Understanding memory through hippocampal remapping

Transcription

1 Review Understanding memory through hippocampal remapping Laura Lee Colgin, Edvard I. Moser and May-Britt Moser Kavli Institute for Systems Neuroscience and Centre for the Biology of Memory, Norwegian University of Science and Technology, 7489 Trondheim, Norway Memory interference is a common cause of forgetting. Interference is a byproduct of the need to balance the formation of well-differentiated representations against the ability to retrieve memories from cues that are not identical to the original experience. How the brain accomplishes this has remained elusive. Here we review how insights can be gained from studies of an apparently unrelated phenomenon in the rodent brain remapping in hippocampal place cells. Remapping refers to the formation of distinct representations in populations of place cells after minor changes in inputs to the hippocampus. Remapping might reflect processes involved generally in decorrelation of overlapping signals. These processes might be crucial for storing large numbers of similar experiences with only minimal interference. Introduction One of the major causes of forgetting is memory interference. Memory interference refers to antagonistic processes whereby new memories impair retrieval of previously learned information (retroactive interference) or earlier memories obstruct future learning (proactive interference). Interference is most likely to occur between memories that are similar. For example, if you went fishing on the fjord with your younger sister on Labour Day and then went fishing there again with your older sister on Constitution Day, interference might cause confusion about which holiday was spent with which sister. The brain might employ a neural mechanism that promotes the formation of dissimilar memory representations to guard against such memory interference (Figure 1). A mechanism of this kind would likely involve the hippocampus, a brain region known to be critical for memory formation. The hippocampus came to the forefront of the neuroscience of memory when Scoville and Milner [1] published the first study on H.M., a patient who had most of his two hippocampi surgically removed for treatment of epilepsy. The procedure caused severe memory deficits described as forgetting of the events of daily life as quickly as they occur. The discovery of a brain area of critical importance for memory formation came about at the same time as psychologists, most prominently Donald O. Hebb [2], formulated the first theories of the neural foundations of behaviour. Hebb s work convinced a generation of young behavioural scientists that the most complex functions of Corresponding authors: Colgin, L.L. (laura.colgin@cbm.ntnu.no); Moser, M.-B. (maybm@cbm.ntnu.no). the brain, including learning and memory, could be understood by studying the activity of neuronal populations. The observations on H.M. pointed to a brain area where such studies could begin. This, together with the development of technology for microelectrode recording, paved the way for the first observation of a reliable behavioural correlate in hippocampal neurons the discovery of place cells [3]. Place cells are neurons that are activated selectively when an animal moves through a particular location in space (the place field ). Based on the observation that a considerable fraction of the cell population in the hippocampus are place cells [4], the hippocampus was proposed to function as a spatial map [5], with many of the same properties of the cognitive map proposed by Tolman some decades earlier [6,7]. Hippocampal cells were shown to represent nonspatial information as well [8 11], suggesting that the hippocampal map also stores experiences associated with places in the environment. Implicit in these suggestions was the idea that place cells are essential carriers of hippocampal memory. Today, place cells remain one of the most remarkable neuronal correlates of behaviour in non-sensory and nonmotor areas of the brain. Place cells have taught much, not only about spatial representation in the brain but also about the general working principles of the hippocampus and cortical circuits in general. The use of place cells as a model system for studying neuronal computation rests on two important developments during the 1990s and 2000s. The first is the development of tools and concepts for population analyses. With the emergence of technology for multisite recording from large neuronal ensembles [12,13], it became possible to test theories of neuronal computation in interacting cell assemblies (e.g. [2,14,15]). It was shown that simultaneously recorded place cells provide highly accurate dynamic representations not only of the animal s current location in the environment [12] but also of environments it had experienced in the past [16 19]. This gradual appreciation of population codes in hippocampal place cells was accompanied by a second and equally important development the recognition that place cells are part of a larger system, consisting of several interrelated networks, each with a distinct function in computation of space and memory [20]. The computational functions of some of the subfields of the hippocampus are beginning to be revealed, and place cells are now known to interact with grid cells and head-direction cells in adjacent parahippocampal regions, including the medial entorhinal cortex [21 23]. With these recent /$ see front matter ß 2008 Elsevier Ltd. All rights reserved. doi: /j.tins Available online 5 August

2 Figure 1. Schematic illustrating how remapping might help prevent interference between similar memories. Separate memory representations for similar experiences can be formed when patterns of neural activity from the entorhinal cortex (EC) are remapped onto largely nonoverlapping cell ensembles in the hippocampus (subfield CA3 in this example). developments, place cells have become one of the best tools for understanding how representations of the outside world are computed in complex neuronal networks. The discovery of remapping One of the major discoveries to come out of studies of hippocampal place cells was the observation that place cells frequently alter their firing patterns in response to apparently minor changes in sensory or cognitive inputs [24]. Place fields can appear, disappear or move to unpredictable locations. Firing rates can also drastically change. Muller and colleagues proposed that such changes in firing activity constitute a remapping of the place cell representation of space. The discovery of remapping emphasised the multi-representational nature of the hippocampus, a property that is ideal for a structure involved in highcapacity memory storage. Moreover, remapping revealed that distinct representations could be produced for similar environments and thus provided researchers with a promising experimental model for studying separation of memories in the brain. In the first quantitative study of remapping, Muller and Kubie [25] set out to test how place cell firing properties would be affected by well-defined changes in the environment. They used a set of simple environments that were variations of the same basic apparatus placed in the same location. For each manipulation, only one aspect of the environment was actively changed while the other features were kept constant. Changing the shape of the testing enclosure from circular to rectangular resulted in a pattern of remapping in which cells either had unrelated fields in the two shapes or had fields in one shape but not the other. When the size of the environment was doubled, approximately half of the cells remapped. In the remaining cells, place fields scaled in size but stayed in the same relative position. Remapping was also observed in a subset of cells when animals were tested in the same environment in light versus darkness [26]. Subsequent studies showed that, in some animals, all simultaneously recorded place cells remapped in response to replacement of a white intramaze cue card with a black one [27] (Figure 2). In another study, remapping was obtained across the entire population of control animals when a familiar testing cylinder was replaced with a novel cylinder of a different colour [28]. At the time of these studies, it was quite remarkable that changes in location-specific firing would occur in response to relatively minor environmental manipulations. However, time has validated these early findings, and there are now numerous reports on remapping in the literature. The emergence of remapping appears to depend upon several factors including extent of differences between environments [29] and animals prior training experience [26,30]. Also, in some cases, remapping can need time to develop. Lever and colleagues [31] reported that repeated exposures to different shapes of enclosures were associated with an increase in remapping across training days, and the probability of remapping also increased with repeated presentations of an altered environment in an earlier study [27]. The studies described above involve remapping related to sensory changes, but motivational state and behavioural context are also important factors for inducing remapping in hippocampal place cells. In one of the first studies supporting this point [32], approximately one-third of cells Figure 2. Colour-coded firing rate maps (yellow = 0 Hz, purple = maximum rate) for hippocampal place cells recorded in a cylinder containing either a white or a black intra-maze cue card. (a) An example place cell recorded across four sessions in a cylinder with either the white or black cue card (as indicated above). Note the difference in place field locations for white and black sessions. (b) Three additional cells recorded in the cylinder for white and black cue card sessions. In these examples, place fields changed location or disappeared in response to the cue card substitution. Modified, with permission, from Bostock et al. [27]. 470

3 exhibited different firing fields when two different tasks, random foraging and moving systematically between goal locations, were performed in the same apparatus located in the same place. When both the task and the apparatus were changed, nearly all of the cells exhibited different place fields. In another study, Moita et al. [33] reported that place cells remapped between two training environments, one which was neutral and another in which rats underwent fear conditioning. In studies in which rats were trained to continuously alternate between left and right turns on T or W mazes [34,35], changes in the firing patterns of cells with fields on the central arm were observed. Some cells fired almost exclusively on either left or right turn trials, and some cells displayed the same fields but changed their firing rates on the two types of trials. Consistent with this, Ferbinteanu and Shapiro [36] found that place cell firing rates in a plus maze increased or decreased according to where the rat started or where it was headed. Remapping that occurs in response to changes in behavioural context might allow the animal to create independent representations of different consequences associated with similar stimuli. In a broader perspective, modulation of place cell activity by behavioural context is in agreement with the idea that place cells are involved in episodic memory operations. Different types of remapping Rate remapping and global remapping The diversity of remapping results seen across multiple studies raises the questions of how and why remapping occurs and what different patterns of remapping might represent in terms of memory encoding. Recently, it was reported that two main categories of remapping signify different types of environmental changes [37]. Training and testing animals in the same location using either different-coloured or different-shaped enclosures resulted in rate remapping, defined as substantial changes in firing rates accompanied by little to no shifts in place field locations (Figure 3, top). When the same animals were tested in identical enclosures placed in different locations, global remapping was observed [37]. Global remapping refers to arbitrary changes in both firing rates and firing fields (Figure 3, bottom) and is similar to the complete remapping described by Muller et al. [24]. Under some circumstances, global remapping can also be induced by salient changes in cue configurations without changing the location of the recording apparatus [27 29,38]. The likelihood of observing global remapping increases if the differences between the environments are more substantial or were exaggerated during the training history. In their 1987 study, Muller and Kubie observed global remapping between rectangular and circular environments in an identical location after animals had been pre-trained in another room where the circular and rectangular enclosures were placed side by side (i.e. in different locations; R.U. Muller and J.L. Kubie, pers. commun.). Wills and colleagues [30] found that global remapping could be more readily obtained within the same place if animals were initially trained in enclosures that differed in shape and colour and were built of different materials. In a later study, global remapping was induced in trials in the same location by changing the shape, colour and construction materials of the recording enclosure and in addition varying the floor texture and type of food reward [29]. Rate remapping and global remapping have been proposed to represent distinct hippocampal encoding systems [37]. In rate remapping, the population of active cells and the location of those cells place fields remain unchanged, suggesting that rate remapping preserves the population code for space and represents nonspatial stimuli through changes in firing rate. Firing rate variations might be a general way for the hippocampus to represent nonspatial aspects of an experience on top of a stable place code. Rate variations can express properties of experiences that change from one trial to the next [10,11] or between particular segments of the trial [34,35]. Rate changes can even reflect slight differences in sensory or motivational inputs on a second-to-second basis within the same trial [39,40]. The degree of rate changes can vary from one condition to the next, possibly as a function of the extent of differences between conditions. Global remapping differs from rate remapping in that it is an all-or-none phenomenon. Although changes in the animal s location virtually always elicit global remapping [37], this coding strategy can also be used to differentiate conditions at a single location [27 29,38]. Whether rate or global remapping takes place might be determined by the degree of difference between experimental conditions, with populations of cells being more likely to express global remapping when environmental changes are relatively substantial. Transitions between representations Most studies of remapping investigated the effects of discrete changes to the environment. Such discrete changes can under some circumstances give rise to slow transformations of hippocampal representations [31]. However, in the natural world, stimuli often change in a continuous manner. The hippocampus must then create dynamically changing representations of continually developing episodes, but how it accomplishes this was not apparent from results of standard remapping studies. Two recent studies [19,30] addressed the subject of how transitions between hippocampal representations occur in response to gradually accumulating changes in the environment. In these experiments, a flexible box that could be configured into many shapes was used as the recording apparatus. Animals were first familiarised with the square and circle versions of the enclosure for several days. Then, on the final test day, the enclosure was morphed through a series of different shapes that were intermediate between square and circle. In the study by Leutgeb and colleagues, rate remapping was observed between the original representations for square and circle enclosures. When the novel intermediate shapes were presented, firing rates increased or decreased gradually as the shapes progressively changed. In the study by Wills and colleagues, a different training protocol was employed to obtain global remapping between initial square and circle representations. Under these circumstances, representations for novel intermediate shapes switched abruptly between the two previously learned representations, falling into one or the other category depending on their degree of similarity to square 471

4 Figure 3. Colour-coded rate maps showing rate remapping and global remapping in six pairs of CA3 place cells (dark blue = 0 Hz; red = maximum firing rate, as shown on the far left and right of each row). Rats were tested in boxes with a different colour configuration in a constant location (rate remapping) or in identical boxes in different locations (A and B) (global remapping). In each panel, the left column shows rate maps for the condition where the cell had the highest peak rate (black or white in the rate remapping condition; room A or room B in the global remapping condition). Peak rates are indicated to the left. The middle column shows rate maps for the same cells in the condition with a lower peak rate. The scale is the same as for the left column. The right column contains the same data as the middle, but the colour maps are now scaled to their own maximum values (indicated to the right of each map). Note that firing locations remained constant in the rate remapping condition, whereas the intensities of firing differed strongly. In the global remapping conditions, both firing locations and firing rates were changed. Modified from Leutgeb et al. [37]. or circle. These results show that small transformations in the environment lead to gradual changes in representations when rate remapping is used to differentiate between stimuli. In global remapping, by contrast, representations are mutually exclusive, and transitions between representations occur abruptly at some point as differences in the environment increase. Results from the two morph studies have important implications for theoretical models of associative memory storage. Some of these models predict that memories are represented as stable activity states in the network known as attractors [15]. When these activity states are discrete and well separated, presentation of stimuli that are similar to previously learned inputs will induce retrieval of the attractor state that is most closely associated with that stimulus pattern [41 43]. Support for discrete attractor networks in hippocampus was provided by the results of Wills et al. [30] in which representations for square and circle shapes were believed to reflect two distinct attractors. By contrast, the results observed in the Leutgeb et al. study [19] are more consistent with an attractor landscape made up of many local attractors along a continuum [44,45]. In this type of scheme, new information can be differentiated via the formation of another local basin of attraction. The threshold for transitioning between neighbouring attractors is very low, such that small external perturbations can easily push the system out of one attractor state and into another [46]. This property of continuous attractors implies that new representations can form in response to minor changes in the environment and be readily associated with previously learned representations having nearby attractor basins. This might facilitate encoding of continuous yet distinguishable sequences or events. Partial remapping Typically, all cells in the place cell population coherently exhibit the same type of remapping in response to environmental manipulations [19,25,28,29]. However, discordance across representations within the population is sometimes observed (e.g. [26,47 50]), and this is known as partial remapping [24]. In partial remapping, different reference frames can be represented by different subsets of cells within the population. In a study in which rats were tested in two identical boxes connected by a corridor, the locations of firing fields in the two boxes were similar for some cells and different for others, indicating that different subsets of cells coded for room and box reference frames [51]. Additionally, in a study employing a rotating arena to dissociate local and global reference frames, different subsets of place cells represented the arena and the stationary background [52]. In a later study, Paz-Villagran and colleagues [53] trained animals in an enclosure consisting of a hexagonal box and a circular box connected by a runway. When the hexagon was replaced with a square, a large majority of cells with fields in that part of the apparatus remapped, whereas only a small percentage of cells with fields in the circle or on the runway remapped, suggesting that different portions of the environment were represented by separate maps. Taken together, the above results suggest that partial remapping might reflect the formation of multiple parallel maps for the various reference frames within a given environment, but these studies did not address the question of whether representations of different reference frames are activated simultaneously. Another study, however, found that different subsets of cells that represented different reference frames were not activated simultaneously [54]. Specifically, during trials in which a variable goal location overlapped with the place 472

5 field of a particular cell, the place cell did not fire, whereas cells that were tied to the goal location reference frame did fire. This result suggests that partial remapping might constitute an unstable state in which the hippocampus constantly fluctuates between maps for different reference frames. Such an unstable state might ultimately transition into a stable configuration. Indeed, in experiments in which local and distal reference frames were rotated in opposite directions, partial remapping patterns in the population evolved over time [47]. Specifically, an increase in novel representations that were not tied to rearrangements of local or distal reference frames was observed across the place cell population after many repetitions of trials. Thus, partial remapping might eventually converge over time toward a coherent population response representing the new relationship between stimuli in the environment. Origins of remapping CA3 To encode an episodic memory, the brain needs to integrate the spatial and nonspatial elements that comprise an individual episode. CA3 has been theorised to carry out this function by acting as an auto-associative memory network [14,41,42,55]. Due to its extensively interconnected recurrent collateral system [56], CA3 is believed to essentially function as a single unit that can associate arbitrary inputs. Sets of concurrent activity, conveying information about where an episode occurred and what occurred there, are associated and stored as a coherent event. The massive interconnectivity in CA3 also allows activation of a portion of the original elements in a memory representation to retrieve the memory representation in its entirety, a process known as pattern completion [41,43,57]. It is easy to imagine that the pattern completion process in CA3 could give rise to memory interference effects if representations for slightly different environments were too similar. Perhaps to counteract these effects, remapping is particularly pronounced in CA3 [37,58]. When identical environments are placed in different locations, resulting in global remapping, completely independent population codes develop in CA3. When different-shaped or -coloured environments are placed in the same location, exceptionally high rate remapping can be seen in CA3, with cells often changing their peak firing rate by an order of magnitude [37]. This strong differentiation of representations might be imposed on CA3 by inputs from dentate gyrus and layer II of medial entorhinal cortex (see below). Dentate gyrus Theoretical models have for several reasons predicted a crucial role for dentate gyrus (DG) in pattern separation, a process that reduces overlap and enhances dissimilarity between representations [42,43]. To reliably produce novel distinct memory representations in response to small changes in input in a model CA3 network, a nonnormally distributed pattern of very strong extrinsic input to CA3 is needed [42]. The unique mossy fiber projection from the DG to CA3 fulfills these requirements. A small number [59] of highly potent [60] synapses combined with sparse firing in the DG [61] create a strong and specific input to CA3 that might be sufficient to orthogonalise incoming patterns of activity. Recent empirical results support this theory. Small differences between environments placed in the same location were associated with rate changes within the different place fields of an individual DG granule cell that were so substantial that rate remapping in the DG was even more pronounced than in CA3 [62]. Another recent study showed that rate remapping between square and circle representations was absent in CA3 in mutant mice lacking the critical NMDA subunit NR1 in the DG [63]. Additionally, a recent human imaging study showed high activation in CA3 and DG during presentation of slightly modified versions of previously acquired stimuli [64]. These results are in line with the theory that DG input is crucial for CA3 pattern separation and further suggest that rate remapping in the hippocampus might originate in the DG. However, it is possible that the origins of rate remapping can be traced back even further, perhaps to lateral entorhinal cortex, which is believed to transmit nonspatial information to hippocampus [65 67]. Medial entorhinal cortex If rate remapping depends on the DG, where and how does global remapping arise? A potential answer to this question is provided by recordings from grid cells in medial entorhinal cortex (MEC). Grid cells are neurons with multiple spatial receptive fields that form a regularly spaced, triangular grid pattern that spans the entirety of a given environment [22]. Changes in environments in a common location that give rise to rate remapping in CA3 produce no changes in grid cell representations in layer II, the MEC layer that provides direct excitatory input to CA3. By contrast, grid cells shift their fields coherently relative to the surrounding environment under conditions in which sensory differences in environments placed in the same location are sufficient to induce global remapping in CA3 [29] (Figure 4a). When identical testing enclosures are presented in different rooms, another manipulation associated with global remapping in CA3, grid cells both shift and rotate their fields coherently. Furthermore, global remapping in CA3 is closely aligned in time with grid realignment in MEC (Figure 4b). These results raise the possibility that global remapping in hippocampus might result from grid realignment in MEC. It is not entirely clear how grid cell realignment could lead to global remapping in hippocampus, but two possible explanations were put forth by Fyhn and colleagues [29]. Both assume that a place cell will only fire when its grid cell inputs sufficiently overlap [68]. The first potential explanation further assumes the existence of column-like processing modules in MEC, an idea that is reasonable considering that neighbouring grid cells have similar spacing, orientation and field size [22]. During remapping, the different modules would undergo different degrees of rotation and translation. A given CA3 place cell would receive input from grid cells in several separate MEC modules along the septotemporal axis. The differential realignment across modules in distinct environments would lead to different patterns of overlap between grid cells and thus produce a new place field, or loss of a 473

6 Figure 4. Remapping and grid realignment in MEC. (a) Examples of colour-coded rate maps (left) for MEC grid cells recorded in square and circle enclosures at a constant location illustrate changes in grid cell firing patterns. A coherent grid cell realignment between square and circle is more clearly visualised in the colour-coded crosscorrelation matrices (Pearson product moment correlations) shown on the right for the same cells. (b) Global remapping in CA3 is closely aligned in time with grid realignment in MEC. Rats were trained to run in the same enclosure in light and darkness, and remapping was observed between the two conditions. When the lights were switched on while the rat was still in the box at the end of a dark session, the maps for the light condition instantly appeared at the same time in CA3 and MEC. Spatial correlations between rate maps for 1 min intervals and the average rate map for the initial light session in its entirety are plotted. Colour-coded rate maps for a pair of simultaneously recorded CA3 and MEC cells are shown (inset). Modified from Fyhn et al. [29]. previously existing place field, in CA3 (i.e. global remapping). The second potential explanation assumes a single continuous map of space in MEC. Identical recording enclosures in different spatial locations, or other conditions that lead to global remapping in CA3, would activate different portions of the universal MEC spatial map. Due to different spacings and orientations between grid cells, points of overlap would vary at different locations in the universal spatial map in MEC, and global remapping would occur in CA3. Considering that the entorhinal hippocampal network is a loop [69,70], one might argue that remapping could begin in CA3, for example, and cycle around to entorhinal cortex. However, there is currently no obvious mechanism to explain how the recruitment of different cell populations in the hippocampus would lead to a coherent shift in grid fields in the MEC. Although it has been proposed that place cell feedback to grid cells might be important for anchoring grids to the environment [71], recent studies have shown that grid cell activity persists for tens of minutes following complete inactivation of hippocampus [72,73]. The slower time course of place field formation in hippocampus compared to MEC [22,58] is also incompatible with a role for place cells in establishing new grid coordinates. These observations suggest that grid fields arise independently from hippocampal activity and thus that grid cell realignment is unlikely to be driven exclusively by place cell remapping. Finally, transfer of representations between hippocampus and entorhinal cortex might not be a linearly sequential process but instead might depend on the background state of the network. Hippocampal output of memory representations to cortex has been reported to occur during sharp wave-related states [74], and CA1 might receive input from CA3 and EC on different phases of theta [75]. These points, so far uninvestigated with regard to remapping, might be important for fully understanding the relationship between remapping in hippocampus and MEC. 474

7 CA1 and onward As described above, an increasing body of evidence suggests that the DG CA3 network plays a large role in the pattern separation process, so it is somewhat puzzling that remapping becomes less robust in CA1 [37,58].Itisof interest to understand the implications of this transformation of representations in CA1 because CA3 output must first pass through CA1 before being transmitted to cortex. According to the auto-associative memory theory, each CA3 cell contains a large amount of information that is vulnerable to degradation as it gets transmitted out of the network. To solve this problem, Treves and Rolls [57] proposed that an intermediate recoding stage is required, and CA1 is hypothesised to fulfill this role. There are more neurons in CA1 than in CA3 [76], and each CA3 pyramidal cell projects to a large number of CA1 pyramidal cells [77,78]. This divergence during recoding is thought to reduce the amount of information that is carried by individual neurons, while at the same time not decreasing the overall information content. It was further suggested that CA1 cells integrate information from CA3 and in this way produce a more conjunctive memory representation [79]. This idea is related to the auto-associative properties of the CA3 network. Retrieval of a memory in its entirety can occur following activation of only part of its elements, suggesting that several different simultaneously active CA3 ensembles make up a memory representation. Each element within the memory is thought to be coded by its own ensemble of CA3 cells with different ensembles linked together by auto-association. Individual CA1 cells would respond to concurrent firing across multiple CA3 ensembles, and consequently CA1 cells would represent combinations of elements of each episodic memory as opposed to individual elements. There is some indirect support for this hypothesis. In addition to the divergent connections described above, there are also convergent connections from CA3 to CA1 [80]. The individual synapses are weak and highly variable [80]. Thus, reliable discharge of a CA1 cell probably requires co-activation of multiple synapses from CA3 within a relatively short window of time, ensuring that CA1 responds preferentially to conjunctive inputs from CA3. As would be expected if CA1 representations were indeed more conjunctive, representations for different environments with common features are more correlated in CA1 than in CA3 [37,58]. It is possible that there is even further convergence and less individual coding as information moves downstream from CA1. Remapping in the subiculum is even less distinct than in CA1 [81], and conjunctive representations of spatial position, head direction and running speed are observed in deep layers of MEC that receive output projections from CA1 and subiculum [23]. Regardless of their conjunctive properties, representations in later processing stages are still likely to effectively separate memories of different environments. A recent study showed that different populations of cells are activated in posterior parietal cortex following experiences in distinct spatial and behavioural contexts [82], suggesting that the distinct memory representations that emerge during hippocampal remapping might be carried downstream to cortical targets. Relationship between remapping and behaviour If remapping is a neural mechanism for memory separation, remapped representations should be associated with different memories and therefore detectable changes in animals behaviour in memory tasks. Dislocation of hippocampal maps that follows displacement of environmental cues is associated with altered performance in place navigation tasks [83 85]. Thus far, however, few studies in the literature have addressed the question of whether remapping would be accompanied by retrieval of different memories. An indirect link between remapping and learning was reported by McHugh and colleagues [63]. In this study, CA3 rate remapping between similar environments was impaired in knockout mice with selective NMDA receptor deletions in DG, and these mice were also deficient at discriminating between similar contexts in a fear conditioning task. However, the results of this study are only correlational, meaning that some additional process might have been disrupted that impaired remapping and contextual conditioning via independent mechanisms. Another test of the relationship between remapping and behavioural performance was conducted by Jeffery and colleagues [86]. In this study, rats were trained to forage in a black square box for grains of rice and then to run to a particular corner when a tone sounded to indicate the imminent delivery of a food reward. When animals reached a criterion of eight consecutive correct choices in the task, the walls of the box were changed from black to white. The majority (85%) of stable place cells remapped in response to this change, meaning that they either started or stopped firing in the new box or shifted the location of their field. Performance following remapping remained significantly above chance. Thus, the authors concluded that place cell remapping is not coupled with navigational behaviour. However, even though task performance was significantly higher than expected after remapping, a significant (p < 0.01) decrease in performance did occur in association with remapping; mean performance decreased from 91% correct in the original black box to 70% correct in the white box. Preserved performance could be at least partially due to the use of a non-hippocampal-dependent beacon strategy [87], meaning that the rat could merely identify a room cue located behind the goal corner and move toward it. Such a strategy would be easy for the animals to implement owing to the richness of extra-maze cues and the simplicity of the box shape [88]. Countering this argument, the authors demonstrated that the task was hippocampal dependent because hippocampal-lesioned animals could not learn to perform above chance levels and were significantly impaired when compared to controls. Still, the lesion group was trained in the early stages of the study, a few weeks before the remapping data were obtained, and thus it is possible that the animals switched from a hippocampal-dependent strategy to a non-hippocampal-dependent strategy during this period. It is also possible that parallel representations were formed in other regions during this interval allowing the task to be solved by brain areas unaffected by remapping. Whatever the case may be, the body of work is too limited at this time to determine whether a link between remapping and behaviour does indeed exist. 475

8 Conclusions Much has been learned over the past two decades regarding the conditions that lead to place cell remapping, and these results have bolstered the view that place cells convey much more than just spatial information. Remapping incorporates information about space together with particular events and contexts that arise within that space, a function that is essential for episodic memory processing. Remapping also serves as a pattern separation mechanism that is likely important for reducing interference between related memories. Pattern separation is a general process that occurs throughout the brain. For example, cells in lateral prefrontal cortex respond differentially to different categories of stimuli even when stimuli features are highly similar [89]. Pattern separation occurs in cerebellum, where it is thought to involve divergence of incoming activity onto a large number of granule cells [90]. 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