Department of Psychology, Boston University, Boston 02215; and Department of Psychology and Program in Neuroscience, Harvard University, Cambridge, Massachusetts 02138
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ABSTRACT |
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Patil, Madhvi M. and Michael E. Hasselmo. Modulation of inhibitory synaptic potentials in the piriform cortex. Intracellular recordings from pyramidal neurons in brain slice preparations of the piriform cortex were used to test results from a computational model about the effects of cholinergic agonists on inhibitory synaptic potentials induced by stimulation of afferent fibers in layer Ia and association/intrinsic fibers in layer Ib. A simple model of piriform cortex as an associative memory was used to analyze how suppression of inhibitory synaptic transmission influenced performance of the network. Levels of suppression of excitatory synaptic transmission were set at levels determined in previous experimental work. Levels of suppression of inhibitory synaptic transmission were then systematically varied within the model. This modeling work demonstrated that suppression of inhibitory synaptic transmission in layer Ib should be stronger than suppression of inhibitory synaptic transmission in layer Ia to keep activity levels high enough for effective storage. Experimental data showed that perfusion of the cholinergic agonist carbachol caused a significant suppression of inhibitory postsynaptic potentials (IPSPs) in the pyramidal neurons that were induced by stimulation of layer Ib, with a weaker effect on IPSPs induced by stimulation of layer Ia. As previously described, carbachol also selectively suppressed excitatory postsynaptic potentials (EPSPs) elicited by intrinsic but not afferent fiber stimulation. The decrease in amplitude of IPSPs induced by layer Ib stimulation did not appear to be directly related to the decrease in EPSP amplitude induced by layer Ib stimulation. The stimulation necessary to induce neuronal firing with layer Ia stimulation was reduced in the presence of carbachol, whereas that necessary to induce neuronal firing with layer Ib stimulation was increased, despite the depolarization of resting membrane potential. Thus physiological data on cholinergic modulation of inhibitory synaptic potentials in the piriform cortex is compatible with the functional requirements determined from computational models of piriform cortex associative memory function.
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INTRODUCTION |
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The dynamical interactions of cortical neurons can be altered by a
range of modulatory substances, including ACh (for review see
Hasselmo 1995). ACh has been shown to suppress excitatory synaptic transmission in the piriform cortex (Hasselmo and Bower 1992
; Linster et al. 1999
; Williams and
Constanti 1988
), in the hippocampus (Hasselmo et al.
1995
; Valentino and Dingledine 1981
; Yamamoto and Kawai 1967
), and in the neocortex
(Hasselmo and Cekic 1996
). This effect shows laminar
selectivity, with stronger suppression of excitatory synaptic
transmission at synapses between pyramidal cells within a region and
weaker suppression at synapses arising from other areas
(Hasselmo and Bower 1992
; Hasselmo and Schnell 1994
). This selectivity may be of particular functional
relevance, as demonstrated in computational models of cortical function
(Hasselmo and Bower 1993
; Hasselmo et al.
1992
). In the presence of cholinergic modulation, suppression
of intrinsic synaptic transmission allows afferent sensory input to
more strongly drive the activity within cortical network models,
setting appropriate dynamics for attention to the external environment
and storage of new information. Cholinergic modulation has been shown
to enhance long-term potentiation of synaptic potentials in the
piriform cortex (Hasselmo and Barkai 1995
; Patil
et al. 1998
), further setting the appropriate dynamics for
storage of new information.
Cholinergic agonists have also been shown to suppress evoked inhibitory
synaptic potentials (Haas 1982; Muller and
Misgeld 1986
; Pitler and Alger 1992
;
Valentino and Dingledine 1981
). This has been shown in
brain slice preparations of the hippocampal formation and cultures of
neocortex but was not previously analyzed in piriform cortex.
Computational models of cortical function can be used to analyze the
functional significance of the cholinergic suppression of inhibitory
synaptic transmission. As described here, computational models of
associative memory function in the piriform cortex generated the
prediction that cholinergic modulation should cause greater suppression
of inhibitory synaptic potentials elicited by stimulation of intrinsic
and association fibers in layer Ib than inhibitory synaptic potentials
elicited by stimulation of afferent fibers in layer Ia. This prediction
was tested with physiological recording of inhibitory synaptic
potentials in brain slice preparations of the piriform cortex.
Experimental work investigated whether the cholinergic modulation of
inhibitory synaptic potentials observed in hippocampus appears in the
piriform cortex as well and whether this modulation shows the laminar
selectivity suggested by the computational modeling work.
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METHODS |
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Mathematical analysis of piriform cortex modeling
ACh simultaneously alters a number of different parameters of
cortical neurons. A simple mathematical model of cortical circuits helps in understanding the interaction of these modulatory effects. In
this model, we assumed that the maximum steady-state activity of
excitatory neurons should remain about the same during changes in ACh
levels. The pattern of responsiveness of individual neurons does change
during cholinergic modulation (Metherate et al. 1990; Sillito and Kemp 1983
), but physiologically realistic
changes in cholinergic modulation do not cause a dramatic change in
overall activity such as a total absence of activity. On a
functional level, modulatory changes would be considerably more useful
if they would change the pattern of neuronal response rather than completely shut off the network or cause it to become overactive. Thus
we assume that the level of activity should remain within a stable
range. Here we evaluated how much modulatory change in inhibitory
synaptic transmission would be necessary to offset the modulation of
excitatory synaptic transmission previously described in the piriform
cortex (Hasselmo and Bower 1992
; Linster et al.
1999
).
This model focuses on the interaction of populations of excitatory
units and a population of inhibitory units mediating feedforward and
feedback inhibition. This representation has a considerable advantage
over other neural network models in which excitatory units and
inhibitory units are not represented separately (Amit 1988). Dynamics of the mathematical representation used
here were first studied by Wilson and Cowan (1972
, 1973
). This type of
representation was used to study the dynamics of cortical networks
including piriform cortex (Hasselmo and Linster 1998a
,b
;
Hasselmo et al. 1997
), hippocampus (Hasselmo et
al. 1995
; Tsodyks et al. 1997
), somatosensory
cortex (Pinto et al. 1996
), and visual cortex
(Hansel and Sompolinsky 1998
). These models leave out
many of the details incorporated in compartmental biophysical
simulations (e.g., Barkai and Hasselmo 1994
;
Hasselmo and Barkai 1995
), such as the Hodgkin-Huxley current underlying spike generation, and the passive properties of
dendritic trees. Thus they are somewhat less constrained with regard to
the intrinsic properties of individual neurons. However, the network
dynamics of these simplified representations show many qualitative
features in common with spiking network models, including attractor
dynamics (Fransen and Lansner 1995
; Hansel and
Sompolinsky 1998
; Pinto et al. 1996
), and the
results of the analysis presented here should apply to network dynamics
in a biophysical simulation.
In these models the firing rate of a population of neurons is simplified into a continuous firing rate variable, which depends on the average membrane potential of the population. The firing of a spike in an individual neuron is an all-or-nothing phenomenon, but the spiking rate within a population can be seen as a continuous variable, which is zero when the average membrane potential of the population is well below threshold, small when the average membrane potential is just above threshold, and large when the average membrane depolarization is large. In the computational simulations described in the next section, we split the population of excitatory neurons into separate populations representing components of different odor patterns while simulating just one population of inhibitory neurons. For the mathematical analysis described in this section, we focus on the average membrane potential (represented by the variable a) of one subpopulation of excitatory neurons and the average membrane potential (represented by the variable h) of the subpopulation of inhibitory neurons that interacts with these excitatory neurons. (These averages correspond to the membrane potential determined by synaptic input and exclude the membrane potential during generation of spikes).
Changes in the average membrane potential of the population of
excitatory and inhibitory neurons are described by the following equations
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(1) |
In these equations, W represents the average strength of excitatory synapses arising from cortical pyramidal cells and synapsing on other excitatory neurons. If neuronal output is in spikes/ms, then synaptic strength reflects the change in membrane voltage per spike (mV/spike) because of the membrane conductance change caused by synaptic transmission. H represents the average strength of inhibitory synapses arising from cortical inhibitory interneurons and synapsing on pyramidal cells. W' represents the average strength of excitatory synapses arising from cortical pyramidal cells and synapsing on inhibitory interneurons. To keep the equations simpler, we left out inhibitory synapses on inhibitory interneurons, which were included in previous work. The simplified system is summarized in Fig. 1A.
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The equilibrium states of networks of this type were evaluated in a
previous article (Hasselmo et al. 1995). Here we use
those equilibrium states to investigate the relationship between
modulation of excitatory synaptic transmission and modulation of
inhibitory synaptic transmission. Previous experimental work has shown
the magnitude of cholinergic suppression of excitatory synaptic
potentials in the afferent and intrinsic fiber layers of the piriform
cortex (Hasselmo and Bower 1992
). The cholinergic
suppression of excitatory synaptic transmission was modeled in the
equations by rescaling the excitatory intrinsic connections
W proportional to a unitless suppression variable
cw and scaling the excitatory connections from
pyramidal cells to interneurons W' proportional to a
suppression variable cw' Given these
values, we evaluated how feedforward and feedback inhibition should
change to keep the equilibrium activity of the network in the same
range of values and for the network to remain stable. Modulation of
feedforward inhibition Hff was represented by
the variable cff, and modulation of
feedback inhibition H was represented by the variable
cfb (all suppression variables
range between 0 and 1.0). As described in the RESULTS,
modulation of excitatory synaptic transmission of the sort described
previously (Hasselmo and Bower 1992
) was more
effectively offset by modulation of feedback inhibitory parameters than
by modulation of feedforward inhibition.
Computational modeling
The functional significance of different levels of the
cholinergic modulation of inhibitory synaptic potentials was analyzed in a simplified computational model of the piriform cortex, showing that selective cholinergic suppression of feedback but not feedforward inhibition is necessary for effective function. This computational model used the same general functional framework as the mathematical analysis described previously, but instead of a single subpopulation of
excitatory neurons the computational model split the population of
excitatory neurons into several excitatory units representing separate
subpopulations of excitatory neurons responding differentially to
different components of different odor patterns. Many aspects of
cholinergic modulation were analyzed in previous compartmental biophysical simulations of the piriform cortex with spiking neurons (Barkai et al. 1994; Hasselmo and Barkai
1995
), but this computational model used the simplified
representation of average firing rate described previously
(Hasselmo et al. 1995
; Pinto et al. 1996
; Wilson and Cowan 1972
). This simplified representation
was used previously to analyze cholinergic modulation in region CA3 of the hippocampus. In fact, that previous simulation used selective cholinergic suppression of feedback but not feedforward inhibition, although the full range of parameter values was not explored. In the
computational simulations described in the next section, we split the
population of excitatory neurons into separate populations representing
components of different odor patterns while simulating just one
population of inhibitory neurons.
In this computational model of cortical memory function, individual
odors are represented as different patterns of afferent input
activating specific subpopulations of excitatory neurons (Hasselmo 1995; Hasselmo and Linster
1998a
; Hasselmo et al. 1992
, 1995
; Linster and Hasselmo, 1997
). The afferent
patterns representing individual odors are stored as self-sustained
equilibrium states (attractor states) in the network, with a particular
pattern of active neurons within the network. Once an odor is stored as
an attractor state, input that resembles that odor attractor state will
put the network into the same attractor state. Thus individual differences in neuronal activity (caused by changes in odor
concentration or background odors) can be ignored in favor of deciding
on a specific odor. Within this general framework, ACh can be seen as
altering the sensitivity to external features of the stimulus relative
to the internal stored representation. As ACh levels are increased in
the network, the relative influence of afferent input increases,
allowing greater sensitivity to variations in the external input.
The network used the activation dynamics described by Eq. 2 (as difference equations). These activation dynamics differ from Eq. 1 to include reversal potentials for excitatory and
inhibitory synaptic inputs
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(2) |
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Additional equations were utilized to simulate the feedback
regulation of cholinergic modulation and the modification of excitatory recurrent synapses for storage of patterns (Hasselmo et al.
1995). Experimental evidence demonstrated that stimulation of
cholinergic input from the horizontal limb of the diagonal band
influences synaptic transmission in piriform cortex, and stimulation of
piriform cortex causes phases of excitation and inhibition in the
horizontal limb (Linster et al. 1997
). Here we focus on
feedback inhibition of cholinergic modulation. ACh levels were
represented by
and depended on a threshold linear function
[
]+ of the average membrane potential
of a
population of cholinergic neurons
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(3) |
Excitatory feedback connections were modified according to learning
rules dependent on postsynaptic activity
ai and presynaptic activity
aj, in keeping with experimental evidence on associative long-term potentiation in piriform cortex (Patil et al. 1998). Modification depended on cumulative
buildup of pre- and postsynaptic variables
si and
sj, which increased with separate
dynamics (dependent on an accumulation constant
and a diffusion
constant
. This could be construed as the buildup of pre- and
postsynaptic calcium or activation of pre- and postsynaptic second
messengers such as protein kinase C. The learning rule also contained
synaptic decay proportional to the current strength
Wij and pre- or postsynaptic activity (scaled with the constants
pre and
post) as a representation of long-term depression
(Levy et al. 1990
). Each rule had parameters for
the overall modification rate
and the postsynaptic modification threshold
w. The rate of synaptic
modification was also scaled to the level of cholinergic modulation, as
suggested by experiments showing cholinergic enhancement of long-term
potentiation in piriform cortex (Hasselmo and Barkai
1995
; Patil et al. 1998
) and hippocampus
(Burgard and Sarvey 1990
; Huerta and Lisman
1993
). The cumulative learning rule took the form
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(4) |
In this computational model, a set of binary input patterns represented
the neuronal activity associated with a series of different odors.
These binary input patterns were stored in the network through Hebbian
modification of excitatory association connections. The storage of
these patterns was subsequently evaluated by presenting one portion of
the input patterns and determining if the network could complete the
missing components of the input patterns. A performance measure based
on normalized dot products was used to evaluate the effectiveness of
retrieval, as in previous articles (Barkai et al. 1994;
Hasselmo et al. 1992
). This performance measure
increased with the effective completion of a learned pattern but
decreased with the similarity between the learned pattern and other
learned patterns. In this simulation, the network is first trained on
one input pattern (1010010010) followed by another input pattern with
considerable overlap (0101010010), as shown in Fig. 4. In the
simulations shown here, high performance occurs when the first pattern
is learned properly but does not interfere with learning of the second
pattern. The performance measure is very low when no learning occurs
and retrieval activity is low. The performance measure also goes to low
values when the first pattern interferes with learning of the second,
causing the response to the degraded input to contain elements of both
the stored patterns.
Brain slice physiology
Intracellular recordings (with sharp electrodes in current-clamp
mode and patch electrodes in current- and voltage-clamp modes) were
obtained from pyramidal neurons in layer II of the piriform cortex in
the in vitro slice preparation. Slices 400 µm thick were obtained
from adult female Sprague-Dawley rats (150-200 gm), by using standard
procedures (Hasselmo and Barka, 1995; Hasselmo and Bower 1992
) in accordance with institutional
guidelines. The animal was lightly anesthetized with halothane and
decapitated. The brain was rapidly removed and placed in chilled
oxygenated artificial cerebrospinal fluid (ACSF) maintained close to
4°C. Slices were cut in the coronal plane, perpendicular to the
laminar organization of the piriform cortex, with a vibratome. Once
cut, the slices were stored at room temperature in a chamber containing oxygenated ACSF solution with the following composition (in mM): 124 NaCl, 5 KCl, 1.2 KH2PO4, 1.3 MgSO4,
2.4 CaCl2, 26 NaHCO3, and 10 D-glucose (pH 7.4-7.5).
After 1 h of incubation at room temperature slices were placed on
a nylon mesh in a submerged chamber with oxygenated ACSF flowing over
the slices at a rate of 1 ml/min. A thermistor, placed just below the
mesh, monitored and controlled the temperature of the ACSF through a
temperature regulator circuit maintaining it between 33 and 34°C.
Intracellular responses to electrical stimuli were recorded once the
pyramidal neurons were impaled. All recorded neurons had a resting
membrane potential of 65 mV or more negative.
The orthodromic stimuli were delivered through a NeuroData PG4000
stimulator, with two fine unipolar tungsten electrodes, one positioned
in the intrinsic layer and other in the afferent layer. The brain slice
preparation is illustrated in Fig. 2. For sharp electrode recording, the glass micropipette electrodes
(resistance = 70-180 M) were filled with 4 M potassium acetate
solution. In four neurons QX314 (50 mM) was introduced to block the
sodium spikes and the slow-inhibitory postsynaptic potential (IPSP)
component to better record the fast-IPSP component. From these four
neurons only IPSP data were recorded; they were not used for threshold firing values. IPSPs were primarily recorded by intracellular current
injection during association-afferent fiber stimulation, as shown by
Tseng and Haberly (1988)
. The fast and slow IPSP components, shown to
be mediated by changes in Cl
and K+
conductances, respectively (Tseng and Haberly 1988
),
were measured at time periods corresponding to the maximum IPSP peaks
in control (between 20 and 40 ms for fast and between 100 and 140 ms
for slow component). The same time reference was used in individual neurons, but it varied slightly over the population. Orthodromic thresholds were examined by varying the strength of the stimulus at
certain stimulus durations (0.2, 0.5, 1.5, and 3 ms in most cells).
Input resistance was calculated from the responses to 200-ms
hyperpolarizing current pulses.
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Whole cell patch recordings for both current clamp and voltage clamp
were carried out with borosilicate glass electrodes, pulled to give an
electrode resistance of ~8-12 M. The electrode solution had the
following composition (in mM): 120 K-gluconate, 10 KCl, 2 CaCl2, 2 MgCl2, 2 EGTA, 2 K-ATP, 0.2 Na-GTP, 20 HEPES, buffered to pH-7.2 with KOH, having an osmolarity of 280 mosm. The solution was filtered before filling the electrodes. An
Axoclamp-2A amplifier was used for recordings, and data
acquisition was performed with pClamp 6.4 software and Digidata 1200 interface board. Data were sampled at 10 or 20 kHz, depending on the
nature of the responses. Unless otherwise noted, holding potential for
voltage-clamp recording of synaptic currents was
60 mV.
Effects of cholinergic modulation were studied by the introduction of
the cholinergic agonist carbachol (carbamylcholine chloride, CCh) into
the bathing medium at a concentration of 50 µM. To isolate IPSPs,
excitatory postsynaptic potentials (EPSPs) were blocked with the
introduction of D()-2-amino-5-phosphonovaleric acid (D-APV) at 40 µM and 6-cyano-7-nitroauinoxaline-2,3-dione
(CNQX) at 20 µM into the bath for some cells before the application
of CCh. The drugs were obtained from Research Biochemicals (Natick, MA). Data were analyzed with two-way ANOVA. Results are quantified as
the means ± SE, and the accepted level for significance was P < 0.05 (unless otherwise stated).
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RESULTS |
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Mathematical analysis
As described in METHODS, the simplified network
allowed mathematical analysis of how much modulation of feedforward and
feedback inhibitory synaptic transmission would be necessary to offset the previously observed modulation of excitatory synaptic transmission (Hasselmo and Bower 1992). This analysis was performed
with the network with a single unit representing average membrane
potential a of a subpopulation of excitatory neurons and a
single unit representing average membrane potential h of the
population of inhibitory neurons (see Fig. 1A).
By using the equations shown in METHODS, we can solve for
the average membrane potential within a subpopulation of excitatory neurons when the network is in equilibrium. This could correspond to a
particular self-sustained memory state toward which the network evolves
(an attractor state), or it could simply represent the steady-state
response to a particular new odor. In either case, the equilibrium can
be determined by observing the state of the equations when there is no
change in the value of a or h, i.e., by setting
da/dt = dh/dt = 0 (Hasselmo et al. 1995). Real
biological networks probably only enter equilibrium states for brief
periods, but the network may be continuously moving toward particular
stable equilibrium states. Equilibrium states can determine the level of network activity even when there are slow oscillatory changes in
particular parameters (Tsodyks et al. 1997
). The value
of a during this equilibrium state is obtained by
algebraically solving for a after setting da/dt = dh/dt = 0 in the equations presented in METHODS. The
average excitatory membrane potential during this equilibrium state
will be designated as Q.
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(5) |
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In each of the following sections, the relationship between
cw and one of the other
modulation variables is analyzed (setting other variables equal to
one). These sections address 1) the suppression of
feedforward inhibition Hff (suppressed in proportion to the variable cff),
2) the suppression of feedback inhibition
H (suppressed in proportion to the variable cfb), and
3) the suppression of the excitatory input to feedback
interneurons W' (suppressed in proportion to the
variable cw'). In
each of these sections, we set the equilibrium state with cholinergic
modulation equal to the equilibrium state without cholinergic
modulation, as follows
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(7) |
Relationship between recurrent excitation (W) and feedforward inhibition (Hff)
This section explores how much modulation of feedforward
inhibition cff can compensate for changes in the
modulation of feedback excitation cw to maintain
approximately the same level of average membrane potential during the
equilibrium state of the network. On an intuitive level, the loss of
excitation in the network should cause a decrease in activity, but this
decrease in activity could be prevented if there is a corresponding
decrease in inhibition. For example, if a subpopulation of excitatory
neurons has a certain activity level in response to a particular odor
and we then alter the strength of feedback excitation caused by
cholinergic modulation, can a change in feedforward inhibition keep the
network activity at approximately the same average level? For the
moment, the cholinergic modulation of feedback inhibition and input to
interneurons will be ignored, so we will keep
cfb = cW' = 1. We can then rearrange Eq. 7 to see how much
cff must change during changes in
cw to prevent cholinergic modulation from
causing a change in the average level of activity (Q).
Algebraic manipulation of Eq. 7 yields the following
relation
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(8) |
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(9) |
If I is increased greatly, cff can compensate for a wider range of values of cw, but great increases in I are inconsistent with the distal termination of afferent synapses on piriform cortex pyramidal cells. In summary, modulation cff of feedforward inhibition Hff is not an effective means to compensate for modulation cw of recurrent excitation W. This generates the prediction that there probably is not a strong effect of cholinergic suppression of inhibitory potentials in layer Ia of the piriform cortex.
Relationship between recurrent excitation (W) and feedback inhibition (H)
We can also compensate for changes in the recurrent excitation
W with changes in the inhibitory synaptic transmission
H arising from interneurons activated by feedback from
pyramidal cells. In this case, we change the levels of feedback
inhibitory transmission H according to the modulation
parameter cfb while keeping other modulatory parameters at cff =
cW' = 1. By algebraically
rearranging Eq. 7, we obtain the following relation
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(10) |
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(11) |
Relationship between recurrent excitation (W) and excitatory input to feedback interneurons (W')
We can also compensate for changes in the recurrent excitation
W with changes in the excitatory input W' to
interneurons mediating feedback inhibition. In this case, we change the
level of excitatory input to interneurons according to the modulation
parameter cw', setting
cff = cfb = 1. From Eq. 7, we obtain the following relation
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(12) |
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(13) |
When considering this analysis of equilibrium state, we should consider
not only the value of the equilibrium state but also the stability of
this equilibrium state. Mathematical analysis demonstrates which
parameters allow stable self-sustained equilibrium states. The value of
cw = 0 is not realistic for equilibrium states because the equilibrium state becomes unstable when the excitatory connection between pyramidal cells (cw × W) drops below the rate of passive membrane potential decay
. On an intuitive level, a network will never be able to sustain
activity if the neurons lose membrane potential more rapidly than
feedback excitation can build it up. To maintain equilibrium with these
parameters, the feedback excitation (cw × W) needs to be larger than the membrane potential decay
= 0.01. This occurs at cw = 0.625, which can be
compensated by cw' = 0.762. Only the suppression
of excitatory input to interneurons can compensate for the full range
of cw values for which the equilibrium remains
stable. Thus suppression of excitatory input to interneurons can be
weaker than suppression of excitatory transmission between pyramidal
cells. In contrast to both the other examples presented previously,
this compensation also does not change for different values of the
afferent input I, suggesting that it might be easier to
implement. This suggests that compensation of changes in feedback
excitation with changes in feedback inhibition may depend strongly on
changes in the excitatory input to inhibitory interneurons rather than
just on changes in the inhibitory transmission from interneurons.
Inhibitory synaptic potentials evoked in layer Ib of the piriform
cortex in the experiments described here contain components of both
W' and H. Thus the analysis suggests that we
should see a much stronger cholinergic suppression of inhibitory
synaptic potentials in layer Ib than in layer Ia.
Relationship between depolarization of interneurons (A') and excitatory input to interneurons (W')
Cholinergic modulation enhances the frequency of spontaneous
inhibitory synaptic currents during recordings from pyramidal cells in
the hippocampus (Behrends and ten Bruggencate 1993;
Pitler and Alger 1992
). This frequency increase is
believed to result from direct cholinergic depolarization of GABAergic
interneurons (McQuiston and Madison 1996
), which would
increase firing rate. This depolarization of interneurons appears
rather paradoxical when combined with the suppression of evoked
inhibitory potentials. Why would the same substance simultaneously
increase inhibition via direct depolarization while suppressing total
feedback inhibition? The analytic framework presented here provides a
possible explanation of this paradox. Starting with Eq. 1,
we can analyze the effect of depolarization of inhibitory interneurons
by representing it as a direct depolarizing afferent input to
interneurons A', yielding the following equilibrium state
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(14) |
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Computational modeling
As described in METHODS, the effect of cholinergic
modulation of inhibitory synaptic potentials was also tested in a
computational model of associative memory function. In this model, the
storage of highly overlapping input patterns was analyzed with
different levels of cholinergic modulation of synaptic transmission.
This gives a notion of how cholinergic modulation affects the overall function of the network. The model shows that cholinergic suppression of feedback inhibition is necessary for effective function, whereas the
cholinergic suppression of feedforward inhibition does not have a
strong role in ensuring effective function. In fact, previous simulations of associative memory function used selective cholinergic suppression of feedback but not feedforward inhibition (Hasselmo et al. 1995), although the full range of parameter values was not previously explored.
Figure 4 demonstrates the basic function
of the network for different values of cholinergic modulation of
feedback inhibition H. Each section of the figure shows the
activity in a network of 10 excitatory neurons and 1 inhibitory neuron
during sequential presentation of different patterns of input
(1010010010 and 0101010010). Pattern number one is first presented,
followed by a degraded version of that input pattern. Then a second
pattern that overlaps with the first pattern is presented, followed by
a degraded version of that second pattern. For each pattern, the
activity of the network is shown during a number of time steps. The
width of black lines represents the activity of individual neurons
within the network. For insufficient cholinergic suppression of
inhibitory feedback (cfb = 0.6),
inhibition is too strong in the network, and the degraded patterns
evoke activity in only two of the normal four neurons. For excessive
cholinergic suppression of inhibitory feedback
(cfb = 1.0), inhibition is
insufficient, and there is severe interference between highly
overlapping stored patterns. This results from the fact that some
inhibition is necessary during learning to prevent interference in the
network (Hasselmo 1993). Appropriate levels of the
cholinergic suppression of inhibitory feedback
(cfb = 0.8) provides effective
function in the model. In this case, the network responds to the
degraded version of the input patterns with the full learned version of
those input patterns.
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Suppression of feedback inhibitory transmission (H) and feedforward inhibition (Hff)
As shown in Fig. 5A suppression of feedback inhibitory transmission (H) is more important for good memory performance in the model than suppression of feedforward inhibition (Hff). Simulations were used to evaluate the memory performance of the network for a large number of different values of suppression of inhibitory transmission (the influence of interneurons on pyramidal cells) in combination with a large number of different values for suppression of feedforward inhibition. As can be seen in the figure, the best performance occurred with high levels of suppression of inhibitory transmission (H), ranging between 65 and 100% suppression (corresponding to cfb = 0.35-0.0). As can be seen from the graph, suppression of feedforward inhibition does not as strongly influence levels of performance. High levels of performance could be obtained across all values for feedforward inhibition, although lower levels of suppression of feedback inhibitory transmission were necessary when there was stronger suppression of feedforward inhibition. The results from these simulations suggest that for effective memory performance strong suppression of feedback inhibition is more necessary than suppression of feedforward inhibition.
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Suppression of excitatory input to interneurons (W') and feedforward inhibition (Hff)
As shown in Fig. 5B, the effect of suppression of
excitatory input to interneurons is similar to the effect of
suppression of inhibitory transmission, with a somewhat broader range
of good performance. Thus good performance can be obtained with
suppression of excitatory input to interneurons (W') set at
30% (corresponding to cw' = 0.7). However,
this lesser requirement for suppression of excitatory input to
interneurons only occurs when the suppression of feedforward inhibition
is quite strong. Thus for this parameter there is a greater interaction
between the level of feedforward inhibition and the level of feedback inhibition, although good function can still be obtained at all values
of feedforward inhibition.
Suppression of excitatory input to interneurons (W') and inhibitory transmission (H)
Figure 6A shows the performance of the network for different values of the cholinergic suppression of both components of feedback inhibition, the suppression of excitatory input to interneurons (W') and the suppression of inhibitory transmission from interneurons to excitatory neurons (H). As can be seen in the figure, effective performance depends on strong modulation of feedback inhibition, but effective function is obtained in the model with suppression of either component of this modulation because the feedback inhibition can be entirely shut down by either type of suppression. Thus with strong suppression of excitatory input to interneurons no suppression of inhibitory transmission is necessary, and with strong suppression of inhibitory transmission no suppression of excitatory input is necessary. Intermediate levels of suppression can provide good performance if they are combined, but this requires >50% suppression of both parameters. Thus, although decreases in excitatory feedback can be more easily compensated for by suppression of excitatory input to interneurons (W'), the actual associative memory function of the network can be aided by suppression of either component of feedback inhibition. When network function was tested with highly overlapping patterns, such as those illustrated in Fig. 4, the range of effective function was more narrow. As shown in Fig. 6B, for low values of suppression of inhibition the performance is low because of insufficient learning of the stored patterns, whereas for high levels of suppression of inhibition the performance is low because of interference between the stored patterns. This demonstrates that effective function is only obtained when modulation of feedback excitation is associated with modulation of feedback inhibition with very specific relative values.
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EXPERIMENTAL DATA |
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Cholinergic modulation of inhibitory synaptic potentials
The resting membrane potential for the pyramidal neurons was
typically approximately 72 mV (72.35 ± 4.1, n = 40).
At this resting membrane potential IPSPs were not always prominent, and the cells were depolarized by injection of a constant DC current to
help observation of the inhibitory synaptic potentials. Inhibitory potentials obtained at different membrane potentials during stimulation of layer Ib are shown in Fig.
7A. Depolarization of the
neuron membrane potential allowed observation of the early and late
components of the IPSPs, as can be seen in Fig. 7A. Laminar
differences in the components of the IPSPs were observed, with the
early Cl
component being more prominent during the
stimulation of the association fiber layer (layer Ib), whereas the late
component was observed during stimulation of both the layers. As shown
in Fig. 9A, inhibitory synaptic potentials elicited during
stimulation of the afferent fiber layer (layer Ia) rarely evoked a
prominent early inhibitory component.
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IPSPs were more easily isolated by pharmacologically blocking the EPSPs with D-APV and CNQX as shown for Ib stimulation in Fig. 8A and for Ia stimulation in Fig. 9, A and B. This blockade reduced the peak amplitude of the excitatory component of potentials by 71.4 ± 7.98% (n = 6) for afferent layer stimulation and 76 ± 9.8% (n = 5) for asociation fiber stimulation.
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Perfusion of the cholinergic agonist carbachol in the slice chamber caused a significant suppression of inhibitory synaptic potentials elicited by stimulation of association/intrinsic fibers in layer Ib, as shown in Figs. 7B and 8, A and B. As can be seen in these figures, carbachol caused suppression of both the early and late components of the inhibitory synaptic potential. The strong suppression of the fast IPSP component made quantitative measurements difficult. Measurement of the slow inhibitory synaptic potentials (n = 14) recorded with sharp electrode techniques revealed that layer Ib inhibitory synaptic potentials were reduced on average by 79.4 ± 2.4%. This effect was highly statistically significant (P < 0.0007). After washing the carbachol out of the slice chamber, the IPSP amplitudes recovered to ~60% of their control value. This partial recovery may be due to insufficient wash period in some slices because wash takes >45 min. Experiments with pharmacological blockade of excitatory currents further confirmed this evidence for suppression of inhibitory currents by carbachol. After postsynaptic blockade of excitatory potentials, the decrease in the late evoked Ib inhibitory potentials was found to be 61.75 ± 4.7% (n = 5). The early evoked Ib inhibitory potentials showed what appeared to be total suppression in carbachol. In carbachol there was no longer a significant difference from baseline at the time point of the early component of the inhibitory potential (see Fig. 8A).
In comparison, perfusion of carbachol had a weaker effect on inhibitory synaptic potentials elicited by stimulation of afferent fibers in layer Ia, as shown in Fig. 9A. Measurement of the change in IPSPs in a number of slices (n = 16) revealed that IPSPs elicited by layer Ia stimulation were decreased on average by 18.5 ± 3.2%. This effect was just statistically significant (P < 0.05). This decrease in the IPSPs caused by carbachol was found to be present in layer Ia when IPSPs were pharmacologically isolated (19.8 ± 8, n = 6) (see Fig. 9, A and B). The mean effect of carbachol on inhibitory synaptic potentials elicited by stimulation in the two layers is summarized in Fig. 10. These results are consistent with the computational model demonstrating that suppression of excitatory transmission between pyramidal cells (layer Ib) can be more effectively offset by suppression of feedback inhibition (layer Ib) than by suppression of feedforward inhibition (layer Ia).
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Cholinergic effects on synaptic inhibition were also examined by
holding membrane potential at 60 mV in the voltage-clamp mode. Figure
8B shows the decrease in the fast and slow IPSCs after
exposure to carbachol in the association layer (these currents are
shown for different holding potentials in Fig. 8C). An
example of the smaller decrease in layer Ia IPSCs is seen in Fig. 9,
A and B, after the blockade of the EPSCs with
D-APV and CNQX.
Cholinergic effect on excitatory synaptic potentials
The effect of carbachol on the height of excitatory synaptic
potentials was also analyzed. Carbachol caused a substantial decrease
in the height of excitatory synaptic potentials elicited by stimulation
of association/intrinsic fibers in layer Ib while having a much weaker
effect on the height of excitatory synaptic potentials elicited in
layer Ia. This effect was demonstrated previously with both
intracellular and extracellular recording (Hasselmo and Bower
1992). Comparison of EPSPs was documented at resting membrane
potentials. The effect on layer Ib EPSPs can be seen in Fig.
7C. Measurement of the change in height of EPSPs elicited by
layer Ib stimulation in a number of slices (n = 17) demonstrated that carbachol caused a decrease in EPSP height by an
average value of 54.1 ± 6.08%. This effect was statistically significant (P < 0.0001). EPSPs recovered to ~75%
of their control value when carbachol was washed from the slice. There
may be some interaction between the decrease in the amplitude of EPSPs
and the fast component of the inhibitory potentials. However, across the population of individual cells, as seen in Fig.
11A, the amount of decrease
in layer Ib IPSPs did not seem to be directly related to the EPSP
decrease, suggesting that these measurements show effects on two
different physiological components of transmission, not just an
increase in EPSP potentials because of decreased inhibitory currents.
As shown in Fig. 10, the mean effect on inhibitory synaptic potentials
elicited by layer Ib stimulation was stronger than the mean effect on
excitatory synaptic potentials elicited by layer Ib stimulation.
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As demonstrated in a set of previous intracellular recordings, the decrease in EPSPs elicited by stimulation of afferent fibers (in layer Ia) was much smaller than the decrease in EPSPs elicited by layer Ib stimulation. Across a number of slices (n = 18), perfusion of carbachol caused a decrease with an average value of 13.5 ± 7.78%. This was still statistically significant (P < 0.03). As in the associational layer, there seemed no obvious relation between the decrease in the afferent IPSPs and EPSPs, as seen in Fig. 11B.
Orthodromic firing threshold decreases in afferent layer and increases in the association layer
The functional advantage of having stronger suppression of
excitatory synaptic transmission in layer Ib compared with layer Ia is
that it provides a mechanism by which cholinergic modulation could
allow afferent input to dominate during storage of new information (Hasselmo et al. 1992). Therefore, we were interested to
directly test whether perfusion of the cholinergic agonist carbachol
makes it easier for afferent input (layer Ia stimulation) to cause
pyramidal cells to spike while making it more difficult for intrinsic
input (layer Ib stimulation) to cause pyramidal cells to spike. To test this effect, we obtained strength-duration threshold curves for both
layers in control conditions and during cholinergic modulation.
Perfusion of carbachol caused pyramidal neurons to generate action
potentials more easily in response to stimulation of afferent input
(layer Ia) than in response to stimulation of intrinsic input (layer
Ib). Figure 12 shows
the threshold stimulus strength-duration curves for a pyramidal neuron
during Ia and Ib orthodromic stimulation. Carbachol exposure decreased
the firing threshold for stimulation of the afferent fiber layer (layer
Ia) in 7 of 10 cells, whereas threshold increased in 3 cells. For the
averages and percent changes presented here, the strength of the
stimulus at 0.5 ms was used for the calculations. The average decrease
in threshold for layer Ia stimulation across the 10 cells was 28.2 ± 5.2%. In contrast, the firing threshold for stimulation of the
intrinsic fiber layer (layer Ib) increased by an average 23.6 ± 4.6% in six cells. This differential effect appeared despite the
common postsynaptic depolarization of membrane potential during
perfusion of carbachol, which alone should make neurons more responsive
to both types of stimulation. In these experiments, carbachol
depolarized the membrane potential by 6.37 ± 2.53 mV
(n = 15) and increased input resistance from 28.2 ± 8.14 M to 36.7 ± 4.03 M
, an increase of ~29.6 ± 4.57%, (n = 16). As demonstrated in previous studies
(Barkai and Hasselmo 1994
), perfusion of
carbachol also decreased the adaptation of pyramidal cells.
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DISCUSSION |
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The experimental data presented here concur with the requirement
of the computational model that cholinergic modulation should more
strongly suppress inhibitory synaptic potentials evoked by stimulation
of layer Ib than inhibitory synaptic potentials evoked by stimulation
of layer Ia. IPSPs and IPSCs evoked by stimulation of layer Ib were
strongly suppressed during perfusion of the cholinergic agonist
carbachol, whereas IPSPs and IPSCs evoked by stimulation of layer Ia
were less strongly suppressed. Although cholinergic suppression of
evoked IPSPs and IPSCs has been shown in the hippocampus (Haas
1982; Pitler and Alger 1992
), this effect was
not demonstrated in piriform cortex nor was laminar selectivity of the
suppression described previously.
Implications of the model
The computational modeling presented here demonstrates the
requirement that activation of cholinergic receptors should have a
stronger effect on intrinsic inhibitory potentials than those activated
by stimulation of the afferent fibers. This prediction arose from the
combination of previous physiological data showing greater cholinergic
suppression of excitatory synaptic transmission in layer Ib than in
layer Ia (Hasselmo and Bower 1992) and assumptions about
the function of excitatory intrinsic connections within the piriform
cortex. Previous modeling demonstrated that cholinergic suppression of
excitatory intrinsic connections may be very important for preventing
retrieval of previously stored representations from interfering with
the storage of new representations (Hasselmo and Schnell
1994
; Hasselmo et al. 1992
, 1995
). Given the
possible functional necessity of the suppression of excitatory
transmission, the network may then need to compensate for the changes
in excitatory transmission through modulation of inhibitory effects.
In our mathematical analysis, we utilized equations for the
interaction of excitatory and inhibitory neurons such as those analyzed
in previous work (Hasselmo et al. 1995; Pinto et
al. 1996
; Wilson and Cowan 1972
, 1973
). In
contrast to many abstract mathematical representations of cortical
function, these equations have the advantage of explicitly representing
separate populations of excitatory and inhibitory neurons, allowing
direct analysis of modulatory effects on inhibition. This will be
useful for analyzing a wide range of modulatory effects, allowing
functional interpretation of a range of specific effects on intrinsic
properties as well as synaptic excitation and inhibition within
cortical structures. In the mathematical analysis, we made the
assumption that equilibrium states of the network should be
approximately equal with and without cholinergic modulation. This would
assist in associative memory function, allowing the network to have
activity patterns of approximately equal amplitude during both encoding
and retrieval, aiding in the consistent processing of these patterns by
subsequent structures. Some changes in activity were noted during in
vivo recording with cholinergic modulation (Beidenbach
1966
; Metherate et al. 1990
; Sillito and
Kemp 1983
). However, those experimental data suggest that
neuronal activity is stronger during cholinergic modulation, which
would require that effects on inhibition should overcompensate for the
effects on excitatory transmission (although the cholinergic depolarization of neurons and suppression of adaptation adaptation can
also contribute to this increased activity). The analysis presented
here is qualitatively similar even if modulation of inhibition
overcompensates for the modulation of excitatory transmission.
In addition to the prediction that modulation of feedforward inhibition is less effective at compensating for reduced excitatory feedback, the model suggests differences in the effectiveness of different components of feedback inhibition in compensating for reduced excitatory feedback. The suppression of inhibitory synaptic transmission H (i.e., release of GABA) can only partly compensate for suppression of excitatory transmission, whereas the suppression of excitatory input to inhibitory interneurons W' (i.e., glutamatergic connections from pyramidal cells to interneurons) more effectively compensates for reduction in excitatory feedback across a wider range of values. This suggests that the cholinergic suppression of inhibitory synaptic potentials evoked by layer Ib stimulation may be more dependent on reduced excitatory input to inhibitory interneurons than on reduced release of the inhibitory transmitter GABA from interneurons. However, some aspects of our data suggest that the effect is not purely due to decreased excitatory input to interneurons, including 1) carbachol still suppressed inhibitory potentials elicited during pharmacological blockade of excitatory currents, 2) the effects on inhibitory potentials often appeared sooner than the effect on excitatory potentials, and 3) our data show independence of effects on excitatory and inhibitory potentials. Further experimental data will be necessary to separately analyze cholinergic effects on excitatory input to interneurons (W') and the release of GABA from interneurons (H) in the piriform cortex.
The network simulations of the piriform cortex allow analysis of the
cholinergic modulation of inhibition in the more specific functional
framework of attractor dynamics and associative memory function. This
work continues previous work exploring the storage of patterns of
activity in a network with separate populations of excitatory and
inhibitory neurons (Hasselmo and Linster 1998b; Hasselmo et al. 1995
, 1997
). This model draws on the
assumption that the excitatory recurrent connections of the piriform
cortex mediate associative memory function, allowing storage of
patterns of activity representing odors, and retrieval of these
patterns given incomplete pattern cues (Bower 1995
;
Haberly 1985
; Haberly and Bower 1989
;
Hasselmo and Linster 1998a
; Hasselmo et
al. 1992
; Wilson and Bower 1988
). The distinct
modeling of separate populations of excitatory and inhibitory neurons
used in the model presented here allows detailed analysis of how
modulation of inhibition could play a role in setting appropriate
functional dynamics in associative memory networks. The exploration of
parameter values shown here in Figs. 5 and 6 demonstrates that strong
cholinergic suppression of feedback inhibition is necessary for
effective function, whereas effective function is obtained at a range
of parameters of feedforward inhibition. In fact, previous published versions of this simulation used selective suppression of feedback but
not feedforward inhibition (Hasselmo et al. 1995
).
Models of the olfactory bulb also demonstrated how modulatory effects on inhibition could play a role in setting appropriate dynamics for
separation and enhancement of odor responses (Linster and Hasselmo 1997
).
More abstract associative memory models assume network activity
is clamped to the input pattern during learning (Amit
1988). In this model, the internally
regulated cholinergic suppression of excitatory feedback combined with
suppression of feedback inhibition allows the network to preferentially
respond more to afferent input during encoding. In our experimental
data, cholinergic modulation also appears to make afferent input the
predominant influence on neuronal activity. As shown in the
strength-duration curves in Fig. 12, the threshold for eliciting an
action potential with afferent fiber stimulation (layer Ia) is strongly
decreased in the presence of carbachol (because of the direct
depolarization of pyramidal cell membrane potential), whereas the
threshold for eliciting an action potential with intrinsic fiber
stimulation (layer Ib) is increased (because of the suppression of
excitatory intrinsic synaptic transmission). These results support the
modeling proposal that cholinergic modulation allows afferent input to dominate without greatly changing the net activity within the network.
Whereas these models assume the importance of stable attractor dynamics
in the network, alternative interpretations are possible. For example,
one model of piriform cortex proposes that sequential cycles of
activity allow hierarchical classification of odor information (Ambros-Ingerson et al. 1990; Granger et al.
1989
). These models are not entirely inconsistent with
the one presented here, in that the afferent connections in the model
presented here could undergo self-organization to form categories such
as those in the Ambros-Ingerson model. The relative strength of
inhibition and excitation is very important for the function of that
previous model as well. In particular, strong excitatory feedback
connections could interfere with effective formation of categories;
therefore it might be important to suppress feedback. At the same time, suppression of inhibition would become important to allow sufficient activity to underlie formation of new categories. This is consistent with evidence that long-term potentiation occurs in the piriform cortex
during cholinergic modulation (Hasselmo and Barkai 1995
; Patil et al. 1998
) and in behavioral contexts requiring
learning (Roman et al. 1987
, 1993a
,b
).
Other functional interpretations of piriform cortex activity have been
developed. For example, the representation of odors was described in
terms of limit cycles or chaotic attractors in modeling and
electroencephalogram work by Freeman and others (Freeman 1975; Freeman et al. 1988
; Liljenstrom
and Hasselmo 1995
; Liljenstrom and Wu
1995
; Yao and Freeman 1990
). These
representations are considerably more complex than the fixed point
attractors shown here, but similar principles apply. If a particular
dynamical state must represent a particular odor stimulus during two
very different arousal states (with different levels of cholinergic modulation), each cholinergic effect on a parameter of cortical function must be compensated for such that the dynamical state has a
consistent and recognizable influence on other cortical regions.
Otherwise, the reduced feedback excitation would cause a very different
dynamical pattern of activity for the same sensory stimulus during
different modulatory states.
The fixed point attractors used here do not differ dramatically in
their properties from limit cycle attractors used in other models, but
limit cycle attractors or sequences of neuronal activity are probably a
more realistic neuronal representation. Here we focused on isolated
storage and retrieval of single patterns, but the real network must
deal with an ongoing interaction with continuously changing olfactory
input and behavioral contingencies. It is very likely that
crosstemporal interitem associations are stored and retrieved in this
context. In fact, unit recording in piriform cortex during performance
of an olfactory discrimination task demonstrates activity to multiple
task components, not just odor sampling (Schoenbaum and
Eichenbaum 1995). The compensation of overall activity would
still be relevant to storage of sequences, but there may be additional
dynamical properties of these influences that will become clearer with
more detailed functional models.
Relation to previous physiological data
Our experimental data show that inhibitory synaptic
potentials evoked by stimulation of the piriform cortex are suppressed by cholinergic modulation in a manner similar to the cholinergic suppression of evoked inhibitory synaptic potentials in the hippocampus (Haas 1982; Pitler and Alger 1992
). The
modulation of inhibition could be important for offsetting a loss of
excitatory transmission in the hippocampus as well because cholinergic
modulation has been shown to suppress excitatory synaptic potentials in
region CA3 and region CA1 of the hippocampus (Hasselmo and
Schnell 1994
; Hasselmo et al. 1995
;
Hounsgaard 1978
; Valentino and Dingledine 1981
). Most of the cholinergic effects on inhibitory potentials and currents shown here could be due to either a decrease in the excitatory synaptic input to interneurons or in the inhibitory transmission from interneurons. However, this study also shows that
evoked inhibitory synaptic potentials and currents recorded in the
presence of CNQX and APV are suppressed during perfusion of carbachol.
This is consistent with previous studies in hippocampus showing
suppression of monosynaptic evoked inhibitory currents (Pitler
and Alger 1992
) and decreased frequency of TTX-insensitive spontaneous inhibitory currents (Behrends and ten Bruggencate 1993
). Suppression of inhibitory transmission was also
demonstrated in cultures of neocortical neurons (Kimura and
Baughman 1997
). This suggests that the reduction in evoked
inhibitory synaptic potentials is not just due to a reduction in the
excitatory input to interneurons.
Here we show cholinergic suppression of both the fast and slow
components of inhibitory synaptic potentials. Previous work on
inhibitory synaptic potentials in the hippocampus reported an effect on
both the fast and slow components (Pitler and Alger 1992), although the bulk of previously presented data concerned the fast GABAA-mediated inhibitory potentials. Other
studies analyzed cholinergic modulation of fast and slow inhibitory
potentials in a more detailed manner (Muller and Misgeld
1989
). In our data, the fast inhibitory synaptic potentials are
much more prominent with stimulation of layer Ib than of layer Ia.
Previous work in the piriform cortex has repeatedly shown both
components after layer Ib stimulation (Tseng and Haberly
1989
). Our data suggest that stimulation in the afferent fiber
layer predominantly evokes slower components of the IPSP. This is
consistent with more recent data showing a greater proportion of slow
GABA currents in more distal dendritic regions (Kapur et al.
1997
). If there is a laminar difference in the relative amount
of fast and slow inhibitory potentials evoked by stimulation, this
would be consistent with data from hippocampal region CA1, where it has
been shown that stimulation of feedforward inhibition in stratum
lacunosum-moleculare selectively activates slower components of
inhibition (Lacaille and Schwartzkroin 1988
).
Cholinergic agonists have been shown to increase frequency of
spontaneous inhibitory potentials recorded intracellularly from pyramidal cells in brain slice preparations of the hippocampus (Pitler and Alger 1992). This effect was also observed
in the piriform cortex in the presence of a range of modulatory
substances (Gellman and Aghajanian 1993
), including the
cholinergic agonist carbachol (R. Gollub, personal communication). This
increase in spontaneous inhibitory potentials was attributed to a
direct depolarization of inhibitory interneurons caused by activation
of cholinergic receptors (Behrends and ten Bruggencate
1993
; Pitler and Alger 1992
), which was
demonstrated with intracellular recording from interneurons in the
hippocampus (McQuiston and Madison 1996
; Reece and Schwartzkroin 1991
). This effect appears somewhat
paradoxical with relation to the observed suppression of evoked
inhibitory potentials but may be due to a requirement for lower tonic
background activity, with greater response to specific evoked patterns
of activity. This could contribute to a change in "signal-to-noise ratio" similar to that proposed for effects of noradrenergic
modulation. As shown in Fig. 3, depolarized interneurons will result in
an overall increase of inhibitory tone, decreasing background activity, whereas suppression of excitatory input to interneurons will results in
less feedback inhibition during activity elicited by afferent input.
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ACKNOWLEDGMENTS |
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The authors thank Dr. Donald Rannie for help with voltage-clamp recordings.
This research was supported by National Institute of Mental Health Grant R29 MH-52732.
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FOOTNOTES |
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Address for reprint requests: M. E. Hasselmo, Dept. of Psychology, Boston University, 64 Cummington St., Boston, MA 02215.
Received 10 September 1997; accepted in final form 6 January 1999.
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REFERENCES |
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