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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 environment compared with a square environment-an effect that was particularly pronounced in the narrow part of the trapezoid. Congruent with changes in the spatial frequency of eigenvector grid patterns, distance estimates between remembered positions were persistently biased, revealing distorted memory maps that explained behaviour better than the objective maps. Our findings demonstrate that environmental geometry affects human spatial memory in a similar manner to rodent grid-cell activity and, therefore, strengthen the putative link between grid cells and behaviour along with their cognitive functions beyond navigation.

Place cells, spatially responsive hippocampal cells, provide the neural substrate supporting navigation and spatial memory. Historically most studies of these neurons have used electrophysiological recordings from implanted electrodes but optical methods, measuring intracellular calcium, are becoming increasingly common. Several methods have been proposed as a means to identify place cells based on their calcium activity but there is no common standard and it is unclear how reliable different approaches are. Here we tested four methods that have previously been applied to two-photon hippocampal imaging or electrophysiological data, using both model datasets and real imaging data. These methods use different parameters to identify place cells, including the peak activity in the place field, compared to other locations (the Peak method); the stability of cells' activity over repeated traversals of an environment (Stability method); a combination of these parameters with the size of the place field (Combination method); and the spatial information held by the cells (Information method). The methods performed differently from each other on both model and real data. In real datasets, vastly different numbers of place cells were identified using the four methods, with little overlap between the populations identified as place cells. Therefore, choice of place cell detection method dramatically affects the number and properties of identified cells. Ultimately, we recommend the Peak method be used in future studies to identify place cell populations, as this method is robust to moderate variations in place field within a session, and makes no inherent assumptions about the spatial information in place fields, unless there is an explicit theoretical reason for detecting cells with more narrowly defined properties. Author summary: Place cells are hippocampal cells that have spatially constrained receptive fields, the place field. These cells have been widely studied in the context of navigation, more recently using virtual reality environments in combination with optical methods of recording neuronal activity. However, there is a lack of consensus regarding how to identify place cells in these data. In this study we tested the sensitivity and specificity of four methods of identifying place cells. By comparing these methods and quantifying the populations of place cells they identify, we aimed to increase our understanding of exactly the populations that are currently being studied under the name "place cells". Although the appropriate method may depend on the experimental design, we generally recommend a single method going forward, which will increase consensus within the field about what should be included in a place cell population, and allow us to better compare results between studies. [ABSTRACT FROM AUTHOR]

The hippocampus has long been observed to encode a representation of an animal's position in space. Recent evidence suggests that the nature of this representation is somewhat predictive and can be modeled by learning a successor representation (SR) between distinct positions in an environment. However, this discretization of space is subjective making it difficult to formulate predictions about how some environmental manipulations should impact the hippocampal representation. Here, we present a model of place and grid cell firing as a consequence of learning a SR from a basis set of known neurobiological features—boundary vector cells (BVCs). The model describes place cell firing as the successor features of the SR, with grid cells forming a low‐dimensional representation of these successor features. We show that the place and grid cells generated using the BVC‐SR model provide a good account of biological data for a variety of environmental manipulations, including dimensional stretches, barrier insertions, and the influence of environmental geometry on the hippocampal representation of space. [ABSTRACT FROM AUTHOR]

How do we know where we are? Orientation in space is key to our daily existence as we follow familiar routes, navigate to a previous location, or just try to get home as quickly as possible. As well as being interesting in its own right, spatial cognition is also a useful model system in which to understand the neural bases of cognition and memory formation more generally. Spatial behavior offers potentially straightforward correlates of neuronal activity that can be studied similarly in adults and infants of both human and non-human animals. The neural mechanisms of spatial behavior can be realistically investigated in a well-controlled way with the aid of virtual reality technologies in humans and rodents. Virtual reality can thus help to bridge the gap between electrophysiological studies in rodents and brain imaging studies using functional magnetic resonance imaging in humans. Within this framework, this article aims to translate findings from the single cell level in rodents to understand the neural and systems level mechanisms of spatial cognition in the human brain. [ABSTRACT FROM PUBLISHER]

The hippocampal formation plays key roles in representing an animal's location and in detecting environmental novelty to create or update those representations. However, the mechanisms behind this latter function are unclear. Here, we show that environmental novelty causes the spatial firing patterns of grid cells to expand in scale and reduce in regularity, reverting to their familiar scale as the environment becomes familiar. Simultaneously recorded place cell firing fields remapped and showed a smaller, temporary expansion. Grid expansion provides a potential mechanism for novelty signaling and may enhance the formation of new hippocampal representations, whereas the subsequent slow reduction in scale provides a potential familiarity signal. [ABSTRACT FROM AUTHOR]

Concepts describe how instances of the same kind are related, enabling the categorization and interpretation of new information. 1,2 How concepts are represented is a longstanding question. Category boundaries have been considered defining features of concept representations, which can guide categorical inference, 3 with fMRI evidence showing category-boundary signals in the hippocampus. 4,5 The underlying neural mechanism remains unclear. The hippocampal-entorhinal system, known for its spatially tuned neurons that form cognitive maps of space, 6,7 may support conceptual knowledge formation, with place cells encoding locations in conceptual space. 4,8,9,10,11 Physical boundaries anchor spatial representations and boundary shifts affect place and grid fields, 12,13,14,15,16 as well as human spatial memory, 17,18,19 along manipulated dimensions. These place cell responses are likely driven by boundary vector cells, which respond to boundaries at specific allocentric distances and directions, 20,21,22,23 the neural correlates of which have been identified in the subiculum and entorhinal cortex 20,24,25. We hypothesize similar patterns of memory adaptations in response to shifting category boundaries. Our findings show that after category boundary shifts, participants' memory for category exemplars distorts along the changed dimension, mirroring place field deformations. We demonstrate that the boundary vector cell model of place cell firing best accounts for these distortions compared with alternative geometric explanations. Our study highlights a role of category boundaries in human cognition and establishes a new complementary link between hippocampal coding properties with respect to boundaries and human concept representation, bridging spatial and conceptual domains. • We tested the influence of category boundary changes on human memory • Memory of specific exemplars was influenced by the geometry of the conceptual space • Category boundaries exert similar effects on memory as spatial boundaries • A BVC population model of place cell activity predicted the observed distortions Theves et al. test whether category boundaries exert similar effects on human memory as environmental boundaries. They observe memory distortions that mimic the effects of environmental boundary changes on hippocampal place fields and spatial memory and explain these with a boundary vector cell model of place cell firing. [ABSTRACT FROM AUTHOR]

Summary Memories are thought to be retrieved by attractor dynamics if a given input is sufficiently similar to a stored attractor state [ 1–5 ]. The hippocampus, a region crucial for spatial navigation [ 6–12 ] and episodic memory [ 13–18 ], has been associated with attractor-based computations [ 5, 9 ], receiving support from the way rodent place cells “remap” nonlinearly between spatial representations [ 19–22 ]. In humans, nonlinear response patterns have been reported in perceptual categorization tasks [ 23–25 ]; however, it remains elusive whether human memory retrieval is driven by attractor dynamics and what neural mechanisms might underpin them. To test this, we used a virtual reality [ 7, 11, 26–28 ] task where participants learned object-location associations within two distinct virtual reality environments. Participants were subsequently exposed to four novel intermediate environments, generated by linearly morphing the background landscapes of the familiar environments, while tracking fMRI activity. We show that linear changes in environmental context cause linear changes in activity patterns in sensory cortex but cause dynamic, nonlinear changes in both hippocampal activity pattern and remembered locations. Furthermore, the sigmoidal response in the hippocampus scaled with the strength of the sigmoidal pattern in spatial memory. These results indicate that mnemonic decisions in an ambiguous novel context relate to putative attractor dynamics in the hippocampus, which support the dynamic remapping of memories. [ABSTRACT FROM AUTHOR]

The hippocampus occupies a central role in mammalian navigation and memory. Yet an understanding of the rules that govern the statistics and granularity of the spatial code, as well as its interactions with perceptual stimuli, is lacking. We analyzed CA1 place cell activity recorded while rats foraged in different large-scale environments. We found that place cell activity was subject to an unexpected but precise homeostasis—the distribution of activity in the population as a whole being constant at all locations within and between environments. Using a virtual reconstruction of the largest environment, we showed that the rate of transition through this statistically stable population matches the rate of change in the animals' visual scene. Thus, place fields near boundaries were small but numerous, while in the environment's interior, they were larger but more dispersed. These results indicate that hippocampal spatial activity is governed by a small number of simple laws and, in particular, suggest the presence of an information-theoretic bound imposed by perception on the fidelity of the spatial memory system. [Display omitted] • Neural activity in rodent CA1 place cell populations is homeostatically balanced • Hippocampal place field size and frequency are governed by proximity to boundaries • Transition rate through place cell population matches rate of change in visual scene New work by Tanni, de Cothi, and Barry finds that place cell activity in the mammalian hippocampus is homeostatically controlled—the distribution of firing rates in the population is stable. During motion, the rate of transition through these cells matches the rate of change in the visual scene, yielding smaller, more numerous fields near walls. [ABSTRACT FROM AUTHOR]

The hippocampal-entorhinal system is important for spatial and relational memory tasks. We formally link these domains, provide a mechanistic understanding of the hippocampal role in generalization, and offer unifying principles underlying many entorhinal and hippocampal cell types. We propose medial entorhinal cells form a basis describing structural knowledge, and hippocampal cells link this basis with sensory representations. Adopting these principles, we introduce the Tolman-Eichenbaum machine (TEM). After learning, TEM entorhinal cells display diverse properties resembling apparently bespoke spatial responses, such as grid, band, border, and object-vector cells. TEM hippocampal cells include place and landmark cells that remap between environments. Crucially, TEM also aligns with empirically recorded representations in complex non-spatial tasks. TEM also generates predictions that hippocampal remapping is not random as previously believed; rather, structural knowledge is preserved across environments. We confirm this structural transfer over remapping in simultaneously recorded place and grid cells. • Common principles for space and relational memory in the hippocampal formation • Explains hippocampal generalization in both spatial and non-spatial problems • Accounts for many reported hippocampal and entorhinal cell types from such tasks • Predicts how hippocampus remaps in both spatial and non-spatial tasks The Tolman-Eichenbaum Machine, named in honor of Edward Chace Tolman and Howard Eichenbaum for their contributions to cognitive theory, provides a unifying framework for the hippocampal role in spatial and nonspatial generalization and unifying principles underlying many entorhinal and hippocampal cell types. [ABSTRACT FROM AUTHOR]