1Center for the Neural Basis of Cognition and 2Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3891
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ABSTRACT |
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Goodridge, Jeremy P. and David S. Touretzky. Modeling Attractor Deformation in the Rodent Head-Direction System. J. Neurophysiol. 83: 3402-3410, 2000. We present a model of the head-direction circuit in the rat that improves on earlier models in several respects. First, it provides an account of some of the unique characteristics of head-direction (HD) cell firing in the lateral mammillary nucleus and the anterior thalamus. Second, the model functions without making physiologically unrealistic assumptions. In particular, it implements attractor dynamics in postsubiculum and lateral mammillary nucleus without directionally tuned inhibitory neurons, which have never been observed in vivo, and it integrates angular velocity without the use of multiplicative synapses. The model allows us to examine the relationships among three HD areas and various properties of their representations. A surprising result is that certain combinations of purported HD cell properties are mutually incompatible, suggesting that the lateral mammillary nucleus may not be the primary source of head direction input to anterior thalamic HD cells.
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INTRODUCTION |
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Previous empirical research
Previous research has identified several
populations of neurons in the rat brain that fire as a function of the
animal's head direction (HD). The firing rate of each HD cell is
maximal whenever the animal's head is pointed in one particular
direction and tapers off as the animal points its head away from that
direction. Different HD cells have different preferred directions so
that the entire 360° space is uniformly sampled by the population of
HD cells. HD cells have been identified in a number of different brain
areas, including the postsubiculum (PoS) (Ranck 1984;
Taube et al. 1990
), anterior dorsal thalamus (AD)
(Taube 1995
), and lateral mammillary nucleus (LMN)
(Stackman and Taube 1998
), although not all cells in
these regions fire in a direction-specific manner. Furthermore the
particular firing characteristics of HD cells vary from region to region.
PoS head direction cells have simple Gaussian-shaped tuning curves. The
firing of these cells is best correlated with the animal's head
direction approximately 10 ms in the past, on average (Blair and
Sharp 1995; Taube and Muller 1998
). In contrast,
the activity of the average HD cell in AD is best correlated with the
animal's heading approximately 25 ms in the future. In other words,
the average AD HD cell has an anticipatory time interval (ATI) of +25
ms. The ATI value for a single cell appears to be constant over a broad
range of angular head velocities.
According to Blair, Lipscomb, and Sharp (1997), the
shapes of AD HD cell tuning curves exhibit distortions as a function of the speed and direction of the animal's turning. Furthermore when the
animal is still, the curves often have a bimodal shape (two peaks),
whereas when the animal is turning, AD HD cell tuning curves are
unimodal but skewed in the opposite direction of the turn. The peak
firing rate also increases with angular head velocity (Taube
1995
).
The extent to which a particular AD HD cell shows this tuning curve
distortion depends on how much the cell anticipates the animal's head
direction. The greater the ATI, the greater the extent of tuning curve
distortion. In addition, the width of an AD HD cell's tuning curve
when determined across all velocity levels is positively correlated
with ATI (Blair et al. 1997). An accurate model of the
HD system must not only capture the average tuning curve
differences between AD and PoS, it must also account for the
distribution of tuning curve characteristics within the AD
and PoS populations.
The phenomenon of AD tuning curve distortion has been called into
question by Taube and Muller (1998), who did not see
this effect in their own experiments. They also did not report
bimodality when the animal was still. In this paper, we will be basing
our modeling efforts on the Blair et al. (1997)
findings. The Taube and Muller findings were based on much shorter
recording sessions: only 8 min as compared with 15-30 min in
(Blair et al. 1997
) and 15-90 min in (Blair and
Sharp 1998
). Thus because the tuning curve effects reported by
Blair et al. are quite subtle, they may very well have been obscured in
the 8-min sessions.
Data on LMN head-direction cells have only recently become available
and are also somewhat contradictory. Stackman and Taube (1998) describe LMN cells as having Gaussian tuning curves
modulated by angular velocity. Cells that prefer clockwise turns
increase their firing rate as angular velocity increases in the
clockwise direction and decrease their firing rate as angular velocity
increases in the counterclockwise direction relative to the baseline
rate observed when the animal's head is not turning at all. The
observed ATI for LMN cells was approximately 95 ms. But Blair
and Sharp (1998)
report that LMN HD cells have an ATI of 40 ms,
and while their peak firing rate increases with velocity, in addition,
the width of the tuning curve contracts as angular velocity increases in the direction of the side containing the cell e.g., counterclockwise turns for left LMN cells or clockwise turns for right LMN cells. The
width stays the same for turns in the opposite direction. In this
paper, we will be reporting on simulations that address the
compatibility of these different LMN findings in a model in which LMN
is the primary projection to AD.
Redish, Elga, and Touretzky (1996) developed a model
that accounted for some of the differences between AD and PoS HD cell firing. Specifically, their model provided an explanation for why AD HD
cell firing was best correlated with the animal's future head
direction, whereas PoS HD cell firing was best correlated with current
head direction. The Redish et al. model also predicted that AD tuning
curves would exhibit distortions as a function of the animal's turn
velocity, which was subsequently confirmed by Blair et al.
(1997)
. However, the model did not account for certain other
important differences.
In this paper, we present a new model that is the successor to the one
described by Redish et al. (1996). Our model accounts for a number of important new results not covered in the previous model, such as the bimodality of AD tuning curves, the constancy of ATI
for all angular velocities, the particular distorted shapes exhibited
by AD HD cells as velocity increases, and some of the tuning curve
changes exhibited by LMN HD cells. Furthermore unlike the model in
Redish et al. (1996)
, our new model does not rely on
directionally tuned inhibitory neurons or multiplicative synapses.
Preliminary data concerning some of the results presented in this paper
have been published previously (Goodridge et al. 1997).
Previous modeling research
Many findings (Blair and Sharp 1996;
McNaughton et al. 1991
; Stackman and Taube
1997
; Taube and Burton 1995
) suggest that HD
cells rely on internal sources of information to obtain information about ongoing changes in the animal's heading. Vestibular,
proprioceptive, and motor efference copy are all possible sources of
self-motion cues. It appears that HD cell activity is updated by
integrating angular velocity signals from one or more of these sources.
A number of previous models have been proposed to account for the
integration of angular velocity by HD cells (Blair 1996; McNaughton et al. 1991
; Redish et al.
1996
; Skaggs et al. 1995
; Zhang
1996a
). Most of these models are based on the attractor hypothesis first put forth by Skaggs et al. (1995)
that the head-direction system is a circular one-dimensional dynamical
system or ring attractor, which integrates angular velocity by moving
an activation bump around the ring.
Attractor models postulate the existence of an extensive network of
interconnection between HD cells such that cells with similar preferred
directions excite more than inhibit one another, whereas cells with
dissimilar preferred directions inhibit more than excite one another.
This scheme of interconnectivity produces a situation in which a bump
or activation hill (the "attractor state") will arise even when the
initial activation levels of the cells are random. The hill is then
self-maintaining in the absence of external input. The activation hill
is made to move around the ring, representing the animal's estimated
heading as it turns, by supplying additional input to units on one
flank of the hill. The amount of the input supplied to a flank
determines the speed at which the hill moves. If the strength of this
input corresponds to the animal's angular velocity, then movement of the activation hill will track the animal's head direction.
Simulations showed that this mechanism is sufficient to allow the HD
system to accurately track complex head movement profiles taken from a
real rat (Redish et al. 1996).
In the Redish et al. model, the integration process was implemented by
coupling two attractor networks together, one corresponding to AD and
the other to PoS. As described in the preceding text, AD contains an HD
signal that is best correlated with the animal's future head direction
(Blair and Sharp 1995; Blair et al. 1997
; Taube and Muller 1998
). In contrast, PoS contains an HD
signal that best corresponds to the animal's present or recent past
head direction. To account for this difference between PoS and AD, Redish et al. (1996)
proposed that there are asymmetric
connections between AD and PoS. In particular, they proposed that PoS
HD cells project to AD HD cells with slightly offset preferred
directions, whereas AD HD cells project to PoS HD cells with matching
preferred directions. This arrangement is plausible because the
anatomical data suggest that AD and PoS are reciprocally connected but
do not reveal which cells in PoS project to which cells in AD. In the
Redish et al. model, when a left turn was being simulated, PoS
projections to AD HD cells with a preferred direction slightly offset
to the left ("left offset" projections) were enabled, whereas right
offset projections were disabled. The converse was true when right
turns were being simulated. As a result of this scheme of interaction,
AD contained a hill of activation that slightly anticipated the
position of the hill in PoS.
Although the Redish et al. model did account for the temporal
difference between AD and PoS HD cell firing, it did not account for
the distorted tuning curves in AD. Redish et al. did show that removing
the attractor dynamics from AD resulted in distorted tuning curves;
however, these distortions were not the same as the ones that AD HD
cells actually exhibit. In addition, after removal of attractor
dynamics, the amount by which AD HD cell firing anticipated PoS HD cell
firing declined with angular velocity, which is not consistent with the
known data (Taube and Muller 1998). Nevertheless, as
noted by Redish et al., there is good anatomical evidence that the AD
does not contain an attractor network. In particular, AD lacks the
GABA-containing interneurons (Bentivoglio et al. 1993
)
that would provide the necessary inhibition for an attractor network.
So in our new model, AD does not contain any attractor dynamics, and
its connectivity to other structures has been altered.
Blair et al. (1997) proposed their own account of how
the distorted tuning curves in AD were generated. They suggested that each AD HD cell received input from two populations of turn-modulated HD cells. One of these populations increased its firing rate when the
animal turned clockwise, whereas the other increased its rate for
counterclockwise turns. In their scheme, (counter-) clockwise turn-modulated cells projected to AD HD cells with preferred directions slightly (counter-) clockwise to that of the projecting cell. They
postulated that AD HD cells that anticipated head direction by a large
amount received input from turn-modulated head-direction cells with
more highly offset connections and that this scheme would produce the
kinds of tuning distortions that AD HD cells exhibited in real rats.
Although not intended to account for distortions in AD HD tuning
curves, a similar proposal was made by Zhang (1996b)
. Whereas in the Redish et al. model, AD tuning curves were a function of
only one set of offset connections at a time, in the Blair and Sharp
model, AD tuning curves were a function of two sets of simultaneously
active offset connections.
In this paper, we present simulations of a model in which the input to
AD is as proposed by Blair and Sharp. We show that the model is capable
of accurately accounting for AD and PoS tuning curve shapes, that it
integrates angular velocity with good accuracy, and that it also
produces a constant ATI in AD. The source of input to AD is another
attractor module that exhibits the velocity modulated tuning curves
described by Stackman and Taube (1998) with respect to
LMN. However, our simulations show that the ATI values reported for LMN
by Stackman and Taube and by Blair and Sharp (1998)
are
not compatible with the AD tuning curve properties described by
Blair et al. (1997)
if it is LMN alone that drives AD.
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METHODS |
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New model of the HD system: overall structure
The organization of our model is shown in Fig.
1. A bump of activation whose location
represented the animal's current head direction was maintained in PoS
by means of attractor dynamics. This signal was passed to two
populations of LMN cells (clockwise: CW and counterclockwise: CCW),
where it was modulated by angular velocity information represented in
the model by two abstract "angular velocity" units. The two LMN
populations projected to AD with opposite offsets, producing bimodal
tuning curves. Thus AD activity was purely a function of the input
received from LMN, a property which allowed AD to show dramatic shape
distortions. AD cells projected to PoS cells with matching preferred
directions, thereby updating the position of the PoS attractor bump.
This connection scheme is consistent with the known anatomy
(Allen and Hopkins 1989; Shibata 1992
;
van Groen and Wyss 1990
, 1995
), but it does not include
all the connections known to exist for these areas.
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Neuronal model
The elements of our model are nonlinear units with
continuous-valued outputs in [0, 1]. Each unit represents a
subpopulation of "real" neurons, which we assume are firing
asynchronously. Furthermore the excitatory neurons in the
ith subpopulation are assumed to all have preferred
direction i. (Inhibitory units, representing
populations of inhibitory interneurons, are nondirectional.) The
output, or "firing rate," of the ith unit, Fi(t), can be regarded as the
fraction of neurons in the subpopulation that were spiking at time
t or as the probability that an individual neuron emitted a
spike at time t. All structures in the model used the same
type of unit, and the preferred directions
i were uniformly distributed around the circle. The following three equations, modified from Pinto, Brumberg, Simons, and Ermentrout (1996)
, determined the activity of unit i
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(1) |
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(2) |
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(3) |
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(4) |
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Model of PoS
Our model of PoS used a modified form of the Wilson-Cowan
equations (Wilson and Cowan 1972) to generate an
attractor bump (Pinto et al. 1996
). The PoS module
contained both excitatory and inhibitory units, denoted by superscripts
in the following equations. The recurrent connectivity within PoS was
structured to allow a hill of activation to form regardless of each
unit's initial activity level. The projection strength between any
pair of excitatory units was a Gaussian function of the difference in
their preferred directions. There was only one inhibitory unit; it
received input from all of the excitatory units and projected back to
all of them with equal strength as shown in Fig.
2. In the rat there are many inhibitory
interneurons, but if each provides input to a different random subset
of the excitatory population independent of their preferred directions,
the net effect would be the same as having a single fully-connected
inhibitory unit.
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The activation equation for the ith excitatory PoS unit was
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(5) |
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(6) |
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(7) |
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Model of LMN
Our model of LMN consisted of two pools of units, LMN(cw) and LMN(ccw), with a preference for CW and CCW turns, respectively. Each pool contained a recurrently connected excitatory population plus a single inhibitory unit. The parameters used for unit interaction within LMN(cw) and LMN(ccw) were identical to those used for the PoS attractor. In addition, the excitatory units in each LMN pool received external input from one of two angular velocity units as shown in Fig. 1. The CW-sensitive angular velocity unit, which projected to units in LMN(cw), fired maximally during a CW high speed turn; it fired minimally during a CCW high speed turn. The CCW-sensitive angular velocity unit, which projected to units in LMN(ccw), responded in the opposite manner.
These angular velocity units have no specific anatomical correlate in
the rat; they are merely a conceptual device for introducing angular
velocity information into LMN. While cells tuned to angular velocity
have been reported in LMN (a different population than the LMN HD
cells), those cells did not discriminate between CW and CCW turns
(Stackman and Taube 1998). LMN HD cells might receive angular velocity information from the dorsal tegmental nucleus, which
is known to project to LMN and to receive projections from the medial
vestibular nucleus and nucleus prepositus hypoglossi (Blair and
Sharp 1998
; Stackman and Taube 1998
). Those
nuclei contain angular velocity cells that are sensitive to
turn direction (Blair and Sharp 1998
).
Direction-sensitive angular velocity cells have also been reported in
PoS (Sharp 1996
), which might contribute to the PoS
projection to LMN.
In agreement with the observations of Stackman and Taube
(1998), the firing rates of our LMN units were modulated by the
angular velocity signal, increasing for turns in one direction and
decreasing for turns in the opposite direction. To achieve this without
distorting the shape of the tuning curve, we relied on a result
described by Salinas and Abbott (1996)
, showing that an
additive input supplied to all the units in a one-dimensional attractor
bump can produce a multiplicative effect on the bump. The activation
equation for the ith excitatory LMN(cw) unit is shown in the
following text. A similar equation governed LMN(ccw) units
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(8) |
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As with PoS, the weight from LMN excitatory unit j to
excitatory unit i within the same pool was a Gaussian
function of the difference in their preferred directions. The same
weight matrix wij was used as
for PoS. Also as in PoS, the inhibitory unit in each LMN pool received
a projection from each of the excitatory units in its pool, governed by
the coupling constant EI, and an inhibitory
projection from itself governed by the constant
II. The inhibitory unit received no input from
units external to LMN. Parameter values are shown in Tables 2 and
3.
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Model of AD
Because AD contained no attractor dynamics, the activity of AD
units was entirely dependent on external input. This input consisted of
the offset projections from the two LMN pools. The ith unit
in AD, with preferred direction i,
received projections from LMN units with preferred directions
i ±
. For example, if the offset
amount was 15°, an AD unit with a preferred direction of 90° would
receive input from an LMN(cw) unit with a preferred direction of 75°
and an LMN(ccw) unit with a preferred direction of 105°. As described
in RESULTS, the size of this offset was important in
determining both the tuning curve shape exhibited by AD units and the
degree of anticipation they exhibit.
AD units were governed by the following equation
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(9) |
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RESULTS |
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Most of the parameters of the model were kept fixed for all
simulations reported here. In particular, the tonic inhibition values
E and
I and time
constants
E and
I for
all units, the weight matrix
wij used in PoS and LMN, the
number of units in each structure, and the coupling strengths among the
AD, PoS, and LMN modules (
AP,
PL,
LA) were all kept
constant. We varied
, the extent of the offset in the connections
from LMN to AD, to fit the tuning curves of several real AD cells. We
also varied the strength of the angular velocity input
Acw(t) to simulate turns at
various speeds. The effects of varying these parameters are described
in the following text.
At the beginning of a simulation, we set the activation levels of units in PoS and LMN to random values. Then at each time step of the simulation, all units updated their activation levels. Soon a stable activation hill formed over the PoS, LMN, and AD populations. This settling process required 10-50 ms of simulation time.
When the activation hills stabilized, we experimentally adjusted the model so that a continuous turn at particular fixed angular velocity values would cause the expected change in the position of the PoS activation hill. We did this by varying the angular velocity input Acw(t) to LMN until it produced the appropriate speed of activation hill movement. We tuned the model parameters to accurately integrate angular velocities from 0 to 700°/s in steps of 100°/s, both CW and CCW, while maintaining desired tuning curve shapes.
Once this tuning process was complete, the behavior of the model in response to velocity changes could be studied. In particular, we assessed the integration accuracy of the model, the shapes of the tuning curves in all three modules, and the degree of anticipation in AD as a function of angular velocity.
Integrating a real angular velocity profile
To examine whether the network was capable of integrating a real
angular velocity profile, we used data obtained from Blair and Sharp
(personal communication). This was the same data as were used in
Redish et al. (1996). The data consisted of a rat head-direction trajectory lasting 12 s, sampled at 60 Hz. To
compute the angular velocity of the animal during each sample, we
grouped each sample with the two samples before and two samples after. Then we defined the angular velocity of the sample as the slope of the
best fit line through these five points. Because the real data were
sampled at 60 Hz, each sample accounted for approximately 16 ms of
time. However, the simulations were performed with a time step of 0.1 ms, so we used linear interpolation to generate an angular velocity
profile with all the intermediate values required for the model.
We defined the speed at which the model was turning as the rate of
change of the location of the PoS activation hill. Location was defined
as the weighted mean of the i values, where the weights were the firing rates Fi. In
other words, we calculated the population vector (Georgopoulos
et al. 1983
) of the PoS population. Assigning a location to the
PoS activation hill is more straightforward than for AD since AD
profiles have distorted shapes. The tuning curves in the PoS module did
not exhibit the shape distortions that were present in AD even though
PoS received direct projections from AD. The primary reason for this
was the recurrent connections in PoS, which tended to minimize the
effects of distortions in the input. The amplitude of the PoS attractor
bump also remained fairly constant, making PoS cells insensitive to
angular velocity.
When we exposed the model to the complex angular velocity trajectory taken from a real rat, we found it maintained heading with good accuracy, comparable with the Redish et al. model.
Shapes of AD tuning curves
According to the proposal of Blair et al. (1997),
one primary source of differences among AD HD cells with identical
preferred directions is the amount of offset in the projections they
receive from angular velocity-tuned HD cells. This proposal suggests
that for each AD cell there is a particular offset value that
determines both the cell's tuning curve shape and the extent to which
it anticipates head direction.
To examine the validity of this hypothesis, we chose the HD cell from Blair and Sharp's population that showed the highest degree of tuning curve distortion. Then we varied the level of offset in the projection between LMN and AD until we were able to best fit the tuning curve of that AD HD cell. Figure 3 shows the activation hills produced in our simulated AD in relation to the tuning curves of the actual AD HD cell.
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As can be seen, the simulation was able to fit this particular HD cell
quite well. The value used to generate the simulated curves was
27°. It should be noted that this cell shows a very visible degree of
tuning curve distortion. We also performed simulations to match another
HD cell and found a similar quality of fit. To fit the tuning curves of
this new cell, we only had to change the extent of offset
in the
projection from LMN to AD.
To measure anticipatory firing in AD, we arbitrarily designated the position of the attractor hill in the PoS module as the simulated animal's actual head direction. Thus the ATI for PoS was always 0, and the ATI for AD was measured in relation to this reference. To measure the ATI at any time step in the simulation, we computed the position of the AD activation hill and subtracted the position of the PoS activation hill from this value. This difference represented the actual offset in degrees between the AD and PoS activation hills. The ATI was then computed by dividing this difference by the current angular velocity of the PoS activation hill.
For the simulated cell shown in Fig. 3, the calculated ATI was 28 ms.
This value was lower than that of the real cell whose tuning curve we
had fit (47 ms), a result that suggests that other mechanisms besides
the one proposed here are acting to increase the level of anticipation
in AD. In our model, one reason a higher level of anticipation could
not be achieved with this offset was the existence of transmission time
delays from PoS to LMN and then from LMN to AD. The delays resulted
from the nonzero time constants of the individual units. The synaptic
drive of a unit (Eq. 3) varied as a function of net input
plus the unit's current drive value at a rate determined by the time
constant E. As a result of this delay, the
maximum achievable offset in degrees between AD and PoS hills was less
than the value of
. For the HD cell modeled in Fig. 3,
was
27°, but simulations showed that even at high velocities, when AD was
receiving virtually all its input from the one LMN activation hill
projecting ahead in time, the actual offset between AD and PoS hills
was only 24°. Thus despite the offset from LMN to AD, the AD
activation hill was not as advanced relative to LMN as it would have
been in a model with instantaneous-time units, i.e., with
E close to zero.
Our simulations showed there was indeed a positive relationship between
the offset parameter and the extent of anticipation in AD. The
result suggests that the degree of offset is a critical factor in
explaining tuning curve variability of AD HD cells. Therefore according
to this model, the reason that many HD cells do not show a visible
degree of distortion is that the
value is too small. With a very
small value for
, there was almost no visible tuning curve
distortion because the two activation hills from LMN overlapped almost
completely. Nevertheless even in these cases, there was still a
measurable ATI.
Another feature of AD tuning curve shapes is that their peak amplitude
rises with increasing velocity (Taube 1995). That result is also consistent with the AD tuning curves generated by this model.
Constancy of anticipation in AD
Taube and Muller (1998) reported that the extent to
which the signal in AD anticipates the signal in PoS does not vary with angular velocity. We were interested in determining whether our model
exhibited similar behavior. To assess this in the current model, we
conducted simulations at a range of different angular velocities. For
each angular velocity, we computed the ATI in AD relative to PoS.
Figure 4 plots the relationship between
angular velocity and temporal difference between AD and PoS for real HD cells and for the model.
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As can be seen from the graph, the model exhibited a nearly constant ATI as a function of angular velocity. However, it is also important to note that there is a great deal of variability in the real HD data, which makes it difficult to determine how constant the anticipation of real AD HD cells actually is.
Constancy of anticipation means that the difference in position of AD versus PoS activation hills varies linearly with the velocity at which the activation hills are moving. In order for a linear relationship to be preserved, we found it is critical that there be conservation of strength in the projections from LMN to AD. If during a turn the increase in total activity of CW LMN units is not exactly balanced by a decrease in activity for CCW LMN units, then AD units will receive more total external input during high-speed turns than during low-speed turns. Depending on how this extra input is distributed, the change in the shape of the AD activation hill will produce an effect on the speed of the PoS hill above and beyond the effect produced by the difference in position between AD and PoS activation hills. As a result, there will not be a linear relationship between the difference in position of AD versus PoS activation hills and the velocity at which the activation hills are moving, which means ATI will not be constant. Thus the model suggests that equal and opposite changes in the two LMN populations' projections to AD are critical for the stability of AD anticipation across different angular velocities.
Does LMN drive AD?
All of our results for AD were obtained by driving AD with
nonanticipatory HD signals offset by a constant amount ±. In the model, AD was driven by a velocity-modulated signal from LMN, which was
in turn driven by PoS, giving LMN an ATI value of
3 ms relative to
PoS due to transmission delay. But observed ATI values for LMN range
from 40 to 95 ms (Blair and Sharp 1998
; Stackman and Taube 1998
). This raises the question of whether the data on AD and LMN response properties are compatible with the hypothesis that LMN drives AD. Although bilateral LMN lesions eliminate the AD
head direction signal (Blair et al. 1999
; Tullman
and Taube 1998
), this does not prove that AD activity is
principally derived directly from the LMN projection.
To investigate this hypothesis, we modified the model to force LMN to anticipate PoS by a modest amount by shifting the PoS input to LMN CW or CCW as a function of angular velocity. This produced ATI values of 31-46 ms in LMN. (The variation is due to the fact that the PoS activity vector was always shifted by a whole number of units, i.e., in steps of 3.56° since there were 101 units per module.) We found that LMN anticipation combined additively with the effect of offset connections from LMN to AD, producing an ATI of 60-80 ms in AD. Thus AD anticipated LMN, when the reverse should be true. In addition, the shapes of the AD tuning curves no longer fit the data of Blair and Sharp, as will be discussed in the following text.
Why does PoS in the model have a smaller ATI than AD, which drives it,
but AD does not have a smaller ATI than LMN? The AD projection to PoS
does not involve offset connections. But more importantly, PoS is an
attractor module: input from AD is only one influence on PoS unit
activity. Recurrent connections in PoS have significant influence by
stabilizing and maintaining the bump that the AD input perturbs. In AD,
which lacks recurrent connections, unit activity is determined solely
by the offset LMN projections, with the offset amount determined by
the distance between the two peaks in the bimodal still tuning curve.
We produced anticipation in AD by increasing the height of one of the
peaks during turns while decreasing the height of the other. The
peak locations do not actually shift with velocity (Fig.
5A), but since their relative
amplitudes change, the recovered direction in AD, calculated
using the population vector, shifts toward the rising peak, producing
an anticipatory effect. For the curves in Fig. 3, the recovered
directions were 173° (fast CW), 176° (slow CW), 184° (still)
192° (slow CCW), and 198° (fast CCW).
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The anticipatory LMN simulation did not produce correct AD tuning curves because the fast tuning curve was shifted in position, not just height, relative to the still curve. Figure 5A compares fast and still tuning curves for the cell shown in Fig. 3. Note that the peak of the fast CCW curve is at the same location as the right peak of the still curve. Figure 5B shows that the model correctly reproduces this relationship when there is no LMN anticipation. But if LMN anticipates PoS, the AD tuning curve shifts in the direction opposite the turn, as shown in Fig. 5C. Hence the AD curve is not correct since the fast peak does not coincide with the still peak.
To further investigate the effect of LMN anticipation on AD, we
constructed a separate linear network model to determine whether there
was any pattern of weights between LMN and AD that would allow LMN to be anticipatory and AD to exhibit the desired tuning curve
shapes. This model was a two-layer network of linear units, where the
input layer's 202 units represented the two LMN pools, and the output
layer's 101 units represented AD. The training set consisted of a set
of LMN activity patterns corresponding to bumps centered at each of 101 positions around the circle, for five different angular velocities from
fast CCW (approximately 300°/s) through still to fast CW. Thus
there were a total of 505 training patterns. The amplitudes of the
activity patterns in the two LMN pools were modulated by angular
velocity in the appropriate way. In simulations where LMN was supposed
to be anticipatory, we also shifted the bumps in the two LMN pools by
appropriate amounts corresponding to an ATI of roughly 46 ms. When
simulating a nonanticipatory LMN, the bumps were not shifted, only
their amplitudes varied.
Given a set of LMN input patterns X and a set of desired AD
output patterns Y, we sought a weight matrix W
that could satisfy WX = Y. The weights can
be derived by gradient descent learning using the LMS (least mean
squares) algorithm (Hertz et al. 1991) or they can be
computed directly as W = YX
1, where
X
1 denotes the pseudo-inverse of the
202 × 505 matrix X. The latter solution is faster to
compute but numerically unstable; it produces more erratic weight
vectors than gradient descent.
Results for these simulations are shown in Fig. 6. The network was easily able to associate nonanticipatory activity profiles in LMN with the proper AD tuning curve shapes (Fig. 6A). It was unable to do this for anticipatory activity profiles in LMN (Fig. 6, B and C). The matrix inverse solution produced the same unsatisfactory result as LMN. The bimodality of AD tuning curves during zero-turn cases was especially hard to reproduce.
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Because the LMS algorithm with a two-layer network is known to converge
to a solution if one exists (Hertz et al. 1991), these additional simulations suggest that there is no linear mapping between
an anticipatory LMN and an AD with the tuning curve profiles described
by Blair et al. (1997)
. Therefore although
velocity-driven amplitude modulation in LMN provides a mechanism that
can account for tuning curve distortions in AD, it would seem that the
anticipatory nature of real LMN cells precludes their providing a full
account of this phenomenon.
PoS has a reciprocal projection to AD. PoS lesions do not eliminate HD
cell firing in the AD, but they do alter the shapes of AD tuning
curves, making them slightly broader and increasing the ATI
(Goodridge and Taube 1997). On the other hand, bilateral lesions of LMN abolish head direction cell firing in AD (Blair et al. 1999
; Tullman and Taube 1998
). To
investigate the possibility that LMN and PoS inputs together could
produce the observed tuning curves in AD, we added a PoS input to the
two-layer linear network model, expanding the input layer to 303 units.
With this addition, the network was able to reproduce the desired AD
response, as shown in Fig. 6D. Our intuition about why the
PoS input helped is that PoS is somewhat "out of phase" with an
anticipatory LMN, so the PoS input vector contributes a new set of
basis functions on which the linear approximator can draw. The idea is
analogous to adding more terms to a Fourier series.
Besides PoS, another area that could be affecting AD tuning curve
shapes is retrosplenial cortex, which like LMN receives projections
from PoS and provides input to AD (van Groen and Wyss 1990,
1992
). In addition, evidence suggests that some cells in retrosplenial cortex fire as a function of the animal's movements (Chen et al. 1994
), implying there is velocity
modulation there.
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DISCUSSION |
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The contributions of this modeling effort to our understanding of
the rodent head-direction system are 1) a confirmation of the Blair and Sharp hypothesis that a single offset angle parameter () can account for both degree of tuning curve distortion and degree
of anticipation in AD; 2) a prediction that to achieve a
constant ATI over a range of angular velocities, the head-direction system must be balancing CW and CCW offset inputs so as to maintain an
overall constant input to AD; 3) a demonstration that an
additive angular velocity signal can produce suitable amplitude
modulation (a multiplicative effect) in an attractor network model of
LMN; 4) a demonstration that LMN anticipation of AD is
incompatible with LMN being the sole input responsible for AD tuning
curves, if AD curves are bimodal and distort with velocity as described by Blair and Sharp. However, 5) a combination of
anticipatory, velocity-modulated LMN input and nonanticipatory,
velocity-independent PoS input does suffice to reproduce AD tuning curves.
Several issues remain. We derived a workable combination of LMN and PoS
to AD weights using a simple linear model, but we have not yet tried to
retrofit these weights into the more complex nonlinear model. Also a
more recent report by Blair and Sharp (1998) describes
LMN cells as being width-modulated as well as showing velocity
modulation; the tuning curves narrow with increasing velocity in the
cell's preferred turning direction. This effect was not reported by
Stackman and Taube (1998)
. We await the results of
further experiments that could resolve the issue. In the mean time, a
model for producing width-modulated LMN tuning curves would be useful
so that the implications for AD tuning curve shapes may be investigated.
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ACKNOWLEDGMENTS |
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We are grateful to T. Blair and P. Sharp of Yale University and B. Stackman and J. Taube of Dartmouth College for sharing head-direction cell data with us. We thank all of the preceding people and D. Redish and K. Zhang, for helpful discussions.
This work was funded by National Science Foundation Grant IBN-9631336.
Present address of J. P. Goodridge: 336 Central Park West, Studio 6, New York, NY 10025.
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FOOTNOTES |
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Address for reprint requests: D. S. Touretzky, Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213-3891.
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 4 January 1999; accepted in final form 16 February 2000.
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REFERENCES |
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