Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115
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ABSTRACT |
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Usrey, W. Martin, John B. Reppas, and R. Clay Reid. Specificity and Strength of Retinogeniculate Connections. J. Neurophysiol. 82: 3527-3540, 1999. Retinal ganglion cells and their target neurons in the principal layers of the lateral geniculate nucleus (LGN) of the thalamus have very similar, center-surround receptive fields. Although some geniculate neurons are dominated by a single retinal afferent, others receive both strong and weak inputs from several retinal afferents. In the present study, experiments were performed in the cat that examined the specificity and strength of monosynaptic connections between retinal ganglion cells and their target neurons. The responses of 205 pairs of retinal ganglion cells and geniculate neurons with overlapping receptive-field centers or surrounds were studied. Receptive fields were mapped quantitatively using a white-noise stimulus; connectivity was assessed by cross-correlating the retinal and geniculate spike trains. Of the 205 pairs, 12 were determined to have monosynaptic connections. Both the likelihood that cells were connected and the strength of connections increased with increasing similarity between retinal and geniculate receptive fields. Connections were never found between cells with <50% spatial overlap between their centers. The results suggest that although geniculate neurons often receive input from several retinal afferents, these multiple afferents represent a select subset of the retinal ganglion cells with overlapping receptive-field centers.
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INTRODUCTION |
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Neurons in the dorsal lateral geniculate nucleus
(LGN) of the thalamus are the major relay for visual information
traveling from the retina to the primary visual cortex. Anatomic
(Hamos et al. 1987) and physiological (Cleland et
al. 1971a
,b
; Mastronarde 1992
) studies estimate that
many geniculate neurons receive convergent input from two or more (up
to 6) retinal ganglion cells. Given the similarities between receptive
fields of neurons in the retina and LGN, several questions arise. What
is the specificity and the strength of retinal inputs to geniculate
neurons and to what extent do geniculate responses reflect those of the
retinal cells that provide either strong or weak input?
Strong retinal inputs to an individual geniculate neuron have been
studied by recording extracellularly a geniculate neuron's action
potential along with its S-potentialthe synaptic potential evoked by
a retinal input (Bishop et al. 1958
, 1962
;
Cleland et al. 1971b
; Freygang 1958
;
Hubel and Wiesel 1961
; Kaplan and Shapley 1984
). In both cat and monkey, S-potential recordings have been used to show that geniculate neurons and their strongest retinal inputs
have closely matching receptive fields, in terms of spatial location,
on versus off responses, color selectivity,
contrast sensitivity and X-Y classification (Hubel and Wiesel
1961
; Kaplan et al. 1987
; Lee et al.
1983
; Reid and Shapley 1992
; So and
Shapley 1981
). A drawback of S-potential recordings, however,
is their limited ability to discriminate nondominant retinal inputs.
Further, it is an untested assumption that all geniculate spikes are
triggered by the S-potential being recorded. Because it is often
difficult to discriminate the S-potential when associated with a spike, many spikes may occur without the S-potential and therefore in principle may be evoked by other inputs.
Both strong and weak inputs from retinal ganglion cells to geniculate
neurons can be revealed using a different approach, by combining
cross-correlation analysis with simultaneous recordings of
monosynaptically connected pairs of neurons in the retina and LGN.
Studies using this approach found that the strengths of connections between pairs of retinal ganglion cells and geniculate cells span a
broad range (Cleland and Lee 1985; Cleland et al.
1971a
,b
; Levick et al. 1972
; Mastronarde 1987
,
1992
; see also Arnett 1975
). For some geniculate
neurons, all retinal input came from single ganglion cells. For many
geniculate neurons, however, only a portion of their activity was
associated with each retinal input. These studies compared many
features of the response properties of synaptically connected ganglion
cells and geniculate cells, such as on versus off
and sustained versus transient responses (Cleland et al.
1971b
; Cleland and Lee 1985
; Levick et
al. 1972
; Mastronarde 1987
, 1992
). Less
consideration was given, however, to how the receptive fields of the
two cells related to the strength of connection, or the extent to which
geniculate receptive-field properties were dictated by particular
retinal inputs.
Previously, we have examined how retinogeniculate transmission in the
cat is modulated by temporal features in the retinal spike train
(Usrey et al. 1998; see Mastronarde
1987
). In the present study we examined the specificity and
strength of retinal inputs to geniculate neurons. Simultaneous
recordings were made from neurons in the retina and LGN of the cat that
had overlapping receptive-field centers or surrounds. Quantitative
receptive-field maps were made using a white-noise stimulus to compare
receptive fields. Independently, cross-correlation analysis was used to determine both the presence and strength of monosynaptic connections. Results show that retinogeniculate connections are very precise; both
the likelihood of connections and their strengths increase as the
similarity of receptive fields increase.
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METHODS |
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Animal preparation
All surgical and experimental procedures conformed to National
Institutes of Health and U. S. Department of Agriculture
guidelines and were performed with the approval of the Harvard Medical
Area Standing Committee on Animals. Twelve adult cats were used.
Surgical anesthesia was induced with ketamine (10 mg/kg, intramuscular) and followed by thiopental sodium (20 mg/kg, iv, supplemented as
needed). Anesthesia was maintained with thiopental sodium (2 mg · kg1 · h
1, iv,
supplemented as needed). A tracheotomy was performed, and animals were
placed in a stereotaxic apparatus. Body temperature was maintained at
37°C using a thermostatically controlled heating blanket.
Temperature, electrocardiogram (EKG), electroencephalogram (EEG), and
expired CO2 were monitored continuously
throughout the experiment. The nictitating membranes were retracted
with 10% phenylephrine, and the eyes were dilated with 1% atropine sulfate. The eyes were refracted, fitted with appropriate contact lenses, and focused on a tangent screen located 172 cm in front of the
animal. A midline scalp incision was made, and wound margins were
infused with lidocaine hydrochloride. A small craniotomy was made above
the LGN, and the dura was reflected.
Once all surgical procedures were complete, the animal was paralyzed
with vecuronium bromide (0.2 mg · kg1 · h
1, iv) and ventilated mechanically. Proper
depth of anesthesia was ensured throughout the experiment by
1) monitoring the EEG for changes in slow-wave and spindle
activity, and 2) monitoring the EKG and expired
CO2 for changes associated with a decrease in the
depth of anesthesia. In some animals, the paralytic agent was withdrawn
to test whether the criteria adequately indicated the depth of
anesthesia. At the end of each experiment, animals were given a lethal
overdose of thiopental sodium (100 mg/kg).
Electrophysiological recordings and visual stimuli
Simultaneous recordings were made from retinal ganglion cells
and geniculate neurons that had complete or partially overlapped receptive fields (Fig. 1). Geniculate
recordings were made with a multielectrode array (Thomas Recording,
Marburg, Germany; Eckhorn and Thomas 1993) consisting of
seven electrodes that could be independently raised and lowered. An
interelectrode spacing of 80-250 µm was achieved by advancing the
electrodes through a glass guide tube (ID at tip: 300 µm) lowered to
2-3 mm above the LGN. All geniculate recordings were made from layer A
of the LGN.
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Retinal recordings were made intraocularly with tungsten electrodes (AM
Systems, Everett, WA), which were advanced through a guide tube that
penetrated the sclera of the eye (Cleland et al. 1971a;
Kuffler 1953
). The guide tube was inserted through an
opening in a ring that was glued to the sclera and supported by a
manipulator on the stereotaxic frame. Both the electrode and guide tube
were attached to a ball joint and manipulator, which allowed easy
access to most regions of the retina. The fundus was visualized by
means of a contact lens, the power of which (
30 diopter)
approximately negated the positive power of the eye's optics. With the
lens in place, the fundus was in focus either by direct inspection or
with a dissecting microscope (Opmi 1, Zeiss) at a normal working
distance. Geniculate receptive fields were located on the retina with a
laser flashed through a supplementary ocular of the microscope. The
light source of the microscope was attenuated with filters so that it
was in the low-mid photopic level; the laser was attenuated so
that it was not much brighter than the background. With the laser as
pointer, the retinal electrode could be targeted to the retinal
position of the geniculate receptive fields. Once the retinal electrode
was in place, the
30-diopter lens was replaced with a lens that
focused the eye on the stimulus monitor, at 172 cm (so that 3 cm on the
screen subtended 1°).
The arrival times and waveforms of action potentials from all eight electrodes were recorded to disk (with 100-µs resolution) by a single computer running the Discovery software package (Datawave Technologies, Longmont, CO). Spike isolation was based on off-line waveform analysis, presence of a refractory period indicated by the shape of autocorrelograms, and, in some cases, inspection of analog data recorded on tape.
Receptive fields of retinal and geniculate neurons were mapped
quantitatively by a correlation method similar to the reverse correlation of Jones and Palmer (1987) (see
Citron et al. 1981
; Wolfe and Palmer
1998
), but using pseudorandom spatiotemporal white-noise
stimuli (m-sequences; Reid et al. 1997
; Sutter
1987
1992
). The stimuli were created with an AT-Vista graphics
card (Truevision, Indianapolis, IN) running at a frame rate of 128 Hz.
The stimulus program was developed with subroutines from a runtime
library, YARL, written by Karl Gegenfurtner. The mean luminance of the
stimulus monitor was 40-50 cd/m2.
The white-noise stimulus consisted of a 16 × 16 grid of squares
(pixels) that were white or black one-half the time, as determined by
an m-sequence of length 215 1. The stimulus was
updated either every frame of the display (7.8 ms) or every other frame
(15.6 ms). The entire sequence (~4 or 8 min) was often repeated
several times. Pixels were either 0.6 or 0.3° on a side. This was
small enough to map receptive fields, which were between 5 and 25°
eccentricity, at a reasonable level of detail. In most cases 8-16
pixels filled the center of a cell's receptive field.
In many cases, sinusoidal grating visual stimuli were also used to
characterize the neurons under study. In particular, in cases for which
the X-Y classification was uncertain, a modified null test was
performed with contrast-reversing gratings at several spatial
frequencies (Enroth-Cugell and Robson 1966;
Hochstein and Shapley 1976
). Because we often recorded
from a number of X and Y cells in the LGN simultaneously, the
differences in the receptive-field sizes usually allowed for an
unambiguous classification without a null test. At each eccentricity,
all X cells had similar sizes as mapped with white noise, and the Y
cells were two to three times larger (Linsenmeier et al.
1982
). Although geniculate cells were not classified explicitly
as lagged or nonlagged (Saul and Humphrey 1990
), the
majority had similar impulse responses (see later description) and were
almost certainly nonlagged.
Once receptive fields were mapped with white-noise stimuli, large numbers of spikes (usually, more than 50,000 retinal spikes, 20,000 geniculate spikes) were collected using a drifting sine-wave grating, during subsequent runs of the white-noise stimulus, and in the absence of any stimulus (eyes covered). These spike trains were used for cross-correlation analysis.
Data analysis
CROSS-CORRELATION ANALYSIS.
Cross-correlograms between retinal and geniculate spike trains were
made to examine connectivity between pairs of cells. Peaks indicative
of monosynaptic connections (monosynaptic peaks) were analyzed in two ways (Reid and Alonso 1995). First, a
statistical test assessed whether peaks that had the time course
associated with monosynaptic connections (Cleland et al.
1971a
,b
) were significant. Second, the magnitude and time
course of statistically significant peaks was measured. For all
quantitative analysis, correlograms were calculated between
10.0 and
+10.0 ms, with 0.1-ms time bins. To display a longer baseline, the
correlograms shown in Figs. 1 and 2
are between
25.0 and +25.0 ms
with 0.5-ms time bins. For most pairs of cells, cross-correlograms were
made with data collected both during white-noise stimulation and during
stimulation by sine-wave gratings drifting at 4 Hz.
Shuffle-subtractions were made with data collected with drifting
gratings, to remove the stimulus-dependent portion of the correlations
(Perkel et al. 1967
).
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Receptive-field mapping: reverse correlation
Spatiotemporal receptive-field maps (kernels) were calculated
from the responses to the white-noise stimulus by a correlation method
(Reid et al. 1997; Sutter 1987
, 1992
; see
Citron et al. 1981
; Jones and Palmer
1987
; Wolfe and Palmer 1998
). For each delay
between stimulus and response and for each of the 16 × 16 pixels,
we summed the stimuli that preceded each spike. In this calculation,
the bright phase is assigned the value +1, the dark phase
1. When
normalized by the total duration of the stimulus, the result is
expressed in units of spikes per second. For each of the pixels, the
kernel can also be thought of as the average firing rate of the neuron,
above or below the mean, after the bright phase of the stimulus at that
pixel. This time course is called the impulse response of
the neuron at a given pixel. For most purposes, time was
binned at the stimulus update period (1 or 2 display frames, which
corresponds to 7.8 or 15.6 ms). The zero bin corresponds to the average
stimulus at the time of each spike; neurons with latencies of <15.6 ms
therefore show responses in the zero bin. The 15.6 ms bin corresponds
to the responses with latency between 15.6 and 31.2 ms, and so on. To
quantify certain parameters, in particular
tmax (defined later), the responses were sampled in 1.9-ms bins.
To assess the time course and magnitudes of both center and surround responses, it was necessary to identify the pixels in the receptive-field center. First, the largest single response in the spatiotemporal receptive field was located. This maximum defined the position of greatest sensitivity at the best delay between stimulus and response. Next, the spatial receptive field was averaged over a range of times (31.2 ms total, or 4 display frames) before and after the best delay, to define the spatial receptive field. This definition is somewhat arbitrary: because the time courses of the center and surround responses are different, a different set of conventions would give a different spatial receptive field. The center was defined as all contiguous spatial positions in this spatial receptive field that were the same sign as the strongest response and were >2 SD above the baseline noise. The baseline noise was taken as the standard deviation of the kernel values for all pixels and for delays (54.4-108.7 ms) that were well beyond the peak response. The surround was defined as all other points that were within a certain distance of the center. The surround width (in pixels) depended on the number of pixels in the center (Ncenter,) according to the following formula: 3 + (Ncenter)1/2. Thus there was a minimum width of the surround, and it increased roughly linearly with the diameter of the center. The impulse responses of all of the pixels in the center and surround were added together to yield the center impulse response and surround impulse response, respectively (Fig. 2). The definition of center and surround are, again, somewhat arbitrary, but it should be noted that several others were tried and none of the results presented were critically dependent on the exact definition.
Parameters quantifying the impulse responses were calculated as
follows. The time of maximum response,
tmax, was calculated from the center
impulse response, sampled in 1.9-ms bins. The rebound time,
trebound, was defined as the first time, after
tmax, that the response was opposite
in sign from the maximum response. The response
magnitudewhich quantifies the first phase of the response, the
response before the rebound
was defined as the integral of the impulse
response for all times before
trebound. Finally, the rebound
magnitude was defined as the integral of the impulse response for
times greater than trebound (up to
108.7 ms).
Overlap between retinal and geniculate receptive fields was assessed in
two ways. First, the size and location of the centers were quantified
by fitting the best single two-dimensional Gaussian to the
spatial receptive field. Note that for the purpose of determining relative size and overlap, a difference of Gaussian model
(Rodieck 1965) was not used. In a difference of Gaussian
fit, the center size is somewhat dependent on the surround parameters
and is less tightly constrained than if the surround is ignored.
Empirically, we found that a circle drawn at 1.75 space constants (
,
or standard deviations) from the peak of the best fitting Gaussian
roughly corresponds to the spatial extent of the receptive field center (Figs. 1 and 2). Note that a different convention was used in Reid and Alonso (1995)
and Alonso et al.
(1996)
; here we used the expression A
exp(
|x
xc|2/
2), whereas
previously we used A exp(
|x
xc|2/2
2).
A is the amplitude, xc the center,
and
the standard deviation of the Gaussian.
Overlap was also assessed in a model-independent way. First, each
spatial receptive field was normalized so that the dot product with
itself was one (the dot product of two receptive fields is equal to the
product of the values at each pixel, summed over all pixels).
Overlap was defined as the dot product of the two different
receptive fields, after normalization. The value for overlap can
therefore range between 1 and +1. If the spatial receptive fields
were identical to within a scale factor, then the overlap would be +1.
If they were identical, but with opposite sign
one
on-center, the other off-center
then the overlap
would be
1. Imperfectly overlapped or dissimilar receptive fields
would yield smaller values (see Fig. 2 for examples).
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RESULTS |
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In total, we recorded the responses of 205 pairs of retinal ganglion cells and geniculate neurons with overlapping receptive-field centers or surrounds. Of the 205 pairs, 12 displayed statistically significant peaks in their cross-correlograms; these peaks indicated the presence of a monosynaptic connection (see METHODS).
Comparing receptive fields and assessing connectivity
The receptive fields of retinal ganglion cells and geniculate neurons were mapped with white-noise stimuli (Fig. 1B) and connectivity between them was assessed with cross-correlation analysis (Fig. 1C). The two panels of Fig. 1B show the receptive fields of a pair of on-center-off-surround X cells, recorded from simultaneously in the retina and LGN. Regions of a cell's receptive field that responded to the bright phase of the stimulus (on responses) are shown in red and regions that responded to the dark phase (off responses) are shown in blue. The circle over the retinal ganglion cell's receptive field is drawn at 1.75 space constants from the peak of the Gaussian that best fitted the center response. The same circle is also shown superimposed on the geniculate cell's receptive field to allow further comparison of the degree of overlap between the two receptive fields. In this example, the two cells have nearly identical receptive fields, with the exception that the center of the retinal ganglion cell's receptive field is slightly larger than that of the geniculate cell.
Cross-correlation analysis was used to determine whether pairs of
simultaneously recorded retinal and geniculate neurons were monosynaptically connected. The cross-correlograms shown in Fig. 1C show the relationship between the retinal and geniculate
cells' firing patterns under different stimulus conditions. Zero in
the correlograms indicates the time a retinal spike occurred and the narrow peak, ~4.5-5.0 ms to the right of zero, indicates that the
geniculate cell often fired in response to a retinal spike. This
narrow, short-latency peak is taken as evidence of a monosynaptic connection (Cleland et al. 1971a,b
). Although spike rate
varied depending on the stimulus used, the peak in each
cross-correlogram retained its latency, narrow width, and rapid rise
and fall under conditions of either grating or white-noise stimulation,
as well as in the absence of a stimulus (spontaneous activity, eyes covered).
Receptive fields, impulse response functions, and cross-correlograms
for each of the 12 pairs of connected retinal ganglion cells and
geniculate neurons are shown in Fig. 2. Column 1 of Fig. 2
(columns are numbered from the left) shows the receptive fields of each of the 12 retinal ganglion cells. As in Fig. 1, circles
drawn at 1.75 space constants (ret) from the peak of the
center response are shown superimposed on the retinal receptive fields.
The same circles are also shown superimposed over the simultaneously
recorded geniculate cell's receptive field (Fig. 2, column
2) to aid comparison of locations and sizes of receptive fields.
Receptive-field overlap was further quantified with a normalized dot
product (see METHODS) for each of the 12 pairs of cells (Fig. 2, column 5). For most cell pairs,
retinal and geniculate receptive fields were well overlapped. Eleven of
the 12 pairs had positive overlap values that ranged from 0.88 (very
precise overlap, same sign) to 0.50 (less overlap, same sign). Pair 135 had a negative value of 0.60 because the centers overlapped, but the
retinal cell was on-center and the geniculate cell was off-center.
Although most cell pairs had corresponding X or Y classification, three
pairs of cells were nonmatching. Pair 135, in addition to its
on-off mismatch, was a mismatched retinal Y cell
connected to a geniculate X cell. Pairs 97 and 109 were from a single
retinal Y cell with connections to two different geniculate X cells.
Despite the X-Y mismatches of these two pairs, their receptive fields were well overlapped and were all off-center. Although
the occurrence of X-Y mismatches may seem surprising, it should be
noted that previous anatomic and physiological studies have also found
X-Y mismatches between retina and LGN (Hamos et al.
1987; Mastronarde 1992
).
The time course of visual responses of pre- and postsynaptic neurons
are plotted in column 3 of Fig. 2. These impulse
responses, shown for both centers and surrounds, were derived
from the spatiotemporal receptive fields as outlined in
METHODS. They can be thought of as the average response to
the bright phase of the stimulus, summed over all of the pixels in
either the center or the surround. Because the stimulus was
binarythat is, if a pixel was not light, it was necessarily dark
a
negative response to the bright stimulus (seen for the
off-center neurons) is formally equivalent to a positive
response to the dark phase of the stimulus.
A number of points can be appreciated from the impulse responses.
First, the impulse responses of the geniculate receptive-field centers
(red thick lines) are usually of lower amplitude and are delayed with
respect to the retina centers (black thick lines). Second, the impulse
responses of both the geniculate and retinal centers have an overshoot,
or rebound, that begins at ~40 ms. The rebounds tended to be greater
for Y cells than X cells, as would be expected (Cleland et al.
1971b; Ikeda and Wright 1972
; see later
discussion), and were also stronger for neurons in LGN than in the
retina. Note finally that the surrounds also tended to be relatively
stronger in the LGN. All of these qualitative points, some of which are
hard to appreciate in Fig. 2, will be examined quantitatively (Fig.
3).
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Finally, for each of the 12 positive correlations, we calculated two
values (Fig. 2, column 5) that quantify the influence of
a presynaptic cell on the firing of its postsynaptic target: efficacy and contribution (Levick
et al. 1972; see METHODS). Efficacy refers to the
percentage of presynaptic spikes that evoke a postsynaptic spike;
contribution refers to the percentage of postsynaptic spikes that are
due to these presynaptic spikes (assuming strict causality). Cell pairs
in Fig. 2 are shown ordered according to their contribution values.
Although earlier studies with dual recordings in the retina and LGN
reported that 8% of cell pairs were completely driven by a single
retinal input (Cleland et al. 1971a
,b
; Levick et
al. 1972
; cf. Cleland and Lee 1985
and
Mastronarde 1992
who found higher percentages), none of
the retinal cells in the present study provided 100% contributions to
a given geniculate neuron. Of the 12 pairs of connected cells in our
study, contribution values ranged from 1 to 82%, and efficacy values
ranged from 0.6 to 36%.
The distribution of contributions reported here is somewhat skewed
toward lower values (Fig. 2, see Fig. 6C) compared with those reported in previous studies (Levick et al. 1972;
Mastronarde 1987
, 1992
). For instance, Levick and
colleagues (1972)
found in their study that aside from the 8 of
105 cell pairs that had contribution values of 100%, "the sample of
contributions was distributed more or less evenly from 3% up." The
difference is probably because, whereas they seemed to have searched
for connected pairs, we used a multielectrode to study a large number
of geniculate neurons, irrespective of whether they were connected to a
given ganglion cell. For this reason, we may have uncovered more
examples of weak connections. Sampling bias is almost certainly another important factor; for instance, we found very few lagged cells in the
LGN, a class that was shown by Mastronarde (1987)
to be driven by a single retinal input.
Another possible explanation for the lower contribution values reported
here is based on bursts in the geniculate spike train. Specifically,
although a single retinal spike may evoke a burst of action potentials
in a geniculate neuron, only the first spike falls in the time interval
we used to measure correlation strength (see METHODS). Many
factors can affect geniculate bursts including arousal, depth and type
of anesthesia, and cortical feedback (Guido and Weyand
1995; Hubel 1960
; Livingstone and Hubel
1981
; Mukherjee and Kaplan 1995
; Sherman
1996
; Sherman and Koch 1986
; Steriade et
al. 1993
). We used the criteria of Sherman and colleagues
(Guido et al. 1992
; Lu et al. 1992
) to
identify geniculate bursts and then replaced each burst with a single
event located at the time of the burst's first action potential.
Efficacy and contribution values from burst-subtracted spike trains are
given (Fig. 2, column 5) below the corresponding values
calculated from nonsubtracted spike trains. When bursts were
subtracted, contribution values increased only slightly (by 8.5 ± 8.0%, mean ± SE) and none of the pairs in our sample reached 100%
contribution. Finally, because burst-subtraction removed geniculate
spikes, efficacy values decreased slightly (by
6.4 ± 7.2%).
Similarities and differences between retinal and geniculate responses
As can be appreciated qualitatively from Fig. 2, the receptive fields of pre- and postsynaptic neurons were subtly different in several ways. To quantify the relations between them, we analyzed the impulse responses of both centers and surrounds and derived several parameters related to their strength and timing. To understand the differences between the time courses of retinal and geniculate visual responses, we first considered whether these differences may be caused by a simple synaptic delay.
In a strictly feedforward system, the location of the correlogram's
peak can be thought of as the latency: the delay between pre- and
postsynaptic responses. Factors affecting latency include conduction
velocity, synaptic transmission and postsynaptic spike generation. The
range of latencies between retinal and geniculate responses for the 12 pairs was 2.3-4.9 ms. In all cases, the peaks were extremely narrow:
the rise from half-maximum was 0.23 ± 0.05 (mean ± SE) ms and the
decay to one-half maximum was 0.33 ± 0.09 ms. The correlation
between latency and the retinal cell's X-Y classification was
extremely tight (Fig. 3A). Consistent with previous
reports that describe faster conduction velocities for Y-cell axons
than X-cell axons (Cleland et al. 1971b; Fukada
1971
), Y cells in our sample had shorter latencies (2.66 ± 0.49 ms) between pre- and postsynaptic response than did X cells
(4.45 ± 0.36 ms).
As noted, the time course of the maximum visual response
of the geniculate neurons was delayed relative to the retinal ganglion cell. To demonstrate this quantitatively, we plotted the geniculate tmax (time of maximum visual response, see
METHODS) versus the retinal tmax
(Fig. 3B). The retinal tmax
ranged between 13.4 and 29.1 ms; the LGN
tmax ranged between 21.4 and 38.8 ms. In
each case the LGN tmax was greater than the
retinal tmax, so all points fell above the
line of unit slope. A similar relationship held for the time of maximum
visual response of the surrounds (not shown). In fact, the change in
the timing of the surround was less than the change in center timing in
most cases. Given the relative noisiness of the surround measurements,
however, it is unlikely that this effect is significant. In Fig.
3C the change in center timing between retina and LGN
(tmax) is plotted versus the latencies
from Fig. 3A. The change in center timing (1.9-15.5 ms)
was in most cases significantly longer than the synaptic delay between
pre- and postsynaptic neurons (again, 2.3-4.9 ms). Thus, the delayed
geniculate visual responses were not solely due to the latency between
retinal and geniculate firing. It is possible, however, that they were
caused by slower interactions between spikes from the retinal afferent,
which can last for tens of milliseconds (Mastronarde
1987
; Usrey et al. 1998
).
It had been noted that retinal receptive-field centers are larger than
those of their geniculate targets (Cleland and Lee 1985;
Cleland et al. 1972a
). To assess this, we compared the
space constants from the two-dimensional Gaussian fits:
ret and
LGN (Fig. 3D; note
that in Figs. 1 and 2, the plotted circles have a radius of 1.75
ret). When the three examples of a Y cell connected to
an X cell are discounted, there were few consistent differences between
the retinal and geniculate centers.
Although the spatial extent of the center changed little between retina
and LGN, we found evidence of increased spatial and temporal antagonism
in the LGN. By temporal antagonism, we mean the rebound seen in the
impulse response. This rebound can be related to a more conventional
measuretransience (Cleland et al.
1971b
; Ikeda and Wright 1972
)
in the following
way. Transience is normally measured by recording the response to a
step function, to yield the step response. Instead of
measuring step responses directly, we studied the time course of visual
responses with white noise. The time course of the responses as
measured with white noise approximates the response to a brief impulse,
the impulse response. Because a step function can be thought of as the
integral of an impulse, the step response (assuming linear temporal
summation) should be the integral of the impulse response (Fig. 2,
column 4). Therefore the magnitude of the rebound should be directly related to the transience of the neuron's response to step
stimuli (see Gielen et al. 1982
).
Quantification of the rebound indicated that it was significantly
greater for most retinal Y cells than for retinal X cells (Fig.
3E, open and shaded diamonds), as would be expected from the greater transience of Y cells (Cleland et al. 1971b;
Ikeda and Wright 1972
). Further, for all cases except
one, in which a retinal Y cell connected to a geniculate X cell, the
rebound in the geniculate was greater than in the retina (Fig.
3E). This demonstration of more transient responses in
the LGN is in agreement with studies of the responses to localized
stimuli (Cleland and Lee 1985
); it is also consistent
with the more band-pass temporal frequency tuning and phase advances
found in the LGN (Kaplan et al. 1987
; Mukherjee
and Kaplan 1995
).
The relative strength of center and surround was quantified by
normalizing the magnitude of the surround response by the center response magnitude (see METHODS). The normalized retinal
surrounds ranged from 0.07 to 0.49, whereas the geniculate surrounds
ranged from 0.39 to 1.01. In all cases but one, the geniculate surround was stronger than the retinal surround (Fig. 3F). This
finding is consistent with the original report of increased surround
antagonism (Hubel and Wiesel 1961) and is confirmed by
subsequent extracellular (Levick et al. 1972
) and
intracellular recordings made in the LGN (Singer and Creutzfeldt
1970
; Singer et al. 1972
), although it has not
been consistently found in all studies (So and Shapley 1981
). The different degrees of spatial antagonism found
between studies may be the result of differing states of anesthesia of the animals; anesthesia has been shown to profoundly affect the degree
of band-pass temporal tuning of LGN cells (Mukherjee and Kaplan
1995
), which is presumably also the result of inhibitory interactions.
Specificity and strength of retinogeniculate connections
To examine how the probability of finding a retinogeniculate connection depends on receptive-field overlap, we used two procedures to characterize the relationship between the retinal and geniculate receptive fields: a Gaussian fit to the receptive field centers and a normalized dot product between the two receptive fields.
The Gaussian fits allowed the relative position and size of the two
receptive fields to be compared. To compare the relationship between
all cell pairs, we normalized the distance between receptive fields, as
well as both space constants (ret and
lgn), by
lgn. We used
this procedure to analyze the spatial relationship between the
receptive-field centers of all of the unconnected and connected pairs
of cells recorded from in this study. In Fig.
4 ( A, B, and
C), the centers of retinal ganglion cell receptive fields are represented as circles (thin lines) that are superimposed on a
common stylized geniculate receptive field (thick lines). In
D, E, and F, the same arrangement of
retinal receptive fields is shown, but with only the center points
indicated. Retinal receptive fields that had the same sign
(on or off) as the geniculate cell are shown as
either thin solid lines (B and C) or × (E and F), whereas retinal receptive fields with
the opposite sign are shown as either dashed lines or triangles
(A and D). A, B,
D, and E in Fig. 4 show the relative
receptive-field locations of the retinal ganglion cells and geniculate
neurons that did not show peaks in their correlograms. Receptive fields
of pairs of cells that showed a monosynaptic peak are illustrated in
C and F. All the retinal cells with connections
to geniculate neurons had receptive field centers that overlapped LGN
centers by
50%. Although all combinations of overlap (center and
surround) were present in our data set (Fig. 4, A,
B, D, and E), only a very small number of cell pairs (Fig. 4, C, and F) were
monosynaptically connected.
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The second measure of overlap, the normalized dot product, allowed us to quantify spatial overlap with a single parameter. Figure 5 shows the relationship between presence of connection and degree of spatial overlap. Most important, all pairs of cells with overlap values >0.75 were monosynaptically connected. As the degree of overlap decreased, the percentage of connected cell pairs also decreased, to 50% (for pairs with 0.51-0.75 overlap) and to 9% (for pairs with 0.26-0.50 overlap).
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Finally, we examined the relationship between receptive-field overlap
and strength of connection by comparing overlap values (the dot product
between receptive fields, see METHODS) with values of
efficacy and contribution (Fig. 6).
Because efficacy and contribution values depend in a complex fashion on
the stimulus used (Cleland and Lee 1987; Hubel
and Wiesel 1961
; Kaplan et al. 1987
; Lee
et al. 1983
; Levick et al. 1972
), it is
necessary to compare values from pairs of cells that are driven by a
similar stimulus. The relationship between efficacy and overlap is
shown for data collected with grating stimuli (Fig. 6A,
shaded diamonds) and with white-noise stimuli (Fig. 6A,
solid squares). Similarly, the relationship between contribution and
overlap is shown in Fig. 6C. In general, efficacy and
contribution values were higher when cells were driven with a grating
stimulus compared with a white-noise stimulus (Fig. 6B and
6D). Under both stimulus conditions, however, there was a
clear trend in the relationship between receptive-field overlap and
strength of connection: the more complete the spatial overlap, the
stronger the connection. A similar relationship between strength of
connection and proximity of retinal and geniculate receptive fields is
described in Mastronarde (1992)
. It should be noted that
we found cases in which the efficacy or contribution of unconnected pairs (open symbols) were nominally higher than those of a few connected pairs (solid squares or shaded diamonds). This is because the
test for monosynaptic connections has an initial stage that rejects
correlations slower than approximately 1 ms in width (see METHODS). Several of the retinogeniculate pairs had
significant but slower correlations, on the order of several
milliseconds. These slower correlations were most likely the result of
correlations found within the retina (Mastronarde 1983a
)
and do not represent monosynaptic connections between retina and LGN.
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DISCUSSION |
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By studying the receptive-field properties and the cross-correlations between retinal ganglion cells and geniculate neurons, we have examined the rules that govern the presence and the strength of individual connections. For strong connections from single ganglion cells, the geniculate receptive field is, of course, very similar to the retinal receptive field, with the same spatial location, response sign (on or off), and X-Y classification. More significantly, even weakly connected pairs are almost always from same-sign retinal cells with nearby receptive fields. As similarity between receptive fields decreases, so does the likelihood of connection and the strength of connection.
In the following section, we examine three topics: 1) the technique of cross-correlation analysis as applied to the examination of retinogeniculate connections, 2) the relationship between specificity, strength, and probability of connections, and 3) the functional implications of converging and diverging retinogeniculate connections.
Cross-correlation analysis and the retinogeniculate pathway
Cross-correlation analysis has been used extensively in studies of
the visual system to examine relationships in the firing patterns of
multiple neurons. The location, size and shape of peaks in a
cross-correlogram can indicate much about the synaptic circuitry
between neurons (Perkel et al. 1967; Usrey and
Reid 1999
). Although peaks in correlograms can often have
multiple causes, correlations between retina and LGN are particularly
stereotyped and easy to interpret. Consistent with previous reports
(Cleland et al. 1971a
,b
; Levick et al. 1972
;
Mastronarde 1987
, 1992
), retinal ganglion cells and
geniculate neurons often displayed peaks in their cross-correlograms,
which were quite narrow (<1 ms, a slightly lower number than found in
previous studies) and occurred with a latency of 2-5 ms (~2.5 ms for
Y-cell inputs, ~4.5 ms for X-cell inputs). These monosynaptic
correlations were often stronger (
83% of the geniculate cell's
spikes) than correlations seen in cross-correlograms made between pairs
of neurons at other locations in the visual pathway, including pairs of
retinal ganglion cells (Mastronarde 1983a
,b
, 1989
;
Meister et al. 1995
; Rodieck 1967
), pairs
of geniculate neurons (Alonso et al. 1996
;
Neuenschwander and Singer 1996
; Sillito et al.
1994
), geniculate neurons and simple cells in layer 4 of visual
cortex (Alonso et al. 1996
; Reid and Alonso
1995
; Tanaka 1983
), or pairs of neurons within
the visual cortex (Alonso and Martinez 1998
;
Singer and Gray 1995
; Toyama et al. 1981
;
Ts'o et al. 1986
).
Many features of the pathway from retina to LGN make it ideal for
applying cross-correlation analysis to determine the presence and
strength of monosynaptic connections. First, the pathway is unidirectional: there is no feedback projection. As a result, the
firing of retinal ganglion cells is not influenced by the firing of
their targets. Second, estimates of convergence suggest that most
geniculate neurons receive input from fewer than six retinal ganglion
cells (Cleland et al. 1971a,b
; Hamos et al. 1987
; Mastronarde 1987
, 1992
). Peaks in retinogeniculate
correlograms therefore tend to be rather large and can be easily
distinguished from baseline activity. Third, direct excitatory
connections have never been demonstrated between LGN cells, and any
correlations between them (Alonso et al. 1996
) are
therefore likely to be due only to common retinal input (Usrey
et al. 1998
). Finally, and perhaps most important, it is
unlikely that intraretinal correlations would cause positive
retinogeniculate correlograms in the absence of a direct connection, as
could happen if two ganglion cells were correlated but only one was
connected monosynaptically to a given geniculate neuron. Such a
"false-positive" retinogeniculate correlation would have the time
structure of the intraretinal correlation. However, most correlations
between ganglion cells tend to occur over relatively slow time scales
(between 2 and 10 ms) compared with the duration of monosynaptic peaks
(full width at half-maximum of 0.56 ± 0.13 ms) seen in
retinogeniculate correlograms. This difference in time scale makes it
unlikely that our criterion for monosynaptic correlations would yield
false positives caused by these slower intraretinal correlations. The one possible exception is for faster (0.5-1.0 ms) correlations seen
between neighboring retinal Y cells (Mastronarde 1983b
). We do not think these correlations are important in our recordings for
two reasons: 1) they are fairly weak (accounting for ~5%
or fewer of the spikes for any pair of Y cells), and 2) they
generally result in a correlogram with two narrow peaks that
are 2.0 ms apart (Mastronarde 1983b
, Fig. 1). Again,
because a false-positive retinogeniculate correlation would inherit the
structure of the intraretinal correlation, this pattern would be
discriminable from the very narrow single peaks we classify as monosynaptic.
Relationship between specificity, strength, and probability of retinogeniculate connections
One way of expressing our findings is that very precise rules
determine the connections between retina and LGN. If retinal and
geniculate receptive fields are very similar and overlap extensively (overlap value >0.75, see Fig. 5), the cells are strongly connected; if the receptive fields differ in any way, the probability of finding
connections declines very rapidly, and any connections are weak. The
first part of this rule may seem obvious: if a geniculate neuron is
receiving most of its excitatory drive from a given retinal cell, the
receptive fields are necessarily similar. The second part, however,
concerning weaker connections, is not a necessary finding. If a retinal
cell contributes only a small percentage of the input to a geniculate
neuron, its influence on the geniculate receptive field should be
similarly weak. In our sample, however, of the six weakly connected
retinogeniculate pairs (contribution <15%) all had centers that were
overlapped by 50%, and all but one had the same response sign.
In a similar study of geniculocortical connections (Reid and
Alonso 1995), geniculate neurons contributed, on average, 3.1% to the firing of their simple-cell targets in the cortex (maximum 10.3%). In this study, it was found that geniculate neurons with receptive-field centers overlapping the appropriate simple-cell subregion (on or off) were very likely to be
connected to the simple cell. Again, because these connections were
relatively weak, none by itself determined a large part of the
receptive field structure. The weaker correlations found in the present study are thus similar to those found in the past study of
geniculocortical connections: connections were found when the pre- and
postsynaptic neurons had similar response properties at the same
location, although this need not have been the case.
Functional implications of converging and diverging retinogeniculate connections
Anatomic (Hamos et al. 1987) and
physiological (Cleland et al. 1971a
; Mastronarde
1987
, 1992
) estimates of convergence between retinal ganglion
cells and geniculate neurons suggest that whereas some geniculate
neurons receive input from only one retinal ganglion cell, many others
receive converging input from two or more ganglion cells. In the
geniculocortical pathway, one role for convergence is clearly the
transformation of receptive field properties: layer 4 simple cells,
which have elongated, orientation-selective receptive fields with
separate on and off subregions, receive
convergent input from geniculate neurons with receptive fields that
overlap the length of the subregions (Hubel and Wiesel
1962
; Reid and Alonso 1995
; see also
Chapman et al. 1991
; Ferster et al.
1996
). In the pathway from retina to LGN, the role of
convergence is much less clear.
A geniculate surround is usually stronger than the surrounds of its
retinal inputs (Hubel and Wiesel 1961). Inhibitory
influences
which are difficult to assess in cross-correlation studies
but have been characterized with intracellular recordings
(Singer and Creutzfeldt 1970
; Singer et al.
1972
)
are likely to play a role in the stronger surround. It
is also possible that geniculate neurons receive convergent input from
retinal ganglion cells with centers that are opposite in sign but
overlap the surround of the geniculate receptive field (suggested by
Hubel and Wiesel 1961
and Maffei and Fiorentini
1971
). In the present study and others (Cleland et al.
1971a
,b
; Mastronarde 1987
, 1992
), however,
monosynaptic connections between retinal ganglion cells and geniculate
neurons the centers of which have opposite signs have been encountered rarely. Of the many pairs of neurons we studied with opposite-sign receptive fields (Fig. 4, A, and D), only one was
weakly connected (Figs. 2, pair 135; and 4, C, and
F).
Although it cannot be ruled out that convergence between retina and LGN
is simply a result of incomplete pruning of aberrant connections during
development, convergence may also be important in the transmission of
information from retina to LGN. One possible role of convergence of
several retinal cells onto one geniculate neuron depends critically on
divergence in the same pathway. Estimates from anatomic studies in the
cat (reviewed in Cleland 1986) suggest that axons from
individual retinal X cells diverge to contact at least three geniculate
neurons; Y cells diverge by a factor of at least 30. Results from our
study suggest that branching axons should most strongly innervate
geniculate neurons with very similar receptive fields and weakly
innervate a subset of the geniculate neurons with receptive fields that
only partially overlap or that differ in receptive-field type (X or Y)
or sign (on or off). As had been
predicted by Cleland (1986)
, a recent study found that
pairs of geniculate neurons with very similar receptive fields often
had strong and narrow peaks (~1 ms) in their cross-correlograms, centered at time zero (Alonso et al. 1996
). These
correlograms provided strong evidence for the presence of common input.
The same study also described smaller peaks, which occurred less
frequently, between pairs of geniculate neurons that had receptive
fields that were either partially overlapped or were mismatched: X and Y or on and off. Taken together, it seems likely
that a retinal ganglion cell with a receptive field similar to and well
overlapped with two geniculate cells almost certainly provides strong
input to those cells, while simultaneously providing weak input to a subset of other geniculate cells that are less similar. Thus divergence in the pathway from retina to LGN establishes small ensembles of
geniculate neurons that fire a variable proportion of their spikes in
tight synchrony.
As we have previously argued (Alonso et al. 1996,
Usrey and Reid 1999
), synchronous activity in the
LGN
caused by divergent connections from the retina (Usrey et
al. 1998
)
may serve several purposes. In development,
geniculate synchrony may be important for the patterning of
geniculocortical connections (cf. Erwin and Miller 1998
;
Meister et al. 1991
; Miller 1994
). In the
adult, synchronous geniculate spikes can be used to derive more
information about the visual stimulus (Dan et al. 1998
).
Finally, perhaps the most important consequence of synchronous activity
in the LGN stems from the fact that many LGN cells provide convergent input to individual layer 4 simple cells in area 17 (Reid and Alonso 1995
; see Peters and Payne 1993
). Some of
these inputs come from tightly synchronized pairs of LGN cells, and
these synchronous inputs have been shown to be especially effective in
driving layer 4 simple cells (Alonso et al. 1996
). Thus
divergence from retina to LGN and reconvergence from LGN to layer 4 may
act as a means to enhance the transfer of visual information from the
retina to cortex.
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ACKNOWLEDGMENTS |
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We thank E. Serra for expert technical assistance and D. Hubel, M. Livingstone, S. M. Sherman, and M. Hawken for insightful comments on the manuscript.
This work was supported by National Eye Institute Grants EY-06604, EY-10115, and EY-12196; The Klingenstein Fund; The Harvard Mahoney Neuroscience Institute; and The Howard Hughes Medical Institute.
Present address of W. M. Usrey: Center for Neuroscience, University of California, 1544 Newton Court, Davis, CA 95616.
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FOOTNOTES |
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Address reprint requests to: R. C. Reid, Dept. of Neurobiology, Harvard Medical School, 220 Longwood Ave. Boston, MA 02115.
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 7 January 1999; accepted in final form 30 July 1999.
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REFERENCES |
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