Grid Cells In Entorhinal Cortex Research Articles

Learning the Vector Coding of Egocentric Boundary Cells from Visual Data.

The use of spatial maps to navigate through the world requires a complex ongoing transformation of egocentric views of the environment into position within the allocentric map. Recent research has discovered neurons in retrosplenial cortex and other structures that could mediate the transformation from egocentric views to allocentric views. These egocentric boundary cells respond to the egocentric direction and distance of barriers relative to an animal's point of view. This egocentric coding based on the visual features of barriers would seem to require complex dynamics of cortical interactions. However, computational models presented here show that egocentric boundary cells can be generated with a remarkably simple synaptic learning rule that forms a sparse representation of visual input as an animal explores the environment. Simulation of this simple sparse synaptic modification generates a population of egocentric boundary cells with distributions of direction and distance coding that strikingly resemble those observed within the retrosplenial cortex. Furthermore, some egocentric boundary cells learnt by the model can still function in new environments without retraining. This provides a framework for understanding the properties of neuronal populations in the retrosplenial cortex that may be essential for interfacing egocentric sensory information with allocentric spatial maps of the world formed by neurons in downstream areas, including the grid cells in entorhinal cortex and place cells in the hippocampus.SIGNIFICANCE STATEMENT The computational model presented here demonstrates that the recently discovered egocentric boundary cells in retrosplenial cortex can be generated with a remarkably simple synaptic learning rule that forms a sparse representation of visual input as an animal explores the environment. Additionally, our model generates a population of egocentric boundary cells with distributions of direction and distance coding that strikingly resemble those observed within the retrosplenial cortex. This transformation between sensory input and egocentric representation in the navigational system could have implications for the way in which egocentric and allocentric representations interface in other brain areas.

Sense of self impacts spatial navigation and hexadirectional coding in human entorhinal cortex

Grid cells in entorhinal cortex (EC) encode an individual’s location in space and rely on environmental cues and self-motion cues derived from the individual’s body. Body-derived signals are also primary signals for the sense of self and based on integrated sensorimotor signals (proprioceptive, tactile, visual, motor) that have been shown to enhance self-centered processing. However, it is currently unknown whether such sensorimotor signals that modulate self-centered processing impact grid cells and spatial navigation. Integrating the online manipulation of bodily signals, to modulate self-centered processing, with a spatial navigation task and an fMRI measure to detect grid cell-like representation (GCLR) in humans, we report improved performance in spatial navigation and decreased GCLR in EC. This decrease in entorhinal GCLR was associated with an increase in retrosplenial cortex activity, which was correlated with participants’ navigation performance. These data link self-centered processes during spatial navigation to entorhinal and retrosplenial activity and highlight the role of different bodily factors at play when navigating in VR.

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.

Can we study 3D grid codes non-invasively in the human brain? Methodological considerations and fMRI findings

Recent human functional magnetic resonance imaging (fMRI) and animal electrophysiology studies suggest that grid cells in entorhinal cortex are an efficient neural mechanism for encoding knowledge about the world, not only for spatial location but also for more abstract cognitive information. The world, be it physical or abstract, is often high-dimensional, but grid cells have been mainly studied on a simple two-dimensional (2D) plane. Recent theoretical studies have proposed how grid cells encode three-dimensional (3D) physical space, but it is unknown whether grid codes can be examined non-invasively in humans. Here, we investigated whether it was feasible to test different 3D grid models using fMRI based on the direction-modulated property of grid signals. In doing so, we developed interactive software to help researchers visualize 3D grid fields and predict grid activity in 3D as a function of movement directions. We found that a direction-modulated grid analysis was sensitive to one type of 3D grid model – a face-centred cubic (FCC) lattice model. As a proof of concept, we searched for 3D grid-like signals in human entorhinal cortex using a novel 3D virtual reality paradigm and a new fMRI analysis method. We found that signals in the left entorhinal cortex were explained by the FCC model. This is preliminary evidence for 3D grid codes in the human brain, notwithstanding the inherent methodological limitations of fMRI. We believe that our findings and software serve as a useful initial stepping-stone for studying grid cells in realistic 3D worlds and also, potentially, for interrogating abstract high-dimensional cognitive processes.

Compromised Grid-Cell-like Representations in Old Age as a Key Mechanism to Explain Age-Related Navigational Deficits

SummaryA progressive loss of navigational abilities in old age has been observed in numerous studies, but we have only limited understanding of the neural mechanisms underlying this decline [1]. A central component of the brain’s navigation circuit are grid cells in entorhinal cortex [2], largely thought to support intrinsic self-motion-related computations, such as path integration (i.e., keeping track of one’s position by integrating self-motion cues) [3, 4, 5, 6]. Given that entorhinal cortex is particularly vulnerable to neurodegenerative processes during aging and Alzheimer’s disease [7, 8, 9, 10, 11, 12, 13, 14], deficits in grid cell function could be a key mechanism to explain age-related navigational decline. To test this hypothesis, we conducted two experiments in healthy young and older adults. First, in an fMRI experiment, we found significantly reduced grid-cell-like representations in entorhinal cortex of older adults. Second, in a behavioral path integration experiment, older adults showed deficits in computations of self-position during path integration based on body-based or visual self-motion cues. Most strikingly, we found that these path integration deficits in older adults could be explained by their individual magnitudes of grid-cell-like representations, as reduced grid-cell-like representations were associated with larger path integration errors. Together, these results show that grid-cell-like representations in entorhinal cortex are compromised in healthy aging. Furthermore, the association between grid-cell-like representations and path integration performance in old age supports the notion that grid cells underlie path integration processes. We therefore conclude that impaired grid cell function may play a key role in age-related decline of specific higher-order cognitive functions, such as spatial navigation.

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.

Functional Architecture of the Rat Parasubiculum.

Grid cells in medial entorhinal cortex (MEC) are crucial components of an internal navigation system of the mammalian brain. The parasubiculum is a major input structure of layer 2 of MEC, where most grid cells are found. Here we provide a functional and anatomical characterization of the parasubiculum and show that parasubicular neurons display unique features (i.e., strong theta rhythmicity of firing, prominent head-direction selectivity, and output selectively targeted to layer 2 pyramidal cell patches of MEC). These features could contribute to shaping the temporal and spatial code of downstream grid cells in entorhinal cortex.

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.

Cellular resolution optical access to brain regions in fissures: imaging medial prefrontal cortex and grid cells in entorhinal cortex.

In vivo two-photon microscopy provides the foundation for an array of powerful techniques for optically measuring and perturbing neural circuits. However, challenging tissue properties and geometry have prevented high-resolution optical access to regions situated within deep fissures. These regions include the medial prefrontal and medial entorhinal cortex (mPFC and MEC), which are of broad scientific and clinical interest. Here, we present a method for in vivo, subcellular resolution optical access to the mPFC and MEC using microprisms inserted into the fissures. We chronically imaged the mPFC and MEC in mice running on a spherical treadmill, using two-photon laser-scanning microscopy and genetically encoded calcium indicators to measure network activity. In the MEC, we imaged grid cells, a widely studied cell type essential to memory and spatial information processing. These cells exhibited spatially modulated activity during navigation in a virtual reality environment. This method should be extendable to other brain regions situated within deep fissures, and opens up these regions for study at cellular resolution in behaving animals using a rapidly expanding palette of optical tools for perturbing and measuring network structure and function.

Entorhinal cortex stellate cell synchronization

Theta-frequency oscillations (8-12 Hz) of large groups of synaptically coupled cells are commonly seen throughout the mammalian brain. The theta oscillations of entorhinal cortex grid cells and hippocampal place cells have received special attention recently due to their role they apparently play in the encoding of positional information. In a previous study [1], we found that biologically realistic computational models of two or three synaptically coupled entorhinal cortex stellate cells [2] generally synchronize their oscillations much more quickly from an initially random phase configuration if their intrinsic firing frequencies are near the theta range (with the absolute minima generally at about 15-20 Hz) than at significantly either higher or lower ranges. However, our model looked only at identical neurons with the same intrinsic frequencies and lacking any channel noise. Here we present results from further studies in which realistic channel noise and frequency heterogeneity are included. We found that channel noise could either increase or decrease the time for coupled stellate cells to synchronize by a significant amount, depending on which channel the noise was applied to. We also found that the synchronization time appears to be largely independent of the underlying cell parameters that determine the intrinsic frequencies (the persistent sodium and hyperpolarization-activated cation conductances) and depend only on the frequencies themselves, potentially simplifying the process of parameter sweeps. We found evidence that synchronization is favored for a mean firing frequency of 18 Hz even when the individual intrinsic frequencies of the neurons differ by up to about 0.5 Hz (see Figure ​Figure1).1). Finally, we found evidence of a bifurcation in the stability of synchronization states of two coupled identical stellate cells, with in-phase synchronization suddenly switching to out-of-phase synchronization as the synaptic coupling strength is increased, suggesting a complex picture of entorhinal cortex synchronization patterns similar to previous studies of Hodgkin-Huxley neurons [3]. Figure 1 Long-term synchronization of two coupled entorhinal cortex stellate cell neurons with slightly different intrinsic firing frequencies. The mean of their intrinsic frequencies is plotted against a parameter (“Delta Coll 1”) relating the ...

A quantitative theory of the functions of the hippocampal CA3 network in memory.

A quantitative computational theory of the operation of the hippocampal CA3 system as an autoassociation or attractor network used in episodic memory system is described. In this theory, the CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The theory is extended to associations between time and object or reward to implement temporal order memory, also important in episodic memory. The dentate gyrus (DG) performs pattern separation by competitive learning to produce sparse representations suitable for setting up new representations in CA3 during learning, producing for example neurons with place-like fields from entorhinal cortex grid cells. The dentate granule cells produce by the very small number of mossy fiber (MF) connections to CA3 a randomizing pattern separation effect important during learning but not recall that separates out the patterns represented by CA3 firing to be very different from each other, which is optimal for an unstructured episodic memory system in which each memory must be kept distinct from other memories. The direct perforant path (pp) input to CA3 is quantitatively appropriate to provide the cue for recall in CA3, but not for learning. Tests of the theory including hippocampal subregion analyses and hippocampal NMDA receptor knockouts are described, and support the theory.

Neural models of episodic memory

A quantitative computational theory of the operation of the hippocampus as an episodic memory system is described. The CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The theory is extended to associations between time and object or reward to implement temporal order memory, also important in episodic memory. The dentate gyrus performs pattern separation by competitive learning to produce sparse representations, producing for example neurons with place-like fields from entorhinal cortex grid cells. The dentate granule cells produce by the very small number of mossy fiber connections to CA3 a randomizing pattern separation effect important during learning but not recall that separates out the patterns represented by CA3 firing to be very different from each other, which is optimal for an unstructured episodic memory system in which each memory must be kept distinct from other memories. The direct perforant path input to CA3 is quantitatively appropriate to provide the cue for recall in CA3, but not for learning. The CA1 recodes information from CA3 to set up associatively learned backprojections to neocortex to allow subsequent retrieval of information to neocortex, providing a quantitative account of the large number of hippocampo-neocortical and neocortical-neocortical backprojections. Tests of the theory including hippocampal subregion analyses and hippocampal NMDA receptor knockouts are described, and support the theory.

Increased Size and Stability of CA1 and CA3 Place Fields in HCN1 Knockout Mice

Hippocampal CA1 and CA3 pyramidal neuron place cells encode the spatial location of an animal through localized firing patterns called "place fields." To explore the mechanisms that control place cell firing and their relationship to spatial memory, we studied mice with enhanced spatial memory resulting from forebrain-specific knockout of the HCN1 hyperpolarization-activated cation channel. HCN1 is strongly expressed in CA1 neurons and in entorhinal cortex grid cells, which provide spatial information to the hippocampus. Both CA1 and CA3 place fields were larger but more stable in the knockout mice, with the effect greater in CA1 than CA3. As HCN1 is only weakly expressed in CA3 place cells, their altered activity likely reflects loss of HCN1 in grid cells. The more pronounced changes in CA1 likely reflect the intrinsic contribution of HCN1. The enhanced place field stability may underlie the effect of HCN1 deletion to facilitate spatial learning and memory.

Contribution of Ih to LTP, Place Cells, and Grid Cells

The brain's grid and place cells, which contribute to spatial representations of the external environment, are thought to be modulated by the hyperpolarization-activated cation current (I(h)). Giocomo et al. and Hussaini et al. now provide new insights into these cells' unique activity patterns by studying transgenic mice lacking I(h).

Using Strategic Movement to Calibrate a Neural Compass: A Spiking Network for Tracking Head Direction in Rats and Robots

The head direction (HD) system in mammals contains neurons that fire to represent the direction the animal is facing in its environment. The ability of these cells to reliably track head direction even after the removal of external sensory cues implies that the HD system is calibrated to function effectively using just internal (proprioceptive and vestibular) inputs. Rat pups and other infant mammals display stereotypical warm-up movements prior to locomotion in novel environments, and similar warm-up movements are seen in adult mammals with certain brain lesion-induced motor impairments. In this study we propose that synaptic learning mechanisms, in conjunction with appropriate movement strategies based on warm-up movements, can calibrate the HD system so that it functions effectively even in darkness. To examine the link between physical embodiment and neural control, and to determine that the system is robust to real-world phenomena, we implemented the synaptic mechanisms in a spiking neural network and tested it on a mobile robot platform. Results show that the combination of the synaptic learning mechanisms and warm-up movements are able to reliably calibrate the HD system so that it accurately tracks real-world head direction, and that calibration breaks down in systematic ways if certain movements are omitted. This work confirms that targeted, embodied behaviour can be used to calibrate neural systems, demonstrates that ‘grounding’ of modelled biological processes in the real world can reveal underlying functional principles (supporting the importance of robotics to biology), and proposes a functional role for stereotypical behaviours seen in infant mammals and those animals with certain motor deficits. We conjecture that these calibration principles may extend to the calibration of other neural systems involved in motion tracking and the representation of space, such as grid cells in entorhinal cortex.

Evidence for grid cells in a human memory network

Grid cells in the entorhinal cortex of freely moving rats provide a strikingly periodic representation of self-location which is indicative of very specific computational mechanisms. However, the existence of grid cells in humans and their distribution throughout the brain are unknown. Here we show that the preferred firing directions of directionally modulated grid cells in rat entorhinal cortex are aligned with the grids, and that the spatial organization of grid-cell firing is more strongly apparent at faster than slower running speeds. Because the grids are also aligned with each other, we predicted a macroscopic signal visible to functional magnetic resonance imaging (fMRI) in humans. We then looked for this signal as participants explored a virtual reality environment, mimicking the rats' foraging task: fMRI activation and adaptation showing a speed-modulated six-fold rotational symmetry in running direction. The signal was found in a network of entorhinal/subicular, posterior and medial parietal, lateral temporal and medial prefrontal areas. The effect was strongest in right entorhinal cortex, and the coherence of the directional signal across entorhinal cortex correlated with spatial memory performance. Our study illustrates the potential power of combining single-unit electrophysiology with fMRI in systems neuroscience. Our results provide evidence for grid-cell-like representations in humans, and implicate a specific type of neural representation in a network of regions which supports spatial cognition and also autobiographical memory.

Entorhinal cortex grid cells can map to hippocampal place cells by competitive learning

‘Grid cells’ in the dorsocaudal medial entorhinal cortex (dMEC) are activated when a rat is located at any of the vertices of a grid of equilateral triangles covering the environment. dMEC grid cells have different frequencies and phase offsets. However, cells in the dentate gyrus (DG) and hippocampal area CA3 of the rodent typically display place fields, where individual cells are active over only a single portion of the space. In a model of the hippocampus, we have shown that the connectivity from the entorhinal cortex to the dentate granule cells could allow the dentate granule cells to operate as a competitive network to recode their inputs to produce sparse orthogonal representations, and this includes spatial pattern separation. In this paper we show that the same computational hypothesis can account for the mapping of EC grid cells to dentate place cells. We show that the learning in the competitive network is an important part of the way in which the mapping can be achieved. We further show that incorporation of a short term memory trace into the associative learning can help to produce the relatively broad place fields found in the hippocampus.