grid

Unpacking the role of the medial septum in spatial coding in the medial entorhinal cortex

Altered grid-like coding in early blind people and the role of vision in conceptual navigation

A specialized role for entorhinal attractor dynamics in combining path integration and landmarks during navigation

During navigation, animals estimate their position using path integration and landmarks. In a series of two studies, we used virtual reality and electrophysiology to dissect how these inputs combine to generate the brain’s spatial representations. In the first study (Campbell et al., 2018), we focused on the medial entorhinal cortex (MEC) and its set of navigationally-relevant cell types, including grid cells, border cells, and speed cells. We discovered that attractor dynamics could explain an array of initially puzzling MEC responses to virtual reality manipulations. This theoretical framework successfully predicted both MEC grid cell responses to additional virtual reality manipulations, as well as mouse behavior in a virtual path integration task. In the second study (Campbell*, Attinger* et al., 2021), we asked whether these principles generalize to other navigationally-relevant brain regions. We used Neuropixels probes to record thousands of neurons from MEC, primary visual cortex (V1), and retrosplenial cortex (RSC). In contrast to the prevailing view that “everything is everywhere all at once,” we identified a unique population of MEC neurons, overlapping with grid cells, that became active with striking spatial periodicity while head-fixed mice ran on a treadmill in darkness. These neurons exhibited unique cue-integration properties compared to other MEC, V1, or RSC neurons: they remapped more readily in response to conflicts between path integration and landmarks; they coded position prospectively as opposed to retrospectively; they upweighted path integration relative to landmarks in conditions of low visual contrast; and as a population, they exhibited a lower-dimensional activity structure. Based on these results, our current view is that MEC attractor dynamics play a privileged role in resolving conflicts between path integration and landmarks during navigation. Future work should include carefully designed causal manipulations to rigorously test this idea, and expand the theoretical framework to incorporate notions of uncertainty and optimality.

No Free Lunch from Deep Learning in Neuroscience: A Case Study through Models of the Entorhinal-Hippocampal Circuit

Research in Neuroscience, as in many scientific disciplines, is undergoing a renaissance based on deep learning. Unique to Neuroscience, deep learning models can be used not only as a tool but interpreted as models of the brain. The central claims of recent deep learning-based models of brain circuits are that they shed light on fundamental functions being optimized or make novel predictions about neural phenomena. We show, through the case-study of grid cells in the entorhinal-hippocampal circuit, that one may get neither. We rigorously examine the claims of deep learning models of grid cells using large-scale hyperparameter sweeps and theory-driven experimentation, and demonstrate that the results of such models are more strongly driven by particular, non-fundamental, and post-hoc implementation choices than fundamental truths about neural circuits or the loss function(s) they might optimize. We discuss why these models cannot be expected to produce accurate models of the brain without the addition of substantial amounts of inductive bias, an informal No Free Lunch result for Neuroscience.

A multi-level account of hippocampal function in concept learning from behavior to neurons

A complete neuroscience requires multi-level theories that address phenomena ranging from higher-level cognitive behaviors to activities within a cell. Unfortunately, we don't have cognitive models of behavior whose components can be decomposed into the neural dynamics that give rise to behavior, leaving an explanatory gap. Here, we decompose SUSTAIN, a clustering model of concept learning, into neuron-like units (SUSTAIN-d; decomposed). Instead of abstract constructs (clusters), SUSTAIN-d has a pool of neuron-like units. With millions of units, a key challenge is how to bridge from abstract constructs such as clusters to neurons, whilst retaining high-level behavior. How does the brain coordinate neural activity during learning? Inspired by algorithms that capture flocking behavior in birds, we introduce a neural flocking learning rule to coordinate units that collectively form higher-level mental constructs ("virtual clusters"), neural representations (concept, place and grid cell-like assemblies), and parallels recurrent hippocampal activity. The decomposed model shows how brain-scale neural populations coordinate to form assemblies encoding concept and spatial representations, and why many neurons are required for robust performance. Our account provides a multi-level explanation for how cognition and symbol-like representations are supported by coordinated neural assemblies formed through learning.

Efficient Random Codes in a Shallow Neural Network

Efficient coding has served as a guiding principle in understanding the neural code. To date, however, it has been explored mainly in the context of peripheral sensory cells with simple tuning curves. By contrast, ‘deeper’ neurons such as grid cells come with more complex tuning properties which imply a different, yet highly efficient, strategy for representing information. I will show that a highly efficient code is not specific to a population of neurons with finely tuned response properties: it emerges robustly in a shallow network with random synapses. Here, the geometry of population responses implies that optimality obtains from a tradeoff between two qualitatively different types of error: ‘local’ errors (common to classical neural population codes) and ‘global’ (or ‘catastrophic’) errors. This tradeoff leads to efficient compression of information from a high-dimensional representation to a low-dimensional one. After describing the theoretical framework, I will use it to re-interpret recordings of motor cortex in behaving monkey. Our framework addresses the encoding of (sensory) information; if time allows, I will comment on ongoing work that focuses on decoding from the perspective of efficient coding.

The 15th David Smith Lecture in Anatomical Neuropharmacology: Professor Tim Bliss, "Memories of long term potentiation

The David Smith Lectures in Anatomical Neuropharmacology, Part of the 'Pharmacology, Anatomical Neuropharmacology and Drug Discovery Seminars Series', Department of Pharmacology, University of Oxford. The 15th David Smith Award Lecture in Anatomical Neuropharmacology will be delivered by Professor Tim Bliss, Visiting Professor at UCL and the Frontier Institutes of Science and Technology, Xi’an Jiaotong University, China, and is hosted by Professor Nigel Emptage. This award lecture was set up to celebrate the vision of Professor A David Smith, namely, that explanations of the action of drugs on the brain requires the definition of neuronal circuits, the location and interactions of molecules. Tim Bliss gained his PhD at McGill University in Canada. He joined the MRC National Institute for Medical Research in Mill Hill, London in 1967, where he remained throughout his career. His work with Terje Lømo in the late 1960’s established the phenomenon of long-term potentiation (LTP) as the dominant synaptic model of how the mammalian brain stores memories. He was elected as a Fellow of the Royal Society in 1994 and is a founding fellow of the Academy of Medical Sciences. He shared the Bristol Myers Squibb award for Neuroscience with Eric Kandel in 1991, the Ipsen Prize for Neural Plasticity with Richard Morris and Yadin Dudai in 2013. In May 2012 he gave the annual Croonian Lecture at the Royal Society on ‘The Mechanics of Memory’. In 2016 Tim, with Graham Collingridge and Richard Morris shared the Brain Prize, one of the world's most coveted science prizes. Abstract: In 1966 there appeared in Acta Physiologica Scandinavica an abstract of a talk given by Terje Lømo, a PhD student in Per Andersen’s laboratory at the University of Oslo. In it Lømo described the long-lasting potentiation of synaptic responses in the dentate gyrus of the anaesthetised rabbit that followed repeated episodes of 10-20Hz stimulation of the perforant path. Thus, heralded and almost entirely unnoticed, one of the most consequential discoveries of 20th century neuroscience was ushered into the world. Two years later I arrived in Oslo as a visiting post-doc from the National Institute for Medical Research in Mill Hill, London. In this talk I recall the events that led us to embark on a systematic reinvestigation of the phenomenon now known as long-term potentiation (LTP) and will then go on to describe the discoveries and controversies that enlivened the early decades of research into synaptic plasticity in the mammalian brain. I will end with an observer’s view of the current state of research in the field, and what we might expect from it in the future.

Spatial uncertainty provides a unifying account of navigation behavior and grid field deformations

To localize ourselves in an environment for spatial navigation, we rely on vision and self-motion inputs, which only provide noisy and partial information. It is unknown how the resulting uncertainty affects navigation behavior and neural representations. Here we show that spatial uncertainty underlies key effects of environmental geometry on navigation behavior and grid field deformations. We develop an ideal observer model, which continually updates probabilistic beliefs about its allocentric location by optimally combining noisy egocentric visual and self-motion inputs via Bayesian filtering. This model directly yields predictions for navigation behavior and also predicts neural responses under population coding of location uncertainty. We simulate this model numerically under manipulations of a major source of uncertainty, environmental geometry, and support our simulations by analytic derivations for its most salient qualitative features. We show that our model correctly predicts a wide range of experimentally observed effects of the environmental geometry and its change on homing response distribution and grid field deformation. Thus, our model provides a unifying, normative account for the dependence of homing behavior and grid fields on environmental geometry, and identifies the unavoidable uncertainty in navigation as a key factor underlying these diverse phenomena.

Neural circuits for novel choices and for choice speed and accuracy changes in macaques

While most experimental tasks aim at isolating simple cognitive processes to study their neural bases, naturalistic behaviour is often complex and multidimensional. I will present two studies revealing previously uncharacterised neural circuits for decision-making in macaques. This was possible thanks to innovative experimental tasks eliciting sophisticated behaviour, bridging the human and non-human primate research traditions. Firstly, I will describe a specialised medial frontal circuit for novel choice in macaques. Traditionally, monkeys receive extensive training before neural data can be acquired, while a hallmark of human cognition is the ability to act in novel situations. I will show how this medial frontal circuit can combine the values of multiple attributes for each available novel item on-the-fly to enable efficient novel choices. This integration process is associated with a hexagonal symmetry pattern in the BOLD response, consistent with a grid-like representation of the space of all available options. We prove the causal role played by this circuit by showing that focussed transcranial ultrasound neuromodulation impairs optimal choice based on attribute integration and forces the subjects to default to a simpler heuristic decision strategy. Secondly, I will present an ongoing project addressing the neural mechanisms driving behaviour shifts during an evidence accumulation task that requires subjects to trade speed for accuracy. While perceptual decision-making in general has been thoroughly studied, both cognitively and neurally, the reasons why speed and/or accuracy are adjusted, and the associated neural mechanisms, have received little attention. We describe two orthogonal dimensions in which behaviour can vary (traditional speed-accuracy trade-off and efficiency) and we uncover independent neural circuits concerned with changes in strategy and fluctuations in the engagement level. The former involves the frontopolar cortex, while the latter is associated with the insula and a network of subcortical structures including the habenula.

Neural Codes for Natural Behaviors in Flying Bats

This talk will focus on the importance of using natural behaviors in neuroscience research – the “Natural Neuroscience” approach. I will illustrate this point by describing studies of neural codes for spatial behaviors and social behaviors, in flying bats – using wireless neurophysiology methods that we developed – and will highlight new neuronal representations that we discovered in animals navigating through 3D spaces, or in very large-scale environments, or engaged in social interactions. In particular, I will discuss: (1) A multi-scale neural code for very large environments, which we discovered in bats flying in a 200-meter long tunnel. This new type of neural code is fundamentally different from spatial codes reported in small environments – and we show theoretically that it is superior for representing very large spaces. (2) Rapid modulation of position × distance coding in the hippocampus during collision-avoidance behavior between two flying bats. This result provides a dramatic illustration of the extreme dynamism of the neural code. (3) Local-but-not-global order in 3D grid cells – a surprising experimental finding, which can be explained by a simple physics-inspired model, which successfully describes both 3D and 2D grids. These results strongly argue against many of the classical, geometrically-based models of grid cells. (4) I will also briefly describe new results on the social representation of other individuals in the hippocampus, in a highly social multi-animal setting. The lecture will propose that neuroscience experiments – in bats, rodents, monkeys or humans – should be conducted under evermore naturalistic conditions.

Distance-tuned neurons drive specialized path integration calculations in medial entorhinal cortex

During navigation, animals estimate their position using path integration and landmarks, engaging many brain areas. Whether these areas follow specialized or universal cue integration principles remains incompletely understood. We combine electrophysiology with virtual reality to quantify cue integration across thousands of neurons in three navigation-relevant areas: primary visual cortex (V1), retrosplenial cortex (RSC), and medial entorhinal cortex (MEC). Compared with V1 and RSC, path integration influences position estimates more in MEC, and conflicts between path integration and landmarks trigger remapping more readily. Whereas MEC codes position prospectively, V1 codes position retrospectively, and RSC is intermediate between the two. Lowered visual contrast increases the influence of path integration on position estimates only in MEC. These properties are most pronounced in a population of MEC neurons, overlapping with grid cells, tuned to distance run in darkness. These results demonstrate the specialized role that path integration plays in MEC compared with other navigation-relevant cortical areas.

Deforming the metric of cognitive maps distorts memory

Environmental boundaries anchor cognitive maps that support memory. However, trapezoidal boundary geometry distorts the regular firing patterns of entorhinal grid cells proposedly providing a metric for cognitive maps. Here, we test the impact of trapezoidal boundary geometry on human spatial memory using immersive virtual reality. Consistent with reduced regularity of grid patterns in rodents and a grid-cell model based on the eigenvectors of the successor representation, human positional memory was degraded in a trapezoid compared to a square environment; an effect particularly pronounced in the trapezoid’s narrow part. Congruent with spatial frequency changes of eigenvector grid patterns, distance estimates between remembered positions were persistently biased; revealing distorted memory maps that explained behavior better than the objective maps. Our findings demonstrate that environmental geometry affects human spatial memory similarly to rodent grid cell activity — thus strengthening the putative link between grid cells and behavior along with their cognitive functions beyond navigation.

Of Grids and Maps

Neuroscientific methods successfully account for a system’s functional properties, but leave out the subjective properties of the accompanying experience. According to IIT, phenomenology can be studied scientifically by unfolding the cause-effect structure specified by a system. To illustrate how, in this talk I compare two systems (a grid and a map) to show that they can be functionally equivalent in performing fixation, but only one can specify a cause-effect structure that accounts for the extendedness of phenomenal space.

Self-organized formation of discrete grid cell modules from smooth gradients

Modular structures in myriad forms — genetic, structural, functional — are ubiquitous in the brain. While modularization may be shaped by genetic instruction or extensive learning, the mechanisms of module emergence are poorly understood. Here, we explore complementary mechanisms in the form of bottom-up dynamics that push systems spontaneously toward modularization. As a paradigmatic example of modularity in the brain, we focus on the grid cell system. Grid cells of the mammalian medial entorhinal cortex (mEC) exhibit periodic lattice-like tuning curves in their encoding of space as animals navigate the world. Nearby grid cells have identical lattice periods, but at larger separations along the long axis of mEC the period jumps in discrete steps so that the full set of periods cluster into 5-7 discrete modules. These modules endow the grid code with many striking properties such as an exponential capacity to represent space and unprecedented robustness to noise. However, the formation of discrete modules is puzzling given that biophysical properties of mEC stellate cells (including inhibitory inputs from PV interneurons, time constants of EPSPs, intrinsic resonance frequency and differences in gene expression) vary smoothly in continuous topographic gradients along the mEC. How does discreteness in grid modules arise from continuous gradients? We propose a novel mechanism involving two simple types of lateral interaction that leads a continuous network to robustly decompose into discrete functional modules. We show analytically that this mechanism is a generic multi-scale linear instability that converts smooth gradients into discrete modules via a topological “peak selection” process. Further, this model generates detailed predictions about the sequence of adjacent period ratios, and explains existing grid cell data better than existing models. Thus, we contribute a robust new principle for bottom-up module formation in biology, and show that it might be leveraged by grid cells in the brain.

Space wrapped onto a grid cell torus

Entorhinal grid cells, so-called because of their hexagonally tiled spatial receptive fields, are organized in modules which, collectively, are believed to form a population code for the animal’s position. Here, we apply topological data analysis to simultaneous recordings of hundreds of grid cells and show that joint activity of grid cells within a module lies on a toroidal manifold. Each position of the animal in its physical environment corresponds to a single location on the torus, and each grid cell is preferentially active within a single “field” on the torus. Toroidal firing positions persist between environments, and between wakefulness and sleep, in agreement with continuous attractor models of grid cells.

Dynamic maps of a dynamic world

Extensive research has revealed that the hippocampus and entorhinal cortex maintain a rich representation of space through the coordinated activity of place cells, grid cells, and other spatial cell types. Frequently described as a ‘cognitive map’ or a ‘hippocampal map’, these maps are thought to support episodic memory through their instantiation and retrieval. Though often a useful and intuitive metaphor, a map typically evokes a static representation of the external world. However, the world itself, and our experience of it, are intrinsically dynamic. In order to make the most of their maps, a navigator must be able to adapt to, incorporate, and overcome these dynamics. Here I describe three projects where we address how hippocampal and entorhinal representations do just that. In the first project, I describe how boundaries dynamically anchor entorhinal grid cells and human spatial memory alike when the shape of a familiar environment is changed. In the second project, I describe how the hippocampus maintains a representation of the recent past even in the absence of disambiguating sensory and explicit task demands, a representation which causally depends on intrinsic hippocampal circuitry. In the third project, I describe how the hippocampus preserves a stable representation of context despite ongoing representational changes across a timescale of weeks. Together, these projects highlight the dynamic and adaptive nature of our hippocampal and entorhinal representations, and set the stage for future work building on these techniques and paradigms.

Using extra-hippocampal cognitive maps for goal-directed spatial navigation

Goal-directed navigation requires precise estimates of spatial relationships between current position and future goal, as well as planning of an associated route or action. While neurons in the hippocampal formation can represent the animal’s position and nearby trajectories, their role in determining the animal’s destination or action has been questioned. We thus hypothesize that brain regions outside the hippocampal formation may play complementary roles in navigation, particularly for guiding goal-directed behaviours based on the brain’s internal cognitive map. In this seminar, I will first describe a subpopulation of neurons in the retrosplenial cortex (RSC) that increase their firing when the animal approaches environmental boundaries, such as walls or edges. This boundary coding is independent of direct visual or tactile sensation but instead depends on inputs from the medial entorhinal cortex (MEC) that contains spatial tuning cells, such as grid cells or border cells. However, unlike MEC border cells, we found that RSC border cells encode environmental boundaries in a self-centred egocentric coordinate frame, which may allow an animal for efficient avoidance from approaching walls or edges during navigation. I will then discuss whether the brain can possess a precise estimate of remote target location during active environmental exploration. Such a spatial code has not been described in the hippocampal formation. However, we found that neurons in the rat orbitofrontal cortex (OFC) form spatial representations that persistently point to the animal’s subsequent goal destination throughout navigation. This destination coding emerges before navigation onset without direct sensory access to a distal goal, and are maintained via destination-specific neural ensemble dynamics. These findings together suggest key roles for extra-hippocampal regions in spatial navigation, enabling animals to choose appropriate actions toward a desired destination by avoiding possible dangers.

Locally-ordered representation of 3D space in the entorhinal cortex

When animals navigate on a two-dimensional (2D) surface, many neurons in the medial entorhinal cortex (MEC) are activated as the animal passes through multiple locations (‘firing fields’) arranged in a hexagonal lattice that tiles the locomotion-surface; these neurons are known as grid cells. However, although our world is three-dimensional (3D), the 3D volumetric representation in MEC remains unknown. Here we recorded MEC cells in freely-flying bats and found several classes of spatial neurons, including 3D border cells, 3D head-direction cells, and neurons with multiple 3D firing-fields. Many of these multifield neurons were 3D grid cells, whose neighboring fields were separated by a characteristic distance – forming a local order – but these cells lacked any global lattice arrangement of their fields. Thus, while 2D grid cells form a global lattice – characterized by both local and global order – 3D grid cells exhibited only local order, thus creating a locally ordered metric for space. We modeled grid cells as emerging from pairwise interactions between fields, which yielded a hexagonal lattice in 2D and local order in 3D – thus describing both 2D and 3D grid cells using one unifying model. Together, these data and model illuminate the fundamental differences and similarities between neural codes for 3D and 2D space in the mammalian brain.

Slow global population dynamics propagating through the medial entorhinal cortex

The medial entorhinal cortex (MEC) supports the brain’s representation of space with distinct cell types whose firing is tuned to features of the environment (grid, border, and object-vector cells) or navigation (head-direction and speed cells). While the firing properties of these functionally-distinct cell types are well characterized, how they interact with one another remains unknown. To determine how activity self-organizes in the MEC network, we tested mice in a spontaneous locomotion task under sensory-deprived conditions. Using 2-photon calcium imaging, we monitored the activity of large populations of MEC neurons in head-fixed mice running on a wheel in darkness, in the absence of external sensory feedback tuned to navigation. We unveiled the presence of motifs that involve the sequential activation of cells in layer II of MEC (MEC-L2). We call these motifs waves. Waves lasted tens of seconds to minutes, were robust, swept through the entire network of active cells and did not exhibit any anatomical organization. Furthermore, waves did not map the position of the mouse on the wheel and were not restricted to running epochs. The majority of MEC-L2 neurons participate in this global sequential dynamics, that ties all functional cell types together. We found the waves in the most lateral region of MEC, but not in adjacent areas such as PaS or in a sensory cortex such as V1.

Linking neural representations of space by multiple attractor networks in the entorhinal cortex and the hippocampus

In the past decade evidence has accumulated in favor of the hypothesis that multiple sub-networks in the medial entorhinal cortex (MEC) are characterized by low-dimensional, continuous attractor dynamics. Much has been learned about the joint activity of grid cells within a module (a module consists of grid cells that share a common grid spacing), but little is known about the interactions between them. Under typical conditions of spatial exploration in which sensory cues are abundant, all grid-cells in the MEC represent the animal’s position in space and their joint activity lies on a two-dimensional manifold. However, if the grid cells in a single module mechanistically constitute independent attractor networks, then under conditions in which salient sensory cues are absent, errors could accumulate in the different modules in an uncoordinated manner. Such uncoordinated errors would give rise to catastrophic readout errors when attempting to decode position from the joint grid-cell activity. I will discuss recent theoretical works from our group, in which we explored different mechanisms that could impose coordination in the different modules. One of these mechanisms involves coordination with the hippocampus and must be set up such that it operates across multiple spatial maps that represent different environments. The other mechanism is internal to the entorhinal cortex and independent of the hippocampus.

Perturbing the spatio-temporal organization of the grid cell network

The Role of Hippocampal Replay in Memory Consolidation

The hippocampus lies at the centre of a network of brain regions thought to support spatial and episodic memory. Place cells - the principal cell of the hippocampus, represent information about an animal’s spatial location. Yet, during rest and awake quiescence place cells spontaneously recapitulate past trajectories (‘replay’). Replay has been hypothesised to support systems consolidation – the stabilisation of new memories via maturation of complementary cortical memory traces. Indeed, in recent work we found place and grid cells, from the deep medial entorhinal cortex (dMEC, the principal cortical output region of the hippocampus), replayed coherently during rest periods. Importantly, dMEC grid cells lagged place cells by ~11ms; suggesting the coordination may reflect consolidation. Moreover, preliminary data shows that the dMEC-hippocampal coordination strengthens as an animal becomes familiar with a task and that it may be led by directionally modulated cells. Finally, on-going work, in my recently established lab, shows replay may represent the mechanism underlying the maturation of episodic/spatial memory in pre-weanling pups. Together, these results indicate replay may play a central role in ensuring the permanency of memories.

Revealing the neural basis of human memory with direct recordings of place and grid cells and traveling waves

The ability to remember spatial environments is critical for everyday life. In this talk, I will discuss my lab’s findings on how the human brain supports spatial memory and navigation based on our experiments with direct brain recordings from neurosurgical patients performing virtual-reality spatial memory tasks. I will show that humans have a network of neurons that represent where we are located and trying to go. This network includes some cell types that are similar to those seen in animals, such as place and grid cells, as well as others that have not been seen before in animals, such as anchor and spatial-target cells. I also will explore the role of network oscillations in human memory, where humans again show several distinctive patterns compared to animals. Whereas rodents generally show a hippocampal oscillation at ~8Hz, humans have two separate hippocampal oscillations, at low and high frequencies, which support memory and navigation, respectively. Finally, I will show that neural oscillations in humans are traveling waves, propagating across the cortex, to coordinate the timing of neuronal activity across regions, which is another property not seen in animals. A theme from this work is that in terms of navigation and memory the human brain has novel characteristics compared with animals, which helps explain our rich behavioural abilities and has implications for treating disease and neurological disorders.

Quantitative modeling of the emergence of macroscopic grid-like representations

Bernstein Conference 2024

Grid cells rapidly integrate novel landmarks

COSYNE 2022

Grid cells rapidly integrate novel landmarks

COSYNE 2022

Long‐term dynamics of the entorhinal grid code

COSYNE 2022

Long‐term dynamics of the entorhinal grid code

COSYNE 2022

Entorhinal grid-like signals reflect temporal context for human timing behavior

COSYNE 2023

State Space Models for Classifying Grid Cell Spatial Sequences

COSYNE 2023

Unveiling the structure of the grid cell code in novel environments

COSYNE 2023

Not so griddy: Internal representations of RNNs path integrating more than one agent

COSYNE 2025

Predictive coding in a biophysically detailed Continuous attractor model of grid cells

COSYNE 2025

Topology-aware, unbiased grid coding for rapid task generalization

COSYNE 2025

Entorhinal grid-like codes and time-locked network dynamics track others navigating through space

Entorhinal grid-like signals reflect temporal context for human timing behavior

Four grid cell modules can code for a precise location at a low error

Functional independence of grid cell modules during hippocampal remapping

Grid cells rescale to match goal distance during path-integration

GRID1 / GluD1 mutations causing intellectual disability with spastic paraplegia impair mGlu1/5 receptor signaling and excitatory synapses

Grid-like codes in the human entorhinal cortex map visual space during memory formation

Long-term dynamics of the entorhinal grid code

Place and grid cell navigation in multiple environments

Spiking neural network simulations reveal the role of place and grid cells in spatial learning

Uniformity of grid phases in large environments

All-optical interrogation of functional connectivity in grid cell networks

FENS Forum 2024

Application of the local activity principle to explain grid cell dynamics

FENS Forum 2024

Grid field anchoring enhances path integration-dependent but not cue-based navigation

FENS Forum 2024

Grid representation for future spatial information in the medial entorhinal cortex

FENS Forum 2024

The GRID1/GLUD1 homozygous variant p.Arg161His linked to intellectual disability and spastic paraplegia impairs excitatory synaptic transmission in the hippocampus

FENS Forum 2024

Local expansion of the grid cell map during a goal-directed navigation task

FENS Forum 2024

Theta modulation of grid cells in the medial entorhinal cortex in a novel environment task

FENS Forum 2024