller environments. When examining the stability of spatial firing patterns over time, differences and similarities were observed across cell types. Within-session stability was higher for grid cells than for non-grid cell populations. Despite differences in baseline stability between cell types, stability levels remained consistent over time between sessions, up to 1 hr. Even for sessions separated by 6 hrs, activity patterns of grid cells and of most non-grid cells lacked any systematic decrease in spatial similarity over time. However, a subset of ~15% of mEC non-grid cells recorded preferentially from layer III exhibited dramatic, time dependent changes in firing patterns across 6 hrs, reminiscent of previous characterizations of the hippocampal CA2 subregion. Collectively, our data suggest that mEC grid cell input to hippocampus in conjunction with many time invariant non-grid cells may aid in stabilizing hippocampal spatial maps, while a subset of time varying non-grid cells could provide complementary temporal information.

Axonal Anatomy Optimizes Spatial Encoding in the Rat Entorhinal-Dentate System: A Computational Study

OBJECTIVE

The network architecture connecting neural regions is defined by the organization and anatomical properties of the projecting axons, but its contributions to neural encoding and system function are difficult to study experimentally.

METHODS

Using a large-scale, spiking neuronal network model of rat dentate gyrus, the role of the anatomy of the entorhinal-dentate axonal projection was evaluated in the context of spatial encoding by incorporating grid cell activity to provide physiological, spatially-correlated input. The dorso-ventral extents of the entorhinal axon terminal fields were varied to generate different feedforward architectures, and the resulting spatial representations and spatial information scores of the network were evaluated. Position was decoded from the population activity using a point process filter to investigate the contributions of network architecture on spatial encoding.

RESULTS

The model predicted the emergence of anatomical gradients within the dentate gyrus for place field size and spatial information along its dorso-ventral axis, which were dependent on the extents of the entorhinal axon terminal fields. The decoding results revealed an optimal performance at an axon terminal field extent of 2 mm that lies within the biological range.

CONCLUSION

The axonal anatomy mediates a tradeoff between encoding multiple place field sizes or achieving a high spatial information score, and the combination of both properties is necessary to maximize spatial encoding by a network.

SIGNIFICANCE

In total, this paper establishes a mechanistic neuronal network model that, in concert with information-theoretic and statistical methods, can be used to investigate how lower level properties contribute to higher level function.

Computational models of memory consolidation and long-term synaptic plasticity during sleep

The brain stores memories by persistently changing the connectivity between neurons. Sleep is known to be critical for these changes to endure. Research on the neurobiology of sleep and the mechanisms of long-term synaptic plasticity has provided data in support of various theories of how brain activity during sleep affects long-term synaptic plasticity. The experimental findings - and therefore the theories - are apparently quite contradictory, with some evidence pointing to a role of sleep in the forgetting of irrelevant memories, whereas other results indicate that sleep supports the reinforcement of the most valuable recollections. A unified theoretical framework is in need. Computational modeling and simulation provide grounds for the quantitative testing and comparison of theoretical predictions and observed data, and might serve as a strategy to organize the rather complicated and diverse pool of data and methodologies used in sleep research. This review article outlines the emerging progress in the computational modeling and simulation of the main theories on the role of sleep in memory consolidation.

Hippocampal Remapping and Its Entorhinal Origin

The activity of hippocampal cell ensembles is an accurate predictor of the position of an animal in its surrounding space. One key property of hippocampal cell ensembles is their ability to change in response to alterations in the surrounding environment, a phenomenon called remapping. In this review article, we present evidence for the distinct types of hippocampal remapping. The progressive divergence over time of cell ensembles active in different environments and the transition dynamics between pre-established maps are discussed. Finally, we review recent work demonstrating that hippocampal remapping can be triggered by neurons located in the entorhinal cortex.

Chemically activated luminopsins allow optogenetic inhibition of distributed nodes in an epileptic network for non-invasive and multi-site suppression of seizure activity

Although optogenetic techniques have proven to be invaluable for manipulating and understanding complex neural dynamics over the past decade, they still face practical and translational challenges in targeting networks involving multiple, large, or difficult-to-illuminate areas of the brain. We utilized inhibitory luminopsins to simultaneously inhibit the dentate gyrus and anterior nucleus of the thalamus of the rat brain in a hardware-independent and cell-type specific manner. This approach was more effective at suppressing behavioral seizures than inhibition of the individual structures in a rat model of epilepsy. In addition to elucidating mechanisms of seizure suppression never directly demonstrated before, this work also illustrates how precise multi-focal control of pathological circuits can be advantageous for the treatment and understanding of disorders involving broad neural circuits such as epilepsy.

Spatial representation in the hippocampal formation: a history

Since the first place cell was recorded and the cognitive-map theory was subsequently formulated, investigation of spatial representation in the hippocampal formation has evolved in stages. Early studies sought to verify the spatial nature of place cell activity and determine its sensory origin. A new epoch started with the discovery of head direction cells and the realization of the importance of angular and linear movement-integration in generating spatial maps. A third epoch began when investigators turned their attention to the entorhinal cortex, which led to the discovery of grid cells and border cells. This review will show how ideas about integration of self-motion cues have shaped our understanding of spatial representation in hippocampal-entorhinal systems from the 1970s until today. It is now possible to investigate how specialized cell types of these systems work together, and spatial mapping may become one of the first cognitive functions to be understood in mechanistic detail.

Early-stage attenuation of phase-amplitude coupling in the hippocampus and medial prefrontal cortex in a transgenic rat model of Alzheimer's disease

Alzheimer's disease (AD) is pathologically characterized by amyloid-β peptide (Aβ) accumulation, neurofibrillary tangle formation, and neurodegeneration. Preclinical studies on neuronal impairments associated with progressive amyloidosis have demonstrated some Aβ-dependent neuronal dysfunction including modulation of gamma-aminobutyric acid-ergic signaling. The present work focuses on the early stage of disease progression and uses TgF344-AD rats that recapitulate a broad repertoire of AD-like pathologies to investigate the neuronal network functioning using simultaneous intracranial recordings from the hippocampus (HPC) and the medial prefrontal cortex (mPFC), followed by pathological analyses of gamma-aminobutyric acid (GABA_A ) receptor subunits α1_, α5, and δ, and glutamic acid decarboxylases (GAD65 and GAD67). Concomitant to amyloid deposition and tau hyperphosphorylation, low-gamma band power was strongly attenuated in the HPC and mPFC of TgF344-AD rats in comparison to those in non-transgenic littermates. In addition, the phase-amplitude coupling of the neuronal networks in both areas was impaired, evidenced by decreased modulation of theta band phase on gamma band amplitude in TgF344-AD animals. Finally, the gamma coherence between HPC and mPFC was attenuated as well. These results demonstrate significant neuronal network dysfunction at an early stage of AD-like pathology. This network dysfunction precedes the onset of cognitive deficits and is likely driven by Aβ and tau pathologies. This article is part of the Special Issue "Vascular Dementia".

A Novel Mechanism for the Grid-to-Place Cell Transformation Revealed by Transgenic Depolarization of Medial Entorhinal Cortex Layer II

The spatial receptive fields of neurons in medial entorhinal cortex layer II (MECII) and in the hippocampus suggest general and environment-specific maps of space, respectively. However, the relationship between these receptive fields remains unclear. We reversibly manipulated the activity of MECII neurons via chemogenetic receptors and compared the changes in downstream hippocampal place cells to those of neurons in MEC. Depolarization of MECII impaired spatial memory and elicited drastic changes in CA1 place cells in a familiar environment, similar to those seen during remapping between distinct environments, while hyperpolarization did not. In contrast, both manipulations altered the firing rate of MEC neurons without changing their firing locations. Interestingly, only depolarization caused significant changes in the relative firing rates of individual grid fields, reconfiguring the spatial input from MEC. This suggests a novel mechanism of hippocampal remapping whereby rate changes in MEC neurons lead to locational changes of hippocampal place fields.

Size Matters: How Scaling Affects the Interaction between Grid and Border Cells

Many hippocampal cell types are characterized by a progressive increase in scale along the dorsal-to-ventral axis, such as in the cases of head-direction, grid and place cells. Also located in the medial entorhinal cortex (MEC), border cells would be expected to benefit from such scale modulations. However, this phenomenon has not been experimentally observed. Grid cells in the MEC of mammals integrate velocity related signals to map the environment with characteristic hexagonal tessellation patterns. Due to the noisy nature of these input signals, path integration processes tend to accumulate errors as animals explore the environment, leading to a loss of grid-like activity. It has been suggested that border-to-grid cells' associations minimize the accumulated grid cells' error when rodents explore enclosures. Thus, the border-grid interaction for error minimization is a suitable scenario to study the effects of border cell scaling within the context of spatial representation. In this study, we computationally address the question of (i) border cells' scale from the perspective of their role in maintaining the regularity of grid cells' firing fields, as well as (ii) what are the underlying mechanisms of grid-border associations relative to the scales of both grid and border cells. Our results suggest that for optimal contribution to grid cells' error minimization, border cells should express smaller firing fields relative to those of the associated grid cells, which is consistent with the hypothesis of border cells functioning as spatial anchoring signals.

Place field detection using grid-based clustering in a large-scale computational model of the rat dentate gyrus

Place cells are neurons in the hippocampus that are sensitive to location within an environment. Simulations of a large-scale, computational model of the rat dentate gyrus using grid cell input have been performed resulting in granule cells that express multiple place fields. The typical method of detecting place fields using a global threshold on this data is unreliable as the characteristics of the place fields from a single neuron can be highly variable. A grid-based implementation of DENCLUE has been developed to calculate local thresholds to identify each place field. An adaptive binning algorithm used to smooth the rate maps was combined with the DENCLUE implementation to adaptively choose the size of the smoothing kernel and reduce the number of free parameters of the total algorithm. A sensitivity analysis was performed using the threshold parameter to demonstrate the robustness of using local thresholds as opposed to using a single global threshold in detecting the place fields resulting from the large-scale simulation. The analysis supports the use of applying local thresholds for place field detection and will be used to further investigate the role of granule cells in hippocampal function.

From grid cells to place cells with realistic field sizes

While grid cells in the medial entorhinal cortex (MEC) of rodents have multiple, regularly arranged firing fields, place cells in the cornu ammonis (CA) regions of the hippocampus mostly have single spatial firing fields. Since there are extensive projections from MEC to the CA regions, many models have suggested that a feedforward network can transform grid cell firing into robust place cell firing. However, these models generate place fields that are consistently too small compared to those recorded in experiments. Here, we argue that it is implausible that grid cell activity alone can be transformed into place cells with robust place fields of realistic size in a feedforward network. We propose two solutions to this problem. Firstly, weakly spatially modulated cells, which are abundant throughout EC, provide input to downstream place cells along with grid cells. This simple model reproduces many place cell characteristics as well as results from lesion studies. Secondly, the recurrent connections between place cells in the CA3 network generate robust and realistic place fields. Both mechanisms could work in parallel in the hippocampal formation and this redundancy might account for the robustness of place cell responses to a range of disruptions of the hippocampal circuitry.

Place and Grid Cells in a Loop: Implications for Memory Function and Spatial Coding

Place cells in the hippocampus and grid cells in the medial entorhinal cortex have different codes for space. However, how one code relates to the other is ill understood. Based on the anatomy of the entorhinal-hippocampal circuitry, we constructed a model of place and grid cells organized in a loop to investigate their mutual influence in the establishment of their codes for space. Using computer simulations, we first replicated experiments in rats that measured place and grid cell activity in different environments, and then assessed which features of the model account for different phenomena observed in neurophysiological data, such as pattern completion and pattern separation, global and rate remapping of place cells, and realignment of grid cells. We found that (1) the interaction between grid and place cells converges quickly; (2) the spatial code of place cells does not require, but is altered by, grid cell input; (3) plasticity in sensory inputs to place cells is key for pattern completion but not pattern separation; (4) grid realignment can be explained in terms of place cell remapping as opposed to the other way around; (5) the switch between global and rate remapping is self-organized; and (6) grid cell input to place cells helps stabilize their code under noisy and/or inconsistent sensory input. We conclude that the hippocampus-entorhinal circuit uses the mutual interaction of place and grid cells to encode the surrounding environment and propose a theory on how such interdependence underlies the formation and use of the cognitive map.SIGNIFICANCE STATEMENT The mammalian brain implements a positional system with two key pieces: place and grid cells. To gain insight into the dynamics of place and grid cell interaction, we built a computational model with the two cell types organized in a loop. The proposed model accounts for differences in how place and grid cells represent different environments and provides a new interpretation in which place and grid cells mutually interact to form a coupled code for space.

Physiological Properties and Behavioral Correlates of Hippocampal Granule Cells and Mossy Cells

The hippocampal dentate gyrus is often viewed as a segregator of upstream information. Physiological support for such function has been hampered by a lack of well-defined characteristics that can identify granule cells and mossy cells. We developed an electrophysiology-based classification of dentate granule cells and mossy cells in mice that we validated by optogenetic tagging of mossy cells. Granule cells exhibited sparse firing, had a single place field, and showed only modest changes when the mouse was tested in different mazes in the same room. In contrast, mossy cells were more active, had multiple place fields and showed stronger remapping of place fields under the same conditions. Although the granule cell-mossy cell synapse was strong and facilitating, mossy cells rarely "inherited" place fields from single granule cells. Our findings suggest that the granule cells and mossy cells could be modulated separately and their joint action may be critical for pattern separation.