Sage Journals: Discover world-class research

Abstract

Spatial memory has fascinated psychologists ever since the discipline began, but a series of findings beginning in the middle of last century propelled its study into the domain of neuroscience and helped bring about the cognitive revolution in psychology. Starting with the discovery that the hippocampus plays a central role in memory, particularly spatial memory, studies of the mammalian hippocampus and related regions over the latter half of the century slowly uncovered an extensive neural system involved in processing place, head direction, objects, speed and other spatially informative parameters. Meanwhile, the concurrent discovery of hippocampal synaptic plasticity allowed theoreticians and experimentalists to collaborate in linking spatial perception and memory, and genetic techniques developed towards the end of the century opened the door to circuit dissections of these processes. Building on these discoveries, spatial cognition and episodic memory may be the first cognitive competences understood across all levels from molecules to behaviour.

Keywords

Cognitive maps hippocampus neurobiology path integration reference frames rat spatial memory

For the first half of last century, behavioural neuroscience was dominated by associative explanations of learning and memory. These had begun with William James’ (1890) insights that memory depends on ‘brain paths’, followed by Pavlov’s (1927) seminal experiments on conditioned association formation, which together led over the next few decades to Behaviourism (Watson, 1958) and related associative learning theories. By the 1940s the first neural network models were emerging, formed from simple elements such as the McCulloch-Pitts neuron (McCulloch and Pitts, 1943), after which it was discovered that by endowing neural networks with the capacity for re-wiring according to simple rules like Hebb’s co-activation rule (Cooper, 2005; Hebb, 1949), networks could store and retrieve patterns of activation in a memory-like way. Two decades later, a physiological correlate of Hebb’s rule was discovered, in the form of the electrically induced phenomenon of ‘long-term potentiation’ (LTP; Bliss and Gardner-Medwin, 1973; Bliss and Lømo, 1973), shown to be dependent on a new type of neurotransmitter receptor, the coincidence-detecting N-methyl-d-aspartate (NMDA) receptor (Collingridge and Bliss, 1995; Collingridge et al., 1983). Thus, the earlier part of the 20th century saw steady recruitment of both theoretical and experimental evidence to support Behaviourism’s idea that learning is formed from associations between neurons, stemming from associations occurring in the real world.

The cognitive map

At around the middle of the 20th century, a quiet revolution against Behaviourism began. The start of this revolution is often attributed to Tolman (1948), who identified that some types of learning, such as the computing of novel short-cuts by rats in a maze, could not easily be explained with simple associative models but seem instead to require the existence of an internal representation of space. Although the ‘cognitivist’ models of Tolman and others did also assume association formation as a critical component of memory, these models also proposed that the brain constructs internal models of the world and operates on these independently of external experience, thus generating something akin to insight (Tolman and Honzik, 1930). In the case of spatial learning, Tolman and Honzik wrote ‘We believe that in the course of learning something like a field map of the environment gets established in the rat’s brain’ (Tolman, 1948), an idea that is generally called the ‘cognitive map’ theory of spatial learning. Such an internal representation, or map, could potentially account for flexible spatial responses such as short-cutting and detour-taking.

The proposal that there might be a map in the brain was not well received, possibly because in the popular conception of a map the terrain is viewed from above, and there is a one-to-one mapping (as it were) between points on the map and points in the world. The idea that a rat brain can make a map of this kind was quite reasonably dismissed. However, this is not what Tolman and Honzik meant. They elaborated as follows:

The stimuli, which are allowed in, are not connected by just simple one-to-one switches to the outgoing responses. Rather, the incoming impulses are usually worked over and elaborated in the central control room into a tentative, cognitive-like map of the environment. And it is this tentative map, indicating routes and paths and environmental relationships, which finally determines what responses, if any, the animal will finally release.

Thus, in their formulation, the representation in the brain is essentially associative except that internal processes also operate on the incoming signals so that spatial relations such as sequences of places (i.e., routes) can be inferred and represented. This prescient idea took many years to be accepted and it was not until single neuron studies began that unequivocal evidence for purely internal spatial processes, operating independently of external stimuli, finally emerged.

Place cells and head direction cells

The single-neuron discovery that helped cause the tide to start turning against Behaviourist explanations of spatial learning was O’Keefe’s discovery of the hippocampal place cells. Neuroscientific interest in the hippocampus had been growing since the report of Scoville and Milner (1957) that bilateral hippocampal damage in humans causes a profound anterograde amnesia. O’Keefe began recording neurons from the dorsal hippocampus of freely exploring rats (equivalent to the posterior hippocampus of humans) and found that single neurons from this region would become selectively active when the animal visited a particular region of the environment (O’Keefe and Dostrovsky, 1971), irrespective of exact sensory conditions (direction of entry to the place, etc.). He called these cells ‘place cells’ and suggested, together with the neuropsychologist Nadel, that the hippocampus might be the site of Tolman’s cognitive map (O’Keefe and Nadel, 1978).

The suggestion that the hippocampus might be for space, rather than memory, was not immediately accepted, but it was rapidly supported by evidence that hippocampally lesioned animals could not solve a radial maze task (Olton and Samuelson, 1976) or other spatial tasks (reviewed in O’Keefe and Nadel, 1978). Nor could lesioned animals learn to find a hidden platform in a tank of water, a task developed by Morris (1981) that lacks local cues to location and thus requires integration of environmental ‘allocentric’ cues for its solution. Hippocampal lesions proved devastating to this task (Morris et al., 1982), particularly when made to the dorsal region – ventral lesions left spatial processing much more intact (Moser et al., 1995), suggesting some other role for this region, perhaps in emotional processing and anxiety (Bannerman et al., 2004). Spatial tasks were also found to be severely disrupted by administration of pharmacologic agents that block synaptic plasticity, particularly those blocking the then-newly-discovered NMDA receptor (Morris et al., 1986), providing important support for the emerging synaptic-plasticity theory of memory formation. Studies of humans followed, and it was found that humans with unilateral medial temporal lobe damage (with spared memory) show subtle spatial mapping impairments (Maguire et al., 1996), and furthermore, that humans navigating virtual environments show functional magnetic resonance imaging (fMRI) activation of the hippocampus (Maguire et al., 1998). Most notably, Maguire et al. (2000) showed that London taxi drivers, who navigate all day long, show enlargement of the posterior hippocampus (homologous to the spatial dorsal pole of rodents), a report that won Maguire a coveted Ig Nobel Prize (Lenzer, 2003). Eventually, place cells were also found in humans too (Ekstrom et al., 2003). It is now generally accepted that the hippocampus is critical for large-scale spatial cognition (small-scale, local space being more the purview of the parietal cortex).

The study of place cells soon began to yield a number of findings of importance to understanding how animals represent space. It was rapidly determined that the representation is not a pixel map so much as a population code, inasmuch as a given place cell may have firing locations (place fields) in many environments (Alme et al., 2014), apparently randomly distributed. It was also soon discovered that although the cells use specifics of the sensory environment to establish their firing locations (Muller and Kubie, 1987), their activity is independent of exact sensory input (e.g., as O’Keefe noted, it does not depend on the direction through which the field is traversed) and seems to form an abstract representation of ‘place’ that uses sensory information in a flexible manner that seems more constructive than reflective. Another revealing early observation was that firing can be sustained in the dark (Quirk et al., 1990) and influenced by self-motion or ‘path integration’ information (Gothard et al., 1996; Jeffery et al., 1997), indicating an interaction between external environmental inputs and intrinsic processes (McNaughton et al., 2006; Samsonovich and McNaughton, 1997). The finding that place cells can switch between static, environmental cues (landmarks) and dynamic, movement information provides unequivocal evidence for Tolman and Honzik’s proposal that ‘the incoming impulses are usually worked over and elaborated’ – that is, the systems form a superordinate representation that is independent of exact sensory information. Thus, the place cell map is cognitive rather than simply associative (though it is associative as well).

The developing spatial view of the hippocampal system soon afterwards received additional support in the 1980s and 1990s from Ranck and colleagues, who reported the discovery of a new type of spatially sensitive neuron in the dorsal part of the presubiculum (Ranck, 1984; Taube et al., 1990a, 1990b). These ‘head direction cells’ did not seem to produce place fields (the localised patches of activity characteristic of place cells) but they would fire only when the rat faced in a particular direction. The head direction (HD) system was subsequently elaborated by Taube and others and found to be remarkably extensive, involving both cortical and subcortical structures (see Taube, 2007 for review), and it steadily became apparent that a directional signal likely forms the core of the spatial representation. An important insight that emerged from work on head direction cells is that activity across the cell population is highly coherent, inasmuch as the relative firing directions of the neurons seems to be the same in every environment. This observation led to proposals that the neurons are interconnected in a so-called attractor network (Skaggs et al., 1995; Zhang, 1996), a concept that was soon after recruited to explain why place cells often show collective responses (Samsonovich and McNaughton, 1997) and indeed, why our subjective sense of place and facing direction tends to be unitary (we never feel as though we are in two places at once).

Use of the place cell map

Following the discovery of place cells, intensive investigations began into how the map is configured and used. The 1990s were a fertile decade in this regard. An important discovery from this time was that during periods of restfulness or early (slow-wave) sleep, local field potential activity would switch from a rhythmic movement-related pattern known as theta to the more sporadic and abrupt, high frequency sharp-wave ripple pattern (Buzsáki et al., 1983), during which ensembles of place cells would reactivate (Pavlides and Winson, 1989; Wilson and McNaughton, 1994), often in the same sequence in which they had been active during waking (Lee and Wilson, 2002). This ‘replay’ activity was suggested to underlie memory consolidation, in line with Marr’s (1971) early suggestion that hippocampal memories are consolidated during offline states in neocortex, and this notion was supported by subsequent observations of neocortical ‘replay’ occurring in tandem with hippocampal replay (Ji and Wilson, 2007). Subsequently, Foster and Wilson (2006) found that replay could occur in reverse when a rat pauses at a goal location, suggesting a possible role for ‘reverse replay’ in learning the recent action sequences that led to reward (Foster et al., 2000). In addition, forward replay, or ‘preplay’, has been seen in animals deciding where to go next (Johnson and Redish, 2007; Ólafsdóttir et al., 2015; Pfeiffer and Foster, 2013), consistent with internal simulation of possible routes. Such observations add to the growing weight of evidence that animals not only form but also actively use internal representations of the world.

Although the place cells seem to form a ‘you are here’ signal, it was, and remains, unclear how the system is used in navigational planning, because little evidence has been seen of a goal signal hidden among place cell activity (Poucet et al., 2004). Indeed, place cells seemed rather uninterested in goals (Speakman and O’Keefe, 1990), although more recent studies have suggested a slight and unexplained tendency for place fields to creep towards goals (Dupret et al., 2010; Hollup et al., 2001; Lee et al., 2006). The replay studies discussed above suggest a potential role for place cells in learning rewarded versus unrewarded routes, but the weight of behavioural evidence suggests that animals are able to navigate more flexibly than simply by rote learning of routes. Although Tolman’s experiment has not been successfully replicated (Gentry et al., 1947; Grieves and Dudchenko, 2013; Muir and Taube, 2004), it is generally assumed that even rodents can likely execute simple forms of spatial inference such as short-cutting and detour planning, and of course, it is well accepted that humans can do this with ease. A challenge for the future then is to understand how the brain represents goals, how it computes optimal paths between them given the constraints of the environmental layout, and how it distinguishes perceived from remembered space.

The start of a new century, grid cells, and the way forward

By the end of the 20th century, it was well accepted that the hippocampal place cells form the core of a spatial cognition system (which most would call a cognitive map, despite a degree of remaining controversy – see Bennett, 1996), but the question of how this system contributes to cognition was only just beginning to find answers. One outstanding issue was whether the place cell map is metric – that is, has explicit information about distance and direction – or whether it is merely associative. The question of direction had seemed settled by the finding of head direction cells, but distance representation was not confirmed until the discovery of grid cells, first reported in 2005, which express multiple, regularly spaced place fields (Hafting et al., 2005). The first sight of the iconic ‘grid cell’ firing pattern was a flashbulb memory moment for many of us – that such a regular pattern could arise from the messy and unpredictable activity of neurons was truly amazing, and the finding electrified the field, being recognised fairly soon after by the award of the 2014 Nobel Prize in Physiology or Medicine to O’Keefe and the Mosers, ‘for their discoveries of cells that constitute a positioning system in the brain’. Intensive investigations of entorhinal cortex soon followed, and a large collection of spatial cell types (cells encoding combinations of direction, objects, borders, context, etc.) was eventually unearthed (Grieves and Jeffery, 2017; Hardcastle et al., 2017), the functions of which are just beginning to be understood.

Grid cell ‘grids’ are apparent from the animal’s first entry into a novel environment, and since there is nothing about most environments that could impart either the sixfold rotational symmetry or the even spacing between firing fields, two hypotheses immediately suggested themselves. The first is that as well as a direction signal, grid cells must be in receipt of intrinsic self-motion information, which as noted earlier is often called ‘path integration’ (although this term is more properly reserved for the spatial computations such as self-localisation, or computation of a homing vector, that arise from use of self-motion signals; Etienne and Jeffery, 2004). This means they can produce regular grids even when the environment is unfamiliar to the animal, which is potentially useful for remaining oriented while exploring a new environment. The second is that the grid cell signal might be how place cells make these computations – for example, positioning a firing field at some distance away from the environment walls, even in the dark. Grid cells have thus been considered to either receive or compute a path integration signal (McNaughton et al., 2006), and more recent work in humans has suggested that there may even be a grid-like system for representing non-spatial cognitive structures (Constantinescu et al., 2016).

The question of what grid cells are actually for is far from solved, and has been confounded by accumulating evidence that place cells can function quite well without a clear grid input (Brandon et al., 2014). Indeed, the question of what place cells are for has not been answered either. Studies of primates (human and non-human) find activity of both place and grid cells that seems to be influenced by visual scene as well as location (Ekstrom et al., 2003; Killian et al., 2012; Rolls, 1999), while much evidence now suggests that the cells encode not just current location but also other aspects of the animal’s situation including its intentions (Wood et al., 2000), recent and future navigational paths (see preceding section) and in some species, vectors to goals (Sarel et al., 2017) or information about items a subject has learned about (Ison et al., 2015).

Increasingly, it is being suggested that the entorhinal-hippocampal system is for more than just place, and that it may be part of a broader memory system, of which place is the fundamental organising principle (Buzsáki and Moser, 2013; Eichenbaum, 2017; Marr, 1971). The remainder of the 21st century will see the deployment of sophisticated new tools including genetic manipulation and new methods for recording neurons, as well as new behavioural methods for making cognitive processing explicit and measurable. These techniques will allow us to not just peer into the brain and observe Tolman’s cognitive map in operation, but to reach in and tweak it so that we can fully understand not just where it is but how it works, and why Nature has seen fit to make it the core of our entire autobiographical memory store.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

This work was supported by a Wellcome Trust award (103896AIA) to K.J.J.

References

Alme

Miao

Jezek

et al . (2014) Place cells in the hippocampus: Eleven maps for eleven rooms. Proceedings of the National Academy of Sciences of the United States of America 111(52): 18428–18435.

Bannerman

Rawlins

JNP

McHugh

et al . (2004) Regional dissociations within the hippocampus – Memory and anxiety. Neuroscience and Biobehavioral Reviews 28(3): 273–283.

Bennett

(1996) Do animals have cognitive maps? Journal of Experimental Biology 199(Pt 1): 219–224.

Bliss

Gardner-Medwin

(1973) Long-lasting potentiation of synaptic transmission in the dentate area of the unanaesthetized rabbit following stimulation of the perforant path. Journal of Physiology 232(2): 357–374.

Bliss

Lømo

(1973) Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. Journal of Physiology 232(2): 331–356.

Brandon

Koenig

Leutgeb

et al . (2014) New and distinct hippocampal place codes are generated in a new environment during septal inactivation. Neuron 82(4): 789–796.

Buzsáki

Moser

(2013) Memory, navigation and theta rhythm in the hippocampal-entorhinal system. Nature Neuroscience 16(2): 130–138.

Buzsáki

Leung

Vanderwolf

(1983) Cellular bases of hippocampal EEG in the behaving rat. Brain Research 287(2): 139–171.

Collingridge

Bliss

(1995) Memories of NMDA receptors and LTP. Trends in Neurosciences 18(2): 54–56.

10.

Collingridge

Kehl

McLennan

(1983) Excitatory amino acids in synaptic transmission in the Schaffer collateral-commissural pathway of the rat hippocampus. Journal of Physiology 334: 33–46.

11.

Constantinescu

O’Reilly

Behrens

TEJ

(2016) Organizing conceptual knowledge in humans with a gridlike code. Science 352(6292): 1464–1468.

12.

Cooper

(2005) Donald O. Hebb’s synapse and learning rule: A history and commentary. Neuroscience and Biobehavioral Reviews 28(8): 851–874.

13.

Dupret

O’Neill

Pleydell-Bouverie

et al . (2010) The reorganization and reactivation of hippocampal maps predict spatial memory performance. Nature Neuroscience 13(8): 995–1002.

14.

Eichenbaum

(2017) The role of the hippocampus in navigation is memory. Journal of Neurophysiology 117(4): 1785–1796.

15.

Ekstrom

Kahana

Caplan

et al . (2003) Cellular networks underlying human spatial navigation. Nature 425(6954): 184–188.

16.

Etienne

Jeffery

(2004) Path integration in mammals. Hippocampus 14(2): 180–192.

17.

Foster

Wilson

(2006) Reverse replay of behavioural sequences in hippocampal place cells during the awake state. Nature 440(7084): 680–683.

18.

Foster

Morris

Dayan

(2000) A model of hippocampally dependent navigation, using the temporal difference learning rule. Hippocampus 10(1): 1–16.

19.

Gentry

Brown

Kaplan

(1947) An experimental analysis of the spatial location hypothesis in learning. Journal of Comparative and Physiological Psychology 40(5): 309–322.

20.

Gothard

Skaggs

McNaughton

(1996) Dynamics of mismatch correction in the hippocampal ensemble code for space: Interaction between path integration and environmental cues. Journal of Neuroscience 16(24): 8027–8040.

21.

Grieves

Dudchenko

(2013) Cognitive maps and spatial inference in animals: Rats fail to take a novel shortcut, but can take a previously experienced one. Learning and Motivation 44(2): 81–92.

22.

Grieves

Jeffery

(2017) The representation of space in the brain. Behavioural Processes 135: 113–131.

23.

Hafting

Fyhn

Molden

et al . (2005) Microstructure of a spatial map in the entorhinal cortex. Nature 436(7052): 801–806.

24.

Hardcastle

Maheswaranathan

Ganguli

et al . (2017) A multiplexed, heterogeneous, and adaptive code for navigation in medial entorhinal cortex. Neuron 94(2): 375.e7–387.e7.

25.

Hebb

(1949) The Organization of Behavior: A Neuropsychological Theory. New York: Wiley.

26.

Hollup

Molden

Donnett

et al . (2001) Accumulation of hippocampal place fields at the goal location in an annular watermaze task. Journal of Neuroscience 21(5): 1635–1644.

27.

Ison

Quiroga

Fried

(2015) Rapid encoding of new memories by individual neurons in the human brain. Neuron 87(1): 220–230.

28.

James

(1890) The Principles of Psychology, vols 1 and 2. New York: Henry Holt.

29.

Jeffery

Donnett

Burgess

et al . (1997) Directional control of hippocampal place fields. Experimental Brain Research 117(1): 131–142.

30.

Wilson

(2007) Coordinated memory replay in the visual cortex and hippocampus during sleep. Nature Neuroscience 10(1): 100–107.

31.

Johnson

Redish

(2007) Neural ensembles in CA3 transiently encode paths forward of the animal at a decision point. Journal of Neuroscience 27(45): 12176–12189.

32.

Killian

Jutras

Buffalo

(2012) A map of visual space in the primate entorhinal cortex. Nature 491(7426): 761–764.

33.

Lee

Wilson

(2002) Memory of sequential experience in the hippocampus during slow wave sleep. Neuron 36(6): 1183–1194.

34.

Lee

Griffin

Zilli

et al . (2006) Gradual translocation of spatial correlates of neuronal firing in the hippocampus toward prospective reward locations. Neuron 51(5): 639–650.

35.

Lenzer

(2003) Study of London taxi drivers wins Ig Nobel prize. British Medical Association 327(7419): 831.

36.

McCulloch

Pitts

(1943) A logical calculus of ideas imminent in nervous activity. Bulletin of Mathematical Biology 5(1–2): 115–133.

37.

McNaughton

Battaglia

Jensen

et al . (2006) Path integration and the neural basis of the ‘cognitive map’. Nature Reviews Neuroscience 7(8): 663–678.

38.

Maguire

Burgess

Donnett

et al . (1998) Knowing where and getting there: A human navigation network. Science 280(5365): 921–924.

39.

Maguire

Burke

Phillips

et al . (1996) Topographical disorientation following unilateral temporal lobe lesions in humans. Neuropsychologia 34(10): 993–1001.

40.

Maguire

Gadian

Johnsrude

et al . (2000) Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences of the United States of America 97(8): 4398–4403.

41.

Marr

(1971) Simple memory: A theory for archicortex. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 262(841): 23–81.

42.

Morris

RGM

(1981) Spatial localization does not require the presence of local cues. Learning and Motivation 12(2): 239–261.

43.

Morris

RGM

Anderson

Lynch

et al . (1986) Selective impairment of learning and blockade of long-term potentiation by an N-methyl-D-aspartate receptor antagonist, AP5. Nature 319(6056): 774–776.

44.

Morris

RGM

Garrud

Rawlins

et al . (1982) Place navigation impaired in rats with hippocampal lesions. Nature 297(5868): 681–683.

45.

Moser

Forrest

et al . (1995) Spatial learning with a minislab in the dorsal hippocampus. Proceedings of the National Academy of Sciences of the United States of America 92(21): 9697–9701.

46.

Muir

Taube

(2004) Head direction cell activity and behavior in a navigation task requiring a cognitive mapping strategy. Behavioural Brain Research 153(1): 249–253.

47.

Muller

Kubie

(1987) The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells. Journal of Neuroscience 7(7): 1951–1968.

48.

O’Keefe

Dostrovsky

(1971) The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Research 34(1): 171–175.

49.

O’Keefe

Nadel

(1978) The Hippocampus as a Cognitive Map. Oxford: Clarendon Press.

50.

Ólafsdóttir

Barry

Saleem

et al . (2015) Hippocampal place cells construct reward related sequences through unexplored space. eLife 4: e06063.

51.

Olton

Samuelson

(1976) Remembrance of places passed: Spatial memory in rats. Journal of Experimental Psychology: Animal Behavior Processes 2(2): 97–116.

52.

Pavlides

Winson

(1989) Influences of hippocampal place cell firing in the awake state on the activity of these cells during subsequent sleep episodes. Journal of Neuroscience 9(8): 2907–2918.

53.

Pavlov

(1927) Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex (ed. Anrep

). Oxford: Oxford University Press.

54.

Pfeiffer

Foster

(2013) Hippocampal place-cell sequences depict future paths to remembered goals. Nature 497(7447): 74–79.

55.

Poucet

Lenck-Santini

Hok

et al . (2004) Spatial navigation and hippocampal place cell firing: The problem of goal encoding. Reviews in the Neurosciences 15(2): 89–107.

56.

Quirk

Muller

Kubie

(1990) The firing of hippocampal place cells in the dark depends on the rat’s recent experience. Journal of Neuroscience 10(6): 2008–2017.

57.

Ranck

Jr (1984) Head-direction cells in the deep cell layers of dorsal presubiculum in freely moving rats. Society for Neuroscience Abstracts 10: 599.

58.

Rolls

(1999) Spatial view cells and the representation of place in the primate hippocampus. Hippocampus 9(4): 467–480.

59.

Samsonovich

McNaughton

(1997) Path integration and cognitive mapping in a continuous attractor neural network model. Journal of Neuroscience 17(15): 5900–5920.

60.

Sarel

Finkelstein

Las

et al . (2017) Vectorial representation of spatial goals in the hippocampus of bats. Science 355(6321): 176–180.

61.

Scoville

Milner

(1957) Loss of recent memory after bilateral hippocampal lesions. Journal of Neurology, Neurosurgery, and Psychiatry 20(1): 11–21.

62.

Skaggs

Knierim

Kudrimoti

et al . (1995) A model of the neural basis of the rat’s sense of direction. Advances in Neural Information Processing Systems 7: 173–180.

63.

Speakman

O’Keefe

(1990) Hippocampal complex spike cells do not change their place fields if the goal is moved within a cue controlled environment. European Journal of Neuroscience 2(6): 544–555.

64.

Taube

(2007) The head direction signal: Origins and sensory-motor integration. Annual Review of Neuroscience 30: 181–207.

65.

Taube

Muller

Ranck

Jr (1990a) Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis. Journal of Neuroscience 10(2): 420–435.

66.

Taube

Muller

Ranck

Jr (1990b) Head-direction cells recorded from the postsubiculum in freely moving rats. II. Effects of environmental manipulations. Journal of Neuroscience 10(2): 436–447.

67.

Tolman

(1948) Cognitive maps in rats and men. Psychological Review 55(4): 189–208.

68.

Tolman

Honzik

(1930) ‘Insight’ in rats. University of California Publications in Psychology 4(14): 215–232.

69.

Watson

(1958) Behaviorism. Piscataway, NJ: Transaction Publishers.

70.

Wilson

McNaughton

(1994) Reactivation of hippocampal ensemble memories during sleep. Science 265(5172): 676–679.

71.

Wood

Dudchenko

Robitsek

et al . (2000) Hippocampal neurons encode information about different types of memory episodes occurring in the same location. Neuron 27(3): 623–633.

72.

Zhang

(1996) Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: A theory. Journal of Neuroscience 16(6): 2112–2126.

Cognitive representations of spatial location

Abstract

Keywords

The cognitive map

Place cells and head direction cells

Use of the place cell map

The start of a new century, grid cells, and the way forward

Footnotes

Declaration of conflicting interests

Funding

References