Grid Cells In Medial Entorhinal Cortex Research Articles

Learning Spatiotemporal Properties of Hippocampal Place Cells.

It is well known that hippocampal place cells have spatiotemporal properties, namely, that they generally respond to a single spatial location of a small environment, and they also display the temporal response property of theta phase precession, namely, that the phase of spiking relative to the theta wave shifts from the late phase to early phase as the animal crosses the place field. Grid cells in Layer II of the medial entorhinal cortex (MEC) also have spatiotemporal properties similar to hippocampal place cells, except that grid cells respond to multiple spatial locations that form a hexagonal pattern. Because the EC is the upstream area that projects strongly to the hippocampus, a number of EC-hippocampus learning models have been proposed to explain how the spatial receptive field properties of place cells emerge via synaptic plasticity. However, the question of how the phase precession properties of place cells and grid cells are related has remained unclear. This study shows how theta phase precession in hippocampal place cells can emerge from MEC input as a result of synaptic plasticity, demonstrating that a learning model based on non-negative sparse coding can account for both the spatial and temporal properties of hippocampal place cells. Although both MEC grid cells and other EC spatial cells contribute to the spatial properties of hippocampal place cells, it is the MEC grid cells that predominantly determine the temporal response properties of hippocampal place cells displayed here.

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.

Effect of boundaries on grid cell patterns

The authors show how the addition of static recurrent inhibition to a system of Turing pattern formation removes pattern alignment to boundaries and explains features of medial entorhinal cortex grid cells.

Disrupted Place Cell Remapping and Impaired Grid Cells in a Knockin Model of Alzheimer's Disease

Patients with Alzheimer's disease (AD) suffer from spatial memory impairment and wandering behavior, but the brain circuit mechanisms causing such symptoms remain largely unclear. In healthy brains, spatially tuned hippocampal place cells and entorhinal grid cells exhibit distinct spike patterns in different environments, a circuit function called "remapping." We tested remapping in amyloid precursor protein knockin (APP-KI) mice with impaired spatial memory. CA1 neurons, including place cells, showed disrupted remapping, although their spatial tuning was only mildly diminished. Medial entorhinal cortex (MEC) neurons severely lost their spatial tuning and grid cells were almost absent. Fast gamma oscillatory coupling between the MEC and CA1 was also impaired. Mild disruption of MEC grid cells emerged in younger APP-KI mice, although the spatial memory and CA1 remapping of the animals remained intact. These results point to remapping impairment in the hippocampus, possibly linked to grid cell disruption, as circuit mechanisms underlying spatial memory impairment in AD.

Self-motion processing in visual and entorhinal cortices: inputs, integration, and implications for position coding.

The sensory signals generated by self-motion are complex and multimodal, but the ability to integrate these signals into a unified self-motion percept to guide navigation is essential for animal survival. Here, we summarize classic and recent work on self-motion coding in the visual and entorhinal cortices of the rodent brain. We compare motion processing in rodent and primate visual cortices, highlighting the strengths of classic primate work in establishing causal links between neural activity and perception, and discuss the integration of motor and visual signals in rodent visual cortex. We then turn to the medial entorhinal cortex (MEC), where calculations using self-motion to update position estimates are thought to occur. We focus on several key sources of self-motion information to MEC: the medial septum, which provides locomotor speed information; visual cortex, whose input has been increasingly recognized as essential to both position and speed-tuned MEC cells; and the head direction system, which is a major source of directional information for self-motion estimates. These inputs create a large and diverse group of self-motion codes in MEC, and great interest remains in how these self-motion codes might be integrated by MEC grid cells to estimate position. However, which signals are used in these calculations and the mechanisms by which they are integrated remain controversial. We end by proposing future experiments that could further our understanding of the interactions between MEC cells that code for self-motion and position and clarify the relationship between the activity of these cells and spatial perception.

Methodological Caveats in the Detection of Coordinated Replay between Place Cells and Grid Cells.

At rest, hippocampal “place cells,” neurons with receptive fields corresponding to specific spatial locations, reactivate in a manner that reflects recently traveled trajectories. These “replay” events have been proposed as a mechanism underlying memory consolidation, or the transfer of a memory representation from the hippocampus to neocortical regions associated with the original sensory experience. Accordingly, it has been hypothesized that hippocampal replay of a particular experience should be accompanied by simultaneous reactivation of corresponding representations in the neocortex and in the entorhinal cortex, the primary interface between the hippocampus and the neocortex. Recent studies have reported that coordinated replay may occur between hippocampal place cells and medial entorhinal cortex grid cells, cells with multiple spatial receptive fields. Assessing replay in grid cells is problematic, however, as the cells exhibit regularly spaced spatial receptive fields in all environments and, therefore, coordinated replay between place cells and grid cells may be detected by chance. In the present report, we adapted analytical approaches utilized in recent studies of grid cell and place cell replay to determine the extent to which coordinated replay is spuriously detected between grid cells and place cells recorded from separate rats. For a subset of the employed analytical methods, coordinated replay was detected spuriously in a significant proportion of cases in which place cell replay events were randomly matched with grid cell firing epochs of equal duration. More rigorous replay evaluation procedures and minimum spike count requirements greatly reduced the amount of spurious findings. These results provide insights into aspects of place cell and grid cell activity during rest that contribute to false detection of coordinated replay. The results further emphasize the need for careful controls and rigorous methods when testing the hypothesis that place cells and grid cells exhibit coordinated replay.

Neuromorphic VLSI realization of the hippocampal formation

The medial entorhinal cortex grid cells, aided by the subicular head direction cells, are thought to provide a matrix which is utilized by the hippocampal place cells for calculation of position of an animal during spatial navigation. The place cells are thought to function as an internal GPS for the brain and provide a spatiotemporal stamp on episodic memories. Several computational neuroscience models have been proposed to explain the place specific firing patterns of the cells of the hippocampal formation — including the GRIDSmap model for grid cells and Bayesian integration for place cells. In this work, we present design and measurement results from a first ever system of silicon circuits which successfully realize the function of the hippocampal formation of brain based on these models.

The hippocampus

The hippocampus

The transformation of grid to place cells is robust to noise in the grid pattern

The neural mechanisms of spatial navigation in rodents are thought to rest on place-selective cells in the hippocampus and/ or medial entorhinal cortex (MEC). These cells have been suggested to be the basis for a cognitive map of the animal's environment and for path integration. However, despite large body of theoretical work, it remains unsettled how the neural responses of hippocampal place cells and the grid cells in MEC are generated. Given the massive projections from the superficial MEC to the hippocampal CA regions, which host the place cells, it was initially postulated that grid cells drive the spatial responses of place cells. The transformation was modeled as a linear weighted summation of input grid cells into place cells firing. Different strategies was used to implement the connecting weights (see [1] for a generalized model and review of earlier models). However, recent experimental evidence appears to cast doubt on this suggestion. During development [2] or, when the theta oscillation was disrupted due to medial septum inactivation [3], stable place cell responses were found at the same time when the grid cell responses were severely distorted. The authors of these studies therefore concluded that place cells cannot rely on grid cells as their exclusive source of input. In this work, we use computational modeling to show that this conclusion might have been premature. We use a simple feedforward network with grid cells in one layer and place cells in another layer to model the grid-to-place cell transformation. The weights in this network depend on the spatial phase of the grid cell according to a relationship that we reported previously [1]. We perturb the grids in the input in four different ways and study the robustness of the place cells response to these perturbations. In two cases, which we do not regard as realistic, we find that the grid-to-place cell transformation is not robust (Figure 1 A and B). However, in the more realistic two cases of grid perturbations, the transformation is very robust (Figure 1 C and D). Figure 1 Robustness of grid to place transformation under different noise strategies. Fraction of simulation runs that lead to stable place cell as a function of added noise to the amplitude (A), phase (B), direction (C) of the grid pattern and accumulated noise ... These robust cases include grid perturbations due to noise in the path integration mechanism that gives rise to grid cell responses and noise in the alignment of the three main axes of the grids. Although these two cases are conceptually quite different, many results are quite similar. Even if current experimental data suggest the involvement of other inputs in driving place cell responses, our work shows that the simple idea that grid cells drive spatial responses of place cells cannot be ruled out at this point.

Functional Split between Parietal and Entorhinal Cortices in the Rat

Posterior parietal cortex (PPC) and medial entorhinal cortex (MEC) are important elements of the neural circuit for space, but whether representations in these areas are controlled by the same factors is unknown. We recorded single units simultaneously in PPC and MEC of freely foraging rats and found that a subset of PPC cells are tuned to specific modes of movement irrespective of the animals' location or heading, whereas grid cells in MEC expressed static spatial maps. The behavioral correlates of PPC cells switched completely when the same animals ran in a spatially structured maze or when they ran similar stereotypic sequences in an open arena. Representations in PPC were similar in identical mazes in different rooms where grid cells completely realigned their firing fields. The data suggest that representations in PPC are determined by the organization of actions while cells in MEC are driven by spatial inputs.

Representation of Non-Spatial and Spatial Information in the Lateral Entorhinal Cortex

Some theories of memory propose that the hippocampus integrates the individual items and events of experience within a contextual or spatial framework. The hippocampus receives cortical input from two major pathways: the medial entorhinal cortex (MEC) and the lateral entorhinal cortex (LEC). During exploration in an open field, the firing fields of MEC grid cells form a periodically repeating, triangular array. In contrast, LEC neurons show little spatial selectivity, and it has been proposed that the LEC may provide non-spatial input to the hippocampus. Here, we recorded MEC and LEC neurons while rats explored an open field that contained discrete objects. LEC cells fired selectively at locations relative to the objects, whereas MEC cells were weakly influenced by the objects. These results provide the first direct demonstration of a double dissociation between LEC and MEC inputs to the hippocampus under conditions of exploration typically used to study hippocampal place cells.

Theta Modulation in the Medial and the Lateral Entorhinal Cortices

Hippocampal neurons show a strong modulation by theta frequency oscillations. This modulation is thought to be important not only for temporal encoding and decoding of information in the hippocampal system, but also for temporal ordering of neuronal activities on timescales at which physiological mechanisms of synaptic plasticity operate. The medial entorhinal cortex (MEC), one of the two major cortical inputs to the hippocampus, is known to show theta modulation. Here, we show that the local field potentials (LFPs) in the other major cortical input to the hippocampus, the lateral entorhinal cortex (LEC), show weaker theta oscillations than those shown in the MEC. Neurons in LEC also show weaker theta modulation than that of neurons in MEC. These findings suggest that LEC inputs are integrated into hippocampal representations in a qualitatively different manner than the MEC inputs. Furthermore, MEC grid cells increase the scale of their periodic spatial firing patterns along the dorsoventral axis, corresponding to the increasing size of place fields along the septotemporal axis of the hippocampus. We show here a corresponding gradient in the tendency of MEC neural firing to skip alternate theta cycles. We propose a simple model based on interference of delta oscillations with theta oscillations to explain this behavior.

PREDICTION OF THE POSITION OF AN ANIMAL BASED ON POPULATIONS OF GRID AND PLACE CELLS: A COMPARATIVE SIMULATION STUDY

The grid cells of the rodent medial entorhinal cortex (MEC) show activity patterns correlated with the animal's position. Unlike hippocampal place cells that are activated at only one specific location in the environment, MEC grid cells increase firing frequency at multiple regions in space, or subfields, that are arranged in regular triangular grids. It has been recently shown that a conjunction of MEC grid cells can lead to unique spatial representations. However, it remains unclear what the key properties of the grids are that allow for an accurate reconstruction of the position of the animal and what the comparison with hippocampal place cells is. Here we use a theoretical approach based on data from electrophysiological recordings of the MEC to simulate the neural activity of grid cells. Our simulations account for the accurate reproduction of grid cell mean firing rates, based on only three grid parameters, that is grid phase, spacing and orientation. The analysis of the key properties of the grids first reveals that for an accurate position reconstruction, it is necessary to combine cells with different grid spacings (which are found at different dorsoventral locations of the MEC) or orientations. Second, the relationship between grid spacing and subfield size observed in physiological data is optimal to predict the animal's position. Third, the regular triangular tessellating patterns of grid cells lead to the best position reconstruction results when compared with all other regular tessellations of two-dimensional space. Finally, the comparison of grid cells with place cells shows that populations of MEC grid cells can better predict the animal's position than equally-sized populations of hippocampal place cells with similar but unique spatial fields. Taken together, our results suggest that the MEC provides highly compact representations of the animal's position, which may be subsequently integrated by the place cells of the hippocampus.