http://metapractice.livejournal.com/379617.htmlPlace Cells, Grid Cells, and the Brain's Spatial Representation SystemAnnual Review of NeuroscienceVol. 31: 69-89 (Volume publication date July 2008)First published online as a Review in Advance on February 19, 2008DOI: 10.1146/annurev.neuro.31.061307.090723Edvard I. Moser,1 Emilio Kropff,1,2 and May-Britt Moser11Kavli Institute for Systems Neuroscience and Centre for the Biology of Memory, Norwegian University of Science and Technology, 7489 Trondheim, Norway2Cognitive Neuroscience Sector, International School for Advanced Studies, Trieste, Italy; email: edvard.moser@cbm.ntnu.noFULL-TEXT| PDFPDF (516 KB)| Permissions | ReprintsCitation: PubMed| Web of Science ®| Download| Email notification|Web of Science ®: Related Records ®| Times Cited: 292Place Cells, Grid Cells, and the Brain's Spatial Representation SystemAnnual Review of Neurosciencehttp://www.annualreviews.org/doi/full/10.1146/annurev.neuro.31.061307.090723ABSTRACTMore than three decades of research have demonstrated a role for hippocampal place cells in representation of the spatial environment in the brain. New studies have shown that place cells are part of a broader circuit for dynamic representation of self-location. A key component of this network is the entorhinal grid cells, which, by virtue of their tessellating firing fields, may provide the elements of a path integration–based neural map. Here we review how place cells and grid cells may form the basis for quantitative spatiotemporal representation of places, routes, and associated experiences during behavior and in memory. Because these cell types have some of the most conspicuous behavioral correlates among neurons in nonsensory cortical systems, and because their spatial firing structure reflects computations internally in the system, studies of entorhinal-hippocampal representations may offer considerable insight into general principles of cortical network dynamics.INTRODUCTIONQuestions about how we perceive space and our place in that space have engaged epistemologists for centuries. Although the British empiricists of the seventeenth and eighteenth centuries thought that all knowledge about the world was ultimately derived from sensory impressions, Kant argued that some ideas exist as a priori intuitions, independent of specific experience. One of these ideas is the concept of space, which he considered an innate organizing principle of the mind, through which the world is, and must be, perceived. With the birth of experimental psychology and neuroscience a century later, the organization and development of spatial behavior and cognition could be analyzed experimentally. We review evidence from the past three decades that indicates the presence of a preconfigured or semipreconfigured brain system for representation and storage of self-location relative to the external environment. In agreement with the general ideas of Kant, place cells and grid cells in the hippocampal and entorhinal cortices may determine how we perceive and remember our position in the environment as well as the events we experience in that environment.PLACE CELLS AND THE HIPPOCAMPAL MAPThe experimental study of spatial representations in the brain began with the discovery of place cells. More than 35 years ago, O'Keefe & Dostrovsky (1971) reported spatial receptive fields in complex-spiking neurons in the rat hippocampus, which are likely to be pyramidal cells (Henze et al. 2000). These place cells fired whenever the rat was in a certain place in the local environment (the place field of the cell; Figure 1a). Neighboring place cells fired at different locations such that, throughout the hippocampus, the entire environment was represented in the activity of the local cell population (O'Keefe 1976, Wilson & McNaughton 1993). The same place cells participated in representations for different environments, but the relationship of the firing fields differed from one setting to the next (O'Keefe & Conway 1978). Inspired by Tolman (1948), who suggested that local navigation is guided by internal “cognitive maps” that flexibly represent the overall spatial relationships between landmarks in the environment, O'Keefe & Nadel (1978) proposed that place cells are the basic elements of a distributed noncentered map-like representation. Place cells were suggested to provide the animal with a dynamic, continuously updated representation of allocentric space and the animal's own position in that space. We now have abundant evidence from a number of mammalian species demonstrating that the hippocampus plays a key role in spatial representation and spatial memory (Nadel 1991, Rolls 1999, Ekstrom et al. 2003, Ulanovsky & Moss 2007), although new evidence suggests that position is only one of several facets of experience stored in the hippocampal network (Eichenbaum et al. 1999, Leutgeb et al. 2005b).Figure 1 GRID CELLS AND THE ENTORHINAL MAPAll subfields of the hippocampal region contain place-modulated neurons, but the most distinct firing fields are found in the CA areas (Barnes et al. 1990). On the basis of the apparent amplification of spatial signals from the entorhinal cortex to the CA fields (Quirk et al. 1992), many investigators thought, until recently, that place signals depended primarily on computations within the hippocampal network. This view was challenged by the observation that spatial firing persisted in CA1 neurons after removal of intrahippocampal inputs from the dentate gyrus (McNaughton et al. 1989) and CA3 (Brun et al. 2002). This raised the possibility that spatial signals were conveyed to CA1 by the direct perforant-path projections from layer III of the entorhinal cortex. Projection neurons in layers II and III of the medial entorhinal cortex (MEC) were subsequently shown to exhibit sharply tuned spatial firing, much like place cells in the hippocampus, except that each cell had multiple firing fields (Fyhn et al. 2004). The many fields of each neuron formed a periodic triangular array, or grid, that tiled the entire environment explored by the animal (Hafting et al. 2005) (Figure 1b). Such grid cells collectively signaled the rat's changing position with a precision similar to that of place cells in the hippocampus (Fyhn et al. 2004). The graphics paper–like shape of the grid immediately indicated grid cells as possible elements of a metric system for spatial navigation (Hafting et al. 2005), with properties similar to that of the allocentric map proposed for the hippocampus more than 25 years earlier (O'Keefe & Nadel 1978).How do grid representations map onto the surface of the entorhinal cortex? Each grid is characterized by spacing (distance between fields), orientation (tilt relative to an external reference axis), and phase (xy displacement relative to an external reference point). Although cells in the same part of the MEC have similar grid spacing and grid orientation, the phase of the grid is nontopographic, i.e., the firing vertices of colocalized grid cells appear to be shifted randomly, just like the fields of neighboring place cells in the hippocampus. The spacing increases monotonically from dorsomedial to ventrolateral locations in MEC (Hafting et al. 2005, Solstad et al. 2007), mirroring the increase in size of place fields along the dorsoventral axis of the hippocampus (Jung et al. 1994, Maurer et al. 2005, Kjelstrup et al. 2007). Cells in different parts of the MEC may also have different grid orientations (Hafting et al. 2005), but the underlying topography, if there is one, has not been established. Thus, we do not know whether the entorhinal map has discrete subdivisions. The entorhinal cortex has several architectonic features suggestive of a modular arrangement, such as periodic bundling of pyramidal cell dendrites and axons and cyclic variations in the density of immunocytochemical markers (Witter & Moser 2006), but whether the anatomical cell clusters correspond to functionally segregated grid maps, each with their own spacing and orientation, remains to be determined.SENSORY CUES AND PATH INTEGRATIONWhich factors control the spatial discharge pattern of place cells and grid cells? Early on, it became apparent that place fields are strongly influenced by distal sensory cues. When rats walked in a circle, rotation of the circumferential cues caused rotation of the place fields, whereas rotation of the proximal environment itself, in the presence of fixed distal cues, failed to change the firing locations (O'Keefe & Conway 1978, O'Keefe & Speakman 1987, Muller & Kubie 1987). Extending the sides of a rectangular recording box stretched or split the fields in the extended direction (O'Keefe & Burgess 1996). These observations indicate a primary role for extrinsic cues, and especially geometric boundaries, in defining the firing location of a place cell, although individual proximal landmarks do exert some influence under certain conditions (Muller & Kubie 1987, Gothard et al. 1996b, Cressant et al. 1997, Shapiro et al. 1997, Zinyuk et al. 2000, Fyhn et al. 2002). A similar dependence on distal landmarks and boundaries has since been demonstrated for entorhinal grid cells (Hafting et al. 2005, Barry et al. 2007). However, place cells and grid cells do not merely mirror sensory stimuli. When salient landmarks are removed while the animal is running in a familiar environment, both cell types continue to fire in the original location (Muller & Kubie 1987, Hafting et al. 2005). Moreover, representations in the hippocampus are often maintained when the recording box is transformed smoothly into another familiar box (J.K. Leutgeb et al. 2005), suggesting that the history of testing may sometimes exert stronger control on place cell activity than would the actual sensory stimuli.Discrete representations of individual places would not be sufficient to support navigation from one place to another. The brain needs algorithms for linking the places in metric terms. When animals move away from a start position, they can keep track of their changing positions by integrating linear and angular self-motion (Barlow 1964, Mittelstaedt & Mittelstaedt 1980, Etienne & Jeffery 2004). This process, referred to as path integration, is a primary determinant of firing in place cells. When rats are released from a movable start box on a linear track with a fixed goal at the end, the firing is initially determined by the distance from the start box (Gothard et al. 1996a, Redish et al. 2000). Although the activity is soon corrected against the external landmarks, this initial firing pattern suggests that place-selective firing can be driven by self-motion information alone.Where is the path integrator? The hippocampus itself is not a good candidate. Without invoking a separate hippocampal circuit for this process, it would be hard to imagine how the algorithm could be adapted for each of the many overlapping spatial maps stored in the very same population of place cells. Place cells may instead receive inputs from a general metric navigational system outside the hippocampus (O'Keefe 1976). Grid cells may be part of this system. The persistence of grid fields after removal or replacement of major landmarks points to self-motion information as the primary source for maintaining and updating grid representations (Hafting et al. 2005, Fyhn et al. 2007). The system has access to direction and speed information required to transform the representation during movement (Sargolini et al. 2006b). However, whereas path integration may determine the basic structure of the firing matrix, the grid map is anchored to geometric boundaries and landmarks unique to each environment. These associations may, under some circumstances, override concurrent path integration–driven processes, such as when the sides of a familiar rectangular recording environment are extended moderately (Barry et al. 2007). The origin of the self-motion signals and the mechanisms for integration of self-motion signals with extrinsic sensory inputs have not been determined.THEORETICAL MODELS OF GRID FORMATIONMost current models of grid field formation suggest that MEC neurons path-integrate speed and direction signals provided by specialized cells, whereas sensory information related to the environment is used for setting the initial parameters of the grid or adjusting it to correct the cumulative error intrinsically associated with the integration of velocity.One class of models suggests that grid formation is a result of local network activity. In these models, a single position is represented by an attractor, a stable firing state sustained by recurrent connections with robust performance in the presence of noise (Hopfield 1982, Amit 1989, Rolls & Treves 1998). A network can store several attractor states associated with different locations and retrieve any of them in response to sensory or path integration cues. When a large number of very close positions are represented, a continuous attractor emerges, which permits a smooth variation of the representation in accordance with the rat's trajectory (Tsodyks & Sejnowski 1995, Samsonovich & McNaughton 1997, Battaglia & Treves 1998). We review two of the models in this class.Fuhs & Touretzky (2006) proposed that MEC is roughly topographically organized; neighboring grid cells display similar activity, and the representation of a single place forms a grid pattern on the neuron layer. Such a pattern emerges naturally at a population level in a network with Mexican hat connectivity, where every neuron is excited by its neighbors, inhibited by neurons at an intermediate distance, and unaffected by those far away. The authors include several alternating excitation and inhibition ranges and an overall decay of the synaptic strength with distance. With this connectivity rule, a grid of activity appears spontaneously in the MEC layer, except at the borders of the layer, where the lack of balance overexcites neurons and additional attenuation is required. To transform the grid pattern in the MEC layer into a grid firing field in each neuron, the representation of a single place must be rigidly displaced along the topographically organized network following the movements of the rat (Figure 2a). This happens if any given neuron receives an input proportional to the running speed only when the rat runs in a preferred direction, which is different for each neuron. Increased speed produces increased excitation and a faster displacement of the grid pattern across the neural layer. The proposed mechanism may fail to displace the initial representation accurately for realistic trajectories of the rat, resulting in neurons that do not express grid fields (Burak & Fiete 2006; see authors' response at http://www.jneurosci.org/cgi/data/26/37/9352/DC1/1).Figure 2 The above model is challenged by the apparent lack of topography in grid phases of neighboring MEC neurons (Hafting et al. 2005). Motivated by this experimental observation, McNaughton et al. (2006) proposed, in an alternative model, that a topographically arranged network is present in the cortex during early postnatal development and serves as a tutor to train MEC cell modules with randomly distributed Hebbian connections and no topographical organization (Figure 2b). Attractor representations of space are formed in each of these modules during training. Because the inputs from the tutor are scrambled, neurons in a similar phase are not necessarily neighbors, but they are associated through synaptic plasticity. If after the training period neurons in a module were rearranged according to their firing phase or connection strength, a single bump of activity would be observed at any time. Because the tutor has the periodicity of a grid, the rearranged network has no borders and resembles the surface of a torus (Figure 2b). To displace representations along the abstract space of the continuous attractor, the model introduces an additional layer of cells whose firing is modulated by place, head direction, and speed. Sargolini et al. (2006b) have identified neurons with such properties. Neurons in this hidden layer may receive input from currently firing grid cells and project back selectively to grid cells that fire next along the trajectory; the activation of target cells depends on the current head direction and velocity of the rat (Samsonovich & McNaughton 1997, McNaughton et al. 2006; Figure 2b).In a second class of models, path integration occurs at the single cell level and is intimately related to phase precession, a progressive advance of spike times relative to the theta phase observed in hippocampal place cells (O'Keefe & Recce 1993) and entorhinal grid cells (Hafting et al. 2006) when rats run through a localized firing field. O'Keefe & Reece (1993) modeled phase precession on a linear track as the sum between two oscillatory signals with frequencies around the theta rhythm but slightly differing by an amount proportional to the rat's running speed. The resulting interference pattern can be decomposed into an oscillation at the mean of the two frequencies, which advances with respect to the slower (theta) rhythm, and a slow periodical modulation with a phase that integrates the rat's speed and thus reflects its position along the track (O'Keefe & Recce 1993, O'Keefe & Burgess 2005, Lengyel et al. 2003). Burgess et al. (2007) extended the interference model to two dimensions by considering the interaction of one somatic intrinsic oscillator of frequency ws (∼ theta rhythm) with several dendritic oscillators, each with a frequency equal to ws plus a term proportional to the projection of the rat velocity in some characteristic preferred direction. The interference of the somatic signal with each of these dendritic oscillators has a slow modulation that integrates the preferred component of the velocity into a linear spatial interference pattern (a plane wave). When combining several of these linear patterns, a triangular grid map is obtained, provided that their directions differ in multiples of 60° and the phases are set in such a way that all maxima coincide, a choice of parameters that could result from a self-organization process maximizing the neuron's overall activity. In a variant of this idea, Blair et al. (2007) proposed that the interference sources could be two theta-grid cells (grid cells with a high spatial frequency associated with the theta rhythm), differing in either their relative size or their orientation, although evidence for such cells has not yet been reported.The above models make different predictions about the organization of the entorhinal grid map. The network models explicitly or implicitly rely on a discontinuous module arrangement with different grid spacing and grid orientation. Fuhs & Touretzky (2006) propose large clusters of MEC cells each with fixed grid spacing and orientation but with continuous topographic variation of phase among neighbors. They estimate ∼17 such clusters in layer II, restricting the variability in grid parameter values in a given environment. Possibly smaller and larger in number, the attractor networks proposed by McNaughton and colleagues inherit in principle the orientation and spacing from their tutor, whereas the phase varies randomly inside each cluster. However, if two networks trained by the same tutor were fed with speed signals of different gain, the displacement of the representations in response to a rat movement would differ, resulting in grids with different spacing, as observed along the dorsoventral axis. Burgess et al. associate such a modulation in spacing with a gradient in the frequency of subthreshold membrane potential oscillations along this axis, a prediction that was recently verified (Giocomo et al. 2007), whereas their model is agnostic to neighboring cells' orientation and phase.PLACE FIELDS MAY BE EXTRACTED FROM GRID FIELDSInspired by Fourier analysis, researchers have proposed that grid fields of different spacing, playing the role of periodic basis functions, combine linearly to generate place fields in the hippocampus (O'Keefe & Burgess 2005, Fuhs & Touretzky 2006, McNaughton et al. 2006). The resulting hippocampal representation would be periodic but because the period would be equal to the least common multiple of the grid spacings, and because it would be further enhanced by differences in grid orientation, only single fields would be observed in standard experimental settings. The peak of the representation would be at the location where most of the contributing grids are in phase. In a computational model, Solstad et al. (2006) showed that in small two-dimensional environments, single place fields can be formed by summing the activity of a modest number (10–50) of grid cells with relatively similar grid phases, random orientations, and a biologically plausible range of spacings corresponding with convergence of inputs from ∼25% of the dorsoventral axis of MEC (Dolorfo & Amaral 1998). Rolls et al. (2006) showed that the choice of grid cells contributing to a given place field need not be hardwired but can result from a competitive Hebbian learning process starting from random connectivity, provided that enough variability in orientation, phase, and spacing is available in the afferent population of grid cells. They also showed that if variability in the in-peak frequency of grid cells is considered, more place cells have a single field, whereas trace learning (a variant of Hebbian learning that uses short time averages of, for example, the postsynaptic firing rate) produces broader fields, more similar to the ones observed in hippocampus.The idea that place fields emerge through LTP-like competitive learning mechanisms receives only partial support from studies of place field formation in the presence of NMDA receptor blockers. When animals explore new environments, it takes several minutes for the firing fields to reach a stable state (Hill 1978, Wilson & McNaughton 1993, Frank et al. 2004). During this period, place fields may fade in or out, or their fields may expand toward earlier parts of the trajectory (Mehta et al. 1997, 2000). The stabilization is slower in CA3 than in CA1 (Leutgeb et al. 2004). Although synaptic plasticity is necessary for experience-dependent field expansions (Ekstrom et al. 2001), synaptic modifications may not be required for manifestation of place-specific firing as such. In the absence of functional NMDA receptors, CA1 place cells continue to express spatially confined firing fields, although some selectivity and stability may be lost (McHugh et al. 1996, Kentros et al. 1998). Whether hardwired connections are sufficient for place cell formation in all parts of the circuit remains to be determined, however. Preliminary data suggest that during exploration of new environments, systemic blockade of NMDA receptors disrupts spatial selectivity in dentate granule cells while CA3 cells continue to exhibit localized activity (Leutgeb et al. 2007b), raising the possibility that on dentate granule cells, unlike hippocampal pyramidal cells, spatial selectivity may be established by competitive selection of active inputs.PLACE CELLS AND HIPPOCAMPAL MEMORYFollowing the discovery of place cells, several studies indicated a broader role for the hippocampus in representation and storage of experience, consistent with a long tradition of work on humans implying hippocampal involvement in declarative and episodic memory (Scoville & Milner 1957, Squire et al. 2004). Not only are hippocampal neurons triggered by location cues, but also they respond to salient events in a temporal sequence (Hampson et al. 1993) and nonspatial stimuli such as texture or odors (Young et al. 1994, Wood et al. 1999). However, nonspatial variables are not represented primarily by a dedicated subset of neurons or a nonspatial variant of the place cells. Fenton and colleagues observed that when a rat passed through a neuron's place field, the rate variation across traversals substantially exceeded that of a random model with Poission variance (Fenton & Muller 1998; Olypher et al. 2002). This excess variance, or overdispersion, raised the possibility that nonspatial signals are represented in place cells on top of the location signal by continuous rate modulation within the field. More direct support for this idea comes from the observation that, in hippocampal cell assemblies, spatial and nonspatial variables (place and color) are represented independently by variation in firing location and firing rate, respectively (Leutgeb et al. 2005a). Together these studies indicate a conjunctive spatial-nonspatial code for representation of experience in the hippocampus.How could memories be stored in the place cell system? On the basis of the extensive intrinsic connectivity and modifiability of the CA3 network, theoretical work has indicated attractor dynamics (Hopfield 1982, Amit 1989) as a potential mechanism for low-interference storage of arbitrary input patterns to the hippocampus (McNaughton & Morris 1987, Treves & Rolls 1992, Hasselmo et al. 1995, McClelland & Goddard 1996, Rolls & Treves 1998). In networks with discrete attractor states (a Hopfield network), associative connections would allow stored memories to be recalled from degraded versions of the original input (pattern completion) without mixing up the memory with other events stored in the network (pattern separation).Many observations suggest that place cells perform both pattern completion and pattern separation. Pattern completion is apparent from the fact that place cells maintain their location specificity after removing many of the landmarks that originally defined the environment (O'Keefe & Conway 1978, Muller & Kubie 1987, O'Keefe & Speakman 1987, Quirk et al. 1990, Nakazawa et al. 2002). Representations are regenerated with greater strength in CA3 than in CA1 (Lee et al. 2004, Vazdarjanova & Guzowski 2004, J.K. Leutgeb et al. 2005), possibly because the relative lack of recurrent collaterals in CA1 makes firing patterns more sensitive to changes in external inputs. As predicted from the theoretical models, pattern completion is disrupted by blockade of NMDA receptor–dependent synaptic plasticity in CA3 (Nakazawa et al. 2002). Pattern separation can be inferred from the ability of place cells to undergo substantial remapping after only minor changes in the sensory input, such as a change in color or shape of the recording enclosure or a change in the overall motivational context (Muller & Kubie 1987, Bostock et al. 1991, Markus et al. 1995, Wood et al. 2000). Two forms of remapping have been reported (Leutgeb et al. 2005b): The cell population may undergo complete orthogonalization of both firing locations and firing rates (global remapping), or the rate distribution may be changed selectively in the presence of stable firing locations (rate remapping). In each instance, remapping tends to be instantaneous (Leutgeb et al. 2006, Fyhn et al. 2007), although delayed transitions occur under some training conditions (Lever et al. 2002). The disambiguation of the firing patterns is stronger in CA3 than in CA1 (Leutgeb et al. 2004, Vazdarjanova & Guzowski 2004). Pattern separation in the CA areas may be facilitated by prior orthogonalization of hippocampal input patterns in the dentate gyrus (Leutgeb et al. 2007a, McHugh et al. 2007, Leutgeb & Moser 2007). The sparse firing of the granule cells (Jung & McNaughton 1993, Chawla et al. 2005, Leutgeb et al. 2007a) and the formation of one-to-one detonator synapses between granule cells and pyramidal cells in CA3 (Claiborne et al. 1986, Treves & Rolls 1992) may jointly contribute to decorrelation of incoming cortical signals in the dentate gyrus (McNaughton & Morris 1987, Treves & Rolls 1992).Although place cells exhibit both pattern completion and pattern separation, we cannot discount that firing is maintained by unidentified stimuli that are present both during encoding with the full set of landmarks and during retrieval with a smaller subset of landmarks. A more direct way to test the attractor properties of the network is to measure the response of the place cell population to continuous or step-wise transformations of the recording environment. Wills et al. (2005) trained rats, on alternating trials, in a square and a circular version of a morph box with flexible walls. Different place cell maps were formed for the two environments. On the test day, rats were exposed to multiple intermediate shapes. A sharp transition from square-like representations to circle-like representations was observed near the middle in the geometric sequence, as predicted if the network had discrete attractor states corresponding to each of the familiar square and circular shapes. Whether the implied attractor dynamics occurs in the hippocampus itself or in upstream areas such as the MEC, or both, remains to be determined. Because the changes in firing patterns indicate global remapping and global remapping is invariably accompanied by realignment and rotation of the entorhinal grid map (Fyhn et al. 2007), it may well be that some of the underlying attractor dynamics lies in the MEC (S. Leutgeb et al. 2007).Hippocampal representations cannot always be discontinuous as in a Hopfield network. Hippocampal memories are characterized by events that are tied together in sequences (Tulving & Markowitsch 1998, Shapiro et al. 2006), just like positions are tied together in two dimensions as spatial maps. A continuous attractor network (Tsodyks & Sejnowski 1995) may be needed to preserve the continuity of both types of representations. Recent observations support this possibility. When two recording environments are morphed in the presence of salient distal landmarks, the firing locations remain constant across the sequence of intermediate shapes, but the rate distribution changes smoothly between the preestablished states (J.K. Leutgeb et al. 2005). Stable states can thus be attained along the entire continuum between two preexisting representations (Blumenfeld et al. 2006). This ability to represent continua may provide hippocampal networks with a capacity for encoding and retrieving consecutive inputs as uninterrupted, distinguishable episodes.SEQUENCE CODING IN PLACE CELLS AND GRID CELLSThe idea that neuronal representations have a temporal dimension can be traced back to Hebb (1949), who suggested that cell assemblies are activated in sequences, and that such phase sequences may provide the neural basis of thought. More recent theoretical studies have proposed a number of mechanisms by which temporal sequences could be formed and stored as distinct entities. Such mechanisms include potentiation of asymmetric connections between serially activated neurons (Blum & Abbott 1996) and orthogonalization of the individual sequence elements (McNaughton & Morris 1987). However, although these and related ideas have nourished important experiments, the mechanisms of sequence coding are still not well understood.The most intriguing example of a temporal code in the rodent hippocampus is probably the expression of theta phase precession in place cells when animals follow a fixed path in a linear environment (O'Keefe & Recce 1993, Skaggs et al. 1996). The reliable tendency of place cells to fire at progressively earlier phases of the theta rhythm during traversal of the place field increases the information about the animal's location in the environment, both in one-dimensional (Jensen & Lisman 2000, Harris et al. 2003, Huxter et al. 2003) and two-dimensional (Huxter et al. 2007) environments. Moreover, when the rat runs through a sequence of overlapping place fields from different cells, the firing sequence of the cells will be partially replicated in compressed form within individual theta periods (Jensen & Lisman 1996, Skaggs et al. 1996, Tsodyks et al. 1996, Dragoi & Buzsaki 2006). The repeated compression of discharges within windows of some tens of milliseconds provides a mechanism for associating temporally extended path segments on the basis of the rules of spike-dependent plasticity (Dan & Poo 2004). Such associations may be necessary for storage of route and event representations in the network.We do not know the neuronal mechanisms of phase precession or their locations in the brain. O'Keefe & Recce (1993) suggested that phase precession is caused by interference between intrinsic and extrinsic neuronal membrane potential oscillations with slightly different frequencies (Figure 2c). Spike times are determined, in this view, by the high-frequency wave of the interference pattern, which advances progressively with reference to the field theta activity. An alternative set of models suggests that phase precession occurs when theta-modulated inhibition interacts with progressively increasing excitation of the place cell over the extent of the firing field (Harris et al. 2002, Mehta et al. 2002). Because of the ramp-up of the excitation, cells would discharge at successively earlier points in the theta cycle. A third type of models puts the mechanism at the network level (Jensen & Lisman 1996, Tsodyks et al. 1996, Wallenstein & Hasselmo 1998). By intrinsic connections between place cells, cells that are strongly activated by external excitation may initiate, at each location, a wave of activity that spreads toward the cells with place fields that are further along the animal's path. Experimental data do not rule out any of these models. To understand the underlying mechanisms, it may be necessary to identify the neural circuits that support phase precession and then determine which of those are able to generate phase precession on their own. Recent observations imply that phase precession is not exclusively hippocampal. Phase precession in CA1 is not frozen by brief inhibition of hippocampal activity (Zugaro et al. 2005), which suggests that the precession may, at least under some circumstances, be imposed on hippocampal place cells by cells from other areas. One such area could be the MEC. Grid cells in this region exhibit phase precession (Hafting et al. 2006). Of particular interest is the stellate cell in layer II of MEC. Because stellate cells exhibit voltage-dependent intrinsic oscillations that may be faster than the field theta rhythm (Alonso & Llinas 1989, Klink & Alonso 1993, Giocomo et al. 2007), these cells may express interference patterns of the type proposed by O'Keefe and colleagues (O'Keefe & Recce 1993, Burgess et al. 2007). Additional assumptions must be invoked, however, to explain the stronger correlation of phase with position, and the presence of intrinsic oscillations does not rule out other potential mechanisms including rampant excitation or neural network properties.REPLAY AND PREPLAY IN PLACE CELL ENSEMBLESAfter a memory trace is encoded during an experience, the memory is thought to undergo further consolidation off-line when the subject is sleeping or is engaged in consummatory activities. Although the cascade of events leading to consolidation of hippocampal memory is not well understood, ensemble recordings in sleeping rats have provided some clues. Cells that are coactivated in the hippocampus during awake behavior continue to exhibit correlated activity during sleep episodes subsequent to behavioral testing (Wilson & McNaughton 1994). The order of firing is generally preserved but the rate may be faster (Skaggs & McNaughton 1996, Lee & Wilson 2002). Such replay or reactivation is associated with hippocampal sharp waves, which are bursts of synchronous pyramidal-cell activity during slow-wave sleep and awake rest (Buzsáki et al. 1983). Sharp-wave bursts can induce plasticity in downstream areas and may therefore be involved in information transfer from the hippocampus to the neocortex during the consolidation time window (Buzsáki 1989). Direct evidence for this hypothesis is still lacking. The correlation of sharp waves in CA1 and upstates in the neocortex suggests interaction between these brain areas during sleep, but the slight delay of membrane potential changes in CA1 compared with neocortex suggests that signals are transferred from neocortex to CA1 and not vice versa (Isomura et al. 2006, Hahn et al. 2007, Ji & Wilson 2007). Much work is still needed to establish the potential significance of sleep-associated reactivation in memory consolidation.Reactivation of place cell discharges is observed not just during sleep. Recent studies have reported reactivation during sharp waves that are “interleaved” in waking activities (Foster & Wilson 2006, O'Neill et al. 2006), which indicates possible mechanisms for maintaining recent memories on shorter time scales when rest is not possible. When the animal stops at the turning points of a linear track, the hippocampus enters sharp-wave mode, and the preceding sequence of place-cell activity on the track is replayed, but now in a time-reversed order (Foster & Wilson 2006). Reverse replay may facilitate the storage of goal-directed behavioral sequences by allowing reinforcement signals that coincide with reward induction (e.g., dopamine release) to strengthen primarily the later parts of the behavioral sequence. The point at which sharp wave-associated reactivation is forward or backward has not been determined, and further work is needed to establish how the two forms of reactivation contribute to memory, if at all.Not all nonlocal activity is retrospective. During theta-associated behaviors, place-cell discharges may sometimes correlate with future locations. Training rats to choose the correct goal location on discrete trials in a plus maze, Ferbinteanu & Shapiro (2003) found that firing on the start arm was in some cells determined by the subsequent choice of goal arm. Johnson & Redish (2007) showed that when rats reach the choice point of a modified T maze, representations sweep ahead of the animal in the direction of the reward location. The forward-looking activity was experience dependent. Together, these observations suggest that before animals choose between alternative trajectories, future locations are preplayed in the hippocampal place-cell ensembles, probably by retrieving stored representations. This interpretation implies a direct involvement of hippocampal networks in active problem solving and evaluation of possible futures, consistent with the recently reported failure to imagine new experiences in patients with hippocampal amnesia (Hassabis et al. 2007).SPATIAL MAPS INCLUDE MORE THAN HIPPOCAMPUS AND ENTORHINAL CORTEXSpatial representation engages a wide brain circuit. A key component is the network of head direction–modulated cells in presubiculum (Ranck 1985, Taube et al. 1990) and upstream areas such as the anterior thalamus (see Taube 1998 for review). Axons from the presubiculum terminate in layers III and V of MEC (Witter & Amaral 2004), where grid cells are modulated by head direction (Sargolini et al. 2006b), possibly as a consequence of the presubicular input. Head-direction cells may also control grid field orientation. The MEC has strong connections with the parasubiculum and the retrosplenial cortex (Witter & Amaral 2004), which contain cells that are tuned to position or head direction (Chen et al. 1994, Taube 1995, Sargolini et al. 2006a). Lesion studies suggest that the retrosplenial cortex is necessary for path integration–based navigation and topographic memory (Sutherland et al. 1988, Takahashi et al. 1997, Cooper & Mizumori 1999), although little is known about the specific role of this area in these processes. The MEC also interacts closely with the lateral entorhinal cortex, where cells apparently do not show spatial modulation (Hargreaves et al. 2005). Although the lateral entorhinal cortex provides a major component of the cortical input to the hippocampus, its function is not known.The parietal cortex may be an important element of the spatial representation and navigation system. Similar to rats with lesions in the entorhinal cortex, rats with parietal cortex lesions fail to navigate back to a refuge under conditions where the return pathway can be computed only on the basis of the animal's own movement (Save et al. 2001, Parron & Save 2004). Rats and humans with parietal cortex lesions also fail to acquire spatial tasks and remember positional relationships (Kolb et al. 1983, DiMattia & Kesner 1988, Takahashi et al. 1997). The parietal cortex of the rat contains neurons that map navigational epochs when the animal follows a fixed route (Nitz 2006). These cells fire in a reliable order at specific stages of the route, but the firing is not determined by landmarks or movement direction. Much additional work is required to determine whether these discharge patterns are path integration based and contribute to a representation of self-location, and whether they are dependent on grid cells (Hafting et al. 2005) and path-associated firing (Frank et al. 2000) in the MEC. Finally, it is possible that navigation can be aided also by action-based neural computations in the striatum, where key positions along the trajectory are reflected in the local activity (Jog et al. 1999; see also Packard & McGaugh 1996, Hartley et al. 2003).DEVELOPMENT OF THE SPATIAL REPRESENTATION SYSTEMIs space an organizing principle of the mind, imposed on experience according to brain preconfiguration, as Kant suggested? Some properties of the spatial representation system certainly indicate a preconfigured network. Grid fields appear from the very first moment of exploration in a new environment and persist following major landmark removal (Hafting et al. 2005), and the spatial phase relationship of different grid cells remains constant across environments (Fyhn et al. 2007). This suggests a rigidly structured map, but whether animals are born with it remains to be determined. Grid structure may appear from genetically specified properties of the entorhinal circuit, but specific maturational programs and experiences may also be necessary for the development of an adult map-like organization (Hubel et al. 1977, McNaughton et al. 2006). Unfortunately, we do not know much about the ontogeny of the spatial representation system. Apparently, only one study has systematically explored the development of spatial representations. Martin & Berthoz (2002) found that sharp and confined place fields were not expressed in CA1 until ∼P50 in the rat. The lack of functional studies is matched by a similarly fragmented understanding of how intrinsic and extrinsic connections of the entorhinal and hippocampal cortices develop relative to each other. The few existing studies suggest that, in the rat, some connections of the system, such as the cholinergic innervation of the entorhinal cortex, appear only at ∼35 days of age (Ritter et al. 1972, Matthews et al. 1974). Although most other connections are apparently present a few days after birth, functional entorhinal-hippocampal circuits may not emerge until all connections are in place. The slow development of the entorhinal-hippocampal system leaves considerable possibilities for postnatal shaping of the spatial map. A major challenge, if we want to address the Kantian question about the a priori nature of space perception, will be to identify the factors that control map formation in young animals.CONCLUSIONThe past few years have witnessed radical advances in our understanding of the brain's spatial representation system. We are beginning to see the contours of a modularly organized network with grid cells, place cells, and head-direction cells as key computational units. Interactions between grid cells and place cells may underlie the unique ability of the hippocampus to store large amounts of orthogonalized information. The mechanisms of this interaction, their significance for memory storage, and their interactions with representations in other cortical regions remain to be determined. More work is also required to establish the computational principles by which grid maps are formed and by which self-location is mapped dynamically as animals move through spatial environments. Perhaps the largest knowledge gap is concerned with how grid structure emerges during ontogenesis of the nervous system. With the emerging arsenal of genetic tools for time-limited selective activation and inactivation of specific neuronal cell types and circuits and with new possibilities for in utero application of these techniques (Callaway 2005, Tervo & Karpova 2007, Zhang et al. 2007), we should be able to address these issues in the near future. If so, spatial navigation may become one of the first nonsensory cognitive functions to be understood in reasonable mechanistic detail at the microcircuit level.SUMMARY POINTS1. The hippocampal-entorhinal spatial representation system contains place cells, grid cells, and head-direction cells.2. Grid cells have periodic firing fields that form a regular triangular grid across the environment. Grid fields are likely generated by path integration to serve as part of a neural map of self-location.3. The integration of speed and direction signals may take place at the population level by virtue of recurrent connectivity or at the single neuron level as a consequence of interference among temporal oscillators with frequencies that depend on speed.4. Hippocampal place fields may be formed by summing convergent input from grid cells with a range of different spacings, similar to a Fourier transform.5. Unlike ensembles of grid cells, place cells participate in a number of highly orthogonalized environment-specific representations. Attractor dynamics may play a role in storing and reactivating representations during memory retrieval.6. Theta-phase precession provides a possible mechanism for sequence representation in place cells.FUTURE ISSUES1. How is the grid pattern generated during nervous system development? Are specific maturational events required, and does grid formation depend on specific experience? Which elements of the map, if any, remain plastic in the adult brain?2. What are the cellular and neural network mechanisms of path integration, and how is the grid representation updated in accordance with the rat's own movement? Which mechanism corrects cumulative error, and on what information does it rely?3. How is the entorhinal map organized? Is the map modular or continuous? How are modularity and continuity generated during development, and how do modules interact in the mature nervous system?4. What is the mechanism of phase precession, where does precession originate, and what is its relationship with spatial periodicity?5. What is the function of the lateral entorhinal cortex, and how does it contribute to representation in the hippocampus?6. How do hippocampal memories influence neocortical memory formation, and what is the function of grid cells or other entorhinal neurons in this process?7. How does the entorhinal-hippocampal spatial map interact with other cortical systems required for spatial navigation, such as the parietal cortex or the striatum?disclosure statementThe authors are not aware of any biases that might be perceived as affecting the objectivity of this review.acknowledgmentsWe thank Neil Burgess, Mark Fuhs, Dave Touretzky, and Alessandro Treves for discussion. The authors were supported by The Kavli Foundation, the Norwegian Research Council, and the Fondation Bettencourt Schueller.
http://www.annualreviews.org/doi/full/10.1146/annurev.neuro.31.061307.0907231. Alonso A, Llinas RR. 1989. Subthreshold Na+-dependent theta-like rhythmicity in stellate cells of entorhinal cortex layer II. Nature 342:175–77 [CrossRef] [Medline] [Web of Science ®]2. Amit DJ. 1989. Modelling Brain Function: The World of Attractor Networks. New York: Cambridge Univ. Press3. Barlow JS. 1964. Inertial navigation as a basis for animal navigation. J. Theor. Biol. 6:76–117 [CrossRef] [Medline] [Web of Science ®]4. Barnes CA, McNaughton BL, Mizumori SJ, Leonard BW, Lin LH. 1990. Comparison of spatial and temporal characteristics of neuronal activity in sequential stages of hippocampal processing. Prog. Brain Res. 83:287–300 [CrossRef] [Medline] [Web of Science ®]5. Barry C, Hayman R, Burgess N, Jeffery KJ. 2007. Experience-dependent rescaling of entorhinal grids. Nat. Neurosci. 10:682–84 [CrossRef] [Medline] [Web of Science ®]6. Battaglia FP, Treves A. 1998. Attractor neural networks storing multiple space representations: a model for hippocampal place fields. Phys. Rev. E 58:7738–53 [CrossRef] [Web of Science ®]7. Blair HT, Welday AC, Zhang K. 2007. Scale-invariant memory representations emerge from moire interference between grid fields that produce theta oscillations: a computational model. J. Neurosci. 27:3211–29 [CrossRef] [Medline] [Web of Science ®]8. Blum KI, Abbott LF. 1996. A model of spatial map formation in the hippocampus of the rat. Neural Comp. 8:85–93 [CrossRef] [Medline] [Web of Science ®]9. Blumenfeld B, Preminger S, Sagi D, Tsodyks M. 2006. Dynamics of memory representations in networks with novelty-facilitated synaptic plasticity. Neuron 52:383–94 [CrossRef] [Medline] [Web of Science ®]10. Bostock E, Muller RU, Kubie JL. 1991. Experience-dependent modifications of hippocampal place cell firing. Hippocampus 1:193–205 [CrossRef] [Medline]11. Brun VH, Otnass MK, Molden S, Steffenach HA, Witter MP, et al. 2002. Place cells and place recognition maintained by direct entorhinal-hippocampal circuitry. Science 296:2243–46 [CrossRef] [Medline] [Web of Science ®]12. Burak Y, Fiete I. 2006. Do we understand the emergent dynamics of grid cell activity? J. Neurosci. 26:9352–54 [CrossRef] [Medline] [Web of Science ®]13. Burgess N, Barry C, O'Keefe J. 2007. An oscillatory interference model of grid cell firing. Hippocampus 17:801–12 [CrossRef] [Medline] [Web of Science ®]14. Buzsáki G. 1989. Two-stage model of memory trace formation: a role for “noisy” brain states. Neuroscience 31:551–70 [CrossRef] [Medline] [Web of Science ®]15. Buzsáki G, Leung LW, Vanderwolf CH. 1983. Cellular bases of hippocampal EEG in the behaving rat. Brain Res. 287:139–71 [CrossRef] [Medline]16. Callaway EM. 2005. A molecular and genetic arsenal for systems neuroscience. Trends Neurosci. 28:196–201 [CrossRef] [Medline] [Web of Science ®]17. Chawla MK, Guzowski JF, Ramirez-Amaya V, Lipa P, Hoffman KL, et al. 2005. Sparse, environmentally selective expression of Arc RNA in the upper blade of the rodent fascia dentata by brief spatial experience. Hippocampus 15:579–86 [CrossRef] [Medline] [Web of Science ®]18. Chen LL, Lin LH, Green EJ, Barnes CA, McNaughton BL. 1994. Head-direction cells in the rat posterior cortex. I. Anatomical distribution and behavioral modulation. Exp. Brain Res. 101:8–23 [CrossRef] [Medline] [Web of Science ®]19. Claiborne BJ, Amaral DG, Cowan WM. 1986. A light and electron microscopic analysis of the mossy fibers of the rat dentate gyrus. J. Comp. Neurol. 246:435–58 [CrossRef] [Medline] [Web of Science ®]20. Cooper BG, Mizumori SJ. 1999. Retrosplenial cortex inactivation selectively impairs navigation in darkness. Neuroreport 10:625–30 [CrossRef] [Medline] [Web of Science ®]21. Cressant A, Muller RU, Poucet B. 1997. Failure of centrally placed objects to control the firing fields of hippocampal place cells. J. Neurosci. 17:2531–42 [Medline] [Web of Science ®]22. Dan Y, Poo MM. 2004. Spike timing-dependent plasticity of neural circuits. Neuron 44:23–30 [CrossRef] [Medline] [Web of Science ®]23. DiMattia BV, Kesner RP. 1988. Role of the posterior parietal association cortex in the processing of spatial event information. Behav. Neurosci. 102:397–403 [CrossRef] [Medline] [Web of Science ®]
24. Dolorfo CL, Amaral DG. 1998. Entorhinal cortex of the rat: topographic organization of the cells of origin of the perforant path projection to the dentate gyrus. J. Comp. Neurol. 398:25–48 [CrossRef] [Medline] [Web of Science ®]25. Dragoi G, Buzsáki G. 2006. Temporal encoding of place sequences by hippocampal cell assemblies. Neuron 50:145–57 [CrossRef] [Medline] [Web of Science ®]26. Eichenbaum H, Dudchenko P, Wood E, Shapiro M, Tanila H. 1999. The hippocampus, memory, and place cells: Is it spatial memory or a memory space? Neuron 23:209–26 [CrossRef] [Medline] [Web of Science ®]27. Ekstrom AD, Kahana MJ, Caplan JB, Fields TA, Isham EA, et al. 2003. Cellular networks underlying human spatial navigation. Nature 425:184–88 [CrossRef] [Medline] [Web of Science ®]28. Ekstrom AD, Meltzer J, McNaughton BL, Barnes CA. 2001. NMDA receptor antagonism blocks experience-dependent expansion of hippocampal “place fields.” Neuron 31:631–38 [CrossRef] [Medline] [Web of Science ®]29. Etienne AS, Jeffery KJ. 2004. Path integration in mammals. Hippocampus 14:180–92 [CrossRef] [Medline] [Web of Science ®]30. Fenton AA, Muller RU. 1998. Place cell discharge is extremely variable during individual passes of the rat through the firing field. Proc. Natl. Acad. Sci. USA 95:3182–87 [CrossRef] [Medline] [Web of Science ®]31. Ferbinteanu J, Shapiro ML. 2003. Prospective and retrospective memory coding in the hippocampus. Neuron 40:1227–39 [CrossRef] [Medline] [Web of Science ®]32. Foster DJ, Wilson MA. 2006. Reverse replay of behavioural sequences in hippocampal place cells during the awake state. Nature 440:680–83 [CrossRef] [Medline] [Web of Science ®]33. Frank LM, Brown EN, Wilson M. 2000. Trajectory encoding in the hippocampus and entorhinal cortex. Neuron 27:169–78 [CrossRef] [Medline] [Web of Science ®]34. Frank LM, Stanley GB, Brown EN. 2004. Hippocampal plasticity across multiple days of exposure to novel environments. J. Neurosci. 24:7681–89 [CrossRef] [Medline] [Web of Science ®]35. Fuhs MC, Touretzky DS. 2006. A spin glass model of path integration in rat medial entorhinal cortex. J. Neurosci. 26:4266–76 [CrossRef] [Medline] [Web of Science ®]36. Fyhn M, Hafting T, Treves A, Moser M-B, Moser EI. 2007. Hippocampal remapping and grid realignment in entorhinal cortex. Nature 446:190–94 [CrossRef] [Medline] [Web of Science ®]37. Fyhn M, Molden S, Hollup SA, Moser M-B, Moser EI. 2002. Hippocampal neurons responding to first-time dislocation of a target object. Neuron 35:555–66 [CrossRef] [Medline] [Web of Science ®]38. Fyhn M, Molden S, Witter MP, Moser EI, Moser M-B. 2004. Spatial representation in the entorhinal cortex. Science 305:1258–64 [CrossRef] [Medline] [Web of Science ®]39. Giocomo LM, Zilli EA, Fransen E, Hasselmo ME. 2007. Temporal frequency of subthreshold oscillations scales with entorhinal grid cell field spacing. Science 315:1719–22 [CrossRef] [Medline] [Web of Science ®]40. Gothard KM, Skaggs WE, McNaughton BL. 1996a. Dynamics of mismatch correction in the hippocampal ensemble code for space: interaction between path integration and environmental cues. J. Neurosci. 16:8027–40 [Medline] [Web of Science ®]41. Gothard KM, Skaggs WE, Moore KM, McNaughton BL. 1996b. Binding of hippocampal CA1 neural activity to multiple reference frames in a landmark-based navigation task. J. Neurosci. 16:823–35 [Medline] [Web of Science ®]42. Hafting T, Fyhn M, Molden S, Moser M-B, Moser EI. 2005. Microstructure of a spatial map in the entorhinal cortex. Nature 436:801–6 [CrossRef] [Medline] [Web of Science ®]43. Hafting T, Fyhn M, Moser M-B, Moser EI. 2006. Phase precession and phase locking in entorhinal grid cells. Soc. Neurosci. Abstr. 32:68.844. Hahn TT, Sakmann B, Mehta MR. 2007. Differential responses of hippocampal subfields to cortical up-down states. Proc. Natl. Acad. Sci. USA 104:5169–74 [CrossRef] [Medline] [Web of Science ®]45. Hampson RE, Heyser CJ, Deadwyler SA. 1993. Hippocampal cell firing correlates of delayed-match-to-sample performance in the rat. Behav. Neurosci. 107:715–39 [CrossRef] [Medline] [Web of Science ®]
46. Hargreaves EL, Rao G, Lee I, Knierim JJ. 2005. Major dissociation between medial and lateral entorhinal input to the dorsal hippocampus. Science 308:1792–94 [CrossRef] [Medline] [Web of Science ®]47. Harris KD, Csicsvari J, Hirase H, Dragoi G, Buzsáki G. 2003. Organization of cell assemblies in the hippocampus. Nature 424:552–56 [CrossRef] [Medline] [Web of Science ®]48. Harris KD, Henze DA, Hirase H, Leinekugel X, Dragoi G, et al. 2002. Spike train dynamics predicts theta-related phase precession in hippocampal pyramidal cells. Nature 417:738–41 [CrossRef] [Medline] [Web of Science ®]49. Hartley T, Maguire EA, Spiers HJ, Burgess N. 2003. The well-worn route and the path less traveled: distinct neural bases of route following and wayfinding in humans. Neuron 37:877–88 [CrossRef] [Medline] [Web of Science ®]50. Hassabis D, Kumaran D, Vann SD, Maguire EA. 2007. Patients with hippocampal amnesia cannot imagine new experiences. Proc. Natl. Acad. Sci. USA 104:1726–31 [CrossRef] [Medline] [Web of Science ®]51. Hasselmo ME, Schnell E, Barkai E. 1995. Dynamics of learning and recall at excitatory recurrent synapses and cholinergic modulation in rat hippocampal region CA3. J. Neurosci. 15:5249–62 [Medline] [Web of Science ®]52. Hebb DO. 1949. The Organization of Behavior. New York: Wiley53. Henze DA, Borhegyi Z, Csicsvari J, Mamiya A, Harris KD, Buzsáki G. 2000. Intracellular features predicted by extracellular recordings in the hippocampus in vivo. J. Neurophysiol. 84:390–400 [Medline] [Web of Science ®]54. Hill AJ. 1978. First occurrence of hippocampal spatial firing in a new environment. Exp. Neurol. 62:282–97 [CrossRef] [Medline] [Web of Science ®]55.Hopfield JJ. 1982. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79:2554–58 [CrossRef] [Medline] [Web of Science ®]56. Hubel DH, Wiesel TN, LeVay S. 1977. Plasticity of ocular dominance columns in monkey striate cortex. Philos. Trans. R. Soc. London B Biol. Sci. 278:377–409 [CrossRef] [Medline] [Web of Science ®]57. Huxter J, Burgess N, O'Keefe J. 2003. Independent rate and temporal coding in hippocampal pyramidal cells. Nature 425:828–32 [CrossRef] [Medline] [Web of Science ®]58. Huxter J, Senior T, Allen K, Csicsvari J. Trajectory and heading in theta-organized spike timing. Soc. Neurosci. Abstr. 33:640.1359. Isomura Y, Sirota A, Ozen S, Montgomery S, Mizuseki K, et al. 2006. Integration and segregation of activity in entorhinal-hippocampal subregions by neocortical slow oscillations. Neuron 52:871–82 [CrossRef] [Medline] [Web of Science ®]60. Jensen O, Lisman JE. 1996. Hippocampal CA3 region predicts memory sequences: accounting for the phase precession of place cells. Learn. Mem. 3:279–87 [CrossRef] [Medline] [Web of Science ®]61. Jensen O, Lisman JE. 2000. Position reconstruction from an ensemble of hippocampal place cells: contribution of theta phase coding. J. Neurophysiol. 83:2602–9 [Medline] [Web of Science ®]62. Ji D, Wilson MA. 2007. Coordinated memory replay in the visual cortex and hippocampus during sleep. Nat. Neurosci. 10:100–7 [CrossRef] [Medline] [Web of Science ®]63. Jog MS, Kubota Y, Connolly CI, Hillegaart V, Graybiel AM. 1999. Building neural representations of habits. Science 286:1745–49 [CrossRef] [Medline] [Web of Science ®]64. Johnson A, Redish AD. 2007. Neural ensembles in CA3 transiently encode paths forward of the animal at a decision point. J. Neurosci. 27:12176–89 [CrossRef] [Medline] [Web of Science ®]65. Jung MW, McNaughton BL. 1993. Spatial selectivity of unit activity in the hippocampal granular layer. Hippocampus 3:165–82 [CrossRef] [Medline] [Web of Science ®]66. Jung MW, Wiener SI, McNaughton BL. 1994. Comparison of spatial firing characteristics of units in dorsal and ventral hippocampus of the rat. J. Neurosci. 14:7347–56 [Medline] [Web of Science ®]67. Kentros C, Hargreaves E, Hawkins RD, Kandel ER, Shapiro M, Muller RU. 1998. Abolition of long-term stability of new hippocampal place cell maps by NMDA receptor blockade. Science 280:2121–26 [CrossRef] [Medline] [Web of Science ®]
68. Kjelstrup KB, Solstad T, Brun VH, Fyhn M, Hafting T, et al. 2007. Very large place fields at the ventral pole of the hippocampal CA3 area. Soc. Neurosci. Abstr. 33:93.169. Klink R, Alonso A. 1993. Ionic mechanisms for the subthreshold oscillations and differential electroresponsiveness of medial entorhinal cortex layer II neurons. J. Neurophysiol. 70:144–57 [Medline] [Web of Science ®]70. Kolb B, Sutherland RJ, Whishaw IQ. 1983. A comparison of the contributions of the frontal and parietal association cortex to spatial localization in rats. Behav. Neurosci. 97:13–27 [CrossRef] [Medline] [Web of Science ®]71. Lee AK, Wilson MA. 2002. Memory of sequential experience in the hippocampus during slow wave sleep. Neuron 36:1183–94 [CrossRef] [Medline] [Web of Science ®]72. Lee I, Yoganarasimha D, Rao G, Knierim JJ. 2004. Comparison of population coherence of place cells in hippocampal subfields CA1 and CA3. Nature 430:456–59 [CrossRef] [Medline] [Web of Science ®]73. Lengyel M, Szatmary Z, Erdi P. 2003. Dynamically detuned oscillations account for the coupled rate and temporal code of place cell firing. Hippocampus 13:700–14 [CrossRef] [Medline] [Web of Science ®]74. Leutgeb JK, Leutgeb S, Moser M-B, Moser EI. 2007a. Pattern separation in dentate gyrus and CA3 of the hippocampus. Science 315:961–66 [CrossRef] [Medline] [Web of Science ®]75. Leutgeb JK, Leutgeb S, Tashiro A, Moser EI, Moser M-B. 2007b. The encoding of novelty in the dentate gyrus and CA3 network. Soc. Neurosci. Abstr. 33:93.976. Leutgeb JK, Leutgeb S, Treves A, Meyer R, Barnes CA, et al. 2005. Progressive transformation of hippocampal neuronal representations in “morphed” environments. Neuron 48:345–58 [CrossRef] [Medline] [Web of Science ®]77. Leutgeb JK, Moser EI. 2007. Pattern separation and the function of the dentate gyrus. Neuron 55:176–78 [CrossRef] [Medline] [Web of Science ®]78. Leutgeb S, Colgin LL, Jezek K, Leutgeb JK, Fyhn M, et al. 2007. Path integration-based attractor dynamics in the entorhinal cortex. Soc. Neurosci. Abstr. 33:93.879. Leutgeb S, Leutgeb JK, Barnes CA, Moser EI, McNaughton BL, Moser M-B. 2005a. Independent codes for spatial and episodic memory in hippocampal neuronal ensembles. Science 309:619–23 [CrossRef] [Medline] [Web of Science ®]80. Leutgeb S, Leutgeb JK, Moser M-B, Moser EI. 2005b. Place cells, spatial maps and the population code for memory. Curr. Opin. Neurobiol. 15:738–46 [CrossRef] [Medline] [Web of Science ®]81. Leutgeb S, Leutgeb JK, Moser EI, Moser M-B. 2006. Fast rate coding in hippocampal CA3 cell ensembles. Hippocampus 16:765–74 [CrossRef] [Medline] [Web of Science ®]82. Leutgeb S, Leutgeb JK, Treves A, Moser M-B, Moser EI. 2004. Distinct ensemble codes in hippocampal areas CA3 and CA1. Science 305:1295–98 [CrossRef] [Medline] [Web of Science ®]83. Lever C, Wills T, Cacucci F, Burgess N, O'Keefe J. 2002. Long-term plasticity in hippocampal place-cell representation of environmental geometry. Nature 416:90–94 [CrossRef] [Medline] [Web of Science ®]84. Markus EJ, Qin YL, Leonard B, Skaggs WE, McNaughton BL, Barnes CA. 1995. Interactions between location and task affect the spatial and directional firing of hippocampal neurons. J. Neurosci. 15:7079–94 [Medline] [Web of Science ®]85. Martin PD, Berthoz A. 2002. Development of spatial firing in the hippocampus of young rats. Hippocampus 12:465–80 [CrossRef] [Medline] [Web of Science ®]86. Matthews DA, Nadler JV, Lynch GS, Cotman CW. 1974. Development of cholinergic innervation in the hippocampal formation of the rat. I. Histochemical demonstration of acetylcholinesterase activity. Dev. Biol. 36:130–41 [CrossRef] [Medline] [Web of Science ®]87. Maurer AP, VanRhoads SR, Sutherland GR, Lipa P, McNaughton BL. 2005. Self-motion and the origin of differential spatial scaling along the septo-temporal axis of the hippocampus. Hippocampus 15:841–52 [CrossRef] [Medline] [Web of Science ®]88. McClelland JL, Goddard NH. 1996. Considerations arising from a complementary learning systems perspective on hippocampus and neocortex. Hippocampus 6:654–65 [CrossRef] [Medline] [Web of Science ®]
89. McHugh TJ, Blum KI, Tsien JZ, Tonegawa S, Wilson MA. 1996. Impaired hippocampal representation of space in CA1-specific NMDAR1 knockout mice. Cell 87:1339–49 [CrossRef] [Medline] [Web of Science ®]90. McHugh TJ, Jones MW, Quinn JJ, Balthasar N, Coppari R, et al. 2007. Dentate gyrus NMDA receptors mediate rapid pattern separation in the hippocampal network. Science 317:94–99 [CrossRef] [Medline] [Web of Science ®]91. McNaughton BL, Barnes CA, Meltzer J, Sutherland RJ. 1989. Hippocampal granule cells are necessary for normal spatial learning but not for spatially-selective pyramidal cell discharge. Exp. Brain Res. 76:485–96 [CrossRef] [Medline] [Web of Science ®]92. McNaughton BL, Battaglia FP, Jensen O, Moser EI, Moser M-B. 2006. Path integration and the neural basis of the “cognitive map.” Nat. Rev. Neurosci. 7:663–78 [CrossRef] [Medline] [Web of Science ®]93. McNaughton BL, Morris RGM. 1987. Hippocampal synaptic enhancement and information storage within a distributed memory system. Trends Neurosci. 10:408–15 [CrossRef] [Web of Science ®]94. Mehta MR, Barnes CA, McNaughton BL. 1997. Experience-dependent, asymmetric expansion of hippocampal place fields. Proc. Natl. Acad. Sci. USA 94:8918–21 [CrossRef] [Medline] [Web of Science ®]95. Mehta MR, Lee AK, Wilson MA. 2002. Role of experience and oscillations in transforming a rate code into a temporal code. Nature 417:741–46 [CrossRef] [Medline] [Web of Science ®]96. Mehta MR, Quirk MC, Wilson MA. 2000. Experience-dependent asymmetric shape of hippocampal receptive fields. Neuron 25:707–15 [CrossRef] [Medline] [Web of Science ®]97. Mittelstaedt ML, Mittelstaedt H. 1980. Homing by path integration in a mammal. Naturwissenschaften 67:566–67 [CrossRef] [Web of Science ®]98. Muller RU, Kubie JL. 1987. The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells. J. Neurosci. 7:1951–68 [Medline] [Web of Science ®]99. Nadel L. 1991. The hippocampus and space revisited. Hippocampus 1:221–29 [CrossRef] [Medline]100. Nakazawa K, Quirk MC, Chitwood RA, Watanabe M, Yeckel MF, et al. 2002. Requirement for hippocampal CA3 NMDA receptors in associative memory recall. Science 297:211–18 [CrossRef] [Medline] [Web of Science ®]
101. Nitz DA. 2006. Tracking route progression in the posterior parietal cortex. Neuron 49:747–56 [CrossRef] [Medline] [Web of Science ®]102. O'Keefe J. 1976. Place units in the hippocampus of the freely moving rat. Exp. Neurol. 51:78–109 [CrossRef] [Medline] [Web of Science ®]103. O'Keefe J, Burgess N. 1996. Geometric determinants of the place fields of hippocampal neurons. Nature 381:425–28 [CrossRef] [Medline] [Web of Science ®]104. O'Keefe J, Burgess N. 2005. Dual phase and rate coding in hippocampal place cells: theoretical significance and relationship to entorhinal grid cells. Hippocampus 15:853–66 [CrossRef] [Medline] [Web of Science ®]105. O'Keefe J, Conway DH. 1978. Hippocampal place units in the freely moving rat: why they fire where they fire. Exp. Brain Res. 31:573–90 [CrossRef] [Medline] [Web of Science ®]106.O'Keefe J, Dostrovsky J. 1971. The hippocampus as a spatial map. Preliminary evidence from unit activity in 107. O'Keefe J, Nadel L. 1978. The Hippocampus as a Cognitive Map. Oxford: Clarendon108. O'Keefe J, Recce ML. 1993. Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3:317–30 [CrossRef] [Medline] [Web of Science ®]109. O'Keefe J, Speakman A. 1987. Single unit activity in the rat hippocampus during a spatial memory task. Exp. Brain Res. 68:1–27 [CrossRef] [Medline] [Web of Science ®]110. Olypher AV, Lansky P, Fenton AA. 2002. Properties of the extrapositional signal in hippocampal place cell discharge derived from the overdispersion in location-specific firing. Neuroscience 111:553–66 [CrossRef] [Medline] [Web of Science ®]111. O'Neill J, Senior T, Csicsvari J. 2006. Place-selective firing of CA1 pyramidal cells during sharp wave/ripple network patterns in exploratory behavior. Neuron 49:143–55 [CrossRef] [Medline] [Web of Science ®]112. Packard MG, McGaugh JL. 1996. Inactivation of hippocampus or caudate nucleus with lidocaine differentially affects expression of place and response learning. Neurobiol. Learn. Mem. 65:65–72 [CrossRef] [Medline] [Web of Science ®]113. Parron C, Save E. 2004. Evidence for entorhinal and parietal cortices involvement in path integration in the rat. Exp. Brain Res. 159:349–59 [CrossRef] [Medline] [Web of Science ®]114. Quirk GJ, Muller RU, Kubie JL. 1990. The firing of hippocampal place cells in the dark depends on the rat's recent experience. J. Neurosci. 10:2008–17 [Medline] [Web of Science ®]115. Quirk GJ, Muller RU, Kubie JL, Ranck JB Jr. 1992. The positional firing properties of medial entorhinal neurons: description and comparison with hippocampal place cells. J. Neurosci. 12:1945–63 [Medline] [Web of Science ®]116. Ranck JB. 1985. Head direction cells in the deep cell layer of dorsal presubiculum in freely moving rats. In Electrical Activity of the Archicortex, ed. G Buzsáki, CH Vanderwolf, pp. 217–20. Budapest: Akademiai Kiado117. Redish AD, Rosenzweig ES, Bohanick JD, McNaughton BL, Barnes CA. 2000. Dynamics of hippocampal ensemble activity realignment: time versus space. J. Neurosci. 20:9298–309 [Medline] [Web of Science ®]118. Ritter J, Meyer U, Wenk H. 1972. Chemodifferentiation of the hippocampus formation in the postnatal development of albino rats. II. Transmitter enzymes. J. Hirnforsch. 13:254–78119. Rolls ET. 1999. Spatial view cells and the representation of place in the primate hippocampus. Hippocampus 9:467–80 [CrossRef] [Medline] [Web of Science ®]120. Rolls ET, Stringer SM, Elliot T. 2006. Entorhinal cortex grid cells can map to hippocampal place cells by competitive learning. Network 17:447–65 [CrossRef] [Medline] [Web of Science ®]
121. Rolls ET, Treves A. 1998. Neural Networks and Brain Function. Oxford, UK: Oxford Univ. Press122. Samsonovich A, McNaughton BL. 1997. Path integration and cognitive mapping in a continuous attractor neural network model. J. Neurosci. 17:272–75123. Sargolini F, Boccara C, Witter MP, Moser M-B, Moser EI. 2006a. Grid cells outside the medial entorhinal cortex. Soc. Neurosci. Abstr. 32:68.11124. Sargolini F, Fyhn M, Hafting T, McNaughton BL, Witter MP, et al. 2006b. Conjunctive representation of position, direction and velocity in entorhinal cortex. Science 312:754–58 [CrossRef] [Web of Science ®]125. Save E, Guazzelli A, Poucet B. 2001. Dissociation of the effects of bilateral lesions of the dorsal hippocampus and parietal cortex on path integration in the rat. Behav. Neurosci. 115:1212–23 [CrossRef] [Medline] [Web of Science ®]126. Scoville WB, Milner B. 1957. Loss of recent memory after bilateral hippocampal lesions. J. Neurol. Neurosurg. Psychiatry 20:11–21 [CrossRef] [Medline] [Web of Science ®]127. Shapiro ML, Kennedy PJ, Ferbinteanu J. 2006. Representing episodes in the mammalian brain. Curr. Opin. Neurobiol. 16:701–9 [CrossRef] [Medline] [Web of Science ®]128. Shapiro ML, Tanila H, Eichenbaum H. 1997. Cues that hippocampal place cells encode: dynamic and hierarchical representation of local and distal stimuli. Hippocampus 7:624–42 [CrossRef] [Medline] [Web of Science ®]129. Skaggs WE, McNaughton BL. 1996. Replay of neuronal firing sequences in rat hippocampus during sleep following spatial experience. Science 271:1870–73 [CrossRef] [Medline] [Web of Science ®]130. Skaggs WE, McNaughton BL, Wilson MA, Barnes CA. 1996. Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences. Hippocampus 6:149–72 [CrossRef] [Medline] [Web of Science ®]131. Solstad T, Brun VH, Kjelstrup KB, Fyhn M, Witter MP, et al. 2007. Grid expansion along the dorso-ventral axis of the medial entorhinal cortex. Soc. Neurosci. Abstr. 33:93.2132. Solstad T, Moser EI, Einevoll GT. 2006. From grid cells to place cells: a mathematical model. Hippocampus 16:1026–31 [CrossRef] [Medline] [Web of Science ®]133. Squire LR, Stark CE, Clark RE. 2004. The medial temporal lobe. Annu. Rev. Neurosci. 27:279–306 [Abstract] [Medline] [Web of Science ®]134. Sutherland RJ, Whishaw IQ, Kolb B. 1988. Contributions of cingulate cortex to two forms of spatial learning and memory. J. Neurosci. 8:1863–72 [Medline] [Web of Science ®]135. Takahashi N, Kawamura M, Shiota J, Kasahata N, Hirayama K. 1997. Pure topographic disorientation due to right retrosplenial lesion. Neurology 49:464–69 [Medline] [Web of Science ®]136. Taube JS. 1995. Place cells recorded in the parasubiculum of freely moving rats. Hippocampus 5:569–83 [CrossRef] [Medline] [Web of Science ®]137. Taube JS. 1998. Head direction cells and the neurophysiological basis for a sense of direction. Prog. Neurobiol. 55:225–56 [CrossRef] [Medline] [Web of Science ®]138. Taube JS, Muller RU, Ranck JB Jr. 1990. Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis. J. Neurosci. 10:420–35 [Medline] [Web of Science ®]139. Tervo DGR, Karpova AY. 2007. Rapidly inducible, genetically targeted inactivation of neural and synaptic activity in vivo. Curr. Opin. Neurobiol. 17:In press [CrossRef] [Web of Science ®]140. Tolman EC. 1948. Cognitive maps in rats and men. Psychol. Rev. 55:189–208 [CrossRef] [Medline] [Web of Science ®]
141. Treves A, Rolls ET. 1992. Computational constraints suggest the need for two distinct input systems to the hippocampal CA3 network. Hippocampus 2:189–99 [CrossRef] [Medline] [Web of Science ®]142. Tsodyks M, Sejnowski T. 1995. Associative memory and hippocampal place cells. Int. J. Neural Syst. 6(Suppl.):81–86143. Tsodyks MV, Skaggs WE, Sejnowski TJ, McNaughton BL. 1996. Population dynamics and theta rhythm phase precession of hippocampal place cell firing: a spiking neuron model. Hippocampus 6:271–80 [CrossRef] [Medline] [Web of Science ®]144. Tulving E, Markowitsch HJ. 1998. Episodic and declarative memory: role of the hippocampus. Hippocampus 8:198–204 [CrossRef] [Medline] [Web of Science ®]145. Ulanovsky N, Moss CF. 2007. Hippocampal cellular and network activity in freely moving echolocating bats. Nat. Neurosci. 10:224–33 [CrossRef] [Medline] [Web of Science ®]146. Vazdarjanova A, Guzowski JF. 2004. Differences in hippocampal neuronal population responses to modifications of an environmental context: evidence for distinct, yet complementary, functions of CA3 and CA1 ensembles. J. Neurosci. 24:6489–96 [CrossRef] [Medline] [Web of Science ®]147. Wallenstein GV, Hasselmo ME. 1998. GABAergic modulation of hippocampal population activity: sequence learning, place field development, and the phase precession effect. J. Neurophysiol. 78:393–408 [Web of Science ®]148. Wills TJ, Lever C, Cacucci F, Burgess N, O'Keefe J. 2005. Attractor dynamics in the hippocampal representation of the local environment. Science 308:873–76 [CrossRef] [Medline] [Web of Science ®]149. Wilson MA, McNaughton BL. 1993. Dynamics of the hippocampal ensemble code for space. Science 261:1055–58 [CrossRef] [Medline] [Web of Science ®]150. Wilson MA, McNaughton BL. 1994. Reactivation of hippocampal ensemble memories during sleep. Science 265:676–79 [CrossRef] [Medline] [Web of Science ®]151. Witter MP, Amaral DG. 2004. Hippocampal formation. In The Rat Nervous System, ed. G. Paxinos, pp. 637–703. San Diego: Academic. 3rd ed.152. Witter MP, Moser EI. 2006. Spatial representation and the architecture of the entorhinal cortex. Trends Neurosci. 29:671–78 [CrossRef] [Medline] [Web of Science ®]153. Wood ER, Dudchenko PA, Eichenbaum H. 1999. The global record of memory in hippocampal neuronal activity. Nature 397:613–16 [CrossRef] [Medline] [Web of Science ®]154. Wood ER, Dudchenko PA, Robitsek RJ, Eichenbaum H. 2000. Hippocampal neurons encode information about different types of memory episodes occurring in the same location. Neuron 27:623–33 [CrossRef] [Medline] [Web of Science ®]155. Young BJ, Fox GD, Eichenbaum H. 1994. Correlates of hippocampal complex-spike cell activity in rats performing a nonspatial radial maze task. J. Neurosci. 14:6553–63 [Medline] [Web of Science ®]156. Zhang F, Wang LP, Brauner M, Liewald JF, Kay K, et al. 2007. Multimodal fast optical interrogation of neural circuitry. Nature 446:633–39 [CrossRef] [Medline] [Web of Science ®]157. Zinyuk L, Kubik S, Kaminsky Y, Fenton AA, Bures J. 2000. Understanding hippocampal activity by using purposeful behavior: place navigation induces place cell discharge in both task-relevant and task-irrelevant spatial reference frames. Proc. Natl. Acad. Sci. USA 97:3771–76 [CrossRef] [Medline] [Web of Science ®]158. Zugaro MB, Monconduit L, Buzsáki G. 2005. Spike phase precession persists after transient intrahippocampal perturbation. Nat. Neurosci. 8:67–71 [CrossRef] [Medline] [Web of Science ®]