J Neurophysiol
88: 829 – 838, 2002; 10.1152/jn.00436.2001.
Mitral Cell Temporal Response Patterns Evoked by Odor Mixtures in
the Rat Olfactory Bulb
PASCALE GIRAUDET,1,2 FRÉDÉRIC BERTHOMMIER,1 AND MICHEL CHAPUT2
Institut de la Communication Parlée, Institut National Polytechnique de Grenoble, 38031 Grenoble Cedex 1; and
2
Neurosciences et Systèmes Sensoriels, Centre National de la Recherche Scientifique-Université Claude Bernard-Lyon I,
69366 Lyon Cedex 7, France
1
Received 29 May 2001; accepted in final form 9 April 2002
INTRODUCTION
Natural odors are often blends of several molecular components, and olfactory perception usually depends on the reception and neural processing of these components. Mixture
perception has been extensively investigated psychophysiologically using binary or more complex odor mixtures in a
number of different species, including monkeys (Laska and
Hudson 1993) and humans (Laing and Willcox 1983, 1987;
Laing et al. 1984, 1994; Laing 1989; Laska and Hudson 1991,
1992; Livermore and Laing 1998). By contrast, neural representation of odor mixtures has been studied electrophysiologically only in insects (Akers and Getz 1993; Getz and Akers
1997; Joerges et al. 1997), crustaceans (Cromarty and Derby
1997; Gentilcore and Derby 1998), and fish (Caprio et al. 1989;
Kang and Caprio 1991, 1995, 1997). The numerous studies
Address for reprint requests: P. Giraudet, Université Toulon-Var, BP 132,
83957 La Garde Cedex, France (E-mail: [email protected]).
www.jn.org
performed in mammals were either limited to pure odorants or
to mixtures that were not analyzed in comparison with their
components (Kashiwadani et al. 1999). The present work is
thus the first electrophysiological investigation of odor mixture
processing in the olfactory bulb (OB) of higher vertebrates.
Both psychophysiological and electrophysiological approaches reported mixture interactions when the perception or
representation of a mixture could not be predicted from the
perception or representation of its components.
At the peripheral level, the factors proposed to account for
component interactions are the competition for receptor sites
and the dependence of transduction pathways. In the olfactory
mucosa, the representation of odor mixtures depends on the
equipment in molecular receptors of individual olfactory receptor neurons (ORNs) and on the mode of action of the
individual components of mixtures on each ORN, i.e., whether
mixture components activate the same or different molecular
receptors and transduction pathways. Two main kinds of functioning were observed. First, in the spiny lobster (Cromarty and
Derby 1997) and channel catfish (Caprio and Byrd 1984; Kang
and Caprio 1995; Ngai et al. 1993), individual ORNs express
multiple types of molecular receptors and were found to use
more or less similar rules to code mixtures. Both enhancement
and suppression can occur, in which responses to mixtures
were respectively greater and less than predicted from the
responses to their individual components. Second, in high
vertebrates, individual ORNs were found to express only a
single molecular receptor each (Malnic et al. 1999), although
most ORNs were responsive to multiple pure odors, even if
these odors had very different chemical structures (DuchampViret et al. 1999). Thus one odorant was recognized by multiple molecular receptors and different odorants were recognized by different combinations of these receptors (Malnic et
al. 1999).
In the OB, the peripheral representation of odorants may be
modified by the organization of the projections from the mucosa to the OB (reviewed in Buck 1996), by intrabulbar inhibitory circuits involving periglomerular and granular cells
(Yokoi et al. 1995), and by centrifugal controls exerted by
more central structures (reviewed in Shipley and Ennis 1996).
Results on how OB neurons responded to mixtures were obThe 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.
0022-3077/02 $5.00 Copyright © 2002 The American Physiological Society
829
Downloaded from http://jn.physiology.org/ by 10.220.33.1 on June 18, 2017
Giraudet, Pascale, Frédéric Berthommier, and Michel Chaput.
Mitral cell temporal response patterns evoked by odor mixtures in the rat
olfactory bulb. J Neurophysiol 88: 829–838, 2002; 10.1152/jn.00436.2001.
Mammals generally have the ability to extract odor information contained in complex mixtures of molecular components. However, odor
mixture processing has been studied electrophysiologically only in
insects, crustaceans, and fish. As a first step toward a better understanding of this processing in high vertebrates, we studied the representation of odor mixtures in the rat olfactory bulb, i.e., the secondorder level of the olfactory pathways. We compared the single-unit
responses of mitral cells, the main cells of the olfactory bulb, to pure
odors and to their binary mixtures. Eighty-six mitral cells were recorded in anesthetized freely breathing rats stimulated with five odorants and their 10 binary mixtures. The spontaneous activity and the
odor-evoked responses were characterized by their temporal distribution of activity along the respiratory cycle, i.e., by cycle-triggered
histograms. Ninety percent of the mixtures were found to evoke a
response when at least one of their two components evoked a response. Mixture-evoked patterns were analyzed to describe the modalities of the combination of patterns evoked by the two components.
In most of the cases, the mixture pattern was closely similar to one of
the component patterns. This dominance of a component over the
other one was related to the responsiveness of the cell to the individual
components of the mixture, to the molecular nature of the stimulus,
and to the coarse shape of individual response patterns. This suggests
that the components of binary mixtures may be encoded simultaneously by different odor-specific temporal distributions of activity.
830
P. GIRAUDET, F. BERTHOMMIER, AND M. CHAPUT
METHODS
Animal preparation and cell recording
Experiments were carried out in accordance with the European
Communities Council Directive of November 24th 1986 (86/609/
EEC) for the care and use of laboratory animals and all efforts were
made to minimize animal suffering and to reduce the number of
animals used. Seventeen adult male Wistar rats weighing 250 – 450 g
were utilized in this study. Animals were anesthetized with Equithesin
(a mixture of pentobarbital sodium and chloral hydrate, 3 ml/kg, ip).
Anesthetic was supplemented as necessary to maintain a deep level of
anesthesia, as determined by the depth and rate of the respiratory
rhythm of the rat and its lack of withdraw reflex of the leg in response
to a moderately intense toe pinch. Rectal temperature was monitored
and maintained at 37 ⫾ 0.5°C by a regulated heating pad and surgical
wounds of the animals were regularly infiltrated with 2% Procaine.
Single-unit discharges were recorded extracellularly using glass
micropipettes filled with a 2 M NaCl solution saturated with PonJ Neurophysiol • VOL
tamine sky blue (impedance 15–20 M⍀). Placement of electrode tips
in the ventral mitral cell layer was determined by the appearance of a
dipole reversal in the field potentials evoked by lateral olfactory tract
stimulation and by the occurrence of large-amplitude spikes (Phillips
et al. 1961). When necessary, the placement of the electrode tip was
confirmed using dye spots deposited iontophoretically by passing a
negative current of 2–5 ␮A for 15 min (10 s on, 10 s off) through the
micropipette. Recordings began once a single unit had been clearly
isolated. In addition to mitral-cell single-unit activity, respiratory
activity was recorded through a thermistor placed just at the entrance
of the nostril of the rat.
Odor stimulation
Five odorants and their 10 binary mixtures were presented for 10-s
periods at intervals of ⱖ60 s. These five reagent-grade chemicals were
acetophenone, cineole, isoamyl acetate, methyl-amyl ketone, and pcymene, abbreviated as A, C, I, M, and P in the text. They were
chosen as representative of four of the different groups (I and M
belong to the same group) of the olfactory space defined in the frog
olfactory epithelium (Sicard et al. 1980).
Odors were delivered with a flow dilution olfactometer described in
detail elsewhere (Vigouroux and Chaput 1988). Briefly, the nozzle of
the olfactometer was continuously supplied with a main flow of pure
and humidified air (28 l/min). Between odor delivery, a second flow
of pure air (2 l/min) was injected in this main flow. It was replaced
during stimulation by an equivalent flow (2 l/min) of odorized air
obtained by pumping a predetermined volume of saturated vapor from
50-l Tedlar bags using preadjusted interchangeable needle valves.
This odorized flow began to be produced 10 –15 s before odor delivery
to allow odor concentration to stabilize in the line and it was exhausted until stimulation onset. Odor delivery was initiated 10 ms
after expiration beginning, so that the first inspiration included in the
stimulation corresponded to a complete stimulation period.
In the present study, we considered it crucial to equilibrate odor
intensities in terms of molecular concentration, instead of equilibrating them in terms of proportion of saturated vapor pressure as done in
previous studies (Chaput and Holley 1980, 1985; Chaput et al. 1992;
Joerges et al. 1997; Sicard et al. 1980). Since the odorants had
different vapor pressures, they were diluted differentially as shown in
Table 1, so as to obtain a final partial pressure of 2.9 Pa. This
concentration corresponded approximately to the dilution of 10⫺2 of
the saturated vapor of cineole in our previous studies, which was
considered high enough to recruit most of the olfactory receptor cells
responding to the delivered stimulus. Odorants were delivered singly
and in mixture at the same concentration through a dynamic generation of the odor flows. Odor blends were obtained by simultaneously
plugging the bags containing the two chosen odors on the injection
port of the olfactometer through their ad hoc needle valves, and single
components were presented by replacing the bag containing one of the
two components by a bag filled with pure air. This mixture concentration was the sum of the concentrations of its individual components.
Data analysis
During experiments, signals were recorded by means of a CED1401 Plus data acquisition system (Cambridge Electronic Design) and
systematically stored for subsequent analysis. Cell activity was digitized at 15 kHz to analyze spike trains off-line. Respiration was
sampled at 1 kHz and stimulation events were stored as their time of
occurrence with respect to the beginning of acquisition. An example
of raw data is given in Fig. 1.
Temporal pattern generation
Spikes were checked for stability and triggered using the Spike2
software (Cambridge Electronic Design). Then, the respiratory signal
88 • AUGUST 2002 •
www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.1 on June 18, 2017
tained solely in the catfish (Kang and Caprio 1995). Responses
were classified as excitatory, suppressive, or null depending on
whether their mean firing frequency was significantly higher,
significantly lower, or not significantly different from the spontaneous firing frequency. In this case, 89% of the responses to
the tested binary mixtures were classified similarly as the
responses to at least one of their components and were therefore predictable when responses to their two components were
both classified in the same type. When two components having
different response types were mixed, the mixture response type
was less predictable and depended on the response types of the
mixed components.
The question that we address in the present study is how
mammalian mitral cell activities evoked by two single components combine in the activity evoked by their mixture. Since
the odorant stimulation and consequently mitral cell odorevoked discharges are time-locked to respiration in freely
breathing mammals (Chaput and Holley 1980; Macrides and
Chorover 1972; Sobel and Tank 1993), odor characteristics are
supposed to be encoded in the OB by the spatio-temporal
patterns of activity they evoke among these second-order neurons (Buonviso and Chaput 1990; Buonviso et al. 1992; Chaput
1986; Meredith 1986; Wilson and Leon 1987). The extensively
used mean firing rate was shown not to be sensitive enough to
discriminate between these patterns (Chaput and Holley 1980;
Chaput et al. 1992). The activity of each cell was thus characterized in this study by its temporal organization along the
respiratory cycle by means of a cycle-triggered histogram.
Furthermore, previous results show that mitral cell temporal
response patterns to pure chemicals were stable and reproducible (Chalansonnet and Chaput 1998). Then, temporal patterns
were utilized to define cellular responsiveness and to compare
mixture-evoked responses with single odor-evoked responses.
A priori, using a temporal representation, the three following
types of response combination could be expected: responses to
binary mixtures could be completely different from the responses of their two components, intermediate between them,
or dominated by one of them. In a first step, a pattern comparison method was elaborated to decide whether two responses
were identical or not. In a second step, assuming the linearity
of the interaction of the components in a mixture, a linear
decomposition method was used to analyze the response to a
mixture as a function of the responses to its components.
MITRAL CELL RESPONSE PATTERNS EVOKED BY ODOR MIXTURES
831
1. Physicochemical characteristics of the five odorants
TABLE
Odors
Formula
SVP
Dilution
(A)
49 Pa
6.0 ⫻ 10⫺2
Cineole
(C)
260 Pa
1.1 ⫻ 10⫺2
Isoamyl acetate
(I)
728 Pa
4.0 ⫻ 10⫺3
Methyl amyl-ketone
(M)
490 Pa
6.0 ⫻ 10⫺3
p-Cymene
(P)
190 Pa
1.5 ⫻ 10⫺2
Saturated vapor pressures (SVP) at 25°C of the 5 odorants were obtained from the Handbook of Chemistry and Physics, 76th ed., and their dilutions are
expressed in terms of proportion of SVP so as to obtain a final partial pressure of 2.9 Pa for all odorants.
was processed to discriminate between inspiratory and expiratory
phases. The spontaneous and odor-evoked spike trains recorded during the 30-s period preceding each stimulation and during the 10-s
stimulation period, respectively, were represented separately as two
cycle-triggered histograms. These spontaneous and odor-evoked patterns (abbreviated as SP and EP in the text) were constructed by
counting the number of spikes in each of the 15 intervals (or bins) of
equal duration utilized to divide each respiratory cycle, and by averaging separately the number of spikes per bin over the prestimulation
and stimulation periods as exemplified in Fig. 1. Since each respiratory cycle was about 1.5 s long, the binwidth was approximately 100
ms, which has been shown to be a good compromise between precision and concision (Giraudet 2000). This type of representation could
be used due to the regularity of the respiratory cycle and since the
chosen binwidth was substantially greater than the imprecision in the
determination of the beginnings and ends of the respiratory cycles.
Pattern comparison
Since the cycle-triggered histograms might contain a very low
number of spikes per bin, they could not be compared using classical
statistical methods, such as the ␹2 test. A probabilistic method was
thus developed to decide whether two temporal patterns were significantly different at the 0.05 level. This method considered that each
cycle-triggered histogram was generated by a nonstationary Poisson
process. As exemplified in Fig. 2, it was first applied to determine the
responsiveness of each cell to each stimulus by comparing its EP to
the mean SP, obtained by averaging its SPs recorded before the
different odor presentations. It was then utilized to compare the
responses of a single cell to various stimuli by performing all pairwise
comparisons between the patterns evoked by the odors to which this
cell was responsive.
This method consisted of two steps, as follows.
In a first step, we calculated how many bins of the two histograms
were significantly different at the p ⬍ 0.1 level and at the p ⬍ 0.01
level, assuming that the number Ni of spikes in each bin i (i ⑀
{1. . .15}) was a random variable generated by a Poisson process
ᏼ(␭i) of density ␭i. This calculation differed depending on whether
the comparison concerned an SP and an EP or two EPs. For EP-SP
comparisons, enough cycles (ⱖ150) were averaged in each mean SP
J Neurophysiol • VOL
to fit ␭i to SPi for each bin i. This allowed us to calculate an interval
Ii,p around each SPi so that if Ni was generated by a Poisson process
ᏼ(SPi), then p(Ni ⰻ Ii,p) ⬍ p. Any bin i of the EP containing a firing
frequency Ni situated out of the interval Ii,p was then considered as
significantly different from the corresponding SP bin at the p significance level.
By contrast, for EP-EP comparisons, too few cycles were recorded
during the stimulation period to know (␭1i)i⑀{1. . .15} and
(␭2i)i⑀{1. . .15}, the densities of the Poisson processes that generated
EP1 and EP2. Therefore we calculated for each bin i, the interval Ii,p
around 1 such that if ␭1i ⫽ ␭2i, then p((N1i ⫹ 1)/(N2i ⫹ 1) ⰻ Ii,p) ⬍ p.
In that case, any pair of frequencies in the same bin (N1i, N2i), whose
ratio was situated out of the interval Ii,p, was considered as significantly different at the p significance level.
In a second step, we calculated the minimal number of pairs of bins
that should be significantly different at the p ⬍ 0.1 and the p ⬍ 0.01
significance level to conclude that the two histograms were different
at the 0.05 significance. Assuming that the 15 bins of each pattern
were independent (Giraudet 2000), these two bin numbers, given by
the binomial laws Ꮾ(n ⫽ 15, p ⫽ 0.1) and Ꮾ(n ⫽ 15, p ⫽ 0.01), were
5 and 2, respectively. To take into account the pairs of histograms
where several bins were slightly different as well as the pairs where a
few bins were very different, two histograms were considered to be
significantly different whenever five bins at least were out of Ii,0.1 or
two bins at least were out of Ii,0.01.
Pattern parameterization
Three parameters, for which it will be shown that no single one was
sensitive enough for pattern comparisons, were defined to characterize
each EP: the extensively used mean firing rate (I៮), the maximum firing
rate (Imax) over the 15 bins of the pattern, and the standard deviation
of the firing rate around I៮ (STD), which increased when the pattern
was more modulated. The mean firing rate per respiratory cycle I៮ was
obtained by summing the firing frequencies contained in the 15 bins
of each EP. It was compared with the mean firing rate I៮sp in the
corresponding SP to determine whether the response was an overall
firing increase, an overall decrease, or a neutral response in terms of
mean firing rate. Responses were classified as excitatory whenever (I៮ ⫺
I៮sp)/(I៮ ⫺ I៮sp) ⬎ S, as suppressive whenever (I៮ ⫺ I៮sp)/(I៮ ⫹ I៮sp) ⬍ ⫺S,
88 • AUGUST 2002 •
www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.1 on June 18, 2017
Acetophenone
832
P. GIRAUDET, F. BERTHOMMIER, AND M. CHAPUT
Downloaded from http://jn.physiology.org/ by 10.220.33.1 on June 18, 2017
FIG. 1. Examples of responses of 4 different cells obtained with 4 different odorants. Each example shows raw data and
elaborate representations of the activity of the same cell recorded before, during, and after a 10-s stimulation with a pure odorant,
the name of which is indicated below the stimulation pulse. Raw data comprise cell activity (Cell activ.), the respiratory signal
(Respi.), and the stimulation event (Stim.). Elaborate representations of the triggered spike train consist of a raster plot, where
spikes occurring during successive respiratory cycles are plotted on consecutive rows as a function of their position in the cycle,
and 2 cycle-triggered histograms before (SP) and during (EP) stimulation. The corresponding mean respiratory cycle is plotted
below each elaborate representation. In these 4 examples, cell activity was not synchronized or slightly synchronized (example 1)
before stimulation, and odor presentation induced a synchronization of the cell activity on respiration. In example 1, the response
consists of a firing increase above the spontaneous level at the end of inspiration, followed by a decrease during expiration. Example
2 presents a simple firing increase above the spontaneous level during the transition between inspiration and expiration. Example
3 shows a firing decrease during the second half of inspiration. Example 4 illustrates a more complex response characterized by
a firing decrease followed by an increase during inspiration, and by another firing decrease during expiration.
and as neutral in the other cases. For consistency with the method of
pattern comparison described above, the arbitrary ratio S was set so
that a majority (80%) of NR will be also classified as neutral in terms
of mean firing rate.
Decomposition of a mixture pattern on its components
ᠬ XY was projected on the plane defined
bin. Then the mixture vector V
ᠬ X and V
ᠬ Y whenever they were not
by its two component vectors V
ᠬ XY ⫽ ␣XV
ᠬ X ⫹ ␣YV
ᠬY ⫹ R
ᠬ , where the vector R
ᠬ,
collinear. Thus V
ᠬ X and V
ᠬ Y, was the residue of the
orthogonal to the plane defined by V
projection, and the scalars ␣X and ␣Y represented the weights of
components X and Y in the mixture pattern, respectively (Fig. 3).
Unknowns were calculated by solving the system
The simplest way to analyze the respective contribution of the
patterns of the two components X and Y in each XY mixture pattern is
to determine their linear combination in the mixture. For this linear
decomposition, the three patterns were considered as three 15-dimensional vectors, each coordinate corresponding to the firing rate in a
J Neurophysiol • VOL
88 • AUGUST 2002 •
再
www.jn.org
ᠬ XY ⫽ ␣XV
ᠬ X ⫹ ␣YV
ᠬY ⫹ R
ᠬ
V
ᠬ ⫽0
ᠬX R
V
ᠬY R
ᠬ ⫽0
V
(1)
MITRAL CELL RESPONSE PATTERNS EVOKED BY ODOR MIXTURES
833
Stimulus effectiveness and cell responsiveness to mixtures
RESULTS
A total of 430 single-odor EPs and 544 mixture EPs obtained
from 86 cells were analyzed in this study. All cells were tested
with the five components alone. Forty-seven were submitted
further to a complete stimulation protocol comprising the 10
binary mixtures; 11 were tested with five to nine mixtures, and
14 with a few mixtures only.
FIG. 3. Schematic representation of the principle of calculation of projection coefficients ␣X and ␣Y (left side of the figure) and
examples of coefficients obtained with a cell response patterns (right side). On the right side, the response patterns are organized
as in Fig. 2. The projection coefficients ␣X and ␣Y obtained for each mixture are given at the left and bottom of the corresponding
histograms, respectively. For instance, ␣A ⫽ 0.1 and ␣C ⫽ 0.7 for AC mixture. No coefficient can be found for IM mixture because
I and M vectors are almost collinear.
J Neurophysiol • VOL
88 • AUGUST 2002 •
www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.1 on June 18, 2017
FIG. 2. Example of a cell response patterns and pattern comparison results.
The bottom left histogram represents the mean SP of the recorded cell.
Histograms labeled A, C, I, P, and M in the frames represent the response
patterns of this cell to the 5 pure odorants. The result of responsiveness
determination is indicated below each pattern: if the evoked pattern is significantly different from the spontaneous pattern, the odorant is said to evoke a
response (R), otherwise, it evokes no response (NR). Histograms labeled AC,
AI, etc., which are located at the intersection of each row and each column,
depict the response patterns of the cell to the corresponding mixtures. The
results of pattern comparison between each mixture and its two components
are given at the left and bottom of the corresponding histograms, respectively.
For instance, AC mixture pattern is significantly different from A pattern, but
no significant difference is found with respect to C pattern. In most of the
cases, the mixture evokes the same pattern as one of its components. However
CP is an example of a mixture identical to both its components, which are not
significantly different, and PM is an example of a mixture different from both
its components likely because it is intermediate between them.
The mitral cells responded on average to three pure odorants
(Fig. 2 depicts a cell responsive to four odorants). Table 2
presents the effectiveness of each stimulus (i.e., the proportion
of cells responsive to this stimulus) based on pattern comparisons. It reveals first a hierarchy in the tendency of five pure
odorants to evoke a response. A, C, and M were the less
effective and P and I were the most effective. Even in this
situation where stimulus concentrations were equilibrated in
terms of molecular concentration, the responsiveness to the
most efficient stimulus (P) was more than two times greater
than the responsiveness to the less efficient stimulus (A).
For the mixtures, this effectiveness ranged from 0.66 (for
AC) to 0.84 (AI and IP), whereas the mean responsiveness to
pure odorants was about 0.6 and the mean responsiveness to
mixtures was 0.77. The simplest hypothesis to explain this
difference was to suppose that cells responded to mixtures if
and only if they responded to at least one of their components.
Under this union hypothesis, the probability p(XY) of obtaining
a response to the mixture XY was predicted from the probabilities p(X) and p(Y) of observing a response to X or to Y alone,
assuming that these two probabilities were independent, and
using the formula: p(XY) ⫽ p(X) ⫹ p(Y) ⫺ p(X)p(Y). As seen
in Table 2, the predicted responsiveness was not significantly
different from the observed one for all mixtures except for
those containing P, which were significantly less efficient than
expected. The lower efficacy of P-containing mixtures was
found to be correlated with the important proportion of suppressive responses evoked by P (43%) with respect to the other
odorants (20% for M and 30% for A, C, and I). This correlation might result from a higher probability of observing no
response to a mixture when one of its components evoked a
suppressive response.
Table 3 presents the cell responsiveness to XY mixtures as a
function of the responsiveness to X and Y alone. It shows that
when cells did not respond to any of the two components of the
mixture, they generally did not respond to the mixture. Likewise, when they responded to one of the two components or to
both, they generally responded also to the mixture. On the
834
P. GIRAUDET, F. BERTHOMMIER, AND M. CHAPUT
2. Effectiveness of the 5 single odorants and their 10
binary mixtures
TABLE 4. Percentages of dominance among the 276 differentpatterned odorant mixtures
TABLE
Single odorants
Effectiveness
A
0.35
C
0.49
I
0.72
M
0.57
P
0.80
Binary mixtures
Observed effectiveness
Predicted effectiveness
Significance
AC
0.66
0.67
NS
AI
0.84
0.82
NS
AM
0.7
0.72
NS
AP
0.73
0.87
*
CI
0.83
0.86
NS
Binary mixtures
Observed effectiveness
Predicted effectiveness
Significance
CM
0.81
0.78
NS
CP
0.7
0.9
*
IM
0.8
0.88
NS
IP
0.84
0.94
*
PM
0.81
0.91
*
Predicted effectiveness was calculated assuming that a cell responded to a
mixture if and only if it responded to 1 of its 2 components at least. NS and *
indicate non-significant and significant differences between observed and
predicted efficacies, respectively (␹2 test, p ⬍ 0.05 level).
Pattern combination in binary mixtures
To evaluate the similarity between mixture patterns and their
respective component patterns, pairwise comparisons were
performed between the EP of each cell to each mixture and the
EPs of its components using the method of pattern comparison.
This analysis concerned a total of 544 triplets (Odor X, Odor Y,
Mixture XY), 10 of them illustrated in Fig. 2. In 75% of the 268
cases in which the two component patterns were not significantly different (not shown), mixture patterns were identical to
their component patterns, and therefore directly predictable.
Among the 276 cases in which the two component patterns
were different (Table 4), the dominance of a single component
TABLE
Pure Odorant
Patterns
Number
of Pairs
NR and R
Two different R
138
138
NR
R
Other
Combinations, %
22
64
80
14
20
One component was said to dominate in a mixture when the mixture pattern
was not significantly different to the pattern of this component. As seen here,
one component clearly dominated the other in 86% (22% ⫹ 64%) of the cases
in NR-R mixtures and in 80% of the cases in R-R mixtures. On the whole, one
component dominated the other in 83% of the cases.
was the most represented modality of pattern combination
(83%). It reached 86% in NR-R mixtures and 80% in R-R
mixtures.
To analyze further how patterns combined in mixtures, the
relative influence of the two component patterns in each mixture pattern was determined by applying the decomposition
procedure described in the methods on the 250 mixtures for
which the two component patterns were represented by noncollinear vectors (i.e., the EPs were not too similar). The
resulting coefficients ␣X and ␣Y gave the relative influences of
X and Y in each XY mixture. For instance, ␣X ⬇ 1 and ␣Y ⬇ 0
were representative of the dominance of X over Y. As seen in
Fig. 4, the different modalities of pattern combination were not
clear-cut situations, but rather a continuum, with an overrepresentation of the dominance modality.
Arbitrary circular frontiers were drawn on Fig. 4 to separate
the different combination modalities observed after applying
the linear decomposition method. Sixty-eight percent of the
pairs (␣1, ␣2) were found in the dominance region, 18% were
in the average combination region, and 14% corresponded to
other modalities of combination. In 86% of cases the sum of ␣1
and ␣2 was close to 1. Binary mixture patterns were therefore
close to linear-weighted averages of their component patterns,
often dominated by one of them.
3. Responsiveness of the 86 cells to 544 binary mixtures
Responsiveness to
Mixtures, %
Responsiveness to
Pure Odorants
Number
of Pairs
NR
R
NR-NR
NR-R
R-R
106
138
300
83
22
2
17
78
98
Odor-evoked activities were classified as nonresponses (NR) and responses
(R). The table indicates how the cells responded to mixtures as a function of
their responsiveness to the 2 components alone. Mixtures of 2 inefficient
components failed to induce a response in 83% of the cases. Mixtures of 2
efficient components evoked a response in 98% and mixtures of an inefficient
and an efficient component induced a response in 78% of the cases.
J Neurophysiol • VOL
FIG. 4. Distribution of the 2 projection coefficients ␣1 ⫽ min (␣X, ␣Y) and
␣2 ⫽ max (␣X, ␣Y) of the 250 triplets (Odor X, Odor Y, Mixture XY) for which
single-odor patterns were represented by noncollinear vectors. This figure
presents the different modalities of combination: independence is represented
by (␣1, ␣2) ⬇ (0, 0), dominance is represented by (␣1, ␣2) ⬇ (0, 1), additive
and average combination is represented by (␣1, ␣2) ⬇ (1, 1) and (0.5, 0.5). It
shows that the different modalities of pattern combinations are not clear-cut
situations, but rather a continuum. The peak in (0, 1) indicates the supremacy
of the dominance effect on the other combination modalities.
88 • AUGUST 2002 •
www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.1 on June 18, 2017
whole, for 90% of the pairs, we observed a response when a
least one of the two components evoked a response. This is
consistent with the previous union hypothesis. However, two
important differences with this model are observed. First, for
17% of 106 pairs, a response to the mixture was observed
despite the lack of response to both components. This involved
mainly mixtures that contained A, C, and M, the less efficient
components, and could be due to an additive effect resulting
from the summation of the concentrations of the two mixed
stimuli. On the other hand, mixing an efficient component with
an inefficient component resulted in a lack of response in 22%
of 138 mixtures. This involved suppressive responses to a
component in 75% of the cases, and this is consistent with the
low efficacy of P-containing mixtures reported in the previous
paragraph.
Dominance,
%
MITRAL CELL RESPONSE PATTERNS EVOKED BY ODOR MIXTURES
FIG. 5. Percentages of dominance of the 5 components in all situation of
dominance (All cases), and separately in the cases of masking dominance
(R/R), response dominance (R/NR), and nonresponse dominance (NR/R).
Single odorants showed the same order of dominance in R/R situation as when
all situations of dominance were pulled together (All cases).
Among the 228 mixtures which components evoked different patterns and where one component dominated, we investigated the factors characterizing the dominant component in
terms of responsiveness, molecular nature, and shape of its EP.
In terms of responsiveness, three situations of dominance
might occur. The response to an effective component might
dominate the response to another effective component (masking dominance) or the lack of response to the other component
(response dominance) or it might be dominated by this lack of
response (nonresponse dominance). According to the pattern
comparison method, among the 138 mixtures composed of an
effective and a noneffective component, 64% were dominated
by the effective component and 22% by the noneffective component (Table 4). Thus in the dissymmetrical situation represented by an effective component mixed with a noneffective
component, the dominance effect observed for each cell was
correlated with the responsiveness of this particular cell.
In terms of molecular nature, a hierarchy in the dominance
was visible among the five odorants utilized in this study. As
shown by the left histogram in Fig. 5 (All cases), dominance
increased from A to M components. The odorant A was the
less likely to dominate, with 19% of the mixture it was involved in, and M was the most dominant with 56%. Since
responses were previously shown to dominate often over nonresponses and since M, I, and P induced the highest response
rates, we tested whether the cell responsiveness by itself was
sufficient to explain this hierarchical order. To jointly analyze
the dominance effect and the efficacy of the components to
induce a response in isolation, we represented separately in
Fig. 5 the cases of masking dominance (R/R) where an effective component dominated over another effective component,
response dominance (R/NR) where an effective component
dominated over a noneffective component, and nonresponse
dominance (NR/R) where a noneffective component dominated over an effective component.
This analysis reveals that the dominance order of the five
odorants observed when all situations of dominance were
pooled together (All cases) was not a simple consequence of
their efficacy to induce a response since it persisted when both
components evoked a response (R/R). By contrast, in the last
two histograms (R/NR and NR/R), this dominance order was
altered by the dominance of a response over a nonresponse.
Last, the correlation between parameters extracted from the
J Neurophysiol • VOL
shape of the EP of each component and its capacity to dominate was analyzed. Three parameters derived from the firing
frequencies in the 15 bins of the odor-evoked histogram were
utilized: the mean and maximum firing frequencies of the EP (I៮
and Imax) and an index of temporal modulation, the standard
deviation (STD) of the pattern around its mean value.
As shown by the first bar in Fig. 6, the mean firing rate was
not highly correlated with the efficacy of a component to
dominate since only 62% of the dominant patterns had the
highest mean firing rate. The maximum firing rate was more
strongly involved in this domination since 69% of the dominant patterns had the highest maximum firing rate. Last, 71%
of the dominant patterns had the highest STD. This relationship
between dominance and STD can be partially explained knowing that most of the response patterns had a higher STD (i.e.,
were more modulated) than the nonresponse patterns and dominated. On the contrary, nonresponse patterns were, similar to
spontaneous patterns, not well synchronized with the respiratory cycle, and seldom dominated. As shown by the second
histogram in Fig. 6, 73% of the dominant patterns still had the
highest STD in R/R situations of dominance. Thus a modulated
response was also more likely to dominate than a nonmodulated response. In NR/R situations (right histogram in Fig. 6),
80% of the NR dominating an R had a higher mean firing rate,
which means that they dominated mostly suppressive responses, as seen previously. We also found (not shown) that
excitatory responses dominated by an NR were significantly
less excitatory than other responses, but we did not find that
suppressive responses dominated by an NR were significantly
less suppressive than other responses.
DISCUSSION
Prediction of mixture responsiveness and response pattern
Before the present study, no quantitative electrophysiological investigation of the responses of single olfactory bulb
neurons to odor mixtures had been performed in mammals.
Two fundamental results came out of this work.
1) In 90% of the cases, mixture responsiveness was predictable from the component responsiveness according to the assumption that mixtures evoked a response when at least one of
their components evoked a response. Only 3% of the mixture
responses could be ascribed to synergism, i.e., when mixing
two inefficient stimuli resulted in a response, and 7% to sup-
FIG. 6. Relationship between some characteristics of EP shape and the
dominance effect, as measured by the percentage of dominant patterns having
a higher mean firing rate (I៮), a higher maximum firing rate (Imax), or a higher
standard deviation around this mean (STD). As shown by the high Imax and
STD values obtained in all cases, dominant components generally induced a
well-synchronized response pattern. Higher I៮ values in NR/R cases revealed
that more than 80% of the R dominated by a NR had a lower mean firing rate
than the spontaneous pattern.
88 • AUGUST 2002 •
www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.1 on June 18, 2017
Characterization of the dominant component
835
836
P. GIRAUDET, F. BERTHOMMIER, AND M. CHAPUT
Responses to similar-patterned odorant mixtures
When the two components evoked similar response patterns,
a majority (75%) of mitral cells was found to show mixture
response patterns similar to their component patterns. This
means that although mitral cells were very likely to receive
more peripheral inputs when two efficient odorants were mixed
together, doubling the total odor concentration had no linear
additive effect on their cycle-triggered rate of output activity.
The neural mechanisms responsible for this observation made
in the temporal domain are currently unknown. It is very
unlikely that there is no additive effect from the peripheral
J Neurophysiol • VOL
level onward since it has been shown in catfish and lobster that
the response of the ORNs to a mixture was always higher than
the most intense of their responses to its components
(Cromarty and Derby 1997; Kang and Caprio 1997). The lack
of additive effect may more likely be ascribed to suppressive
intrabulbar interactions such as the lateral inhibition between
mitral cells via granule cells, to inhibitory descending central
influences, or to presynaptic inhibition.
In the remaining 25% of the cases, the responses to mixtures
of components that evoked similar response patterns were
unpredictable. Since a switch from an NR pattern to R pattern
(17% of the NR/NR mixtures) was more likely to occur than
the opposite (2% of the R/R mixtures), most of this unpredictability might simply result from mitral cell intensity-response
functions. One may argue that some mitral cells did not respond to single components, but to mixtures, due to the summation of their component effects. This summation may take
place at the peripheral level if ORN subliminal responses to the
individual components combine in mixtures. Due to the anatomic convergence of 1,000 olfactory receptor neurons onto
one single mitral cell, the summation may also take place at a
more central level (Duchamp-Viret et al. 1989; Satou 1990). In
this latter case, a mixture whose components resulted individually in a response slightly, but not significantly, different from
the spontaneous pattern, will result in a statistically significant
response pattern. However, a low proportion of the R/R mixtures (2%) resulted in a lack of response. The unpredictability
of this 2% of the mixture responses might simply result from
the lack of sensitivity of the method of response determination.
Responses to different-patterned odorant mixtures
In 83% of the cases, the response pattern to a mixture of
components evoking two different patterns was similar to one
of these two patterns. Thus one component clearly dominated
the other.
Dominance of a response over a nonresponse pattern occurred in 64% of the cases. It might simply result from the
inability of the inefficient component to activate any of the
receptor neurons connected to the recorded mitral cell, either
directly or by way of intrabulbar neurons. Therefore this component could not influence the activity of this mitral cell,
whether it was presented alone or in a mixture.
The preponderance of one response pattern over another one
may involve peripheral competitive or noncompetitive interactions between odorants at the receptor sites. It may also result
from a difference in the time of arrival of odor molecules on
different regions of the mucosa due to their transport in the
inhaled air and mucus (Hahn et al. 1994; Kent et al. 1996;
Keyhani et al. 1997; Mozell 1970; Mozell and Jagodowicz
1973). It may finally be ascribed to intrabulbar interactions
such as lateral inhibition between mitral cells via granule cells,
or to descending central influences, resulting in local suppressions of the response to one component in favor of the response
to the other.
In 22% of the cases, a noneffective component masked an
effective component. This possibility has been extensively
shown in brain interneurons of the spiny lobster (Ache 1989;
Derby and Ache 1984; Derby et al. 1985), second-order neurons of the potato beetle (Jong 1988), and olfactory bulb
neurons of the channel catfish (Kang and Caprio 1995). In this
88 • AUGUST 2002 •
www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.1 on June 18, 2017
pression, i.e., when no response was evoked by mixing an
efficient and an inefficient stimuli or two efficient stimuli
together. Thus in terms of population of activated neurons, our
results support the hypothesis that the bulbar population responding to a binary mixture is quite similar to the union of the
populations responding to its components.
2) In 79% of the cases, the mixture response pattern was
identical to the pattern evoked by at least one of its components. When the two components evoked similar patterns, the
prediction of the mixture pattern is straightforward. Otherwise,
the prediction of the mixture pattern can be reduced to the
two-choice prediction of the dominant component. Then, this
prediction is based on the responsiveness of the cell, on the
temporal organization of its response patterns to the two components, and on the nature of the stimulus. According to the
combination of these parameters, the dominant pattern could
be predicted with more or less certainty. For instance, a wellsynchronized response to methyl-amyl ketone had a high probability of dominance over a lack of response to acetophenone.
Result 1 seems different from the conclusions of Kang and
Caprio (1995) in the channel catfish since they reported that the
mixture of an effective and a noneffective component was
likely to be noneffective in 43% of cases. The fish bulbar
population responsive to a mixture was therefore much smaller
than the union of the neural populations responsive to its
components. This apparent contradiction may be explained by
the difference in the criteria chosen to determine the cell
responsiveness (mean firing rate or temporal pattern comparison). To test this assumption, we applied in the present work
the same criterion as Kang and Caprio to determine the existence of a response. By using mean firing rates instead of
temporal patterns, mixtures of an effective and a noneffective
component also failed to induce a response in 46% of the cases.
This percentage corresponds in our study to the mixtures
whose mean firing rates were not significantly different from
the spontaneous firing rates, but whose temporal patterns were
significantly different from the spontaneous patterns. Thus our
results are consistent with those of Kang and Caprio, but the
union model drawn from result 1 is only verified with our
definition of the responsiveness.
By contrast, result 2 is more obviously in agreement with the
conclusions of Kang and Caprio about the three-choice prediction of the type of mixture responses (Kang and Caprio 1995).
Indeed, response types to binary amino acid mixtures in the
catfish were generally predictable when component responses
were both excitatory, both suppressive, or both null. Otherwise,
the predictability depended on the mixture type. The percentage of predictability was globally the same in both studies.
MITRAL CELL RESPONSE PATTERNS EVOKED BY ODOR MIXTURES
We thank D. Piau (Laboratoire de probabilités, UCB-Lyon 1, VilleurbanneFrance) for helpful contribution in establishing a probabilistic method to
compare temporal patterns, J. W. Scott (Emory University, Atlanta, GA), and
N. Buonviso (Neurosciences et Systèmes Sensoriels, UCB-Lyon 1, France) for
helpful comments on the manuscript.
This study was supported by the Centre National de la Recherche Scientifique, the University Claude Bernard, Lyon 1, and the Institut National Polytechnique de Grenoble.
REFERENCES
ACHE B. Central and peripheral bases for mixture suppression in olfaction: a
crustacean model. In: Perception of Complex Smells and Tastes. San Diego:
Academic Press, 1989, p. 101–114.
AKERS R AND GETZ W. Response of olfactory receptor neurons in honeybees
to odorants and their binary mixtures. J Comp Physiol [A] 173: 169 –185,
1993.
BUCK L. Information coding in the vertebrate olfactory system. Ann Rev
Neurosci 19: 517–544, 1996.
BUONVISO N AND CHAPUT M. Response similarity to odors in olfactory output
cells presumed to be connected to the same glomerulus: electrophysiological
study using simultaneous single-unit recordings. J Neurophysiol 63: 447–
454, 1990.
BUONVISO N, CHAPUT M, AND BERTHOMMIER F. Temporal pattern analyses in
pairs of neighboring mitral cells. J Neurophysiol 68: 417– 424, 1992.
CAPRIO J AND BYRD R. Electrophysiological evidence for acidic, basic, and
neutral amino acid receptor sites in the catfish. J Gen Physiol 84: 403– 422,
1984.
CAPRIO J, DUDEK J, AND ROBINSON J. II. Electro-olfactogram and multiunit
olfactory receptor responses to binary and ternary mixtures of amino acids
in the channel catfish, Ictalurus punctatus. J Gen Physiol 93: 245–262,
1989.
CHALANSONNET M AND CHAPUT M. Olfactory bulb output cell temporal response patterns to increasing odor concentrations in freely breathing rats.
Chem Senses 23: 1–9, 1998.
J Neurophysiol • VOL
CHAPUT M. Respiratory-phase-related coding of olfactory information in the
olfactory bulb of awake freely-breathing rabbits. Physiol Behav 36: 319 –
324, 1986.
CHAPUT M, BUONVISO N, AND BERTHOMMIER F. Temporal patterns in spontaneous and odor-evoked mitral cell dischages recorded in anaesthetized
freely breathing animals. Eur J Neurosci 4: 813– 822, 1992.
CHAPUT M AND HOLLEY A. Single unit responses of olfactory bulb neurons to
odor presentation in awake rabbits. J Physiol (Paris) 76: 551–558, 1980.
CHAPUT M AND HOLLEY A. Responses of olfactory bulb neurons to repeated
odor stimulations in awake freely-breathing rabbits. Physiol Behav 34:
249 –258, 1985.
CROMARTY S AND DERBY C. Multiple excitatory receptor types on individual
olfactory neurons: implications for coding of mixtures in the spiny lobster.
J Comp Physiol [A] 180: 481– 491, 1997.
DERBY C AND ACHE B. Electrophysiological identification of the stimulatory
and interactive components of a complex odorant. Chem Senses 9: 201–218,
1984.
DERBY C, ACHE B, AND KENNEL E. Mixture suppression in olfaction: electrophysiological evaluation of the contribution of peripheral and central neural
components. Chem Senses 10: 301–316, 1985.
DUCHAMP-VIRET P, CHAPUT M, AND DUCHAMP A. Odor response properties of
the rat olfactory receptor neurons. Science 284: 2171–2174, 1999.
DUCHAMP-VIRET P, DUCHAMP A, AND VIGOUROUX M. Amplifying role of
convergence in the olfactory system. A comparative study of receptor cell
and second-order neuron sensitivities. J Neurophysiol 61: 1085–1094, 1989.
GENTILCORE L AND DERBY C. Complex binding interactions between multicomponent mixture and odorant receptors in the olfactory organ of the
caribbean spiny lobster Panulirus argus. Chem Senses 23: 269 –281, 1998.
GETZ W AND AKERS R. Coding properties of peak and average response rates
in American cockroach olfactory sensory cells. BioSystems 40: 55– 63,
1997.
GIRAUDET P. Représentation Neuronale des Mélanges Odorants dans le Bulbe
Olfactif des Mammifères (PhD thesis). Grenoble, France: Institut National
Polytechnique de Grenoble, 2000.
HAHN I, SCHERER P, AND MOZELL M. A mass transport model of olfaction. J
Theoret Biol 167: 115–128, 1994.
HOSHINO O, KASHIMORI Y, AND KAMBARA T. An olfactory recognition model
based on spatio-temporal encoding of odor quality in the olfactory bulb. Biol
Cybern 79: 109 –120, 1998.
JOERGES J, KÜTTNER A, GALIZIA C, AND MENZEL R. Representations of odours
and odour mixtures visualized in the honeybee brain. Nature 387: 285–288,
1997.
JONG RD. Integration of olfactory information in the colorado potato beetle
brain. Brain Res 447: 10 –17, 1988.
KANG J AND CAPRIO J. Electro-olfactogram and multiunit olfactory receptor
responses to complex mixtures of amino acids in the channel catfish,
Ictalurus punctatus. J Gen Physiol 98: 699 –721, 1991.
KANG J AND CAPRIO J. Electrophysiological responses of single olfactory bulb
neurons to binary mixtures of amino acids in the channel catfish, Ictalurus
punctatus. J Neurophysiol 74: 1435–1443, 1995.
KANG J AND CAPRIO J. In vivo responses of single olfactory receptor neurons
of channel catfish to binary mixtures of amino acids. J Neurophysiol 77:
1– 8, 1997.
KASHIWADANI H, SASAKI YF, UCHIDA N, AND MORI K. Synchronized oscillatory discharges of mitral/tufted cells with different molecular ranges in the
rabbit olfactory bulb. J Neurophysiol 83: 1786 –1792, 1999.
KENT P, MOZELL M, MURPHY S, AND HORNUNG D. The interaction of imposed
and inherent olfactory mucosal activity patterns and their composite representation in a mammalian species using voltage-sensitive dyes. J Neurosci
16: 345–353, 1996.
KEYHANI K, SCHERER P, AND MOZELL M. A numerical model of nasal odorant
transport for the analysis of human olfaction. J Theoret Biol 186: 279 –301,
1997.
LAING D. The role of physiochemical and neural factors in the perception of
odor mixtures. In: Perception of Complex Smells and Tastes. Sydney:
Academic Press, 1989, p. 65– 81.
LAING D, EDDY A, AND BEST D. Perceptual characteristics of binary, trinary,
and quaternary odor mixtures consisting of unpleasant constituents. Physiol
Behav 56: 81–93, 1994.
LAING D, PANHUBER H, WILLCOX M, AND PITTMAN F. Quality and intensity of
binary odor mixtures. Physiol Behav 33: 303–319, 1984.
88 • AUGUST 2002 •
www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.1 on June 18, 2017
latter study, compounds that individually did not evoke a
response were found to cancel the effect of excitatory as well
as of suppressive components. However, when tested individually, these excitatory or suppressive components were significantly less excitatory or less suppressive than the components
involved in mixtures in which one component dominated. Thus
a noneffective component could mask a weakly, but not a
strongly, effective component in a binary mixture. Similar
results were observed in this study. Noneffective components
generally failed to mask excitatory components, and when this
occurred, masked only weak excitatory components. By contrast, nonresponses were often found to mask suppressive
responses, either strong or weak. P and M, two of the three
odorants having the highest stimulating power, were more able
to dominate when they were inefficient to induce a response.
For the remaining 17% of the mixtures, none of the two
components dominated, and the response pattern was either a
linear combination of its component patterns or totally independent. In the latest case, the linear function may not be
sufficient to take into account complex component interactions.
Thus, as shown in Fig. 4, independence, composition, and
dominance could not be considered clear-cut situations, but
rather as a continuum. Thus even if we had no information on
the spatial distribution of the recorded neurons in the mitral cell
layer and cannot draw strong conclusions about the spatial
characteristics of the populations engaged in mixture coding,
we can conclude that binary mixtures are likely encoded at the
output of the OB by the temporal characteristics specific of
their individual components (Hoshino et al. 1998) and by some
information more specific of the nature of the mixture contained in the responses not dominated by a single component.
837
838
P. GIRAUDET, F. BERTHOMMIER, AND M. CHAPUT
J Neurophysiol • VOL
MOZELL M AND JAGODOWICZ M. Chromatographic separation of odorants by
the nose: retention times measured across in vivo olfactory mucosa. Science
181: 1247–1249, 1973.
NGAI J, DOWLING M, BUCK L, AXEL R, AND CHESS A. The family of genes
encoding odorant receptors in the channel catfish. Cell 72: 657– 666, 1993.
PHILLIPS C, POWELL T, AND SHEPHERD G. The mitral cells of the rabbit’s
olfactory bulb. J Physiol (Lond) 156: 26 –53, 1961.
SATOU M. Synaptic organization, local neuronal circuitry, and functional
segregation of the teleost olfactory bulb. Prog Neurobiol 34: 115–142, 1990.
SHIPLEY M AND ENNIS M. Functional organization of olfactory system. J Neurobiol 30: 123–176, 1996.
SICARD G, DUCHAMP A, REVIAL M, AND HOLLEY A. Odour discrimination by
frog olfactory receptor cells: a recapitulative study. Proc 7th Olfaction and
Taste, Noordwijkerhout, Netherlands, 1980, p. 171–174.
SOBEL E AND TANK D. Timing of odor stimulation does not alter patterning of
olfactory bulb unit activity in freely breathing rats. J Neurophysiol 69:
1331–1337, 1993.
VIGOUROUX M AND CHAPUT M. A simple and flexible device to odorize large
stimulation areas. Chem Senses 13: 587–596, 1988.
WILSON D AND LEON M. Evidence of lateral synaptic interactions in olfactory
bulb output cell responses to odors. Brain Res 417: 175–180, 1987.
YOKOI M, MORI K, AND NAKANISHI S. Refinement of odor molecule tuning by
dendrodendritic synaptic inhibition in the olfactory bulb. Proc Natl Acad Sci
USA 92: 3371–3375, 1995.
88 • AUGUST 2002 •
www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.1 on June 18, 2017
LAING D AND WILLCOX M. Perception of components in binary odour mixtures.
Chem Senses 7: 249 –264, 1983.
LAING D AND WILLCOX M. An investigation of the mechanisms of odor
suppression using physical and dichorhinic mixtures. Brain Res 26: 79 – 87,
1987.
LASKA M AND HUDSON R. A comparison of the detection thresholds of odour
mixtures and their components. Chem Senses 16: 651– 662, 1991.
LASKA M AND HUDSON R. Ability to discriminate between related odor mixtures. Chem Senses 17: 403– 415, 1992.
LASKA M AND HUDSON R. Discriminating parts from the whole: determinants
of odor mixture perception in squirrel monkeys Saimiri sciureus. J Comp
Physiol 173: 249 –256, 1993.
LIVERMORE A AND LAING D. The influence of odor type on the discrimination
and identification of odorants in multicomponent odor mixtures. Physiol
Behav 65: 311–320, 1998.
MACRIDES F AND CHOROVER S. Olfactory bulb units: activity correlated with
inhalation cycles and odor quality. Science 175: 84 – 87, 1972.
MALNIC B, HIRONO J, AND BUCK L. Combinatorial receptor codes for odors.
Cell 96: 713, 1999.
MEREDITH M. Pattern response to odor in mammalian olfactory bulb: the
influence of intensity. J Neurophysiol 56: 572–597, 1986.
MOZELL M. Evidence for a chromatographic model of olfaction. J Gen Physiol
56: 46 – 63, 1970.