The single place fields of CA3 cells: a two-stage transformation from grid cells

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.

Skipping ahead: A circuit for representing the past, present, and future

Envisioning the future is intuitively linked to our ability to remember the past. Within the memory system, substantial work has demonstrated the involvement of the prefrontal cortex and the hippocampus in representing the past and present. Recent data shows that both the prefrontal cortex and the hippocampus encode future trajectories, which are segregated in time by alternating cycles of the theta rhythm. Here, we discuss how information is temporally organized by these brain regions supported by the medial septum, nucleus reuniens, and parahippocampal regions. Finally, we highlight a brain circuit that we predict is essential for the temporal segregation of future scenarios.

Multi-Scale Extension in an Entorhinal-Hippocampal Model for Cognitive Map Building

Neuroscience research shows that, by relying on internal spatial representations provided by the hippocampus and entorhinal cortex, mammals are able to build topological maps of environments and navigate. Taking inspiration from mammals' spatial cognition mechanism, entorhinal-hippocampal cognitive systems have been proposed for robots to build cognitive maps. However, path integration and vision processing are time-consuming, and the existing model of grid cells is hard to achieve in terms of adaptive multi-scale extension for different environments, resulting in the lack of viability for real environments. In this work, an optimized dynamical model of grid cells is built for path integration in which recurrent weight connections between grid cells are parameterized in a more optimized way and the non-linearity of sigmoidal neural transfer function is utilized to enhance grid cell activity packets. Grid firing patterns with specific spatial scales can thus be accurately achieved for the multi-scale extension of grid cells. In addition, a hierarchical vision processing mechanism is proposed for speeding up loop closure detection. Experiment results on the robotic platform demonstrate that our proposed entorhinal-hippocampal model can successfully build cognitive maps, reflecting the robot's spatial experience and environmental topological structures.

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.

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.

Functional properties of stellate cells in medial entorhinal cortex layer II

Layer II of the medial entorhinal cortex (MEC) contains two principal cell types: pyramidal cells and stellate cells. Accumulating evidence suggests that these two cell types have distinct molecular profiles, physiological properties, and connectivity. The observations hint at a fundamental functional difference between the two cell populations but conclusions have been mixed. Here, we used a tTA-based transgenic mouse line to drive expression of ArchT, an optogenetic silencer, specifically in stellate cells. We were able to optogenetically identify stellate cells and characterize their firing properties in freely moving mice. The stellate cell population included cells from a range of functional cell classes. Roughly one in four of the tagged cells were grid cells, suggesting that stellate cells contribute not only to path-integration-based representation of self-location but also have other functions. The data support observations suggesting that grid cells are not the sole determinant of place cell firing.

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.

Spatial Representations of Granule Cells and Mossy Cells of the Dentate Gyrus

Granule cells in the dentate gyrus of the hippocampus are thought to be essential to memory function by decorrelating overlapping input patterns (pattern separation). A second excitatory cell type in the dentate gyrus, the mossy cell, forms an intricate circuit with granule cells, CA3c pyramidal cells, and local interneurons, but the influence of mossy cells on dentate function is often overlooked. Multiple tetrode recordings, supported by juxtacellular recording techniques, showed that granule cells fired very sparsely, whereas mossy cells in the hilus fired promiscuously in multiple locations and in multiple environments. The activity patterns of these cell types thus represent different environments through distinct computational mechanisms: sparse coding in granule cells and changes in firing field locations in mossy cells.

Hippocampal Remapping after Partial Inactivation of the Medial Entorhinal Cortex

Hippocampal place cells undergo remapping when the environment is changed. The mechanism of hippocampal remapping remains elusive but spatially modulated cells in the medial entorhinal cortex (MEC) have been identified as a possible contributor. Using pharmacogenetic and optogenetic approaches, we tested the role of MEC cells by examining in mice whether partial inactivation in MEC shifts hippocampal activity to a different subset of place cells with different receptive fields. The pharmacologically selective designer Gi-protein-coupled muscarinic receptor hM4D or the light-responsive microbial proton pump archaerhodopsin (ArchT) was expressed in MEC, and place cells were recorded after application of the inert ligand clozapine-N-oxide (CNO) or light at appropriate wavelengths. CNO or light caused partial inactivation of the MEC. The inactivation was followed by substantial remapping in the hippocampus, without disruption of the spatial firing properties of individual neurons. The results point to MEC input as an element of the mechanism for remapping in place cells.

Grid Cells and Place Cells: An Integrated View of their Navigational and Memory Function

Much has been learned about the hippocampal/entorhinal system, but an overview of how its parts work in an integrated way is lacking. One question regards the function of entorhinal grid cells. We propose here that their fundamental function is to provide a coordinate system for producing mind-travel in the hippocampus, a process that accesses associations with upcoming positions. We further propose that mind-travel occurs during the second half of each theta cycle. By contrast, the first half of each theta cycle is devoted to computing current position using sensory information from the lateral entorhinal cortex (LEC) and path integration information from the medial entorhinal cortex (MEC). This model explains why MEC lesions can abolish hippocampal phase precession but not place fields.

New and distinct hippocampal place codes are generated in a new environment during septal inactivation

The hippocampus generates distinct neural codes to disambiguate similar experiences, a process thought to underlie episodic memory function. Entorhinal grid cells provide a prominent spatial signal to hippocampus, and changes in their firing pattern could thus generate a distinct spatial code in each context. We examined whether we would preclude the emergence of new spatial representations in a novel environment during muscimol inactivation of the medial septal area, a manipulation known to disrupt theta oscillations and grid cell firing. We found that new, highly distinct configurations of place fields emerged immediately and remained stable during the septal inactivation. The new place code persisted when theta oscillations had recovered. Theta rhythmicity and feedforward input from grid cell networks were thus not required to generate new spatial representations in the hippocampus.

Place cell rate remapping by CA3 recurrent collaterals

Episodic-like memory is thought to be supported by attractor dynamics in the hippocampus. A possible neural substrate for this memory mechanism is rate remapping, in which the spatial map of place cells encodes contextual information through firing rate variability. To test whether memories are stored as multimodal attractors in populations of place cells, recent experiments morphed one familiar context into another while observing the responses of CA3 cell ensembles. Average population activity in CA3 was reported to transition gradually rather than abruptly from one familiar context to the next, suggesting a lack of attractive forces associated with the two stored representations. On the other hand, individual CA3 cells showed a mix of gradual and abrupt transitions at different points along the morph sequence, and some displayed hysteresis which is a signature of attractor dynamics. To understand whether these seemingly conflicting results are commensurate with attractor network theory, we developed a neural network model of the CA3 with attractors for both position and discrete contexts. We found that for memories stored in overlapping neural ensembles within a single spatial map, position-dependent context attractors made transitions at different points along the morph sequence. Smooth transition curves arose from averaging across the population, while a heterogeneous set of responses was observed on the single unit level. In contrast, orthogonal memories led to abrupt and coherent transitions on both population and single unit levels as experimentally observed when remapping between two independent spatial maps. Strong recurrent feedback entailed a hysteretic effect on the network which diminished with the amount of overlap in the stored memories. These results suggest that context-dependent memory can be supported by overlapping local attractors within a spatial map of CA3 place cells. Similar mechanisms for context-dependent memory may also be found in other regions of the cerebral cortex.

The activity of ‘place cells’ in hippocampal area CA3 systematically changes as a function of the animal's position in an arena as well as contextual variables like the color or shape of enclosing walls. Large changes to the local environment, e.g. moving the animal to a different room, can induce a complete reorganization of place-cell firing locations. Such ‘global remapping’ reveals that memory for different environments is encoded as separate spatial maps. Smaller changes to features within an environment can induce a modulation of place cell firing rates without affecting their firing locations. This kind of ‘rate remapping’ is still poorly understood. In this paper we describe a computational model in which discrete memories for contextual features were stored locally within a spatial map of place cells. This network structure supports retrieval of both positional and contextual information from an arbitrary cue, as required by an episodic memory structure. The activity of the network qualitatively matches empirical data from rate remapping experiments, both on the population level and the level of single place cells. The results support the idea that CA3 rate remapping reflects content-addressable memories stored as multimodal attractor states in the hippocampus.

A signature of attractor dynamics in the CA3 region of the hippocampus

The notion of attractor networks is the leading hypothesis for how associative memories are stored and recalled. A defining anatomical feature of such networks is excitatory recurrent connections. These "attract" the firing pattern of the network to a stored pattern, even when the external input is incomplete (pattern completion). The CA3 region of the hippocampus has been postulated to be such an attractor network; however, the experimental evidence has been ambiguous, leading to the suggestion that CA3 is not an attractor network. In order to resolve this controversy and to better understand how CA3 functions, we simulated CA3 and its input structures. In our simulation, we could reproduce critical experimental results and establish the criteria for identifying attractor properties. Notably, under conditions in which there is continuous input, the output should be "attracted" to a stored pattern. However, contrary to previous expectations, as a pattern is gradually "morphed" from one stored pattern to another, a sharp transition between output patterns is not expected. The observed firing patterns of CA3 meet these criteria and can be quantitatively accounted for by our model. Notably, as morphing proceeds, the activity pattern in the dentate gyrus changes; in contrast, the activity pattern in the downstream CA3 network is attracted to a stored pattern and thus undergoes little change. We furthermore show that other aspects of the observed firing patterns can be explained by learning that occurs during behavioral testing. The CA3 thus displays both the learning and recall signatures of an attractor network. These observations, taken together with existing anatomical and behavioral evidence, make the strong case that CA3 constructs associative memories based on attractor dynamics.

Spatial scale and place field stability in a grid-to-place cell model of the dorsoventral axis of the hippocampus

The rodent hippocampus and entorhinal cortex contain spatially modulated cells that serve as the basis for spatial coding. Both medial entorhinal grid cells and hippocampal place cells have been shown to encode spatial information across multiple spatial scales that increase along the dorsoventral axis of these structures. Place cells near the dorsal pole possess small, stable, and spatially selective firing fields, while ventral cells have larger, less stable, and less spatially selective firing fields. One possible explanation for these dorsoventral changes in place field properties is that they arise as a result of similar dorsoventral differences in the properties of the grid cell inputs to place cells. Here, we test the alternative hypothesis that dorsoventral place field differences are due to higher amounts of nonspatial inputs to ventral hippocampal cells. We use a computational model of the entorhinal-hippocampal network to assess the relative contributions of grid scale and nonspatial inputs in determining place field size and stability. In addition, we assess the consequences of grid node firing rate heterogeneity on place field stability. Our results suggest that dorsoventral differences in place cell properties can be better explained by changes in the amount of nonspatial inputs, rather than by changes in the scale of grid cell inputs, and that grid node heterogeneity may have important functional consequences. The observed gradient in field size may reflect a shift from processing primarily spatial information in the dorsal hippocampus to processing more nonspatial, contextual, and emotional information near the ventral hippocampus.

Neuron-specific expression of tomosyn1 in the mouse hippocampal dentate gyrus impairs spatial learning and memory

Tomosyn, a syntaxin-binding protein, is known to inhibit vesicle priming and synaptic transmission via interference with the formation of SNARE complexes. Using a lentiviral vector, we specifically overexpressed tomosyn1 in hippocampal dentate gyrus neurons in adult mice. Mice were then subjected to spatial learning and memory tasks and electrophysiological measurements from hippocampal slices. Tomosyn1-overexpression significantly impaired hippocampus-dependent spatial memory while tested in the Morris water maze. Further, tomosyn1-overexpressing mice utilize swimming strategies of lesser cognitive ability in the Morris water maze compared with control mice. Electrophysiological measurements at mossy fiber-CA3 synapses revealed impaired paired-pulse facilitation in the mossy fiber of tomosyn1-overexpressing mice. This study provides evidence for novel roles for tomosyn1 in hippocampus-dependent spatial learning and memory, potentially via decreased synaptic transmission in mossy fiber-CA3 synapses. Moreover, it provides new insight regarding the role of the hippocampal dentate gyrus and mossy fiber-CA3 synapses in swimming strategy preference, and in learning and memory.

Functional circuits of new neurons in the dentate gyrus

The hippocampus is crucial for memory formation. New neurons are added throughout life to the hippocampal dentate gyrus (DG), a brain area considered important for differential storage of similar experiences and contexts. To better understand the functional contribution of adult neurogenesis to pattern separation processes, we recently used a novel synapse specific trans-neuronal tracing approach to identify the (sub) cortical inputs to new dentate granule cells (GCs). It was observed that newly born neurons receive sequential innervation from structures important for memory function. Initially, septal-hippocampal cells provide input to new neurons, including transient innervation from mature GCs as well as direct feedback from area CA3 pyramidal neurons. After about 1 month perirhinal (PRH) and lateral entorhinal cortex (LEC), brain areas deemed relevant to integration of novel sensory and environmental information, become substantial input to new GCs. Here, we review the developmental time-course and proposed functional relevance of new neurons, within the context of their unique neural circuitry.

The computational influence of neurogenesis in the processing of spatial information in the dentate gyrus

This study was designed to analyze the effect of hippocampal neurogenesis on the spatial maps of granule cells. Accordingly, we developed and improved an artificial neural network that was originally proposed by Aimone. Many biological processes were included in this revised model to improve the biological relevance of the results. We proposed a novel learning-testing protocol to analyze the activation of encoding place cells across contexts and over time in the dentate gyrus. We observed that, regardless of the presence of neurogenesis, the quantity and morphology of the place fields were represented in the same manner by granule cells. Additionally, we observed that neurogenesis was an effective mechanism for reducing the degree of rate remapping that occurred in the place fields of the granule cells.

The operation of pattern separation and pattern completion processes associated with different attributes or domains of memory

Pattern separation and pattern completion processes are central to how the brain processes information in an efficient manner. Research into these processes is escalating and deficient pattern separation is being implicated in a wide array of genetic disorders as well as in neurocognitive aging. Despite the quantity of research, there remains a controversy as to precisely which behavioral paradigms should be used to best tap into pattern separation and pattern completion processes, as well as to what constitute legitimate outcome measures reflecting impairments in pattern separation and pattern completion. This review will discuss a theory based on multiple memory systems that provides a framework upon which behavioral tasks can be designed and their results interpreted. Furthermore, this review will discuss the nature of pattern separation and pattern completion and extend these processes outside the hippocampus and across all domains of information processing. After these discussions, an optimal strategy for designing behavioral paradigms to evaluate pattern separation and pattern completion processes will be provided.

Selective reduction of AMPA currents onto hippocampal interneurons impairs network oscillatory activity

Reduction of excitatory currents onto GABAergic interneurons in the forebrain results in impaired spatial working memory and altered oscillatory network patterns in the hippocampus. Whether this phenotype is caused by an alteration in hippocampal interneurons is not known because most studies employed genetic manipulations affecting several brain regions. Here we performed viral injections in genetically modified mice to ablate the GluA4 subunit of the AMPA receptor in the hippocampus (GluA4^HC−/− mice), thereby selectively reducing AMPA receptor-mediated currents onto a subgroup of hippocampal interneurons expressing GluA4. This regionally selective manipulation led to a strong spatial working memory deficit while leaving reference memory unaffected. Ripples (125–250 Hz) in the CA1 region of GluA4^HC−/− mice had larger amplitude, slower frequency and reduced rate of occurrence. These changes were associated with an increased firing rate of pyramidal cells during ripples. The spatial selectivity of hippocampal pyramidal cells was comparable to that of controls in many respects when assessed during open field exploration and zigzag maze running. However, GluA4 ablation caused altered modulation of firing rate by theta oscillations in both interneurons and pyramidal cells. Moreover, the correlation between the theta firing phase of pyramidal cells and position was weaker in GluA4^HC−/− mice. These results establish the involvement of AMPA receptor-mediated currents onto hippocampal interneurons for ripples and theta oscillations, and highlight potential cellular and network alterations that could account for the altered working memory performance.

A goal-directed spatial navigation model using forward trajectory planning based on grid cells

A goal-directed navigation model is proposed based on forward linear look-ahead probe of trajectories in a network of head direction cells, grid cells, place cells and prefrontal cortex (PFC) cells. The model allows selection of new goal-directed trajectories. In a novel environment, the virtual rat incrementally creates a map composed of place cells and PFC cells by random exploration. After exploration, the rat retrieves memory of the goal location, picks its next movement direction by forward linear look-ahead probe of trajectories in several candidate directions while stationary in one location, and finds the one activating PFC cells with the highest reward signal. Each probe direction involves activation of a static pattern of head direction cells to drive an interference model of grid cells to update their phases in a specific direction. The updating of grid cell spiking drives place cells along the probed look-ahead trajectory similar to the forward replay during waking seen in place cell recordings. Directions are probed until the look-ahead trajectory activates the reward signal and the corresponding direction is used to guide goal-finding behavior. We report simulation results in several mazes with and without barriers. Navigation with barriers requires a PFC map topology based on the temporal vicinity of visited place cells and a reward signal diffusion process. The interaction of the forward linear look-ahead trajectory probes with the reward diffusion allows discovery of never-before experienced shortcuts towards a goal location.

Self-organization of multiple spatial and context memories in the hippocampus

One obstacle to understanding the exact processes unfolding inside the hippocampus is that it is still difficult to clearly define what the hippocampus actually does, at the system level. Associated for a long time with the formation of episodic and semantic memories, and with their temporary storage, the hippocampus is also regarded as a structure involved in spatial navigation. These two independent perspectives on the hippocampus are not necessarily exclusive: proposals have been put forward to make them fit into the same conceptual frame. We review both approaches and argue that three critical developments need consideration: (a) recordings of neuronal activity in rodents, revealing beautiful spatial codes expressed in entorhinal cortex, upstream of the hippocampus; (b) comparative behavioral results suggesting, in an evolutionary perspective, qualitative similarity of function across homologous structures with a distinct internal organization; (c) quantitative measures of information, shifting the focus from who does what to how much each neuronal population expresses each code. These developments take the hippocampus away from philosophical discussions of all-or-none cause-effect relations, and into the quantitative mainstream of modern neural science.

Ensemble place codes in hippocampus: CA1, CA3, and dentate gyrus place cells have multiple place fields in large environments

Previously we reported that the hippocampus place code must be an ensemble code because place cells in the CA1 region of hippocampus have multiple place fields in a more natural, larger-than-standard enclosure with stairs that permitted movements in 3-D. Here, we further investigated the nature of hippocampal place codes by characterizing the spatial firing properties of place cells in the CA1, CA3, and dentate gyrus (DG) hippocampal subdivisions as rats foraged in a standard 76-cm cylinder as well as a larger-than-standard box (1.8 m×1.4 m) that did not have stairs or any internal structure to permit movements in 3-D. The rats were trained to forage continuously for 1 hour using computer-controlled food delivery. We confirmed that most place cells have single place fields in the standard cylinder and that the positional firing pattern remapped between the cylinder and the large enclosure. Importantly, place cells in the CA1, CA3 and DG areas all characteristically had multiple place fields that were irregularly spaced, as we had reported previously for CA1. We conclude that multiple place fields are a fundamental characteristic of hippocampal place cells that simplifies to a single field in sufficiently small spaces. An ensemble place code is compatible with these observations, which contradict any dedicated coding scheme.

The mechanism of rate remapping in the dentate gyrus

Rate remapping is a recently revealed neural code in which sensory information modulates the firing rate of hippocampal place cells. The mechanism underlying rate remapping is unknown. Its characteristic modulation, however, must arise from the interaction of the two major inputs to the hippocampus, the medial entorhinal cortex (MEC), in which grid cells represent the spatial position of the rat, and the lateral entorhinal cortex (LEC), in which cells represent the sensory properties of the environment. We have used computational methods to elucidate the mechanism by which this interaction produces rate remapping. We show that the convergence of LEC and MEC inputs, in conjunction with a competitive network process mediated by feedback inhibition, can account quantitatively for this phenomenon. The same principle accounts for why different place fields of the same cell vary independently as sensory information is altered. Our results show that rate remapping can be explained in terms of known mechanisms.