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The Journal of Physiology logoLink to The Journal of Physiology
. 2016 Jan 5;594(22):6501–6511. doi: 10.1113/JP270624

Environmental boundaries as a mechanism for correcting and anchoring spatial maps

Lisa M Giocomo 1,✉
PMCID: PMC5108900  PMID: 26563618

Abstract

Ubiquitous throughout the animal kingdom, path integration‐based navigation allows an animal to take a circuitous route out from a home base and using only self‐motion cues, calculate a direct vector back. Despite variation in an animal's running speed and direction, medial entorhinal grid cells fire in repeating place‐specific locations, pointing to the medial entorhinal circuit as a potential neural substrate for path integration‐based spatial navigation. Supporting this idea, grid cells appear to provide an environment‐independent metric representation of the animal's location in space and preserve their periodic firing structure even in complete darkness. However, a series of recent experiments indicate that spatially responsive medial entorhinal neurons depend on environmental cues in a more complex manner than previously proposed. While multiple types of landmarks may influence entorhinal spatial codes, environmental boundaries have emerged as salient landmarks that both correct error in entorhinal grid cells and bind internal spatial representations to the geometry of the external spatial world. The influence of boundaries on error correction and grid symmetry points to medial entorhinal border cells, which fire at a high rate only near environmental boundaries, as a potential neural substrate for landmark‐driven control of spatial codes. The influence of border cells on other entorhinal cell populations, such as grid cells, could depend on plasticity, raising the possibility that experience plays a critical role in determining how external cues influence internal spatial representations.

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Abbreviations

MEC

medial entorhinal cortex

Introduction

The ability of mammals to self‐localize, or determine one's location within a spatial environment, is thought to depend, in part, on neural circuits in the medial entorhinal cortex (MEC). As a population, several functionally defined MEC neurons have coding properties that provide many of the components needed to generate an internal map of the external spatial world (Fig. 1). MEC grid cells provide the neural metric of this map, firing in place‐specific locations that repeat periodically to tile the entire spatial environment (Fyhn et al. 2004; Hafting et al. 2005). Head direction cells afford compass orientation relative to local environmental cues, firing at a high rate only when the animal faces a particular direction, irrespective of the animal's position or behaviour at the time (Taube et al. 1990). Recently discovered speed cells serve as a neural speedometer, providing information potentially critical to generating an accurate internal map of space (Kropff et al. 2015). Finally, providing a neural code for environmental landmarks, border cells fire at a high rate only near environmental boundaries (Barry et al. 2006; Savelli et al. 2008; Solstad et al. 2008; Lever et al. 2009) and a subset of hippocampal place cells fire only in specific spatial locations (O'Keefe & Dostrovsky, 1971). Combined, these neurons could support the creation of an internal cognitive map of space, which animals then use to navigate, explore and remember their spatial world.

Figure 1. Spatial neurons in medial entorhinal cortex .

Figure 1

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A, grid cell recorded from a mouse exploring a 1 m × 1 m open field, shown from a bird's eye view. Left, grey lines indicate the mouse's trajectory with spikes (red dots) overlaid. Middle and right, rate map and autocorrelation are colour coded for maximum (red) and minimum (blue) values. The autocorrelation (right) is twice the size of the rate map (middle). B, examples of two border cells. Coded as in A. C, example of a head direction cell. The polar plot (right) indicates firing rate as a function of head direction. The cell fires at the highest rate only when the animal faces one particular direction (in this example, east). Left and middle plots coded as in A.

What types of input support the emergence of these spatially selective neural responses? Computational and experimental work points to a strong dependence of spatial neurons, such as grid and head direction cells, on intrinsic self‐motion cues (Hafting et al. 2005; Terrazas et al. 2005; McNaughton et al. 2006; Burak & Fiete, 2009). The potential use of self‐motion cues to generate spatial signals, such as grid cells, has led to the proposal that parahippocampal neurons provide the neural basis for path integration. Utilizing self‐motion cues such as optic flow, vestibular information and proprioception, path integration allows an animal to track its location within the environment on a moment‐by‐moment basis (Fig. 2 A). A series of recent studies, however, highlight the presence of more complex influences of self‐motion‐based path integration and landmark‐based navigational cues on MEC spatial responses. Here, we consider evidence for how landmarks shape intrinsically generated spatial codes, the functional implications of the interactions between landmark and self‐motion cues in determining MEC spatial responses and the potential mechanisms supporting these interactions.

Figure 2. Navigational strategies and error accumulation .

Figure 2

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A, in path integration an animal uses idiothetic cues to calculate its current location, allowing it to take a direct path back (black dotted line) to its starting point (black circle) rather than re‐tracing its circuitous outbound trajectory (black line). B, path integration is prone to accumulated error. The black mouse indicates the actual location of the mouse at three different time steps and the dotted black line illustrates the accurate vector back to its starting point. The grey mouse indicates the estimated location of the mouse at the same three time steps, assuming some degree of error. Note that the iterative nature of path integration results in error growing with each step, resulting in an incorrect vector back to the mouse's starting point (grey dotted line). C, landmark navigation complements path integration and allows an animal to determine its location in space based on familiar landmarks.

Path integration and the problem of noise

With the discovery of MEC grid cells, researchers began to propose that the MEC grid map could serve as the neural basis for path integration (Hafting et al. 2005; McNaughton et al. 2006), which allows an animal to navigate from a home base and, even in the absence of sensory cues, calculate a direct path back. Consistent with the proposal that grid cells support path integration, initial studies demonstrated an invariant and stable grid structure across multiple environmental contexts, including complete darkness (Hafting et al. 2005). The continuous integration of self‐motion cues, however, results in a cumulative path integration signal, in which any errors in the current estimate of position get added to the next integrative step (Fig. 2 B). In the presence of noise, a ubiquitous feature of the nervous system, this leads to rapidly accumulating error with a magnitude that depends on both distance travelled and time elapsed (Etienne & Jeffery, 2004; Faisal et al. 2008). This means that for grid cells to serve their proposed role in mammalian navigation they either operate in a regime where they path‐integrate perfectly, which is unlikely given the presence of biological noise in self‐motion signals, or they depend on sensory input to calibrate their response properties.

Error correction of path integration and grid signals could depend on the complementary strategy of landmark navigation (Fig. 2 C). Landmark cues can derive from a variety of sensory inputs: visual, auditory, olfactory or tactile. To support accurate navigation, landmark‐ and path integration‐based strategies are likely to continuously interact, with an organism able to flexibly rely on both sources of information, depending on which is more appropriate at the time (Gothard et al. 1996). Landmarks could thus serve to correct accumulated error and help bind internally generated grid or head direction representations to the external spatial world.

Spatial maps and the accumulation of error

If MEC neural circuits do indeed support path integration‐based navigation, grid and head direction cells would be expected to accumulate error over time and distance travelled. Consistent with this prediction, experimental work reports slow drift and accumulating error in grid, head direction and even hippocampal place cells. The observation of error accumulation is most acute in dark recordings, in which visual landmarks are no longer available and the animal must rely more strongly on a path integration‐based navigational strategy. In darkness, head direction cells begin to significantly drift and degrade after a few minutes (Goodridge et al. 1998). This drift and error accumulation in head direction signals impacts the navigational performance of an animal, with a rat's error in its homing vector on a path integration task strongly correlating with the degree of shift in the head direction cell's preferred firing angle (Valerio & Taube, 2012). Hippocampal place cells also show slow drift in the darkness, with spatial information, coherence and firing rates all decreasing (S. Zhang et al. 2014). The degree of drift may depend on the rat's recent experience, however, as place cells remain more stable when the animal has extensive experience in the arena with the lights on, compared with only experiencing the arena in complete darkness (Quirk et al. 1990). This hints at a crucial role for non‐visual sensory information, available even in darkness, as a potential source for correcting error and stabilizing spatial maps.

Grid cells in the dark can retain a coherent firing map for at least 10 min, both in familiar arenas and novel arenas that retain some of the features of the familiar arena (e.g. familiar open arena square versus novel open arena circle) (Hafting et al. 2005). Despite the appearance of a coherent grid map in the darkness, however, more recent research has found that even in lighted conditions grid cells do accumulate significant error when animals take trajectories through the centre of open arenas (Hardcastle et al. 2015). Trajectories in this case were defined as boundary to boundary trips through the arena and for long trajectories (> 60 s), grid cells accumulated significant error over time and distance travelled (∼0.01 cm s−1) since the animal last encountered an environmental boundary (Hardcastle et al. 2015). Grid error accumulation is likely to result from coherent drift in the grid pattern during times the animal is away from environmental boundaries, a phenomenon consistent with predictions by computational models regarding the nature of grid error accumulation (Burak & Fiete, 2009). Combined, this work suggests that environmental boundaries may serve as critical non‐visual landmark cues for generating an accurate map of our spatial environment.

Boundaries as an error correction mechanism

Computational and theoretical work has long proposed that boundaries in the environment could act as landmarks capable of providing an initial localization signal and correcting the error inherent to path integration‐based navigation (McNaughton et al. 1991, 1996; Touretzky & Redish, 1996; Redish & Touretzky, 1997; O'Keefe & Burgess, 2005; Fuhs & Touretzky, 2006; Burgess, 2008; Savelli et al. 2008; Burak & Fiete, 2009; Samu et al. 2009; Cheung et al. 2012). A potential neural substrate for boundary‐driven error correction (Hardcastle et al. 2015) is entorhinal border cells (Savelli et al. 2008; Solstad et al. 2008). Border cells may act as a higher‐order and versatile neural representation of a boundary, as they fire near large impassable walls, short but passable walls and boundaries characterized by a drop rather than a wall (Solstad et al. 2008). Visual cues alone can activate border cell responses (Aronov & Tank, 2014), although this does not rule out the idea that somatosensory interactions with environmental boundaries could also activate border cells, allowing border cells to code for boundaries in the dark. Border cells could thus serve as a robust landmark cue important to the grid cell system, as they are characterized by a highly geometric nature but are active across many spatial locations, typically firing along all boundaries in a given cardinal direction (Solstad et al. 2008; Lever et al. 2009).

Supporting the potential role of border cells in error correction, regular boundary encounters improve the stability of place fields in the dark (S. Zhang et al. 2014). In addition, at post‐natal days where border but not grid cells have developed, place cells in rat pups show stronger stability and coherence near environmental boundaries (Muessig et al. 2015). Place cells in adult rodents also anchor to particular arena walls, such as the north‐west wall, and remain anchored to this wall even when the box transiently shrinks or expands (O'Keefe & Burgess, 1996). Combined, these data raise the important possibility that border cells provide a crucial input for stabilizing the hippocampal place map across dark and light conditions (Hartley et al. 2000; Barry et al. 2006; Lever et al. 2009; Muessig et al. 2015). Boundary‐driven error correction is also observed in the grid cell population (Hardcastle et al. 2015). This error correction is direction dependent with, for example, a west boundary encounter correcting error in the east–west direction but not the north–south direction. The correction of grid error in the perpendicular direction of the boundary encounter provides further evidence that neural signals regarding boundaries, such as border cells, may provide the neural substrate for grid error correction. Taken together, the accumulation of error and boundary‐driven correction of this error provide compelling evidence that spatially responsive parahippocampal neurons and the interactions amongst themselves play a critical role in supporting path integration‐based spatial navigation (Cheung et al. 2012).

However, the possibility certainly also remains that other types of spatially selective neurons, such as hippocampal place cells or lateral entorhinal object cells, provide an additional error correction signal. Several computational models have successfully used place cells as a mechanism to reduce error and drift in the entorhinal grid code (Guanella et al. 2007; Pastoll et al. 2013). Compared with place cells, however, border cells have an advantage in that their firing patterns generalize across different environmental geometries, conditions under which place cells often remap (J. K. Leutgeb et al. 2005; S. Leutgeb et al. 2005; Savelli et al. 2008; Solstad et al. 2008). This stability of border cells across multiple environmental geometries would reduce the need to re‐establish associations between error‐correction inputs and grid cells across different environments, highlighting this cell population as a potentially robust neural signal for error correction.

Boundaries, geometry and symmetry in spatial maps

Theoretical, computational and experimental work all point to input regarding environmental landmarks as a potential mechanism to correct the accumulation of error in path integration‐based neural signals, such as grid cells. More recent evidence, however, hints at a more complex relationship between path integration‐based firing patterns and the external sensory environment, with sensory features also serving to anchor, distort and re‐set the grid pattern (Derdikman et al. 2006; Barry et al. 2007, 2012; Fyhn et al. 2007; Krupic et al. 2015; T. Stensola et al. 2015).

Grid cells vary along three parameters: the orientation of the grid axes, the spatial phase of the grid firing pattern and the spatial frequency of individual grid nodes (Fig. 3). In familiar environments, the grid cell pattern anchors to environment specific landmarks, as evidenced by grid orientation rotating in concert with the rotation of a predominant distal or proximal environmental cue (Hafting et al. 2005; Fyhn et al. 2007; Krupic et al. 2015). Anchoring of the grid map to specific environmental cues may serve a critical role in aligning entorhinal spatial maps across multiple trials, even when the starting or departure point differs across these trials (Hafting et al. 2005). However, grid cells do not appear to simply anchor to a single prominent environment cue, as the geometry of the local environment exerts a strong influence over the orientation of the grid pattern. In square open arenas, the orientation of grid cells across individual animals clusters to share a rotational offset from environmental boundaries of a few degrees (∼7–9 deg) (Krupic et al. 2015; T. Stensola et al. 2015; Sun et al. 2015), while in circular arenas grid orientation is dispersed across multiple angles (Krupic et al. 2015; T. Stensola et al. 2015). The functional implications of geometrically based grid orientation remain untested behaviourally, but theoretical work suggests orientation offset by several degrees in square arenas could reduce the degree of symmetry between the grid pattern and environmental boundaries (T. Stensola et al. 2015). This reduction in symmetry would mean that the grid pattern near, for example, the south boundary would not be redundant with the grid pattern observed near the north boundary of the environment, which could happen if the orientation of the grid paralleled the orientation of a square box.

Figure 3. Parameters of the grid map .

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A, top, rate map of a grid cell coded for maximum (red) and minimum (blue) values. Bottom, approximate locations of grid fields (blue) for the cell shown in the top panel. The three orientation axes of the grid are marked with black lines. B, top, rate map of a different grid cell recorded from the same mouse as shown in A. Bottom, approximate locations of the grid fields (red) for the cell shown in the top panel, with fields from the cell in A overlaid (blue). Note the shift in grid phase between the red and blue cell. C, top, rate map of a different grid cell recorded from the same mouse as shown in A. Bottom, approximate locations of the grid fields (green) for the cell shown in the top panel, with fields from the cell in A overlaid (blue). Note the larger distance between fields for the green compared to the blue cell.

The influence of boundary geometry on grid cell orientation provides further evidence that border cells may serve as a neural substrate for binding the internal grid map to the external spatial world. Environmental boundaries appear not only to set orientation, but also to re‐set the grid pattern. In a linearized hairpin maze (Fig. 4 A), grid cells re‐set at turning points. This effect does not reflect the animal's stereotyped motor movements or the change in the animal's ability to visualize the entire arena, but rather, directly relates to the presence of boundaries (Derdikman et al. 2006). Distortions in the grid pattern also occur in geometric shapes such as trapezoids, in which the grid pattern distorts near the compressed end of the shape but retains its standard 60 deg periodicity near the longer end of the shape (Krupic et al. 2015). The distortion in the trapezoid may relate to the linearization of the grid pattern in the hairpin maze, in which the presence of impassable boundaries shifted the spatial location of the grid nodes to generate a non‐hexagonal structure (Derdikman et al. 2006; Krupic et al. 2013) (Fig. 4 B). In addition, in highly familiar square arenas, grid cells have been recently shown to compress in a single direction, potentially due to shearing forces that could reflect asymmetric influences on the grid map from environmental boundaries. As the environment grows in size, grid cells begin to fragment and appear to find unique anchoring points along multiple environmental axes (T. Stensola et al. 2015) (Fig. 4 C). Combined these studies raise the possibility that, even in familiar environments, the grid pattern is not pre‐set but rather the reflection of intrinsic factors controlling various grid parameters (e.g. grid scale) and environmental boundaries. However, the mechanisms underlying these distortions and perhaps more importantly, the functional implications of such distortions, are not yet entirely clear.

Figure 4. Illustrations of re‐setting and distortions of the grid map .

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Maps of grid activity are coded for maximum (red) and minimum (blue) values. A, illustration of a grid cell in a 1 m × 1 m open arena (left) and the firing pattern of the same grid cell in a hairpin maze (right). B, example of grid pattern distortion in a trapezoid shaped open arena. C, grid orientation and distortion in a 1 m × 1 m open square arena. The grid map distortion in the familiar arena (right) reflects shearing forces (grey arrows). D, grid scale expands in a novel environment (left). In a familiar environment, grid scale contracts and the pattern distorts (right).

Mechanisms of sensory control of intrinsic map computations

How do spatially responsive neurons weigh the influence of internal versus external cues in determining their response properties (Fig. 5)? Recently, insight into the nature of the relationship between internal and external drive in generating spatially responsive neural codes comes from studies examining the influence of visual versus self‐motion cues on place codes. By combining virtual reality environments with electrophysiological recordings in behaving rodents, researchers gain tight control over self‐motion versus sensory inputs. This approach revealed that self‐motion and visual cues combine non‐linearly to generate the CA1 place code in mice, with neither cue sufficient to robustly drive the entire cell population (Chen et al. 2013; Aghajan et al. 2015). Instead, in virtual reality, normal place cell firing appears to emerge from the combination of visual cues at the start of a run down the virtual linear track and subsequent self‐motion cues that update the animal's estimate of its current position (Chen et al. 2013). This suggests that while idiothetic cues may provide an input crucial to generating place cell firing, coherent place maps also depend on the influence of the learned, modifiable connections regarding sensory landmarks (Gothard et al. 1996).

Figure 5. Diagram illustrating the variety of signals received by entorhinal grid and head direction cells .

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The complex rules for how extrinsic sensory cues, partially carried by neurons such as border, place and object cells (left), and idiothetic cues (right) combine to influence entorhinal spatial representations (middle) remain to be fully elucidated.

What is the mechanism for generating spatial neural codes based on a particular learned weighting between internal and external cues? Insight into this question comes from the head direction system, where the balance between external and internal forces of directional coding has been studied for decades (Taube, 2007). Updated by self‐motion but bound to visual landmarks, head direction cells represent a neural code that blends path integration and landmark navigation modalities, reporting an animal's orientation relative to local environmental landmarks (Taube et al. 1990). Over the last several decades, several computational models have proposed that pre‐wired intrinsic connectivity allows idiothetic cues to provide the primary input to head direction cells, with modifiable extrinsic connections mediating a learned influence of sensory landmarks on directional responses (Skaggs et al. 1995; K. Zhang, 1996; Knierim et al. 1998). Supporting this idea, head direction cells anchor to visual cues within the first few minutes of exploration, suggesting the presence of a rapid learning mechanism that supports the development of associations between directional neural circuits and landmark cues (Goodridge et al. 1998). This rapid association could be achieved by a simple plasticity learning rule, which would allow landmarks to not only set the origin of the path integrator but also correct accumulated error (Skaggs et al. 1995) (Fig. 6). A similar type of plasticity mechanism could be used to correct error in the grid cell population, with border cells serving as the neural input for landmark cues (Hardcastle et al. 2015).

Figure 6. Illustration of a simplified theoretical ring attractor model .

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Individual spatial neurons are colour‐coded and recurrently connected to each other via excitatory connections (black lines). Inhibitory connectivity not shown. Movement to the east (A), activates a population of excitatory cells coding for east movement (light grey circles). Movement to the west (B), activates a population of cells coding for west movement (dark grey circles). Connectivity between spatial neurons and movement cells drives firing activity in the appropriate direction around the ring. A, top, an eastbound trajectory. Colour‐coded spikes shown as lines below a long linear track. A border cell is shown firing in black as the animal leaves the boundary. Co‐active with the border cell is the pink spatial neuron. Bottom, the activity of the pink spatial neuron activates the brown and green neuron, as well as exciting an eastward movement cell. The co‐activity of the pink spatial neuron and eastward movement cell more strongly excites the green cell and activity is moved along the attractor. This pattern of activation continues until the mouse reaches the west portion of the environment. B, coded as in A. Note that error is accumulating as the mouse travels. For example, the orange cell fires in a different part of the linear track compared to its location in panel A. As the mouse approaches the west boundary, however, the border cell (black) activates, which excites the pink cell, thus causing the correct cell (pink) to be active at the appropriate spatial location (near the boundary).

It remains less clear, however, what precise rules dictate how idiothetic and sensory information combine to generate and, later, distort spatial firing patterns. Even so, plasticity mechanisms are likely to be involved in sensory‐driven distortions of grid patterns, as the development of these distortions may depend on experience. Recent observations of asymmetric anchoring and grid pattern distortions in highly familiar environments may be less dramatic in novel spatial environments (T. Stensola et al. 2015), in which previous research has shown the grid periodicity transiently expands (Barry et al. 2012; Carpenter et al. 2015; Krupic et al. 2015). This raises the possibility that asymmetric grid distortions develop with experience, possibly from border cell or boundary related inputs that non‐uniformly push or drag the grid pattern, resulting in a non‐symmetric grid geometry (Krupic et al. 2012; T. Stensola et al. 2015). For example, plasticity combined with the presence of multiple fixed points near boundaries or corners of the environment could, in theory, asymmetrically anchor the grid pattern in novel environments (T. Stensola et al. 2015). As the animal gains experience in the environment, grid fields begin to compress, a phenomenon previously documented (Barry et al. 2012). Resistance of this compression by several of the fixed points established during the initial exploration period could result in an asymmetric grid pattern (T. Stensola et al. 2015). Computational modelling also proposes that plasticity between the grid network and location‐specific input from external cues may be crucial to the development of the grid cell network (Widloski & Fiete, 2014). One of these inputs could be border cells, but place cells and non‐spatial lateral entorhinal cells could sub‐serve the same purpose. The grid network, once set, could then support path integration and self‐localization in novel environments and across featureless parts of familiar environments (Widloski & Fiete, 2014). This model thus explicitly predicts that stable grid cell development requires spatial exploration. Consistent with this proposal, the development of border and place cells leads the development of grid cells, suggesting that border cells may provide an early stable cue for location and raising the possibility that border or place cell inputs play a key role in the development of stable grid cells (Langston et al. 2010; Wills et al. 2010; Brandon et al. 2014; Muessig et al. 2015).

On the other hand, several experiments indicate that while sensory cues can transiently influence grid patterns, in some cases, idiothetic cues can override the sensory coordinate frame and convert the grid map back to its baseline structure. This was most recently observed in a study where rats had to shuffle between two boxes with identical spatial cues. While the maps originally anchored locally to environmental landmarks, perhaps due to landmark‐based connections established during training, the grid map eventually reverted to relying on idiothetic cues, and a global map across the two fragmented spaces emerged (Carpenter et al. 2015). In addition, the distance between grid nodes (grid scale) will compress or expand in response to a sudden decrease or increase in the size of a familiar environment. This re‐scaling is experience dependent, however, with grid cells returning to their original scale and structure with continued experience in the altered spatial environment (Barry et al. 2007; H. Stensola et al. 2012).

In conclusion, the complex relationship between intrinsic idiothetic and extrinsic sensory cues in driving the response properties of grid cells raises the possibility that more than one mechanism may determine the influence of sensory cues on internal grid representations. Indeed, a recent paper highlights that cells that project to the dentate gyrus and CA1 show grid coding properties, suggesting that different genetically identifiable grid populations could serve different functions in navigation or memory (Sun et al. 2015). The possibility also exists that these two genetic populations may be differentially influenced by self‐motion versus external sensory inputs, giving rise to the seemingly varied responses of grid cells to these two types of input.

Conclusion

There is increasing evidence that intrinsic idiothetic and extrinsic sensory cues drive the response properties of spatially selective neurons in a rich and complex way. The variability in how these sets of cues determine the responses of spatial neurons raises the possibility of multiple network mechanisms for determining the influence of sensory cues on internally generated spatial representations. For example, in the grid cell population, plasticity between sensory inputs and grid cells could support error‐correction, anchoring and resetting phenomena (Skaggs et al. 1995; Widloski & Fiete, 2014; Hardcastle et al. 2015). A deeper understanding of grid coding errors, and the mechanisms necessary to correct such errors, may provide crucial insight into how networks composed of stochastic dynamics generate stable neural representations, not only in MEC, but in other high‐order cortical systems. On the other hand, repulsive forces from boundaries could push grid fields away from environmental borders and help explain the changes in the grid pattern observed in novelty‐induced compression or the linearization of the grid pattern (Krupic et al. 2013; Widloski & Fiete, 2014). The precise plasticity rules, neural inputs and wiring diagrams of such repulsive mechanisms however, remain to be demonstrated. Finally, although this review focuses on boundary and border representations as key players in error correction and the binding of internal spatial representations to the external world, additional mechanisms may come into play, particularly in natural environments. For example, a population of neurons in lateral entorhinal cortex represent the location of objects and could serve a role similar to that of border cells in more complex environments (Deshmukh & Knierim, 2011; Tsao et al. 2013).

Additional information

Competing interesting

The author has no conflicts of interest.

Funding

L.M.G. is a New York Stem Cell Foundation – Robertson Investigator and supported by The New York Stem Cell Foundation, Whitehall Foundation, Simons Foundation, a Sloan Research Fellowship and a Klingenstein–Simons Fellowship.

Biography

Lisa M. Giocomo is an Assistant Professor of Neurbiology at Stanford University School of Medicine. Her lab integrates electrophysiology, behaviour, gene manipulations and computational modelling to study how single‐cell biophysics and network dynamics interact to mediate spatial memory and navigation.

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This review was presented at the symposium ‘Knowing where you are: circuit mechanisms for estimating location’, which took place at the British Neuroscience Association's 2015 Festival of Neuroscience, Edinburgh, UK, between 12‐15 April 2015.

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