Download Receptive Field Properties of Single Neurons in Rat Primary Visual

Receptive Field Properties of Single Neurons in Rat Primary
Visual Cortex
SERGEJ V. GIRMAN,1 YVES SAUVÉ,2 AND RAYMOND D. LUND2
1
Institute of Developmental Biology, Russian Academy of Science, Moscow 117808, Russia; and 2Neural Transplant Unit,
Institute of Ophthalmology, University College, London EC1V 9EL, United Kingdom
Girman, Sergej V., Yves Sauvé, and Raymond D. Lund. Receptive
field properties of single neurons in the rat primary visual cortex. J.
Neurophysiol. 82: 301–311, 1999. The rat is used widely to study
various aspects of vision including developmental events and numerous pathologies, but surprisingly little is known about the functional
properties of single neurons in the rat primary visual cortex (V1).
These were investigated in the anesthetized (Hypnorm-Hypnovel),
paralyzed animal by presenting gratings of different orientations,
spatial and temporal frequencies, dimensions, and contrasts. Stimulus
presentation and data collection were automated. Most neurons (190/
205) showed sharply tuned (#30° bandwidth at half height) orientation selectivity with a bias for horizontal stimuli (31%). Analysis of
response modulation of oriented cells showed a bimodal distribution
consistent with the distinction between simple and complex cell types.
Orientation specific interactions occurred between the center and the
periphery of receptive fields, usually resulting in strong inhibition to
center stimulation when both stimuli had the same orientation. There
was no evidence for orientation columns nor for orderly change in
optimal orientation with tangential tracks through V1. Responses were
elicited by spatial frequencies ranging from zero (no grating) to 1.2
cycle/degree (c/°), peaking at 0.1 c/°, and with a modal cutoff of 0.6
c/°. Half of the neurons responded optimally to drifting gratings rather
than flashing uniform field stimuli. Directional preference was seen
for 59% of oriented units at all depths in the cortex. Optimal stimuli
velocities varied from 10 to 250°/s. Some units, mainly confined to
layer 4, responded to velocities as high as 700°/s. Response versus
contrast curves (best fit with Naka-Rushton) varied from nearly linear
to extremely steep (mean contrast semisaturation 50% and threshold
6%). There was a trend for cells from superficial layers to be more
selective to different stimulus parameters than deeper layers cells. We
conclude that neurons in rat V1 have complex and diverse visual
properties, necessary for precise visual form perception with low
spatial resolution.
INTRODUCTION
The primary visual cortex, area 17 of several species shows
adaptive responses both during development and at maturity to
modification of visual inputs or manipulation of the primary
optic pathway. Such adaptations are manifested by changes in
visual field representation (Gilbert and Wiesel 1992), patterns
of ocular dominance (Hubel et al. 1977), anatomic indices
(Darian-Smith and Gilbert 1994), immediate early gene expression (Rosen et al. 1992), and growth factor levels (Castren
et al. 1992; Schoups et al. 1995) among others. Although most
studies of plasticity in the visual system have used larger
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animals (cats, ferrets, primates), rodents have been particularly
important when performing exploratory experimental procedures such as when examining factors affecting development
(for a review, see Lund 1978) or strategies for promoting
regeneration of the primary optic pathway (Berry et al. 1996;
Harvey and Tan 1992; So and Yip 1998; Vidal-Sanz et al.
1987) or when using genetic mutants or transgenics that serve
as models for human disease states.
There are a number of rodent diseases in which photoreceptor degeneration mimics human conditions such as retinitis
pigmentosa and age-related macular degeneration (Dowling
and Sidman 1962; LaVail 1981). A range of potential therapies, including transplantation (Gouras et al. 1989; Li and
Turner 1988; Little et al. 1996), growth factor injections (Faktorovich et al. 1992), and gene therapy (Bennett et al. 1996) are
being developed in the rodent models. Most attention has been
given to examining how such therapies affect retinal integrity,
but equally important is how they modify the visual cortical
response properties that underlie the conscious experience of
vision. To achieve this, a more complete description than is
presently available of unit properties in the normal cortex
(Burne et al. 1984; Girman 1985; Mangini and Pearlman 1980;
Parnavelas et al. 1981; Shaw et al. 1975; Tiao and Blakemore
1976; Wiesenfeld and Kornel 1975) is needed. This study has
provided such data for the rat.
METHODS
Animals, preparation, and maintenance
The results presented here are based on recordings done in the
primary visual cortex (V1) of 25 female adult (Lister Hooded) rats,
weighing between 150 and 250 g. This excludes animals, mostly from
pilot experiments, in which recordings were considered less than
optimal due to the condition of the animal, the cortex, or recording
electrodes. Animals were housed and handled in conformity with the
policy of the American Physiological Society. They were anesthetized
with an intraperitoneal injection of 2.7 ml/kg of a solution of fentanyl
citrate and fluanisone (Hypnorm: 0.315 mg/ml, fluanisone: 10 mg/ml;
Janssen-Cilag) and midazolam (Hypnovel: 5 mg/ml; Roche), one part
of each in two parts of distilled water. Preliminary studies using a
range of anesthetics showed that some depressed cortical activity
(urethane, chloral hydrate), others caused spontaneous spiking that
confounded data collection (barbiturates); these anesthetics also gave
unstable baseline conditions or were not suitable for long recording
sessions. In contrast, Hypnorm-Hypnovel gave stable and tightly
tuned responses over many hours.
The anesthetized animal was placed in a stereotaxic apparatus. EEG
electrodes were installed to monitor anesthetic depth by the presence
of EEG sleep spindles; supplementary injections of anesthetic were
0022-3077/99 $5.00 Copyright © 1999 The American Physiological Society
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S. V. GIRMAN, Y. SAUVÉ, AND R. D. LUND
given as necessary. The animal was paralyzed with D-tubocurarine
chloride (Sigma, 3 mg/kg im) to minimize eye movements and artificially ventilated through the nose. Body temperature was maintained
at 37°C with a thermostatically controlled heating pad. The heart rate
was monitored continuously. The cornea was protected with a nonrefractive contact lens. Pupil dilation and accommodation paralysis
with atropine were not used. The pupil diameter remained between 0.5
and 1 mm. throughout the experiment.
Recording and visual stimulation
Most recordings were from the region of V1 receiving input from
the frontal third of the horizontal meridian (coordinates: 1 mm nasal
from lambda, 3.5 mm lateral from the midline). In experiments
requiring tangential microelectrode penetrations, a horizontal channel
was drilled along the skull. At the end of the channel, a small opening
was made (approximate coordinates: 3.0 mm nasal from lambda, 6.0
mm lateral). Tungsten-in-glass microelectrodes (Levick 1972) were
used for recording single units extracellularly. The recording microelectrode was introduced into the tiny craniotomy made over V1. As
the microelectrode was advanced slowly, various stimuli were displayed across the animal’s visual field to activate cells, many of which
had very low or no spontaneous activity. When a single unit was
isolated, we first mapped the location and size of the corresponding
visual receptive field (RF). The RF was centered on a BARCO ICD
451B monitor (stimuli driven by an AT Truevision Vista Graphic
board). The distance between the eye and monitor was set at 57 cm.
At this viewing distance, the screen subtended 20 3 20° of visual
angle. Preliminary tests showed that spatial resolution was not improved by varying the distance or by placing corrective lenses in front
of the eye to correct for the previously reported hypermetropia (up to
19D: Hughes 1977; 14.5 to 18.5D: Mutti et al. 1997) or myopia (22
to 22.8: Brown and Roja 1965; 23D: Montero et al. 1968) of the rat
eye. Only black-and-white display mode was used in the experiments;
the average screen luminance was 30 cd/m2. Quantitative experiments
proceeded under computer control. Each experiment consisted of
several blocks of trials in which stimuli with particular parameter
values were presented in a random order to minimize the possible
effects of response variability. Results of several repeated blocks
(typically 5, but not less than 3) were averaged. Responses to a
uniform field of averaged luminance also were recorded to measure
the neuron’s spontaneous firing rate. Responses were compiled into
average histograms synchronized with respective temporal cycles of
the stimulus. On-line Fourier analysis was performed to calculate the
mean firing rate and the response at the fundamental stimulus spatial
frequency (SF). The experimental protocol and software for stimulus
presentation and analysis followed exactly those used in studies of
monkey visual cortex used by Gegenfurtner et al. (1997).
At the end of some recording sessions, electrolytic lesions (2 mA
for 3 s) were made at three levels along the recording track: at the
ventral limit of the cortex, at an intermediate depth of 100 mm below
the brain surface. Precise readings of the depth indicator on the
hydraulic micromanipulator were noted at each lesion site. The rat
then was killed with an overdose of tribromoethanol (1,000 mg/kg ip)
and perfused intracardially with buffered 4% paraformaldehyde. The
brain was removed and the tissue block containing V1 cortex was
taken and put in 30% sucrose. Frozen sections then were cut at 40-mm
thickness in coronal planes and stained with cresyl violet, for reconstruction of the electrode track.
RESULTS
We recorded from 205 visually responsive cells in the rat V1
cortex, from within the area of maximum visual acuity in the
rat (corresponding to the temporal retinal representation) (Dre-
her et al. 1985; Fukuda 1977). As many as 20 units could be
isolated in a single vertical penetration, but for full analysis, 9
units on average (range 4 –13) per penetration were studied. In
a single vertical track, receptive fields (RF) were roughly in the
same position in the animal’s visual field.
In healthy animals, nearly all cells in V1 were visually
responsive. Nonresponding cells usually were observed in animals with deteriorating conditions, i.e., bradycardia, corneal
clouding and mydriasis, and decay of electroencephalographic
amplitude. We were able to hold single cells for as much as
3 h; axonal units were excluded from the analysis. Sometimes
discharges of two cells were recorded simultaneously. In such
cases, the single units were isolated using a window discriminator, permitting analysis of response properties of neighboring cells.
The receptive field size in 54 of the cells tested failed within
the limits imposed by the stimulus presentation screen (,20°).
However, the other cells responded to stimuli larger than the
screen dimension, making it impossible to define the actual
field size using this experimental set up.
For each neuron, a battery of tests was performed to study its
tuning for orientation and direction, spatial and temporal frequency, size of receptive field, orientation tuning of surround
modulation, and contrast sensitivity. Figure 1 illustrates typical
experimental data obtained from a single cell, recorded at a
depth of 420 mm (within layers II–III).
Figure 1A shows the responses of the cell to sinusoidal
gratings of different orientation. This cell responded to gratings
with an optimal orientation of 210°. It responded slightly more
weakly to gratings of the same orientation but moving in the
opposite direction (30°). No response could be recorded for
any other orientation or direction of movement. Other examples of properties studied are: spatial tuning (Fig. 1B), temporal
tuning (Fig. 1C), response versus contrast (Fig. 1D), and dependence on stimulus dimension (Fig. 1 E). For cells strongly
inhibited by stimuli presented outside their classical RF (as
shown in Fig. 1E), we studied how this inhibition was influenced by the orientation of the surround stimuli (Fig. 1F). As
with all units tested, stimulation of the surround on its own
(right on the x axis), regardless of orientation, did not elicit a
response. In this particular case, presentation of surround at
any orientations (left) had an inhibitory effect on the response
to center stimulation. This inhibition tended to be strongest
when the surround orientation corresponded to that of the
center.
Cell reactivity and selectivity to stimulus orientation and
direction of movement
A summary of the reaction index of all recorded cells is
presented in Fig. 2A. The majority of cells (140/205, ;70%)
had a reaction index (RI) that exceeded 0.7; no cells had RI 5
0. A cell is considered as orientation selective if its orientation
index (ORI) is $0.7 and as directional selective if its direction
index (DI) is $0.7. For the cell shown in Fig. 1, the value of
ORI is close to 1.0 and DI is nearly 0.1.
Figure 2B illustrates the orientation preference for all oriented cells encountered. Of all cells recorded, 77% (158/205)
fulfilled the criterion for being categorized as orientation selective (ORI . 0.7), whereas an additional 16% (32/205)
showed an orientation bias (0.5 , ORI , 0.7). Therefore the
RECEPTIVE FIELD PROPERTIES OF RAT V1 NEURONS
303
FIG. 1. Data obtained on-line from a single cell at 420-mm depth (layer 2–3). In all graphs, the y axis shows the response as
impulse per second (imp/s). Spontaneous activity (SA; discharge rate with uniform field of average luminance) is presented to the
right of each x axis as 2 horizontal lines: SA 1 SE and SA 2 SE, respectively; SE is the standard error of the mean. In this cell,
SA approximates 0. Solid line in all graphs links the average responses (AR) of individual experimental points. Dashed lines
correspond to AR 1 SE and AR 2 SE, respectively. Orientation index (ORI) 5 (max resp 2 orth resp)/max resp, where
max resp and orth resp are responses to optimal and orthogonal to optimal orientation respectively. Direction index (DI) 5
(optim resp 2 oppos resp)/optim resp, where optim resp and oppos resp are responses to optimal and opposite to optimal
direction respectively. A: responses to sinusoidal gratings of different orientations. As a convention, the orientation noted as 0°
corresponds to vertically oriented bars moving from temporal to nasal visual field; 90° corresponds to horizontally oriented bars
moving downward in the field. B: responses to gratings of different spatial frequencies. C: responses to different temporal
frequencies. D: responses to gratings of different contrast. E: responses to grating patches of different sizes. F: responses for testing
center-surround interactions. This particular cell was strongly end-stopped.
majority (93%) of the 205 visual units recorded were tuned at
some level for orientation, whereas 7% (15/205) had no orientation bias (ORI , 0.5); about half of these (7/15) had no
modulation to any orientation (ORI 5 0). There was a signif-
icant bias of orientation preference for horizontal grating stimuli. Table 1 also shows that 35% (55/158) of all oriented cells
responded optimally to stimuli oriented at 90 or 270°. As can
be seen in Fig. 2C, the majority of orientation-selective cells
FIG. 2. Cell reactivity and orientation selectivity. A: histogram showing the reaction index of all recorded cells. Reaction index
(RI) 5 u(max dev 2 SA)u/(max dev 1 SA); 0 . RI , 1, where max dev is the neuronal response, to a specific stimulus, with
maximal deviation from its SA. For example, max dev 5 SA and RI 5 0 in the case where there is no neuronal response to any
pattern of stimulus presentation. B: histogram showing the preferred orientation in the overall population of recorded cells. C:
histogram of tuning for all orientation selective cells.
304
TABLE
S. V. GIRMAN, Y. SAUVÉ, AND R. D. LUND
1.
Distribution of oriented cells by preferred orientation
Angle
n
Percent
0°, 180°
30°, 210°
60°, 240°
90°, 270°
120°, 300°
150°, 330°
20
16
22
55
26
19
13
10
14
35
16
12
Orientation index 5 $0.7.
(99/158) have an orientation tuning (bandwidth at half height)
of 60° or sharper (30°).
Of all oriented cells, 37% (59/158) were directionally selective (DI $ 0.7), whereas a further 22% (35/158) showed a
directional bias (0.5 # DI , 0.7). Thus more than half of the
oriented cells (59%) responded differentially to directional
stimuli. No preferred direction was evident.
Using criteria based on the nonlinear versus linear properties
of V1 neurons (Skottun et al. 1991), we found that oriented
cells could be divided in two populations: complex-like (43%;
F1/F0 , 1), responding mainly with unmodulated elevation in
discharge, and simple-like (57%; F1/F0 . 1), having their
frequency of discharges mainly modulated at the fundamental
stimulus frequency. This distribution of cells according to their
F1/F0 ratio is presented in Fig. 3.
We observed a few individual cells (n 5 6) with the very
unusual characteristic of responding to two stimulus orientations which were nearly orthogonal to one another. This surprising observation has also been made in the cat V1 (Shevelev
et al. 1995). We recorded these cells for a long enough (from
1 to 3 h in several cases) to document this fact and show that
it was repeatable over time. The optimal stimulus parameters
for each cell were identical for the two preferred orientations.
About orientation columns in the rat visual cortex
Cells with very different preferred orientations often could
be encountered in a single vertical penetration through the
cortex (Fig. 4B). The regular shift of orientation preference
with progressive depth that is seen in cat or monkey for
example with a slightly oblique penetration was never encountered. Furthermore when recording from two cells simultaneously, their preferred orientations often could be quite different (29/38, 76%) and in 18% of cases were even tuned for
orthogonal orientations. Such observations do not support the
presence of orientation columns in rat V1 cortex.
To examine further the spatial array of orientation representation across V1, we made a series of horizontal microelectrode
penetrations. Tracks were restricted to superficial layers where
there is a higher proportion of oriented cells (see Depth distribution of cell properties). Unlike previous studies examining
changes in orientation across the cortex in cats and monkeys
(Hubel and Wiesel 1968; Murphy and Sillito 1986), there was
no evidence of a regular change in orientation with progress
across the cortex. The results of these experiments are summarized in Fig. 4. Reconstruction of horizontal electrode penetrations indicated no systematic changes in orientation preference with increasing distance between the cells (Fig. 4A).
Similar exercises with vertical tracks showed large variability
in orientation preference (Fig. 4B). Furthermore there was no
correlation between the distance separating any two neighboring units in horizontal penetrations and the difference in their
respective orientation (Fig. 4C). These results thus fail to
support the existence of ordered representation of orientation in
the rat visual cortex.
Spatial tuning
The spatial tuning of 189 cells was examined using gratings
in which the SF was varied and other parameters kept optimal.
Rat V1 neurons are sensitive to spatial frequencies of #1.2 c/°
(Fig. 5A). Approximately half of the cells (94/189) had low
band-pass higher than zero (Fig. 5C); that is, they had no or
very weak responses to uniform stationary ON-OFF stimuli and
responded optimally to moving gratings. Most cells had optimal responses at #0.08 c/° (Fig. 5B). Twenty-one cells (11%)
responded optimally to flashing uniform field stimuli (0 c/°). In
general, neurons were tuned to relatively low SF. The majority
had sharp SF tuning. The mode of band-pass width distribution
was 2 octaves. Approximately 64% of the cells had band-pass
widths less than two octaves (Fig. 5D).
The spatial frequency properties of cells tuned to horizontally oriented gratings (90 and 270°) were compared with those
of cells tuned to other orientations. The distribution of the peak
SF and cutoffs for these two cell populations did not differ
significantly (P . 0.60; x2). These findings indicate that the
bias for horizontal orientations is unlikely to be due to astigmatism.
Temporal tuning
As can be seen from Fig. 6, cells in the rat V1 cortex
responded best to high temporal frequencies. The distribution
of the highest temporal frequency giving a response (high
cutoff) peaked at 27.5 Hz (Fig. 6A, top). The temporal frequencies associated with maximal responses were distributed almost regularly from 0.43 to 6.88 Hz (Fig. 6B, top). Cells
responding optimally to SF . 0 could be characterized further
by stimulus velocity parameters. The distribution of high cutoff
for stimulus velocities peaked at 500°/s (Fig. 6A, bottom).
FIG. 3. Distribution of F1/F0 ratio, where F1 is the fundamental harmonic
of a neuron’s response to sinusoidal gratings and F0 is the 0 frequency or d.c.
of the response.
RECEPTIVE FIELD PROPERTIES OF RAT V1 NEURONS
305
FIG. 4. Reconstruction of horizontal (A) and vertical (B) tracks. Orientation of each unit encountered is
indicated by bars inside ellipses. Two bars inside the
same ellipse signify 2 distinct units recorded at the
same location. C: scatter diagram representing the
relation between distance and orientation change for
45 units recorded along two horizontal tracks. x axis
shows the distance between 2 consecutively recorded
cells (0 distance means simultaneously recorded
cells), and the y axis shows the difference in orientation preference of these respective cells.
About 25% of the cells responded to higher stimulus velocities,
at least #700°/s (limit of display by BARCO monitor). Response peak velocities had a more or less flat distribution,
ranging from 10 to 250°/s (Fig. 6B, bottom).
Contrast sensitivity
A wide range of shapes was observed for response versus
contrast curves. Some curves showed a linear increase in the
firing rate with increasing contrast (Fig. 7A), whereas others
showed nonlinear responses that saturated at various contrast
levels (Fig. 7, B and C). To characterize quantitatively the
response versus contrast relationship, the contrast sensitivity of
a given cell was calculated as the inverse of the threshold
contrast. The latter was defined as the minimal contrast level
where the respective standard errors for the cell’s response and
for the spontaneous activity did not intersect. In cases where
FIG. 5. Spatial frequency tuning properties of V1 neurons. A: histogram of response cutoffs. B: histogram of stimulus spatial
frequencies that gave an optimal response. C: histograms of low band-pass. D: histogram of band-pass width.
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S. V. GIRMAN, Y. SAUVÉ, AND R. D. LUND
FIG. 6. Temporal frequency tuning properties of V1 neurons. A: histogram of response cutoffs for temporal frequencies (top)
and velocities (bottom). B: histogram of temporal frequencies (top) and velocities (bottom) producing optimal responses.
there was no spontaneous activity, the minimal contrast corresponded to the point at which the response minus the standard
error exceeded zero. Moreover, the nonlinear regression using
the Naka-Rushton equation, R 5 y0 1 a/[11(Cs/C)b], enabled
us to find the best fit for the experimental data (R, response; C,
contrast; Cs, contrast level at the point of midsaturation; and y0,
a, b: mathematical constants giving the best fit). Regression
lines are shown in the figures as - - -. Fig. 7, D–F, shows the
distributions of the midsaturation contrasts, the contrast response exponent and the contrast sensitivity of all cells. Half of
the cells analyzed (64/128) had a sensitivity that exceeded 10.
Approximately 20% of the cells had almost linear response
increments with increasing contrasts (Cs exceeding 100%).
Approximately half of the cells (57/128) had nonlinear curves
(b . 8). There was no correlation between the three parameters: the cross-correlation coefficients were 0.15 (P , 0.01),
0.15 (P , 0.01), and 0.02 (P 5 0.08) for the pairs Cs-b,
Cs-sensitivity, b-sensitivity, respectively. Therefore any combinations of these three contrast response properties can be
encountered in rat V1 cells.
Center-surround interactions
For many cells, mainly recorded in the upper third of the
cortex, increasing the stimulus diameter decreased the response. The RF center size of these cells varied from 3 to 13°
in diameter. Of the 158 cells with strong orientation selectivity
(ORI . 0.7), 54 had clear inhibition when presented with
stimuli extending beyond their RF center. The response to the
largest stimuli was often , 20% of the response to center
illumination alone. For 18 of these cells, the center-surround
interactions were studied in detail. The following stimuli were
used: optimal center size (size producing the maximal response) with other parameters adjusted for highest reaction
index (marked as 0 in Fig. 8); peripheral ring of different
orientations, adjusted to be immediately outside the area of
optimal stimulus (right on x axis); and stimuli 1 and 2 presented simultaneously (left on x axis). All stimuli were presented in a random order. In all cases, peripheral stimulation
alone did not elicit any significant reactions (note the flat curve
on the right part of all graphs in Fig. 8). However, when
presented in combination with stimulation of the center, it
could modulate the size of the response to center stimulation
dramatically.
Three types of center-surround interactions were found:
peripheral stimuli, regardless of their orientation, produced a
strong inhibition of the response to RF center stimulation (n 5
4, Fig. 8A); the level of inhibition on RF center responsiveness
depended on the orientation of the peripheral stimulus (this
inhibition was usually maximal for matching orientations of
central and peripheral stimuli) (n 5 10, Fig. 8B); and peripheral stimuli had strong orientation-specific excitatory and inhibitory effects on the response to RF center stimulation (n 5
4, Fig. 8C). Twelve of the 18 units were simple-like (F1/F0 .
1), 4 were complex-like (F1/F0 , 1), and the remaining 2 were
undetermined (F1/F0 ;1). The majority (16/18) of the units
had sharp SF tuning. There was no apparent relation between
the type of center-surround interactions and the type of response versus contrast curve. However, in comparison with all
the neurons studied, these units had relatively lower contrast
sensitivity.
Depth distribution of cell properties
On reconstructing recording tracks, following electrolytic
lesions made at different depths, a good correspondence was
found between the values read on the depth indicator of the
micromanipulator and the distance between the brain surface
and the lesion sites as seen on coronal sections. The most
superficial units that could be isolated successfully were lo-
RECEPTIVE FIELD PROPERTIES OF RAT V1 NEURONS
FIG. 7. Representative examples of response vs. contrast curves for V1 cells. Experimental points (●) have been fitted using
Naka-Rushton equation (- - -). Spontaneous activity (response to 0 contrast) is presented as in Fig. 1. A: nearly linear contrast
response (the midsaturation is out of the contrast range). B: nonlinear contrast response from a highly sensitive neuron. C: highly
nonlinear response with a steep threshold at ;25% contrast. Histograms of contrast response parameters are presented in D–F:
midsaturation contrast (D), exponent (E), and sensitivity (F).
FIG. 8. Examples of several types of end-stopping effects. Orientation of gratings presented in the periphery of the RF are
indicated at the left and right of the x axis. Central 0 corresponds to center RF stimulus alone. All center RF stimulation (central
0 and left of curve) consisted of the same stimulus, with optimal orientation. A: any grating orientation in RF periphery inhibits the
cell’s response to RF center stimulation (orientation 270°). B: inhibitory effect of peripheral stimuli on response to optimal central
stimulus (60°) is quite orientation selective. C: response to central stimulus (120°) is excited or inhibited by stimuli presented in
the RF periphery depending on their orientation. Note, x axis represents the orientation of the surround (deg); the value 0 in the
middle of the x axis corresponds to the stimulation of the center of the RF alone, using optimal orientation.
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S. V. GIRMAN, Y. SAUVÉ, AND R. D. LUND
FIG. 9. Changes of response properties
with recording depth. Curves represent the
average values calculated at 100-mm steps.
cated at a depth of ;200 mm below the cortical surface (upper
border of layer II). From this depth down to 500 – 600 mm
(encompassing layers II and III), cells had mainly no or very
low spontaneous activity, with high reaction index, orientation
index, and sharpness of orientation. Layers II–III cells responded to stimuli of relatively high spatial frequencies and
low temporal frequencies. Cells with center-surround interactions were found mostly in these superficial layers. The highest
temporal resolution was attributed mainly to cells confined to
layer IV (;900 mm). Cells from layer IV as well as from layers
V–VI had the highest spontaneous activity. Many of these cells
responded optimally to uniform ON-OFF stimuli or to gratings of
low SF. However, these cells also were intermixed with other
cells having characteristics resembling those of cells in layers
II–III. The changes in response properties with recording depth
are summarized in Fig. 9. Statistical analysis (Student’s t-test)
confirmed the differences between several properties of neurons recorded at 200 – 600 mm and of those recorded deeper.
These properties include: spontaneous activity level: mean
0.66 and 1.67 imp/s, respectively, P , 0.01; reaction index:
mean 0.87 and 0.72, P , 0.001; orientation index: mean 0.90
and 0.80, P 5 0.015; bandwidth of orientation tuning: mean 58
and 73°, P , 0.001; peak SF: mean 0.11 and 0.07 c/°, P , 0.01
(the bandwidth of SF tuning shows a trend that however does
not reach statistical significance: mean 1.94 and 2.12 octaves,
P 5 0.08; SF cutoff did not show any depth differences at all);
peak temporal frequency: mean 2.4 and 4.8 Hz, P , 0.001;
bandwidth of temporal frequency tuning: mean 3.0 and 3.6
octaves, P , 0.001; and cutoff of temporal frequency: mean
16.5 and 25.4, P , 0.001.
DISCUSSION
The present study has focused on the quantitative assessment
of receptive field properties in the primary visual cortex of the
rat. The results show that the response properties of rat V1
neurons are surprisingly well tuned. Cells respond to a highly
specific set of stimulus parameters and many have receptive
field organization characterized by complex interactions be-
tween center and surround components. Such findings indicate
that the responses of these rodent V1 neurons are as specialized, in many ways, as those of highly visual animals (cat,
primate). The conclusion reached by some of the earlier studies
that cells in the rat visual cortex only responded to relatively
large, nondescript stimuli and therefore that the rat had very
poor visual capabilities is not supported by the present studies.
Technical considerations
Differences between the present and previous studies of rat
visual cortical physiology largely stem from technical advances in animal preparation and data analysis. The use of
computer-generated stimuli and associated data analysis permits objective, quantitative assessment of response properties.
Several investigators have relied on urethan or barbiturate
anesthesia. In our hands, although urethan does not seem to
interfere with visual responsiveness in the superior colliculus,
it depresses neuronal activity in the primary visual cortex.
ON/OFF characteristics rather than tuning to orientation, spatial
or temporal frequencies can be emphasized; a high proportion
of ON/OFF units in a study suggests that the anesthetic is depressing responsiveness. The use of barbiturates also causes
spontaneous or bursting activity that can contaminate the results. Paralysis has not been used routinely in rodents as in cats
or monkeys, leaving the possibility for eye movements to
interfere with the interpretation of the results. Recordings
usually were done with large craniotomies; this can result in
spreading depression and in restriction of the blood supply to
the cortex. Such conditions affect predominantly units from
superficial layers (where the proportion of orientation tuned
units is highest), while preserving units from deeper layers,
which are generally less tightly tuned. We have found that very
small craniotomies (in the order of 150 mm) permit much better
recordings, especially from the superficial layers, and ensure
that more units can be characterized per penetration. The
condition of the preparation clearly has a major bearing on the
variability encountered in previous results, for example in the
proportion of oriented units, which varies among studies from
RECEPTIVE FIELD PROPERTIES OF RAT V1 NEURONS
30 to 80% (Burne et al. 1984; Montero 1981; Parnavelas et al.
1981; Shaw et al. 1975; Wiesenfeld and Kornel 1975).
Orientation encoding
The majority of neurons in the rat V1 cortex showed clear
orientation bias with tuning (defined as the bandwidth at half
height) ;60° or even sharper and in this respect compares with
cat (Murphy and Berman 1979) and monkey (Celebrini et al.
1993; DeValois et al. 1982b). More than half the cells were
directionally selective, again placing the rat within the range
achieved in other mammals (DeValois et al. 1982b; Gizzi et al.
1990; Hammond and Pomfrett 1989).
We observed a bias for orientation-selective cells toward
horizontal gratings. This observation was confirmed repeatedly
for all neuron types recorded in either vertical or horizontal
penetrations. Orientation preference for horizontal bars, and
less frequently for vertical bars, has been reported in several
other species (mouse: Drager 1975; hamster: Tiao and Blakemore 1976; rabbit: Bousfield 1977; Murphy and Berman 1979;
cat: Bauer and Jordan 1993; Murphy and Berman 1979; monkey: DeValois et al. 1982b; human: Campbell et al. 1966;
Leehey et al. 1975) but has not been noticed previously in rat.
Although behavioral tests indicated that acuity did not vary
with differently oriented gratings (Dean 1981), rats nevertheless learned to detect low spatial frequency gratings with fewer
errors for horizontal or vertical orientations compared with
obliques. It has been suggested that this orientation preference
might rely on anisotropy intrinsic to the physiological processing of visual information (Bauer and Jordan 1993; Leehey et al.
1975), but our finding that spatial-, temporal-, or contrastrelated parameters were similar for cells irrespective of orientation tuning argues against this.
Relation of classification to classical terminology
In the present study, we avoided using the classification of
V1 neurons as simple, complex, and hypercomplex (Hubel and
Wiesel 1962, 1968) in the primary analysis. However, quantitative analysis shows that simple cells tend to be modulated
while complex cells are unmodulated: this is expressed using
the ratio F1/F0 (Skottun et al. 1991). Our findings show that the
distribution of rat V1 cells according to the F1/F0 ratio is
clearly bimodal, as in the cat or monkey V1 (Skottun et al.
1991).
Orientation columns
The previous literature in rats is unclear with respect to the
presence or absence of orientation columns. Although not
examined systematically, orientation columns were not described in mice (Metin at al. 1988), hamsters (Tiao and Blakemore 1976), and rabbits (Bousfield 1977; Murphy and Berman
1979). Nearly all of these authors assumed the existence of
clusters of cells with similar functional properties, however.
None attempted horizontal penetrations to see whether there
was any indication of progressive change across the cortex. We
found evidence neither for the existence of iso-orientation
clusters nor for progressive change in orientation preference in
horizontal penetrations. When units were recorded simultaneously, they were more likely to respond to different orientations than to the same. The commonality of this finding,
309
coupled with the lack of regular change in horizontal penetrations, argued against recordings being from ‘‘break points’’
(Braitenberg and Braitenberg 1979) or ‘‘pin wheels’’ (Maldonaldo et al. 1997) in an otherwise regular array of orientation
representations as has been shown in cats and monkeys. Although unlikely, it is still plausible that orientation columns do
exist in the rat but on such a small scale that they cannot be
detected using microelectrode recordings. It should be noted,
however, using a more accurate discrimination between neighboring units in cat V1 (using the tetrode technique) that orientation appears more heterogeneously represented locally than
was previously thought (Maldonaldo and Gray 1996).
An interesting corollary of the absence of orientation columns is the pattern of horizontal connections in the upper
layers of the cortex. In a number of animals, in which orientation columns have been identified, punctate horizontal projections connect points responding to similar orientations (ferret: Weliky et al. 1995; cat: Kisvarday et al. 1997; tree shrew:
Bosking et al. 1997; monkey: Malach et al. 1993; Yoshioka et
al. 1996). Such lattice connections intrinsic to V1 appear to be
absent in the rat (Tyler et al. 1998). This might be expected
given the absence of regular iso-orientation zones.
Spatial frequency
The SF tuning of V1 neurons covers a range of 0 –1.2 c/°
with an optimal response at 0.08 c/°. Very similar figures were
found when recording visually evoked cortical potentials (Silveira et al. 1987): the maximal response was observed at 0.1 c/°
with a cutoff at 1.2 c/°. The upper limit of the spatial resolution
corresponds particularly well with that found in behavioral
experiments using rats (1.0 –1.6 c/°: Seymoure and Juraska
1997; 1.0 c/°: Dean 1978, 1981; Lashley 1938; 0.9 c/°: Wiesenfeld and Branchek 1976; 1.6 c/°: Yagi et al. 1995) and is near
to the limit of 1.8 c/° estimated from the maximal density of
ganglion cells in the rat retina (6,500 cells/mm2) (Fukuda
1977). Brain lesion studies (Dean 1981) showed that the striate
cortex is responsible for discrimination of spatial frequencies
of up to ;1 c/°. From this, Dean concluded that the rat cortex
is responsible for the analysis of fine details. This is in agreement with the present study where V1 neurons were shown to
have a high-frequency cutoff of #1.2 c/°. As in cats (Maffei
and Fiorentini 1977), the highest SF to which a cell responded
appeared to be related to the receptive field width: units with
maximal cutoff tended to have the smallest RFs. In addition, rat
cells had similar band-pass widths as in cats (1–2 octaves
bandwidth for a large population of neurons in cat V1) (De
Valois et al. 1982a). However, the lowest SF and highest cutoff
were much less in rats (0 –1.2 c/°, respectively) than in cats
(0.5–15 c/°) (De Valois et al. 1982a). In fact, half of the rat V1
neurons responded to flashing uniform field stimuli (0 SF),
which is in agreement with the absence of low SF cutoff found
in behavioral studies in rats (Dean 1978).
Temporal frequency
With respect to temporal frequency tuning, our results show
that, as in mouse (Mangini and Pearlman 1980), rat V1 neurons
can respond at much higher stimulus velocities (#700°/s) than
cat V1 neurons (Orban et al. 1981). Such high cutoffs are found
more often for cells in the vicinity of layer IV and resemble
310
S. V. GIRMAN, Y. SAUVÉ, AND R. D. LUND
more closely those of cat geniculate afferents to V1 (700°/s)
(Orban et al. 1981). Perhaps those units that respond to the
highest velocities in rats correspond to V1 neurons receiving
direct geniculate inputs.
Response versus contrast
An important point that needs to be addressed is the influence of the background illumination on the response properties
of rat V1 neurons. Although we did not examine directly the
level of rod saturation at the background illumination used in
the present study (30 cd/m2), it is a reasonable assumption that
the rod system has not reached full saturation. Previous behavioral measurements based on rod activation failed to detect any
signs of saturation up to the maximal level tested of 20 cd/m2
(Jacobs and Birch 1975). Furthermore analysis of the pupillary
light reflex under different levels of background illumination
have shown a linear response for a range encompassing 6 log
units (Trejo and Cicerone 1982), where the pupil reached a
minimal diameter of 0.2 mm. In our study, the smallest pupil
diameter was in the region of 0.5 mm.
The wide range of response versus contrast curves we obtained for rat V1 units is in agreement with findings in cats and
monkeys where there is a ‘‘great deal of variation, from cell to
cell, in the shape and location of the contrast response function’’ (Albrecht and Hamilton 1982).
Differential distribution of response properties with depth
The present distribution of functional properties with depth
is consistent with most of the previous observations in mouse
(Drager 1975; Mangini and Pearlman 1980; Metin et al. 1988)
and rabbit (Murphy and Berman 1979); there is a trend for cells
in the superficial layers to be more selective to different stimulus parameters than deeper layer cells. Orientation-selective
neurons are more tightly tuned in the upper rather than lower
half of the visual cortex while nonoriented cells are distributed
preferentially in the lower layers. Cells with the smallest receptive fields, having low or no spontaneous activity, and
responding most selectively to different stimulus parameters,
are typical for layers II–III. As in the cat (Bullier and Henry
1979; Mustari et al. 1981), cells in rat with the highest temporal
frequency cutoff tended to be confined around lamina IV.
Conclusions
In summary, our findings suggest that the functional organization of area V1 in the rat is designed to extract cues from
the visual image that are necessary for precise visual form
perception with low spatial resolution. Where parallels can be
made, the physiological properties of rat V1 neurons correlate
with the findings from rat psychophysical experiments. In
addition to providing information about the substrates of visual
information processing in V1 of the rat, the present results
provide essential insights for understanding the impact of inherited retinal degeneration on visual function. In addition, it
provides the background for evaluating the possible effects of
potential treatments including retinal pigment epithelium cell
transplantation into the subretinal space of RCS rats eyes
(Sauvé et al. 1998).
We are especially grateful for the invaluable advice on data acquisition and
analysis provided by Drs. J. B. Levitt and J. S. Lund.
This work was supported by European Community INTAS Grant 96-1652,
the Foundation Fighting Blindness, and the British Medical Research Council
(G9224336). Further support was provided by British MRC Grant G9408137
to J. S. Lund; this grant also allowed acquisition of necessary software.
Address reprint requests to Y. Sauvé.
Received 22 December 1998; accepted in final form 23 March 1999.
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