Department of Psychological & Brain Sciences, Center for Cognitive Neuroscience, Dartmouth College, 6207 Moore Hall, Hanover, NH 03755, USA
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Abstract |
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Introduction |
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Fundamental Properties |
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Brain Areas that Contain HD Cells |
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The percentage of cells that are classified as HD cells varies among different areas. No brain area contains solely HD cells. They are most abundant in the ADN where about 60% of the cells exhibit directional firing (Taube, 1995). The percentage of HD cells in other brain areas are estimated to be as follows: postsubiculum, 25% (Taube et al., 1990a
); LMN, 25% (Stackman and Taube, 1998
); retrosplenial cortex, 10% (Cho and Sharp, 2001
); lateral dorsal thalamus, 30% (Mizumori and Williams, 1993); striatum, 6% (Mizumori et al., 2000
). Because the ADN contains such a high percentage of HD cells, it might be interesting to consider how many total HD cells are present within this nucleus. Since each population of ADN HD cells potentially constitutes a complete representation of 360° of space, knowing the absolute number of cells in this population may set useful constraints for modeling how directionally selective networks arise and behave. We therefore estimated the number of cells in the rat ADN by estimating the neuronal density in one coronal brain section stained with thionin at 1.35 mm posterior to bregma. Neurons occupied
51.6% of tissue space. We then estimated the total volume of ADN based on the cross-sectional area through seven equally spaced 30 µm sections and calculated the total number of cells based on the average neuronal density. With a total volume of 145.81 (106) µm3 and an average cell volume of 3900 µm3 we estimate that the total number of neurons in the ADN is
19 307. If 60% of the cells in this nucleus are classified as HD cells, then there are
11 584 total HD cells in the ADN on one side of the brain. While this number should be viewed as a rough estimate, it nonetheless provides a starting point for constructing realistic network models concerned with HD cell discharge. Mulders et al. (1997) estimated the number of cells in layers II/III of the rat presubiculum to be 334 000 and estimated the number of cells in layers V/VI of combined presubiculum + parasubiculum to be 218 000. Using these values, we estimate the total number of cells in the postsubiculum (the dorsal portion of presubiculum) to be
227 000 and if 25% of these cells are classified as HD cells, then there are
56 750 HD cells in the postsubiculum. Table 1 summarizes these estimates.
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Temporal HD Cell Properties |
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Generation of the HD Cell Signal |
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To date, all lesion studies are consistent with this hierarchical processing scheme. Lesions of the ADN abolish directional tuning in the postsubiculum, but not the reverse (Goodridge and Taube, 1997). Bilateral, but not unilateral, lesions of the LMN abolishes HD cell activity in the ADN (Tullman and Taube, 1998
; Blair and Sharp, 1999). The primary projections to the LMN originate in the DTN and lesions of this structure abolish directional firing in the ADN (Bassett and Taube, 2001b
). Lesions in other brain areas that either contain HD cells, or have intimate connections with ADN, do not abolish the directional signal. Thus, lesions of the hippocampus, lateral dorsal thalamus, or retrosplenial cortex do not disrupt the directional-specific firing of cells in ADN (Golob and Taube, 1997
; Golob et al., 1998
; Bassett and Taube, 1999
). Preliminary studies also indicate that lesions of the posterior parietal cortex do not abolish HD cell activity in ADN (Calton and Taube, 2001
). Together with the results from the labyrinthectomies, these lesion studies clearly indicate that subcortical sites are critical for the generation of HD cell activity in limbic system areas.
What types of signals are important for generating HD cell activity? For the most part, HD cells appear confined to rostral areas within the brain including diencephalic and telencephalic structures. But the generation of the directional signal stems from areas within the brainstem that contain cells that are sensitive to the animals angular head velocity most notably the DTN and nPH. Except for a few cells that were sensitive to directional heading, the most common neural correlate for DTN cells was angular head velocity, with 75% of the cells sensitive to this parameter (Bassett and Taube, 2001a
; Sharp et al., 2001b
). Bassett and Taube (2001a
) observed two different types of angular head velocity cells. One type increased its firing rate proportionately to the speed to which the animal turned its head in either direction clockwise or counter-clockwise (Fig. 1D). These cells were referred to as symmetric angular head velocity cells and accounted for almost 50% of the total cells in the DTN. The firing rate of the second type was positively correlated to the speed at which the animal turned its head, but only in one turn direction. Turning in the opposite direction led either to no change (Fig. 1F) or a decrease in the cells activity (Fig. 1E). These cells were referred to as asymmetric angular head velocity cells and composed
25% of the DTN population. Most, but not all, asymmetric cells were localized to the hemisphere opposite the preferred turning direction. The firing of about half of the angular head velocity cells, both symmetric and asymmetric, was also modulated by the animals linear velocity, although no cells firing rate correlation to linear velocity exceeded its correlation to angular head velocity.
Vestibular nucleus neurons are often classified by their pattern of firing when the head is stationary (Goldberg and Fernández, 1971). Regular firing neurons discharge in a regular, periodic pattern with a fixed interspike interval. In contrast, irregular firing neurons discharge more variably with no definitive peak in the interspike interval histogram. Both symmetric and asymmetric angular head velocity neurons in the DTN were found to contain irregular discharge patterns.
Angular head velocity neurons are also found in the LMN where they constitute 43.7% of the cells (Stackman and Taube, 1998
; Bassett and Taube, 2001a
). Both symmetric and asymmetric cells are found within the LMN (termed fast angular head velocity cells and compose 52.5% of the angular head velocity cells), in addition to a third type of angular head velocity cell, where firing rate is negatively correlated with the speed of head turn for both turn directions. These latter cells are referred to as slow angular head velocity cells and were not observed in the DTN. These slow angular head velocity cells accounted for 47.5% of the angular head velocity cells within LMN. In general, the properties of fast LMN angular head velocity cells are similar to those in the DTN, although the correlation to angular head velocity is not as tight in the LMN compared to the DTN. Furthermore, the slope of the relationship between firing rate and angular head velocity is steeper for DTN than LMN cells. Finally, the firing of
10% of the cells in the postsubiculum was correlated to angular head velocity all the cells were classified as the asymmetric type (Sharp, 1996
).
Cell count analyses for LMN and DTN similar to those used above for ADN resulted in a total of 4256 and 1832 cells for each area, respectively. These values indicate that LMN contains 1064 HD cells, 979 fast angular head velocity cells, and 881 slow angular head velocity cells. The DTN contains
916 symmetric and 458 asymmetric angular head velocity cells. The number of HD cells estimated for LMN compared to ADN on the order of 1000 versus 10 000 raises an interesting question: if the directional signal is already generated at the level of the LMN, then what function is served by having a tenfold increase in the number of cells encoding directional heading at the next rostral brain site?
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Active versus Passive Motion |
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How these seemingly disparate findings are to be resolved is presently unclear. It is possible that the angular head velocity signals in DTN represent motor and/or proprioceptive information rather than processed vestibular signals. The importance of motor/proprioceptive cues to HD cell discharge has to date been underestimated. Its importance is supported by recent findings showing that vestibular inputs were insufficient to maintain a HD cells preferred firing direction when the animal was passively transported to a novel environment in the dark active locomotion between the familiar and novel environments did not lead to this shift in the cells preferred direction (Stackman et al., 2003). Passive transportation to the novel environment would have activated the vestibular system without normal motor inputs. Other findings supporting a role for motor cues are the anticipatory properties of HD cells discussed above. These properties are more easily accounted for by motor/proprioceptive cues rather than vestibular or other sensory cues. In addition, it is clearly evident that the angular head velocity signals in the DTN have been highly processed because all angular velocity signals in the vestibular nucleus are of the asymmetric type (respond to head turns with increased firing in one direction and decreased firing in the opposite direction) whereas a significant portion of the neurons in the DTN show symmetric characteristics (respond with increased firing in both turn directions). In sum, motor and/or proprioceptive inputs are important contributors to the HD cell signal.
For these reasons it would be interesting to know how angular head velocity signals in the DTN or LMN respond to passive rotations. Would their firing remain robust as in the second order vestibular neurons or would their firing be attenuated similar to HD cells? The one study that addressed this issue examined passive rotations for 12 angular head velocity cells in the DTN and obtained mixed results (Sharp et al., 2001b). Most cells became quiescent or lost their angular head velocity correlate during the passive rotations while only a few cells maintained their angular velocity correlates. These results emphasize the importance of motor cues for many of the DTN cells and provide further support for the notion that motor cues play an integral role in HD cell firing.
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Modeling the HD Signal Using Attractor Networks |
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This type of network architecture is particularly suitable for modeling the HD signal because attractor networks are generally stable and do not require external inputs to maintain their stable state. Instead, the external inputs function to move the hill to a new stable state when the animal turns its head. Attractor networks are also capable of performing a mathematical integration in time. Again, this is an attractive characteristic because starting from a known directional heading, a mathematical integration in time of angular head velocity yields angular head displacement. Perhaps a neural integrator, similar to the one involved in the vestibulo-ocular reflex (VOR), is present in the DTN LMN pathway and is responsible for generating the HD cell signal. According to this view, the synaptic connections between different HD cells would maintain a stable state when the head is fixed. When the head moves, an angular head velocity signal from either vestibular or motor inputs would be integrated in time by the attractor network and move the activity hill to a new stable state. The amount the hill moves corresponds to the amount the head is displaced which is the integral of the angular head velocity signal. The activity of the hill is sustained (i.e. no adaptation of firing) either through the tonic firing of excitatory afferents in the recurrent network or by the internal membrane properties of the cells. In either case, once the new hill of activity is initiated it is maintained indefinitely until the next perturbation. Attractor network dynamics can also account for the slightly higher firing rates seen for faster head turns in LMN and ADN HD cells (Goodridge and Touretzky, 2000
).
While attractor networks are appealing models for generating the HD cell signal they must be able to fulfill several experimental conditions in addition to satisfying the basic firing properties described above:
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Key Information Needed |
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Each of the brain areas where HD cells are present contains many neurons that are not characterized by direction-specific firing. What is the role of these neurons? Are they interconnected with the local HD cells? Are angular head velocity cells within the LMN interconnected with HD cells in this same area? Is the connection only one way or is it reciprocal? Moreover, are all three types of angular head velocity cells within LMN (fast symmetric, fast asymmetric, slow) connected with the HD cells? Which types of DTN angular head velocity cells (symmetric or asymmetric) project to LMN? Finally, what cell types project back to DTN is it the HD cells or is it angular head velocity cells, and if the latter, which type of angular head velocity cells? It is known that the LMN cells that project to the ADN are the same cells that project back to the DTN (Hayakawa and Zyo, 1989). As one can see, these are all important anatomical questions that must be addressed if researchers want to model the generation of the HD cell signal accurately.
An even more fundamental issue is whether the HD cells in postsubiculum, ADN, and LMN project directly to their principal target regions namely the entorhinal cortex, postsubiculum and ADN, respectively. While it is assumed that HD cells are the projection cells in these brain areas, there is no direct evidence supporting this notion, and it is quite possible that non-HD cells in these areas are the projection neurons. Simultaneous recording of a HD cell while trying to stimulate it antidromically from a downstream projection area could provide information on this important issue. In addition, as mentioned above, peripheral vestibular lesions abolish the direction-specific activity of HD cells. How these lesions influence the angular head velocity cells in the DTN and LMN is not known. Addressing this issue would provide important information on how HD cell information is processed.
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Comparison to the Oculomotor System |
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Oculomotor integrator neurons have been observed in both mammals (Lopez-Barneo et al., 1982; McFarland and Fuchs, 1992
) and fish (Pastor et al., 1994
); they discharge linearly as a function of eye position, with higher firing rates reached at more eccentric positions in the orbit. Bursting inputs to the integrator neurons correspond to motor signals that drive movement of the eye, while sustained firing persisting at a constant rate corresponds to the integrated position signal (Robinson, 1989
). At this level, there is a clear parallel between the excitatory or inhibitory bursts that drive oculomotor integrator neurons to higher or lower persistent firing rates, and putative bursts of clockwise or counterclockwise vestibular modulation signaling angular head velocity that forces the activity hill around a HD cell ring attractor. This parallel makes clear several critical comparisons between the two systems.
In the oculomotor integrator, a single neuron may vary its firing rate linearly with eye position and will not fire at all below a threshold position. HD cells fire linearly as a function of angular displacement from the preferred firing direction and will cease firing beyond a threshold angle that corresponds with the directional firing range. As with the peak firing rate and the directional firing range of HD cells, the slope of firing rate to position and the positional threshold of oculomotor integrator neurons vary from one neuron to another. A property of oculomotor integrator neurons that is useful for experimental purposes is that they are not perfect; there is a tendency to leak in these imperfect integrators. An animal attempting to hold an eccentric eye position in the dark (thereby having no visual stimuli to direct gaze) will inevitably exhibit a slow drift of the eye back toward a neutral position in the orbit, often referred to as the zero point. This neutral position is determined by the alignment and elasticity of the eye muscles. The drift in eye position toward orbit-zero correlates to a decline in the firing rate of the oculomotor integrator neurons according to a characteristic time constant. As the eye continues to drift, the oculomotor neurons reach quiescence below their positional thresholds.
HD cells, in contrast, do not have a clearly identifiable directional zero. If we are to continue the analogy between the two systems further, then an important question is whether the neural integrator in the HD system is perfect or imperfect. Clearly, the HD cell system does not have the same physical constraints as the oculomotor system, which requires mechanisms to overcome the elastic properties of the eye muscles and the viscous drag of the orbit. Since the HD cell network is not considered a motor system controlling head movement, there is no a priori reason for why the HD cell system could not contain a perfect integrator. On the other hand, if the integrator is imperfect, then how are we to conceptualize leak in the HD cell system? In the oculomotor system, leak corresponds both to a decline in firing rate, and movement of the eye toward zero, and the two are closely correlated because they are causally related. The firing rate of a HD cell and its preferred firing direction do not have such a clear relationship. In fact, if one were to record a declining firing rate from a single HD cell, it would be impossible to distinguish between movement of the activity hill away from the cells preferred firing direction and decay of activity in the system overall, yet leak in the HD integrator could be conceptualized as taking either of these two forms.
When HD cells are recorded in the dark, the preferred firing directions often drift continuously over several min (Goodridge et al., 1998). This drift in preferred firing direction is reminiscent of the drift of the eye in the dark from an eccentric to a central position , and suggests a failure of an integrator in the HD cell network. Thus, the eventual outcome of leak in the HD cell system might be an activity hill drifting freely with respect to the animals actual directional heading. To test this hypothesis, we would want to record HD cells in the dark while the animal is immobile, pointing in the direction of a recorded cells preferred firing direction. If the cell continues to fire indefinitely at its peak rate, then the integrator is perfect. If, over time, the peak firing rate of the cell declined, and the firing rate of a cell with a different preferred firing direction increased, then we could conclude that leak in the HD cell system results in drift of the directional signal (Fig. 3A). But should such a drift be thought of as leak? It could be, from the standpoint of the single HD cell being monitored. In this scenario, after a head turn, the cell loses its activation in the absence of visual or vestibular information. On the other hand, what makes it unlike an oculomotor integrator neuron is that the activation is preserved elsewhere in the network, so as the firing rate declines in one cell, it must increase in another by virtue of the network connectivity. The zero point, then, would correspond to a state of constant instability of the activity hill, driven simply by the aggregation of small asymmetries in the velocity input while the head is motionless, rather like an eye suddenly cut free from the ocular muscles and left to drift in the socket. Furthermore, drift of the activity hill would imply that the persistent activity observed in HD cells is attributable primarily to the internal connectivity of the HD network, rather than depending upon extrinsic excitation, since some signal would persist even with no afferent input.
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The results from vestibular lesion studies imply that the role of the vestibular system goes beyond moving the hill of activity around the network. It indicates that the vestibular system is particularly involved in driving the generation and persistence of the HD cell signal. If this situation is true, then a HD cell firing constantly in its preferred firing direction following a head turn may be seen as the integration of the velocity signal that corresponds to the head turn, just as the position-related firing of the oculomotor integrator neuron represents the integration of the velocity-related saccadic burst that moved the eye to its new position. It should be noted that when a rat is motionless and the head is at zero angular velocity, there is still a strong signal that could feed forward onto the HD cell network because of the tonic high firing rates in neurons within the vestibular nuclei. This condition is in contrast to the situation following labyrinthectomies when the activity hill in the attractor network has permanently broken down despite the eventual recovery of tonic firing rates within the vestibular nuclei (Stackman and Taube, 1997; Ris and Godaux, 1998
).
Oculomotor integrator neurons continue to fire while holding the eye in an eccentric position, but some authors have postulated another type of integrator that resets to zero at the completion of each saccade (Jurgens et al., 1981; Kustov and Robinson, 1995
). In these models, a velocity-to-displacement integration occurs, but the eye displacement signal resets to zero immediately after each saccade by a rapid leak, so that the next saccade can be coded in terms of displacement from the current position. If an updated directional signal following a head turn is the result of an integration of angular velocity over time, then the HD cell system may also be comparable to the resettable displacement integrator in models of saccadic eye movements. Thus, each stable head position becomes a relative zero point to which the next integrated displacement is added when the head turns. In the light, resetting may be caused or facilitated by visual feedback from the environment. In the dark, the successive addition of displacement values without visual feedback may be responsible for the accumulation of error and observed as the preferred firing direction drifting in the dark. This suggestion is intriguing because it implies that a memory of the previous head direction is briefly preserved in the network, and is compared to a representation of desired head direction, an idea that is notable in light of the anticipatory firing properties of HD cells in the LMN and ADN (Blair and Sharp, 1995
; Taube and Muller, 1998
). As a measurable phenomenon, leak in this case would be altogether different than in our previous comparison and would not be evident in the firing activity of an isolated HD cell. In our postulated experiment described above, if the head were fixed in the dark after a single head turn, we would expect the HD integrator to reset to zero at the new direction and an external perturbation of the system would be required to elicit a drift of the activity hill back toward the original head direction. Such a finding would serve as indirect evidence of an incompletely reset displacement signal.
In addition to the oculomotor integrator and the resettable integrator of the saccade system, there is another example of a neural integrator that prolongs a velocity signal, in this case one for the angular velocity of head turns. Within the brainstem circuits that mediate vestibular, vestibulo-ocular, and optokinetic reflexes, vestibular signals outlast the afferent signals in the VIIIth nerve. The longer time constant for the decay rate of vestibular signals in secondary vestibular neurons and other brainstem areas, including the nucleus prepositus, is referred to as velocity storage. This process is thought to play an important role in improving the VOR at low frequency (long duration) head turns and also functions to realign the eye movement axes with the direction of the current gravito-inertial acceleration of the organism (Leigh and Zee, 1999). There is also evidence that the vestibular signal subserving the subjective experience of angular velocity is affected by velocity storage (Okada et al., 1999
). Thus, one might wonder whether this mechanism makes a similar contribution to the HD cell signal, improving the accuracy of updated heading during low frequency head turns. Experimental manipulations of the velocity storage integrator may produce measurable effects on HD cell activity.
This article has summarized the major properties of HD cell activity. The strength of this signal and the ability to measure it accurately provide an ideal system for understanding how the nervous system processes raw sensory information and transforms it into a high level cognitive signal that encodes the animals perception of its directional heading in allocentric space. The nature of the HD cell makes it an attractive candidate for using neural network models to elucidate the underlying mechanisms.
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Notes |
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Address correspondence to J.S. Taube, Dartmouth College, 6207 Moore Hall, Hanover, NH 03755, USA. Email: jeffrey.taube{at}dartmouth.edu.
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