Dual Inhibitory Mechanisms for Definition of 
Receptive Field Characteristics in Cat Striate 
Cortex 
A. B. Bonds 
Dept. of Electrical Engineering 
Vanderbilt University 
Nashville, TN 37235 
Abstract 
In single cells of the cat striate cortex, lateral inhibition across orienta- 
tion and/or spatial frequency is found to enhance pre-existing biases. A 
contrast-dependent but spatially non-selective inhibitory component is also 
found. Stimulation with ascending and descending contrasts reveals the 
latter as a response hysteresis that is sensitive, powerful and rapid, sug- 
gesting that it is active in day-to-day vision. Both forms of inhibition are 
not recurrent but are rather network properties. These findings suggest 
two fundamental inhibitory mechanisms: a global mechanism that limits 
dynamic range and creates spatial selectivity through thresholding and a 
local mechanism that specifically refines spatial filter properties. Analysis 
of burst patterns in spike trains demonstrates that these two mechanisms 
have unique physiological origins. 
1 
INFORMATION PROCESSING IN STRIATE 
CORTICAL CELLS 
The most popular current model of single cells in the striate cortex casts them 
in terms of spatial and temporal filters. The input to visual cortical cells from 
lower visual areas, primarily the LGN, is fairly broadband (e.g., Soodak, Shapley & 
Kaplan, 1987; Maffei & Fiorentini, 1973). Cortical cells perform significant band- 
width restrictions on this information in at least three domains: orientation, spatial 
frequency and temporal frequency. The most interesting quality of these cells is 
76 Bonds 
therefore what they reject from the broadband input signal, rather than what they 
pass, since the mere passage of the signal adds no information. Visual cortical cells 
also show contrast-transfer, or amplitude-dependent, nonlinearities which are not 
seen at lower levels in the visual pathway. The primary focus of our lab is study of 
the cortical mechanisms that support both the band limitations and nonlinearities 
that are imposed on the relatively unsullied signals incoming from the LGN. All of 
our work is done on the cat. 
2 
THE ROLE OF INHIBITION IN ORIENTATION 
SELECTIVITY 
Orientation selectivity is one of the most dramatic demonstrations of the filtering 
ability of cortical cells. Cells in the LGN are only mildly biased for stimulus ori- 
entation, but cells in cortex are completely unresponsive to orthogonal stimuli and 
have tuning bandwidths that average only about 40-50  (e.g., Rose  Blakemore, 
1974). How this happens remains controversial, but there is general consensus that 
inhibition helps to define orientation selectivity although the schemes vary. The 
concept of cross-orientation inhibition suggests that the inhibition is itself orienta- 
tion selective and tuned in a complimentary way to the excitatory tuning of the cell, 
being smallest at the optimal orientation and greatest at the orthogonal orientation. 
More recent results, including those from our own lab, suggests that this is not the 
case. 
We studied the orientation dependence of inhibition by presenting two superim- 
posed gratings, a base grating at the optimal orientation to provide a steady level 
of background response activity, and a mask grating of varying orientation which 
yielded either excitation or inhibition that could supplement or suppress the base- 
generated response. There is some confusion when both base and mask generate 
excitation. In order to separate the response components from each of these stimuli, 
the two gratings were drifted at differing temporal frequencies. At least in simple 
cells, the individual contributions to the response from each grating could then be 
resolved by performing Fourier analysis on the response histograms. 
Experiments were done on 52 cells, of which about 2/3 showed organized suppression 
from the mask grating (Bonds, 1989). Fig. i shows that while the mask-generated 
response suppression is somewhat orientation selective, it is by and large much flat- 
ter than would be required to account for the tuning of the cell. There is thus some 
orientation dependence of inhibition, but not specifically at the orthogonal orienta- 
tion as might be expected. Instead, the predominant component of the suppression 
is constant with mask orientation, or global. This suggests that virtually any 
stimulus can result in inhibition, whether or not the recorded cell actually "sees" 
it. What orientation-dependent component of inhibition that might appear is ex- 
pressed in suppressive side-bands near the limits of the excitatory tuning function, 
which have the effect of enhancing any pre-existing orientation bias. 
Thus the concept of cross-orientation inhibition is not particularly correct, since 
the inhibition is found not just at the "cross" orientation but rather at all orien- 
tations. Even without orientation-selective inhibition, a scheme for establishment 
of true orientation selectivity from orientation-biased LGN input can be derived 
Dual Inhibitory Mechanisms 77 
o -10 2 Hz Ree) resp. : No rnnsk (tunkxa) ---o ...... _ 
i i Oh i 'o i i 
  gO 1SO 210 270  
Mask Orientation (deg) 
Figure 1: Response suppression by mask gratings of varying orientation. A. Impact 
of masks of 2 different contrasts on 2 Hz (base-generated) response, expressed by 
decrease (negative imp/sec) from response level arising from base stimulus alone. 
B. Similar example for mask orientations spanning a full 360 . 
by assuming that the nonselective inhibition is graded and contrast-dependent and 
that it acts as a thresholding device (Bonds, 1989). 
3 
THE ROLE OF INHIBITION IN SPATIAL 
FREQUENCY SELECTIVITY 
While most retinal and LGN cells are broadly tuned and predominantly low-pass, 
cortical cells generally have spatial frequency bandpasses of about 1.5-2 octaves (e.g., 
Maffei k Fiorentini, 1973). We have examined the influence of inhibition on spatial 
frequency selectivity using the same strategy as the previous experiment (Bauman & 
Bonds, 1991). A base grating, at the optimal orientation and spatial frequency, drove 
the cell, and a superimposed mask grating, at the optimal orientation but at different 
spatial and temporal frequencies, provided response facilitation or suppression. 
We defined three broad categories of spatial frequency tuning functions: Low pass, 
with no discernible low-frequency fall-off, band-pass, with a peak between 0.4 and 
0.9 c/deg, and high pass, with a peak above i c/deg. About 75% of the cells 
showed response suppression organized with respect to the spatial frequency of 
mask gratings. For example, Fig. 2A shows a low-pass cells with high-frequency 
suppression and Fig. 2B shows a band-pass cell with mixed suppression, flanking 
the tuning curve at both low and high frequencies. In each case response suppression 
was graded with mask contrast and some suppression was found even at the optimal 
spatial frequency. Some cells showed no suppression, indicating that the suppression 
was not merely a stimulus artifact. In all but 2 of 42 cases, the suppression was 
appropriate to the enhancement of the tuning function (e.g., low-pass cells had high- 
frequency response suppression), suggesting that the design of the system is more 
78 Bonds 
than coincidental. No similar spatial-frequency-dependent suppression was found 
in LGN cells. 
10 
o 0 
-20 
o. NO mask (tuning)--.o--- A 
'"' 14% mask  
",. 28% mask * 
-, Simple 
 , LV9:R4.05 
I I I I I 
0.2 0.3 0.5 1 2 
No mask (tuning) "e'" B 
10% mask * .'"" "', Simple 
' 14% mask + o" LV7R2.05 
20% mask  '" " 
I I I t 
0,2 0.3 0.5 1 2 
Spatial Frequency (cyc/deg) 
Figure 2: Examples of spatial frequency-dependent response suppression. Upper 
broken lines show excitatory tuning functions and solid lines below zero indicate 
response reduction at three different contrasts. A. Low-pass cell with high frequency 
inhibition. B. Band-pass cell with mixed (low and high frequency) inhibition. Note 
suppression at optimal spatial frequency in both cases. 
4 
NON-STATIONARITY OF CONTRAST TRANSFER 
PROPERTIES 
The two experiments described above demonstrate the existence of intrinsic cortical 
mechanisms that refine the spatial filter properties of the cells. They also reveal a 
global form of inhibition that is spatially non-specific. Since it is found even with 
spatially optimal stimuli, it can influence the form of the cortical contrast-response 
function (usually measured with optimal stimuli). This function is essentially loga- 
rithmic, with saturation or even super-saturation at higher contrasts (e.g., Albrecht 
& Hamilton, 1982), as opposed to the more linear response behavior seen in cells 
earlier in the visual pathway. Cortical cells also show some degree of contrast adap- 
tation; when exposed to high mean contrasts for long periods of time, the response 
vs contrast curves move rightward (e.g., Ohzawa, Sclar& Freeman, 1985). We 
addressed the question of whether contrast-response nonlinearity and adaptation 
might be causally related. 
In order to compensate for "intrinsic response variability" in visual cortical cells, 
experimental stimulation has historically involved presentation of randomized se- 
quences of pattern parameters, the so-called multiple histogram technique (Henry, 
Bishop, Tupper  Dreher, 1973). Scrambling presentation order distributes time- 
dependent response variability across all stimulus conditions, but this procedure can 
be self-defeating by masking any stimulus-dependent response variation. We there- 
fore presented cortical cells with ordered sequences of contrasts, first ascending then 
descending in a stepwise manner (Bonds, 1991). This revealed a clear and powerful 
response hysteresis. Fig. 3A shows a solid line representing the contrast-response 
Dual Inhibitory Mechanisms 79 
function measured in the usual way, with randomized parameter presentation, over- 
laid on an envelope outlining responses to sequentially increasing or decreasing 3-sec 
contrast epochs; one sequential presentation set required 54 secs. Across 36 cells 
measured in this same way, the average response hysteresis corresponded to 0.36 log 
units of contrast. Some hysteresis was found in every cortical cell and in no LGN 
cells, so this phenomenon is intrinsically cortical. 
 o 
o 
CV6:R4.04 
3 5 10 20 30 50 100 
B 
Complex  
14% peak conb'. / 
O. 14 Log Cont.  
3 5 10 
Contrast (%) 
Figure 3: Dynamic response hysteresis. A. A response function measured in the 
usual way, with randomized stimulus sequences (filled circles) is overlaid on the 
function resulting from stimulation with sequential ascending (upper level) and 
descending (lower level) contrasts. Each contrast was presented for 3 seconds. B. 
Hysteresis resulting from peak contrast of 14%; 3 secs per datum. 
Hysteresis demonstrates a clear dependence of response amplitude on the history 
of stimulation: at a given contrast, the amplitude is always less if a higher contrast 
was shown first. This is one manifestation of cortical contrast adaptation, which 
is well-known. However, adaptation is usually measured after long periods of stim- 
ulation with high contrasts, and may not be relevant to normal behavioral vision. 
Fig. 3B shows hysteresis at a modest response level and low peak contrast (14%), 
suggesting that it can serve a major function in day-to-day visual processing. The 
speed of hysteresis also addresses this issue, but it is not so easily measured. Some 
response histogram waveforms show consistent amplitude loss over a few seconds 
of stimulation (see also Albrecht, Fartar 2 Hamilton, 1984), but other histograms 
can be fiat or even show a slight rise over time despite clear contrast adaptation 
(Bonds, 1991). This suggests the possibility that, in the classical pattern of any 
well-designed automatic gain control, gain reduction takes place quite rapidly, but 
its effects linger for some time. 
The speed of reaction of gain change is illustrated in the experiment of Fig. 4. 
A "pedestal" grating of 14% contrast is introduced. After 500 msec, a contrast 
increment of 14% is added to the pedestal for a variable length of time. The response 
during the first and last 500 msec of the pedestal presentation is counted and the 
ratio is taken. In the absence of the increment, this ratio is about 0.8, reflecting the 
adaptive nature of the pedestal itself. For an increment of even 50 msec duration, 
this ratio is reduced, and it is reduced monotonically-by up to half the control 
80 Bonds 
level-for increments lasting less than a second. The gain control mechanism is thus 
both sensitive and rapid. 
0.8 
0.6 
0.4 
0.2  
0  
CV9:L11.06-7 
I  I i I * I , I  
0 0.2 0.4 0.6 0.8 1 
Blip Duration (sec) 
"Blip" 
14%_3 tl 'Probe' 12 L_ 
0.0 0.5 1.0 1.5 2.0 
Time (sec) 
Norm. Ampi. = spikes (t2)/spikes(tl) 
Figure 4: Speed of gain reduction. The ratio of spikes generated during the last and 
first 500 msec of a 2 sec pedestal presentation can be modified by a brief contrast 
increment (see text). 
5 
PHYSIOLOGICAL INDEPENDENCE OF TWO 
INHIBITORY MECHANISMS 
The experimental observations presented above support two basic phenomena: 
spatially-dependent and spatially-independent inhibition. The question remains 
whether these two types of inhibition are fundamentally different, or if they stem 
from the same physiological mechanisms. This question can be addressed by exam- 
ining the structure of a serial spike train generated by a cortical cell. In general, 
rather than being distributed continuously, cortical spikes are grouped into discrete 
packets, or bursts, with some intervening isolated spikes. The burst structure can 
be fundamentally characterized by two parameters: the burst frequency (bursts per 
second, or BPS) and the burst duration (spikes per burst, or SPB). 
We have analyzed cortical spike trains for these properties by using an adaptive 
algorithm to define burst groupings; as a rule of thumb, spike intervals of 8 msec 
or less were considered to belong to bursts. Both burst frequency (BPS) and struc- 
ture (SPB) depend strongly on mean firing rate, but once firing rate is corrected 
for, two basic patterns emerge. Consider two experiments, both yielding firing rate 
variation about a similar range. In one experiment, firing rate is varied by vary- 
ing stimulus contrast, while in the other, firing rate is varied by varying stimulus 
orientation. Burst frequency (BPS) depends only on spike rate, regardless of the 
type of experiment. In Fig. 5A, no systematic difference is seen between the exper- 
iments in which contrast (filled circles) and orientation (open squares) are varied. 
To quantify the difference between the curves, polynomials were fit to each and the 
quantity gamma, defined by the (shaded) area bounded by the two polynomials, 
was calculated; here, it equalled about 0.03. 
Dual Inhibitory Mechanisms 81 
16 
(.) 14 
(3) 
(/ 12 
 6 
 2 
o 
o 
Gamma: 0.0290 A 
 , Varia)n ofsti,mus od,entaon 
10     
3.6 
3.4 
3.2 
3.0 
2.8 
2.6 
2.4 
2.0 
60 0 
Gamma: 0.2485 B 
t 
' (3 v 'naon of stimulus orientation 
CV4:R4.06,7 
10 20 30 40 50 60 
Response (imp/sec) 
Figure 5: A. Comparison of burst frequency (bursts per second) as function of firing 
rate resulting from presentations of varying contrast (filled circles) and varying 
orientation (open squares). B. Comparison of burst length (spikes per burst) under 
similar conditions. Note that at a given firing rate, burst length is always shorter 
for experiment parametric on orientation. Shaded area (gamma) is quantitative 
indicator of difference between two curves. 
Fig. 5B shows that at similar firing rates, burst length (SPB) is markedly shorter 
when firing rate is controlled by varying orientation (open squares) rather than 
contrast (filled circles). In this pair of curves, the gamma (of about 0.25) is nearly 
ten times that found in the upper curve. This is a clear violation of univariance, since 
at a given spike rate (output level), the structure of the spike train differs depending 
on the type of stimulation. Analysis of cortical response merely on the basis of 
overall firing rate thus does not give the signalling mechanisms the respect they are 
properly due. This result also implies that the strength of signalling between nerve 
cells can dynamically vary independent of firing rate. Because of post-synaptic 
temporal integration, bursts of spikes with short interspike intervals will be much 
more effective in generating depolarization than spikes at longer intervals. Thus, 
at a given average firing rate, a cell that generates longer bursts will have more 
influence on a target cell than a cell that distributes its spikes in shorter bursts, all 
other factors being equal. 
This phenomenon was consistent across a population of 59 cells. Gamma, which 
reflects the degree of difference between curves measured by variation of contrast 
and by variation of orientation, averaged zero for curves based on number of bursts 
(BPS). For both simple and complex cells, gamma for burst duration (SPB) aver- 
aged 0.15. 
At face value, these results simply mean that when lower spike rates are achieved 
by use of non-optimal orientations, they result from shorter bursts than when lower 
spike rates result from reduction of contrast (with the spatial configuration remain- 
ing optimal). This means that non-optimal orientations and, from some preliminary 
results, non-optimal spatial frequencies, result in inhibition that acts specifically to 
shorten bursts, whereas contrast manipulations for the most part act to modulate 
both the number and length of bursts. 
82 Bonds 
These results suggest strongly that there are at least two distinct forms of cortical in- 
hibition, with unique physiological bases differentiated by the burst organization in 
cortical spike trains. Recent results from our laboratory (Bonds, Unpub. Ohs.) con- 
firm that burst length modulation, which seems to reflect inhibition that depends on 
the spatial characteristics of the stimulus, is strongly mediated by GABA. Microion- 
tophoretic injection of GABA shortens burst length and injection of bicuculline, a 
GABA blocker, lengthens bursts. This is wholly consistent with the hypothesis that 
GABA is central to definition of spatial qualities of the cortical receptive field, and 
suggests that one can indirectly observe GABA-mediated inhibition by spike train 
analysis. 
Acknowledgements 
This work was done in collaboration with Ed DeBruyn, Lisa Bauman and Brian 
DeBusk. Supported by NIH (RO1-EY03778-09). 
References 
D. G. Albrecht & D. B. Hamilton. (1982) Striate cortex of monkey and cat: contrast 
response functions. Journal of Neurophysiology 48,217-237. 
D. G. Albrecht, S. B. Farrar & D. B. Hamilton. (1984) Spatial contrast adaptation 
characteristics of neurones recorded in the cat's visual cortex. Journal of Physiology 
347, 713-739. 
A. B. Bonds. (1989) The role of inhibition in the specification of orientation selec- 
tivity of cells of the cat striate cortex. Visual Neuroscience 2, 41-55. 
A. B. Bonds. (1991) Temporal dynamics of contrast gain control in single cells of 
the cat striate cortex. Visual Neuroscience 6,239-255. 
L. A. Bauman & A. B. Bonds. (1991) Inhibitory refinement of spatial frequency 
selectivity in single cells of the cat striate cortex. Vision Research 31,933-944. 
G. Henry, P. O. Bishop, R. M. Tupper & B. Dreher. (1973) Orientation specificity 
of cells in cat striate cortex. Vision Research 13, 1771-1779. 
L. Maffei & A. Fiorentini. (1973) The visual cortex as a spatial frequency analyzer. 
Vision Research 13, 1255-1267. 
I. Ohzawa, G. Sclar& R. D. Freeman. (1985) Contrast gain control in the cat's 
visual system. Journal of Neurophysiology 54, 651-667. 
D. Rose & C. B. Blakemore. (1974) An analysis of orientation selectivity in the 
cat's visual cortex. Experimental Brain Research 20, 1-17. 
R. E. Soodak, R. M. Shapley & E. Kaplan. (1987) Linear mechanism of orientation 
tuning in the retina and lateral geniculate of the cat. Journal of Neurophysiology 
58,267-275. 
