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The medial entorhinal cortex is part of a neural system for mapping the position of an individual within a physical environment 1 . Grid cells, a key component of this system, fire in a characteristic hexagonal pattern of locations 2 , and are organized in modules 3 that collectively form a population code for the animal’s allocentric position 1 . The invariance of the correlation structure of this population code across environments 4 , 5 and behavioural states 6 , 7 , independent of specific sensory inputs, has pointed to intrinsic, recurrently connected continuous attractor networks (CANs) as a possible substrate of the grid pattern 1 , 8 – 11 . However, whether grid cell networks show continuous attractor dynamics, and how they interface with inputs from the environment, has remained unclear owing to the small samples of cells obtained so far. Here, using simultaneous recordings from many hundreds of grid cells and subsequent topological data analysis, we show that the joint activity of grid cells from an individual module resides on a toroidal manifold, as expected in a two-dimensional CAN. Positions on the torus correspond to positions of the moving animal in the environment. Individual cells are preferentially active at singular positions on the torus. Their positions are maintained between environments and from wakefulness to sleep, as predicted by CAN models for grid cells but not by alternative feedforward models 12 . This demonstration of network dynamics on a toroidal manifold provides a population-level visualization of CAN dynamics in grid cells. Simultaneous recordings from hundreds of grid cells in rats, combined with topological data analysis, show that network activity in grid cells resides on a toroidal manifold that is invariant across environments and brain states.

The encoding of time and its binding to events are crucial for episodic memory, but how these processes are carried out in hippocampal–entorhinal circuits is unclear. Here we show in freely foraging rats that temporal information is robustly encoded across time scales from seconds to hours within the overall population state of the lateral entorhinal cortex. Similarly pronounced encoding of time was not present in the medial entorhinal cortex or in hippocampal areas CA3–CA1. When animals’ experiences were constrained by behavioural tasks to become similar across repeated trials, the encoding of temporal flow across trials was reduced, whereas the encoding of time relative to the start of trials was improved. The findings suggest that populations of lateral entorhinal cortex neurons represent time inherently through the encoding of experience. This representation of episodic time may be integrated with spatial inputs from the medial entorhinal cortex in the hippocampus, allowing the hippocampus to store a unified representation of what, where and when. Temporal information that is useful for episodic memory is encoded across a wide range of timescales in the lateral entorhinal cortex, arising inherently from its representation of ongoing experience.

The hippocampus receives its major cortical input from the medial entorhinal cortex (MEC) and the lateral entorhinal cortex (LEC). It is commonly believed that the MEC provides spatial input to the hippocampus, whereas the LEC provides non-spatial input. We review new data which suggest that this simple dichotomy between ‘where’ versus ‘what’ needs revision. We propose a refinement of this model, which is more complex than the simple spatial–non-spatial dichotomy. MEC is proposed to be involved in path integration computations based on a global frame of reference, primarily using internally generated, self-motion cues and external input about environmental boundaries and scenes; it provides the hippocampus with a coordinate system that underlies the spatial context of an experience. LEC is proposed to process information about individual items and locations based on a local frame of reference, primarily using external sensory input; it provides the hippocampus with information about the content of an experience.

The hippocampus and the medial entorhinal cortex are part of a brain system that maps self-location during navigation in the proximal environment 1 , 2 . In this system, correlations between neural firing and an animal’s position or orientation are so evident that cell types have been given simple descriptive names, such as place cells 3 , grid cells 4 , border cells 5 , 6 and head-direction cells 7 . While the number of identified functional cell types is growing at a steady rate, insights remain limited by an almost-exclusive reliance on recordings from rodents foraging in empty enclosures that are different from the richly populated, geometrically irregular environments of the natural world. In environments that contain discrete objects, animals are known to store information about distance and direction to those objects and to use this vector information to guide navigation 8 – 10 . Theoretical studies have proposed that such vector operations are supported by neurons that use distance and direction from discrete objects 11 , 12 or boundaries 13 , 14 to determine the animal’s location, but—although some cells with vector-coding properties may be present in the hippocampus 15 and subiculum 16 , 17 —it remains to be determined whether and how vectorial operations are implemented in the wider neural representation of space. Here we show that a large fraction of medial entorhinal cortex neurons fire specifically when mice are at given distances and directions from spatially confined objects. These ‘object-vector cells’ are tuned equally to a spectrum of discrete objects, irrespective of their location in the test arena, as well as to a broad range of dimensions and shapes, from point-like objects to extended surfaces. Our findings point to vector coding as a predominant form of position coding in the medial entorhinal cortex. Cells in the mouse medial entorhinal cortex that fire when mice are at a specific distance and direction from a stationary object suggest that vector coding is important for rodent navigation.

As animals navigate on a two-dimensional surface, neurons in the medial entorhinal cortex (MEC) known as grid cells are activated when the animal passes through multiple locations (firing fields) arranged in a hexagonal lattice that tiles the locomotion surface 1 . However, although our world is three-dimensional, it is unclear how the MEC represents 3D space 2 . Here we recorded from MEC cells in freely flying bats and identified 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 neighbouring fields were separated by a characteristic distance—forming a local order—but lacked any global lattice arrangement of the fields. Thus, whereas 2D grid cells form a global lattice—characterized by both local and global order—3D grid cells exhibited only local order, creating a locally ordered metric for space. We modelled grid cells as emerging from pairwise interactions between fields, which yielded a hexagonal lattice in 2D and local order in 3D, thereby 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. Recordings from the brains of freely flying bats show that grid cells that represent 3D space have multiple firing fields and are organized with local rather than global order.

Mounting evidence shows that dopamine in the striatum is critically involved in reward-based reinforcement learning 1 , 2 . However, it remains unclear how dopamine reward signals influence the entorhinal–hippocampal circuit, another brain network that is crucial for learning and memory 3 – 5 . Here, using cell-type-specific electrophysiological recording 6 , we show that dopamine signals from the ventral tegmental area and substantia nigra control the encoding of cue–reward association rules in layer 2a fan cells of the lateral entorhinal cortex (LEC). When mice learned novel olfactory cue–reward associations using a pre-learned association rule, spike representations of LEC fan cells grouped newly learned rewarded cues with a pre-learned rewarded cue, but separated them from a pre-learned unrewarded cue. Optogenetic inhibition of fan cells impaired the learning of new associations while sparing the retrieval of pre-learned memory. Using fibre photometry, we found that dopamine sends novelty-induced reward expectation signals to the LEC. Inhibition of LEC dopamine signals disrupted the associative encoding of fan cells and impaired learning performance. These results suggest that LEC fan cells represent a cognitive map of abstract task rules, and that LEC dopamine facilitates the incorporation of new memories into this map. Cell-type-specific electrophysiological recording, fibre photometry and optogenetic manipulations in mice show that dopamine signals from the ventral tegmental area to the lateral entorhinal cortex have a key role in cue–reward associative learning.

We quantified genome-wide patterns of lysine H3K27 acetylation (H3K27ac) in entorhinal cortex samples from Alzheimer’s disease (AD) cases and matched controls using chromatin immunoprecipitation and highly parallel sequencing. We observed widespread acetylomic variation associated with AD neuropathology, identifying 4,162 differential peaks (false discovery rate < 0.05) between AD cases and controls. Differentially acetylated peaks were enriched in disease-related biological pathways and included regions annotated to genes involved in the progression of amyloid-β and tau pathology (for example, APP, PSEN1, PSEN2, and MAPT), as well as regions containing variants associated with sporadic late-onset AD. Partitioned heritability analysis highlighted a highly significant enrichment of AD risk variants in entorhinal cortex H3K27ac peak regions. AD-associated variable H3K27ac was associated with transcriptional variation at proximal genes including CR1, GPR22, KMO, PIM3, PSEN1, and RGCC. In addition to identifying molecular pathways associated with AD neuropathology, we present a framework for genome-wide studies of histone modifications in complex disease.

Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go 1 , 2 . Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcement learning 3 – 5 failing to rival the proficiency of mammalian spatial behaviour, which is underpinned by grid cells in the entorhinal cortex 6 . Grid cells are thought to provide a multi-scale periodic representation that functions as a metric for coding space 7 , 8 and is critical for integrating self-motion (path integration) 6 , 7 , 9 and planning direct trajectories to goals (vector-based navigation) 7 , 10 , 11 . Here we set out to leverage the computational functions of grid cells to develop a deep reinforcement learning agent with mammal-like navigational abilities. We first trained a recurrent network to perform path integration, leading to the emergence of representations resembling grid cells, as well as other entorhinal cell types 12 . We then showed that this representation provided an effective basis for an agent to locate goals in challenging, unfamiliar, and changeable environments—optimizing the primary objective of navigation through deep reinforcement learning. The performance of agents endowed with grid-like representations surpassed that of an expert human and comparison agents, with the metric quantities necessary for vector-based navigation derived from grid-like units within the network. Furthermore, grid-like representations enabled agents to conduct shortcut behaviours reminiscent of those performed by mammals. Our findings show that emergent grid-like representations furnish agents with a Euclidean spatial metric and associated vector operations, providing a foundation for proficient navigation. As such, our results support neuroscientific theories that see grid cells as critical for vector-based navigation 7 , 10 , 11 , demonstrating that the latter can be combined with path-based strategies to support navigation in challenging environments. Grid-like representations emerge spontaneously within a neural network trained to self-localize, enabling the agent to take shortcuts to destinations using vector-based navigation.

The medial (MEC) and lateral entorhinal cortex (LEC), widely studied in rodents, are well defined and characterized. In humans, however, the exact locations of their homologues remain uncertain. Previous functional magnetic resonance imaging (fMRI) studies have subdivided the human EC into posteromedial (pmEC) and anterolateral (alEC) parts, but uncertainty remains about the choice of imaging modality and seed regions, in particular in light of a substantial revision of the classical model of EC connectivity based on novel insights from rodent anatomy. Here, we used structural, not functional imaging, namely diffusion tensor imaging (DTI) and probabilistic tractography to segment the human EC based on differential connectivity to other brain regions known to project selectively to MEC or LEC. We defined MEC as more strongly connected with presubiculum and retrosplenial cortex (RSC), and LEC as more strongly connected with distal CA1 and proximal subiculum (dCA1pSub) and lateral orbitofrontal cortex (OFC). Although our DTI segmentation had a larger medial-lateral component than in the previous fMRI studies, our results show that the human MEC and LEC homologues have a border oriented both towards the posterior-anterior and medial-lateral axes, supporting the differentiation between pmEC and alEC.

Sex and apolipoprotein E (APOE) genotype have been shown to influence the risk and progression of Alzheimer’s disease (AD). However, the impact of these factors on the functional connectivity of the entorhinal cortex (ERC) in clinically unpaired older adults (CUOA) with amyloid-β (Aβ +) pathology remains unclear. A total of 1022 cognitively normal older adults with Aβ + (603 females and 586 APOE ε4 +) from the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s (A4) study were included in this study. The 2 × 2 (gender, 2 APOE genotypes) analysis of covariance was performed to compare the demographic information, cognitive performance, and volumetric MRI data among these groups. Voxel-wise comparisons of bilateral ERC functional connectivity (FC) were conducted, and partial correlation analyses were used to explore the associations between cognitive performance and ERC-FC strength. We found that the APOE genotype influenced ERC functional connectivity mainly in the sensorimotor network (SMN). Males exhibited higher ERC-FC in the salience network (SN), while females displayed higher ERC-FC in the default mode network (DMN), executive control network (ECN), and reward network. The interplay of sex and APOE genotype on ERC-FC was observed in the SMN and cerebellar lobe. The ERC-FC was associated with executive function and memory performance in individuals with CUOA-Aβ + . Our findings provide evidence of sex-specific ERC functional connectivity compensation mechanism in cognitively normal older adults with Aβ + pathology. This study may contribute to a better understanding of the mechanisms underlying the early stages of AD and may help develop personalized interventions in preclinical AD.