1Computational Neurobiology Laboratory, Howard Hughes Medical Institute, Salk Institute, La Jolla 92037; and 2Department of Biology, University of California San Diego, La Jolla, California 92093
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ABSTRACT |
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Durstewitz, Daniel, Jeremy K. Seamans, and Terrence J. Sejnowski. Dopamine-Mediated Stabilization of Delay-Period Activity in a Network Model of Prefrontal Cortex. J. Neurophysiol. 83: 1733-1750, 2000. The prefrontal cortex (PFC) is critically involved in working memory, which underlies memory-guided, goal-directed behavior. During working-memory tasks, PFC neurons exhibit sustained elevated activity, which may reflect the active holding of goal-related information or the preparation of forthcoming actions. Dopamine via the D1 receptor strongly modulates both this sustained (delay-period) activity and behavioral performance in working-memory tasks. However, the function of dopamine during delay-period activity and the underlying neural mechanisms are only poorly understood. Recently we proposed that dopamine might stabilize active neural representations in PFC circuits during tasks involving working memory and render them robust against interfering stimuli and noise. To further test this idea and to examine the dopamine-modulated ionic currents that could give rise to increased stability of neural representations, we developed a network model of the PFC consisting of multicompartment neurons equipped with Hodgkin-Huxley-like channel kinetics that could reproduce in vitro whole cell and in vivo recordings from PFC neurons. Dopaminergic effects on intrinsic ionic and synaptic conductances were implemented in the model based on in vitro data. Simulated dopamine strongly enhanced high, delay-type activity but not low, spontaneous activity in the model network. Furthermore the strength of an afferent stimulation needed to disrupt delay-type activity increased with the magnitude of the dopamine-induced shifts in network parameters, making the currently active representation much more stable. Stability could be increased by dopamine-induced enhancements of the persistent Na+ and N-methyl-D-aspartate (NMDA) conductances. Stability also was enhanced by a reduction in AMPA conductances. The increase in GABAA conductances that occurs after stimulation of dopaminergic D1 receptors was necessary in this context to prevent uncontrolled, spontaneous switches into high-activity states (i.e., spontaneous activation of task-irrelevant representations). In conclusion, the dopamine-induced changes in the biophysical properties of intrinsic ionic and synaptic conductances conjointly acted to highly increase stability of activated representations in PFC networks and at the same time retain control over network behavior and thus preserve its ability to adequately respond to task-related stimuli. Predictions of the model can be tested in vivo by locally applying specific D1 receptor, NMDA, or GABAA antagonists while recording from PFC neurons in delayed reaction-type tasks with interfering stimuli.
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INTRODUCTION |
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The prefrontal cortex (PFC) and its dense
dopaminergic input are critically involved in working-memory functions
(Brozoski et al. 1979; Fuster 1989
;
Goldman-Rakic 1995
; Müller et al.
1998
; Petrides 1995
; Sawaguchi and
Goldman-Rakic 1994
; Seamans et al. 1998
).
Working memory refers to the ability to hold temporally active
goal-related information and to use it in preparing actions and guiding
behavior. During working-memory tasks, which involve a delay component,
many PFC neurons show stimulus- and/or goal-specific, sustained
activity during the delay. This activity is presumed to reflect the
active holding of task-related information or motor preparation while
external cues are absent (Funahashi and Kubota 1994
;
Fuster 1989
; Goldman-Rakic 1990
;
Quintana and Fuster 1999
), and it can be maintained even
in the presence of interfering stimuli (Miller et al.
1996
).
Task-related electrical activity in the PFC is modulated by dopamine
(DA), mainly via the D1 receptor (Sawaguchi et al. 1988, 1990a
,b
; Williams and Goldman-Rakic 1995
).
Dopaminergic midbrain neurons are activated at the onset of
working-memory tasks (Schultz et al. 1993
), and DA
levels in the PFC increase during delay-task performance
(Watanabe et al. 1997
). Blockade of the dopaminergic input to the PFC or of dopaminergic D1 receptors in the PFC disrupt delay-task performance (Brozoski et al. 1979
;
Sawaguchi and Goldman-Rakic 1994
; Seamans et al.
1998
; Simon et al. 1980
). DA has been shown in
vitro to influence the biophysical properties of multiple intrinsic ionic and synaptic currents of PFC neurons (Gulledge and Jaffe 1998
; Kita et al. 1999
; Law-Tho et al.
1994
; Seamans et al. 1999
; Shi et al.
1997
; Yang and Seamans 1996
; Zheng et al.
1999
; Zhou and Hablitz 1999
). However, it is
unclear how these relate to DA's role in working memory. Thus although
it is clear that DA plays an important role in working memory and
alters the properties of PFC single neurons and synapses, its specific
functions and the underlying biophysical mechanisms remain elusive.
One function of DA may be to stabilize neural representations in the
PFC and thus enable PFC networks to sustain task-related activity even
in the presence of interfering input (Durstewitz et al.
1999a), which could be a unique feature of prefrontal networks (Miller et al. 1996
). In other words, DA might increase
the robustness of (sustained) delay activity with respect to
distracting input and noise. In a previous study, the general concept
of DA-induced stability was explored in a simple model with
leaky-integrator units that lacked detailed channel kinetics and
spiking behavior (Durstewitz et al. 1999a
). Here we
confirm and extend the general results of this study under more
realistic conditions in a network of compartmental model neurons with
Hodgkin-Huxley-like channel kinetics devised to reproduce in vitro and
in vivo results from deep layer PFC neurons. Greater physiological
detail allowed dopaminergic effects on network activity to be explored
in ways that were beyond the scope of the former model. In addition, we
investigate the possible functional implications of the differential
dopaminergic modulation of N-methyl-D-aspartate
(NMDA) and AMPA synaptic conductances (Cepeda et al.
1992
; Kita et al. 1999
; Law-Tho et al.
1994
; Seamans et al. 1999
) and provide a
functional interpretation of the DA-induced increase in
GABAA currents for working-memory processes
(Rétaux et al. 1991
; Seamans et al.
1999
; Yang et al. 1997
; Zhou and Hablitz 1999
).
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METHODS |
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In vitro recordings
To obtain voltage traces for adjustment of the model neurons, in
vitro recordings from PFC layer V intrinsically bursting (IB) pyramidal
cells were made. Details for recording methods can be found in
Seamans et al. (1997). Briefly, the brains of male
Sprague-Dawley rats (14-28 days) were rapidly dissected and immersed
for 1 min in cold (4°C) oxygenated artificial cerebrospinal fluid
(ACSF). After cutting, 300-µm slices containing the
prelimbic/infralimbic region of the PFC were transferred to ACSF
containing (in mM) 126 NaCl, 3 KCl, 26 NaHCO3,
1.3 MgCl2, 2.3 CaCl2, and
10 glucose at 30°C. Thick-walled borosilicate pipettes (serial
resistance = 4-25 M
for somatic recordings was 80%
compensated) were filled with (in mM) 130 K-gluconate, 10 KCl, 1 ethylene glycol-bis(
-aminoethyl ether)-N,
N,N',N'-tetraacetic acid (EGTA), 2 MgCl2, 2 NaATP, and 10 N-2-hydroxyethylpiperazine-N'
2-ethanesulfonic
acid (HEPES). Pipettes were connected to the headstage of an
Axoclamp-2B or Axopatch-200B amplifier (Axon Instruments) with Ag/AgCl wire.
Pyramidal cell model
A compartmental model was developed that reproduced somatic
voltage recordings from a layer V IB pyramidal cell in rat PFC (Fig.
1A). Deep layer pyramidal
cells are the ones most strongly innervated by dopaminergic fibers in
the rat and primate PFC and express the highest levels of mRNA for all
DA receptor subtypes (Berger et al. 1988, 1991
;
Goldman-Rakic et al. 1992
; Joyce et al.
1993
; Lewis et al. 1992
; Lidow et al.
1998
). Furthermore they constitute the major portion of neurons
exhibiting sustained delay activity (Fuster 1973
).
Intrinsically bursting pyramidal cells were chosen because they are the
most common pyramidal cell type in the deep layers of the rat PFC as
assessed by intracellular in vitro recordings (>60%) (Yang et
al. 1996
). The model layer V neurons consisted of a soma, a
basal dendritic, a proximal and a distal apical dendritic compartment,
as depicted in Fig. 1A. The cellular dimensions of the model
were in agreement with a morphological reconstruction of a PFC layer V
pyramidal cell obtained in our laboratory. (Test simulations performed
with a more detailed 20-compartment model yielded the same basic
results, not shown here.)
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The passive properties of the model were adjusted to approximately
reproduce the input resistance (RIN),
membrane time constant (m), and resting
potential (Vrest) of prefrontal IB
cells recorded in vitro. The resulting values for the specific membrane
resistance, membrane capacity, and cytoplasmatic (axial) resistivity
were, respectively, Rm = 30 k
-cm2, Cm = 1.2 µF/cm2, and
Ri = 150
-cm, which are well in the
range of empirically derived estimations for pyramidal cells from other
studies (Destexhe and Paré 1999
; Spruston
et al. 1994
; Stuart and Spruston 1998
). The
leakage reversal potential (Eleak) was
70 mV. These values (together with the active processes) gave rise to
a Vrest ~
66 mV (matching the
empirically measured mean, about
66 mV),
RIN ~ 164 M
(empirically measured
mean, ~163 M
),
m=
RmCm= 36 ms (empirically, ~33 ms), which conjointly with the active processes resulted in an effective time constant at the soma of ~44 ms, which
is well within the range of recordings from IB cells in vitro (range
~16-55 ms). Dendritic spines were implemented by increasing the
effective dendritic Cm and dividing
the effective dendritic Rm by a factor
of 1.92, accounting for a 92% increase in dendritic membrane area due
to spines as estimated from data of Larkman (1991
; see
also Rhodes and Gray 1994
).
DA controls various active ionic processes in the soma and dendrites of deep layer prefrontal pyramidal cells, and as we were interested specifically in the effects of DA on network behavior, the selection of active conductances included in our model was motivated primarily by this aim. Six different ionic conductances were distributed over the soma and the dendritic compartments with densities estimated from empirical data. All conductance kinetics were modeled by Hodgkin-Huxley-like equations, where ionic conductance per unit area is given by a product of powers of one or two voltage-, time-, and, sometimes, [Ca2+]i-dependent, dimensionless gating variables and a maximum conductance density (Tables 1 and 2).
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Gating variables develop in time according to the first-order
differential equation dx/dt = [x(V)
x(V,
t)]/
x(V), where
x
is the voltage-dependent steady
state and
x a voltage-dependent time
constant. Table 2 provides an overview over the gating variables and
their respective powers for all ionic conductances used in the present
study. Voltage gradients were computed according to the spatially
discrete form of the cable equation (e.g., Rall 1989
).
SODIUM CURRENTS.
A fast, spike-generating Na+ current
(INa) was distributed uniformly across
all three dendritic compartments (Huguenard et al. 1989;
Magee and Johnston 1995
; Stuart and Sakmann
1994
) but was given a higher density at the soma. This was done
to transfer the lower threshold spike-generating mechanism of the axon
(Colbert and Johnston 1996
), which was not explicitly
modeled and is probably partly due to much higher nodal
Na+ channel densities (Black et al.
1990
; Westenbroek et al. 1989
), to the soma.
Conductance densities in the dendrites were adjusted to ensure spike
back-propagation (Spruston et al. 1995b
; Stuart and Sakmann 1994
) with spike amplitudes and widths as recorded in vitro (Seamans et al. 1997
). The biophysical
description of INa was taken from a
computational study by Warman et al. (1994)
and adapted
to fit kinetics determined by Cummins et al. (1994)
, who
analyzed Na+ currents in rat and human
neocortical pyramidal cells.
CALCIUM CURRENTS AND CALCIUM ACCUMULATION.
The biophysical description of a high-voltage-activated (HVA)
Ca2+ current was taken from Brown et al.
(1993), who studied these currents in dissociated rat
sensorimotor cortex pyramidal cells. For simplicity we did not
distinguish between L- and N-type Ca2+ currents,
which according to Brown et al. (1993)
have the same activation kinetics, but assumed that the HVA
Ca2+ current implemented in the model represents
a mixture of the two. Immunocytochemical, Ca2+
influx, and electrophysiological data suggest that HVA
Ca2+ channels are highly clustered in the
proximal dendrites around the soma, are present with significant
densities along the apical stem, but are quite low in density in the
distal apical tuft, in neocortical including prefrontal pyramidal cells
(Hell et al. 1993
; Schiller et al. 1995
;
Seamans et al. 1997
; Westenbroek et al. 1990
,
1992
). Densities of the HVA channel in the present study were
adjusted accordingly (Table 1).
POTASSIUM CURRENTS AND POTASSIUM ACCUMULATION.
The Hodgkin-Huxley-like formulas for the delayed rectifier (DR) channel
were taken from a computational study by Warman et al.
(1994) on hippocampal neurons. Following Rhodes and Gray
(1994)
, the deactivation of the DR was sped up by 1.5 to better
reproduce spike repolarization observed in PFC IB neurons in vitro. The distribution of DR conductance densities matched that of the fast Na2+ current (Table 1).
Model of fast-spiking GABAergic interneurons
A basket-type fast spiking (FS) neocortical aspiny interneuron
was implemented by a two-compartment model as depicted in Fig. 1A (Kawaguchi 1993, 1995
; Kawaguchi
and Kubota 1997
) [the morphological dimensions of this cell
were estimated roughly from data on FS cells in frontal cortex given in
Kawaguchi (1995)
]. FS interneurons provide most of the
inhibition in the neocortex including the PFC (Gabbott et al.
1997
; Kawaguchi and Kubota 1997
), are involved in working memory (Rao et al. 1999
; Wilson et al.
1994
), and are the major type of interneuron modulated by DA
(Gorelova and Yang 1998
; Muly et al.
1998
; Sesack et al. 1998
; Zhou and
Hablitz 1999
). Passive membrane properties were as follows:
Rm = 100 k
-cm2,
Cm = 1.0 µF/cm2, Ri = 150
-cm, and Eleak =
68 mV (the
behavior of the interneurons in the network was largely insensitive to
the exact values of these parameters). K+
accumulation dynamics were the same as for the pyramidal cells. The
fast Na+ and K+ DR channels
included in the somatic and dendritic compartment (Table 1) were
sufficient to reproduce the fast-spiking, nonadapting behavior and the
strong, brief afterhyperpolarizations after each spike exhibited by FS
basket-type cells (Kawaguchi 1993
, 1995
; Kawaguchi and Kubota 1997
). The kinetics of these
channels (taken from Lytton and Sejnowski 1991
) differed
from those of the pyramidal neuron to reproduce the much shorter spike
duration in interneurons compared with pyramidal cells
(Kawaguchi 1993
, 1995
; Rao et al. 1999
;
Wilson et al. 1994
) and the faster recovery from
Na+ channel inactivation (Martina
and Jonas 1997a
), allowing higher spike firing rates (Table 2).
Network architecture and synaptic currents
Because little is known about the detailed connectivity of
neurons within the PFC and the associated synaptic strengths, we intentionally kept the network model as general and as simple as
possible (Fig. 1). A total of 20 deep layer pyramidal cells and 10 GABAergic interneurons were simulated. (Test simulations with larger
networks suggested that network size is not a crucial factor for the
questions addressed in the present paper.) All pyramidal cells and
GABAergic interneurons were fully connected (but see following text).
Extensive lateral connections between layer V pyramidal cells (ranging
up to millimeters) in the PFC have been demonstrated by Levitt
et al. (1993) and Kritzer and Goldman-Rakic
(1995)
. According to Lübke et al. (1996)
and Markram et al. (1997a)
, these connections
between deep layer pyramidal cells involve about four to eight synaptic
contacts distributed mainly on the proximal dendritic tree. Thus
pyramidal cells in the present model were connected reciprocally within
the proximal dendritic region; that is, synapses were placed both on
the basal and proximal apical dendritic compartment (Fig.
1A). Pyramidal-to-GABAergic cell synapses were placed on
the dendritic compartments of the interneurons. Inhibitory feedback
connections from interneurons to pyramidal cells consisted of
GABAA-like synapses on the somata of pyramidal cells where
most inhibitory synapses converge (Douglas and Martin
1990
; Thomson and Deuchars 1997
). Direct
reciprocal interactions between pyramidal cells and FS interneurons are
suggested both by in vitro data (Tarczy-Hornoch et al.
1998
; Thomson and Deuchars 1997
) and by
the observation of correlated firing of pyramidal cells and
interneurons in the PFC in vivo during working-memory tasks
(Constantinidis et al. 1999
; Rao et al.
1999
). GABAergic interneurons also were interconnected
reciprocally by GABAA-like conductances on their somata.
All axonal transmission delays varied in the range of 2-4 ms.
Both pyramidal-to-pyramidal and pyramidal-to-GABAergic cell connections
involved both AMPA- and NMDA-like synaptic conductances [for evidence
on NMDA-like synaptic conductances on FS interneurons in frontal
cortex, see Kawaguchi (1993)]. AMPA-like synaptic
currents were modeled by a double exponential function of the form
gAMPA,max × [
1
2/(
2
1)] × [exp(
t/
2)
exp(
t/
1)] with time constants
1= 0.55 ms and
2 = 2.2 ms. NMDA-like
synaptic currents were modeled as in Mel (1993)
by the
product of a voltage-dependent gate s = 1.50265 × [1 + 0.33 exp(
0.06
Vm)]
1 with
s = 0.1 ms (implementing the
voltage-dependent Mg2+ block) and a double exponential with
time constants
1 = 10.6 ms and
2 = 285.0 ms. Time constants for the AMPA and NMDA
currents were taken directly from a study of glutamate receptor
channels by Spruston et al. (1995a)
. The voltage
dependency of the NMDA current as given by the s-gate in
the preceding text also matched the one measured by Spruston et
al. (1995a
; see their Fig. 4F). Furthermore the
relative contributions of AMPA- and NMDA-like currents to excitatory
postsynaptic currents (EPSCs; i.e., the ratio
gAMPA,max:gNMDA,max)
were matched to data from Spruston et al. (1995a)
. In
the absence of more specific knowledge,
gAMPA,max and
gNMDA,max for pyramidal cells and
interneurons were assumed to be of equal strength
(gAMPA,max = 15.1392 nS,
gNMDA,max = 0.0912 nS, for the baseline
configuration, see following text). These conductances were set high
enough to allow in concert with spontaneous synaptic inputs (see
following text) the maintenance of recurrent activity in the small
network for some time similar to that observed in PFC neurons during
delayed reaction-type tasks (Funahashi and Kubota 1994
;
Fuster 1989
; Goldman-Rakic 1995
).
Synaptic reversal potentials were set to
EAMPA = ENMDA = 0 mV (Angulo et al.
1997
; Seamans et al. 1997
; Spruston et
al. 1995a
). GABAA-like currents were modeled by
alpha-functions of the form gGABAA,max × t/
GABAA × exp(
t/
GABAA + 1) with
GABAA = 1.5 ms and
gGABAA,max = 8.4 nS, yielding fast
inhibitory postsynaptic potentials as found in neocortical pyramidal
cells (Thomson and Deuchars 1997
). The reversal
potential was EGABAA =
75 mV
(Ling and Benardo 1998
; Thomson et al.
1996
).
Stimulus- and/or response-specific delay activity and "opponent
memory fields" have been observed in the PFC during working-memory tasks (Funahashi et al. 1989; Goldman-Rakic 1995
,
1996
; Quintana et al. 1988
; Rainer et al.
1998a
; Rao et al. 1997
). Thus for example, in
the oculomotor delayed response task (Funahashi et al.
1989
; Goldman-Rakic 1995
, 1996
), neurons in the
PFC show enhanced firing for a preferred target direction but
suppressed firing for targets opposite to the preferred direction. To
produce stimulus/response-specific activity patterns in the small model
network used here, two partly overlapping subsets of 10 neurons each
(Fig. 1B) were connected by high synaptic weights
(gmax values as given in the preceding text)
within each group and by low synaptic weights (10% of the gmax values given in the preceding text)
between neurons not belonging to the same cell assembly. These two
subsets were meant to represent two different stimuli or motor plans
encoded by the synaptic connections of the network (for the present
purposes the precise nature of the information encoded in the delay
activity is not relevant). Formation of such cell assemblies as
originally proposed by Hebb (1949)
is suggested by
Hebb-like long-term synaptic plasticity mechanisms in the
cortex (Levy and Steward 1983
; Markram et al. 1997
) and is supported by recent multiple-electrode recordings from the PFC (Brody et al. 1999
; Constantinidis
et al. 1999
). Specific activity patterns were evoked by
stimulating one of the stored cell assemblies via afferent synapses
(see following text) or current injections.
To achieve low spontaneous firing rates in the network as observed in
vivo in the PFC (Fuster 1989; Fuster et al.
1985
; Rosenkilde et al. 1981
), random background
synaptic activity was delivered to all pyramidal and GABAergic cells,
generated according to Poisson processes convolved with the excitatory
(AMPA and NMDA) or inhibitory (GABAA) synaptic conductance
changes defined in the preceding text. Background excitatory synaptic
inputs were placed on all dendritic compartments of pyramidal cells and
interneurons, whereas inhibitory inputs were limited to the proximal
stems and somata where pyramidal cells receive most of their inhibitory
input (Douglas and Martin 1990
; Thomson and
Deuchars 1997
; Thomson et al. 1996
). These
inputs mimicked synaptic connections from other neurons within the PFC
as well as afferent connections from other cortical or subcortical
areas. In low-activity states, the network was driven mainly by this
random background activity, which accounted for >90% of the total
synaptic current in pyramidal cells. In contrast, in high-activity
states, the network was dominated by recurrent synaptic activity and
the relative contribution of background synaptic inputs was ~50%.
The strength and number of background synaptic inputs furthermore were
adjusted to approximately produce membrane voltage fluctuations as
observed in vivo (Destexhe and Paré 1999
;
Paré et al. 1998
).
Specific afferent network inputs
The main question of the present study was how DA might affect
the robustness of delay (sustained) activity in PFC circuits with
respect to distracting input. A distracting input could be any
environmental or internally generated stimulus that is incompatible with (or irrelevant to) the current behavioral goal and that tends to
evoke a specific representation (activity pattern) in the PFC networks.
It thereby interferes (or competes) with the current prefrontal delay
activity pattern that encodes information related to the present
behavioral goal (Miller et al. 1996; Quintana et al. 1988
; Rainer at al. 1999
). In
delayed-reaction tasks, interfering stimuli have been introduced as
part of the experimental design (Fuster 1973
;
Miller et al. 1996
).
Distracting input might arise from many different anatomic sources,
involving inputs to the PFC from subcortical (e.g., thalamic), sensory
neocortical, or even other prefrontal areas (Fuster
1989; Fuster et al. 1985
; Goldman-Rakic
1988
; Pandya and Yeterian 1990
). Depending on
the site of origin, association and transcallosal fiber connections
target mainly layers I, III, and V, layers I, IV, and VI, or all layers
in the prefrontal cortices (Goldman-Rakic 1988
;
Isseroff et al. 1984
; Melchitzky et al.
1998
; Pucak et al. 1996
). Hippocampal inputs
predominantly contact layers I and V (Swanson 1981
).
Thalamic inputs distribute mainly throughout layers I, III, and V-VI
(Berendse and Groenewegen 1991
; Kuroda et al. 1993
, 1996
) where they contact the dendrites of layer V-VI
pyramidal cells (Kuroda et al. 1993
). Thus distracting
inputs might basically affect all dendritic compartments of deep layer
pyramidal cells in the PFC, probably exerting their strongest impact in
the proximal basal and apical dendrite region. Hence, in addition to
nonspecific background inputs (see preceding text) to all compartments,
afferent excitatory (NMDA and AMPA) synapses that specifically target
one of the stored cell assemblies were placed on the proximal basal and
apical dendrite compartments of the model cells (placing them in
addition on the distal model dendrites did not change the results).
To probe stability of PFC representations and to vary the strength of
an afferent stimulus, the afferent stimulation frequency rather than
the synaptic conductance strength was varied because the subjective
importance and behavioral relevance of stimuli in prefrontal areas is
correlated with firing frequency (Tremblay and Schultz
1999; Watanabe 1996
). The synaptic (AMPA- and
NMDA-like) conductances of the afferent connections were arbitrarily
set five times higher than those of the recurrent synapses. This choice yielded a physiologically reasonable range of afferent stimulation frequencies (i.e., Fcrit values, see
RESULTS) and allowed sufficient discrimination between conditions.
Dopaminergic modulation
DA has physiological effects that might vary between different
brain regions [for example, DA has effects on NMDA currents in the
hippocampal CA1 region (Hsu 1996; Otmakhova and
Lisman 1999
) opposite from those observed in the PFC and
striatum (see following text)]. Hence only effects of DA reported for
PFC neurons were used in the present study. We also focused on
D1-mediated effects because D1 receptors are much more abundant in the
PFC than D2 receptors (Joyce et al. 1993
; Lidow
et al. 1991
) and, more importantly, both working-memory
performance as well as delay-activity recorded in vivo in behaving
animals is susceptible mainly or exclusively to D1 but not D2 receptor
agonists and antagonists (Arnsten et al. 1994
;
Müller et al. 1998
; Sawaguchi and
Goldman-Rakic 1994
; Sawaguchi et al. 1988
,
1990b
; Seamans et al. 1998
; Williams and
Goldman-Rakic 1995
; Zahrt et al. 1997
). Hence,
the short-lasting D2 effects (Godbout et al. 1991
;
Gulledge and Jaffe 1998
; Pirot et al.
1992
) may subserve other functions not directly related to
holding representations in working memory (see also Durstewitz et al. 1999a
). Both deep layer pyramidal cells (Berger
et al. 1990
; Bergson et al. 1995
; Smiley
et al. 1994
; Yang and Seamans 1996
) and FS
interneurons (Gorelova and Yang 1998
; Muly et al. 1998
) in the PFC are equipped with D1 receptors.
DA-induced parameter shifts in the model were varied systematically
over some range (see RESULTS) but for some simulations, "baseline" (0% DA shift) and "high DA" (100% DA shift)
standard configurations were defined as in Tables 1 and 2 and as
described below. The high-DA configuration is based on the average
shifts in DA-dependent parameters observed in vitro (Gorelova
and Yang 1997; Seamans et al. 1999
; Yang
and Seamans 1996
; Yang et al. 1997
). The
following effects of DA on intrinsic ionic and synaptic currents were
implemented (see Tables 1 and 2):
1) DA shifts the activation threshold of the
persistent Na+ current toward more hyperpolarized
potentials and slows the inactivation process of this current
(Gorelova and Yang 1997; Seamans et al. 1999
; Yang and Seamans 1996
) (Table 2). This
likely contributes to the DA-induced increase in firing rate and
reduced adaptation as observed in vitro (Cepeda et al.
1992
; Seamans et al. 1999
; Shi et al.
1997
; Yang and Seamans 1996
) and in vivo
(Sawaguchi et al. 1988
, 1990a
,b
) in prefrontal pyramidal cells.
2) DA reduces a slowly inactivating
K+ current in PFC pyramidal cells (Yang
and Seamans 1996), as is the case in striatal neurons (Nisenbaum et al. 1998
). This was modeled as a reduction
in gKS,max (Table 1).
3) DA reduces the half-width and amplitude of isolated
dendritic Ca2+ spikes (Yang and Seamans
1996). The data of Yang and Seamans (1996)
made
it likely that this reduction is due to a diminishing effect of DA on a
HVA Ca2+ current located more in the distal
dendrites and thus probably of the N type, which reaches a local
maximum in the distal dendrites of pyramidal cells (Westenbroek
et al. 1992
; Yuste et al. 1994
). A reduction of
N-type HVA Ca2+ currents by DA has been shown
more directly in striatal neurons (Surmeier et al.
1995
), in isolated dorsal root ganglia sensory neurons
(Formenti et al. 1998
), and recently in PFC neurons by Yang et al. (1998)
. As L- and N-type HVA
Ca2+ channels for simplicity were collapsed into
a single description in the present model, HVA
Ca2+ conductances in the proximal and distal
dendrites were affected differentially by DA, based on the assumption
that DA diminishes the N-type current only. L-type channels are highly
clustered within the proximal region and strongly decline toward the
distal dendrites (Hell et al. 1993
; Westenbroek
et al. 1990
). In contrast, immunolabeling for N-type channels
falls off in the middle of the apical stem of deep layer neurons but
rises again within layers II-III (Westenbroek et al.
1992
). Thus we assumed that the total HVA
Ca2+ current in the distal dendritic compartment
was of the N-type, whereas in the proximal region it contributed only
40% to the total HVA Ca2+ current as shown in
vitro by Brown et al. (1993)
. DA-induced reductions in
the maximum HVA Ca2+ conductance
(gHVA,max) were implemented
accordingly (Table 1).
4) Recent evidence shows that DA via the D1 receptor enhances
NMDA-like synaptic currents in the PFC (Kita et al.
1999; Moore et al. 1998
; Seamans et al.
1999
; Zheng et al. 1999
), as in striatal neurons
(reviewed in Cepeda and Levine 1998
; Cepeda et
al. 1993
; Levine et al. 1996
) and as shown
earlier in slices from human frontal neocortex (Cepeda et al.
1992
). In the high DA configuration, this was modeled by an
increase of 40% in gNMDA,max
(Seamans et al. 1999
).
5) In contrast to NMDA-like synaptic currents, non-NMDA or
AMPA-like currents seem to be reduced in the frontal neocortex including the PFC (Cepeda et al. 1992; Kita et
al. 1999
; Law-Tho et al. 1994
), as in the
striatum (Cepeda et al. 1993
). However, this reduction
may only be slight in the PFC (Seamans et al. 1999
). Nevertheless we investigated the effect of a reduction in AMPA currents
as possibly induced by DA by decreasing
gAMPA,max by 20% in the high DA configuration.
The overall effect of the combined DA-induced changes in AMPA and NMDA
currents was to reduce the excitatory postsynaptic potential (EPSP)
amplitude but prolong the duration as suggested by in vitro experiments
(Cepeda et al. 1992; Kita et al. 1999
; Law-Tho et al. 1994
; Seamans, unpublished observations).
6) The DA-induced enhancement of
GABAA-like synaptic currents in the PFC
(Penit-Soria et al. 1987; Pirot et al.
1992
; Rétaux et al. 1991
; Seamans
et al. 1999
; Yang et al. 1997
; Zhou and
Hablitz 1999
) was modeled by an increase in
gGABAA,max of 30% in the high-DA configuration (Seamans et al. 1999
). In addition, DA
might enhance spontaneous activity of GABAergic neurons or GABAergic
transmitter release in the PFC (Rétaux et al.
1991
; Yang et al. 1997
; Zhou and Hablitz
1999
). This effect was accounted for by increasing the
spontaneous background firing rate of GABAergic inputs by 10% in the
high DA condition.
Computational techniques
The simulation software was written in C++ and run on Pentium
PCs using the LINUX operating system. The system of differential equations was integrated numerically using a semi-implicit
extrapolation method as described in Press et al. (1992,
Ch. 16.6) with an adaptive time step procedure. The (local truncation)
error criterion was set to 10
4, and the minimum
time step was limited to 0.1 µs. To produce random background
activity, a (uniform) random-number generator based on data encryption
methods as described in Press et al. (1992
, Ch. 7.5) was
used because it has much better statistical properties than the C++
standard library "rand" function (see Press et al.
1992
). A NEURON implementation of the pyramidal cell model is
available under ftp://ftp.cnl.salk.edu/pub/dd/pcell.
![]() |
RESULTS |
---|
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---|
The first two sections serve to illustrate that the model as devised in the METHODS can reproduce basic electrophysiological features of PFC neurons recorded in vitro and in vivo. The main part of the RESULTS then will examine how DA-induced parameter variations affect network states indicated by these electrophysiological features and which functional implications for working memory this might have.
Reproduction of the firing pattern of PFC IB pyramidal cells
The passive properties of the model neuron matched the
average Vrest,
RIN, and m of
prefrontal IB cells recorded in vitro (see METHODS). In
addition, the single pyramidal cell model reproduced the basic
properties of spiking behavior of these neurons (Fig. 2A). Current injections into
the model cell soma elicited a spike doublet followed by a train of
action potentials with adaptation properties similar to those of IB
neurons recorded in vitro. With the dopaminergic modulation of
intrinsic ion channels in place (high-DA condition; see Tables 1 and
2), the spike frequency of the cell increased almost threefold to the
same somatic current step (compare Fig. 2, B with 3rd
row in A), as reported for PFC pyramidal cells recorded
in vitro after bath application of DA (Yang and Seamans
1996
).
|
Basic network properties
Figures 3 and
4 illustrate that neurons in the fully
connected network model with all internal and external synaptic inputs in place (see METHODS) could reproduce the most salient
electrophysiological features of PFC neurons recorded in vivo. Without
any additional input or stimulation, neurons in the model network
driven by the spontaneous background activity fired at low rates (mean
1.4 Hz), comparable with the low spontaneous firing rates of 1-3 Hz
observed for the majority of pyramidal neurons in the primate and rat
PFC [Fuster 1973; Fuster et al. 1985
;
Jung et al. 1998
; Rosenkilde et al.
(1981)
provide a distribution of spontaneous rates;
Sawaguchi et al. 1990a
]. The spontaneous activity
alone, however, was not sufficient to drive the network spontaneously
into a state of high, sustained activity, although transient episodes
of burst-like activity occasionally appeared in the baseline condition
(see Fig. 3A, left). When a high-activity
state was evoked by a short-lasting stimulation of one of the stored
cell assemblies (either by a current injection or by stimulation of
afferent synapses), this activity was sustained at ~17.3 Hz for
prolonged periods of time if the background noise was not too high
(Fig. 3A). Thus in a manner similar to delay-active
neurons in a working-memory task that hold active a representation of
the stimulus or the forthcoming action, high activity was maintained in
the network for many seconds after removal of the eliciting stimulus.
The rasterplot in Fig. 3A confirms in addition that this
activity was stimulus specific, i.e., it was only present in a subset
of encoding neurons. GABAergic feedback inhibition ensures that the
spread of activity in the network is limited; competing attractors
are suppressed and only one pattern can become active at a time.
|
|
In general, delay frequencies in the present model network ranged
from ~12 to 36 Hz (depending on condition, see following text) and
were thus well within the range of what has been observed during the
delay periods of working-memory tasks (e.g., Di Pellegrino and
Wise 1993; Funahashi et al. 1989
; Fuster
1973
; Miller et al. 1996
; Rainer et al.
1998b
). Firing frequencies, however, could climb transiently to
much higher values, especially during the presentation of a stimulus.
The frequency of the interneurons during delay activity ranged from
~45 to 100 Hz, in agreement with available in vivo data
(Wilson et al. 1994
). The fact that stimulus-specific,
recurrent activity could be maintained in the network at quite low
rates (<20 Hz) is in itself not trivial because of the short axonal
propagation and transmission delays (2-4 ms in the present model) and
the fast AMPA-kinetics. In addition, selective high activity could be
maintained in the presence of considerable noise (see
METHODS). These characteristics depended critically on the
slow time course and voltage-dependence of NMDA conductances. Finally,
in high-activity states, spike trains were highly irregular (Figs. 3,
A and B, and 4, A and
B) with coefficients of variation
(Cv) ranging from 0.5 to 0.8, as observed in
in vivo recordings from neocortical neurons (Bodner et al.
1997
; Shadlen and Newsome 1994
; Softky
and Koch 1993
).
In summary, these simulations demonstrate that the network model established here exhibits network states and behavior as observed in the PFC in vivo, thus providing a physiologically plausible starting point to explore the effects of DA-induced parameter variations on these network states and their functional implications.
Differential dopaminergic modulation of activity states
In the present network, a simulated rise in DA level by shifting intrinsic and synaptic ion channel parameters into the high-DA configuration led to suppression of the low firing state (mean 0.3 Hz; Fig. 3B). This suppression was caused by the relative dominance of the inhibitory over the excitatory actions of DA in the low-activity state (i.e., by the increased GABAergic inhibition and the reduced N-type Ca2+ and AMPA currents). In contrast, when a high-activity state was elicited by a transient stimulus, DA enhanced this sustained activity, and the average firing frequency rose from ~17.3 Hz in the baseline condition to ~25.8 Hz in the high-DA condition. Conversely the activity of neurons not participating in the representation of the evoked pattern (the "background neurons") was more strongly suppressed in the high-activity state in the high-DA condition compared with the baseline condition (compare raster plots in Fig. 3, A and B). In the high-activity state, the excitatory actions of DA dominated its inhibitory actions. Thus the net effects of DA on neural firing depended on the initial activity state of the network. In the high-DA condition, sustained "delay activity" was also more robust with respect to the random fluctuations in background activity: If the impact of noise on network activity was increased, sustained activity in the baseline condition had the tendency to break down much earlier than in the high-DA condition (Fig. 3C).
The switch from a predominantly "inhibitory" to a predominantly "excitatory" action of DA from low to high levels of activity stems mainly from the highly increased contribution of the slow NMDA currents in the high-activity state: During high activity, DA causes a large boost in long-lasting recurrent excitation, further amplified by the increase in firing frequency by the enhancement of INaP. In addition, the fact that the persistent Na+ and the NMDA but not the GABAA and AMPA conductances are voltage-dependent means that the former two but not the latter increase at higher levels of activity. Thus the relative impact of the INaP and NMDA conductances grows with network activity.
The simulations reported above highlight a functionally important
principle, namely that DA's effects depend on the activity state of a
cell assembly such that foreground in relation to background activity
is enhanced. It is important to note that the differential modulation of low- and high-activity states by DA is an intrinsic property of the activity/voltage and time dependencies of the particular conductances affected by DA and as such does not
depend on the particular parameter configuration. Thus even for
parameters for which the low-activity state also was amplified by DA,
there was always a much larger enhancement of the high-activity state, which in turn led to increased GABAergic feedback suppressing the
background neurons. The differential DA-induced enhancement of
sustained activity in the network is also consistent with the enhancements of task-related activity demonstrated in vivo during working-memory tasks (Sawaguchi et al. 1988, 1990a
,b
).
Effects of DA on the stability of active patterns
In the following sections, the possible functional implications of
the differential modulation and the DA-induced parameter changes will
be examined. Sustained (delay) activity of neurons in the PFC
selectively encodes stimulus or motor information relevant to the
current behavioral goal (Asaad et al. 1998; Di
Pellegrino and Wise 1993
; Funahashi et al. 1989
;
Fuster 1989
; Quintana and Fuster 1999
;
Rainer et al. 1998a
,b
, 1999
; Rao et al.
1997
; Rosenkilde et al. 1981
). Both disrupted
delay activity as well as delay activity coding for the wrong stimulus
or response are correlated with subsequent behavioral errors
(Funahashi et al. 1989
; Fuster 1973
; Quintana et al. 1988
). In the following, the activity
pattern that has to be maintained during the delay for successful goal achievement or behavioral performance will be referred to as the "target pattern." A stimulus that is not relevant to the present task or behavioral goal and hence interferes with the target pattern will be referred to in the following as the "distractor pattern." Durstewitz et al. (1999a)
showed how the PFC might
detect patterns that are important to the present goal (and thus
differentiate them from distractor patterns) via "match enhancement
neurons" (e.g., Miller et al. 1996
), and how these
neurons could generate a signal that via a cortico-striatonigral
feedback loop finally terminates target pattern activity again on
achieving the goal.
To explore the idea that DA increases the robustness of target pattern representations (encoded by delay-active neurons) with respect to distracting patterns, parameters modulated by DA were linearly varied, either in combination or independently, within a physiologically reasonable range, using the difference between the baseline and the high-DA configuration as a standard. Thus the differences between the baseline and the high-DA configuration as given in METHODS and in Tables 1 and 2 were defined arbitrarily as a 100% shift in DA-dependent parameters, and other conditions were expressed relative to these standard difference values (i.e., normalized to them). For each of these different "DA levels" (parameter shifts), the minimal frequency of an afferent synaptic stimulation of the distractor pattern neurons that was sufficient to disrupt the target pattern and evoke a transition to the distractor pattern was determined. This is illustrated in Fig. 4, A and B, for the baseline and the high-DA situation, respectively: First a target pattern was evoked by a 250-ms current injection (0.45 nA) into the somata of the neurons coding for that pattern. (Synaptic stimulation yielded the same results, but current injection was used instead to ensure that at the time when the interfering pattern arrived, target pattern activity was driven only by recurrent network inputs and was no longer aided by afferent inputs.) Next, 1 s after the target pattern stimulation was shut off, afferent synapses to the distractor pattern were stimulated for 100 ms. For low-frequency stimulation of the distractor pattern, the target pattern remained stable, but at a certain stimulation frequency (Fcrit), it broke down (due to the increased GABAergic feedback induced by the distractor pattern stimulation), and a transition to a new activity state occurred. At this critical frequency, the current contents of working memory are lost.
As a criterion for stability, target pattern activity had to be
maintained at frequencies >10 Hz for 1 s after offset of the
distractor pattern stimulation. Fcrit
values were determined in steps of 10 Hz such that each step meant one
additional afferent spike within the 100-ms stimulation period. Hence
Fcrit also can be read as the number
of equally spaced afferent spikes within the stimulation period. In a
few cases (<6%), stability was not a monotone function of stimulation
frequency but could exhibit "jumps" within a narrow critical range.
In these cases, Fcrit was defined as
the first stimulation frequency where disruption of the target pattern
occurred. Note from Fig. 4, A and B, that even
suprathreshold activity in the distractor pattern neurons may not be
sufficient to shut down the target pattern and enforce a transition to
the distractor pattern unless the distractor pattern neurons gain
sufficiently high firing rates.
Highly irregular firing patterns and strong membrane voltage
fluctuations are characteristic features of neocortical neurons recorded in vivo (Destexhe and Paré 1999;
Paré et al. 1998
; Shadlen and Newsome
1994
; Softky and Koch 1993
) and are prominent in
PFC neurons during the delay periods of working-memory tasks (Bodner et al. 1997
). Hence we especially were
interested in how DA might affect the robustness of active
representations in the presence of high noise and high variance of the
membrane potentials and interspike intervals. Because the random
synaptic background activity made a major contribution to the total
synaptic current (see METHODS), eight sets of simulations
with different synaptic background patterns were run, and
Fcrit values were averaged across these conditions. This procedure also provided a robustness check because different background patterns produced quite different spiking
patterns in the recurrent network, thus demonstrating that our results
hold despite high levels of noise and with different firing patterns.
The Fcrit values reported in the
following should be interpreted in relative rather than absolute terms
because the absolute values depend on other parameters such as the
synaptic strength of the afferent inputs (see METHODS) and
the delay times, which were kept fixed for these simulations.
Figure 4C shows that the critical afferent frequency (Fcrit) increased more than threefold across the range of simulated DA levels, i.e., with the magnitude of the changes in the DA-modulated parameters. Hence over a range of physiologically plausible parameters, the dopaminergic modulation leads to an increase in stability of the currently active representation with respect to interfering stimuli.
Additional simulations confirmed that an increase in stability with
increasing DA-induced parameter shifts holds under the following
conditions: for longer or shorter time intervals (delays) after target
pattern onset; with background activity shut off completely (i.e., no
noise); stimulation of the distractor pattern by DC injections into the
dendrites of the distractor pattern cells; additional stimulation of
GABAergic interneurons conjointly with the distractor pattern pyramidal
cells (i.e., including a strong feedforward inhibitory component); and
no overlap between the target pattern and the distractor pattern (i.e.,
no shared neurons). In the latter case, an increase in overall
stability occurred while the ordinal relationships between different DA levels were preserved, consistent with previous findings
(Durstewitz et al. 1999a).
Ionic currents contributing to the DA-induced stabilization
To assess which of the DA-modulated conductances could contribute
to an enhancement of stability, each was varied independently while the
other parameters affected by DA were set to the high-DA values given in
METHODS. As shown in Fig. 5,
A and B, the DA-induced alterations in the
persistent Na+ conductance, and, somewhat
counterintuitively, both, the increase in the
NMDA conductance as well as the decrease in the AMPA
conductance increased stability of the target pattern over the range of
parameters tested. The stability-increasing effects of the DA-induced
alterations in these conductances were present in each of the eight
different sets of simulations as well as in the control simulations
listed above. The enhancement of INaP
and NMDA conductances might increase stability through several
mechanisms. Most importantly, the spike rate of the pyramidal cells
increased due to enhanced recurrent excitation and
INaP-mediated amplification of EPSPs
(Schwindt and Crill 1995, 1996
; Stuart and
Sakmann 1995
). This in turn elevated the firing rate of the
GABAergic interneurons, which receive inputs from the pyramidal cells,
resulting in an enhanced GABAergic feedback to the pyramidal cells that
is more difficult for any interfering input to overcome. The increased
firing of the target pattern neurons also implies that a higher firing
rate has to be obtained in the distractor pattern neurons to achieve
the same level of NMDA conductance activation as in the target pattern
neurons. The slow kinetics and voltage dependence of the NMDA
conductance are critical for this effect as evidenced by the fact that
a decrease in gAMPA, which
is voltage-independent and has a short time course, also enhances
stability.
|
The increased stability with a reduction in gAMPA may be partly due to the reduction in the afferent excitatory synaptic current to the distractor pattern neurons: When gAMPA was changed only for the internal recurrent excitatory synapses but not for the external inputs, the increase in stability with a reduction in gAMPA became less pronounced or disappeared in two simulations with different random background. Hence both the target and the distractor pattern neurons may suffer about equally from a reduction in recurrent AMPA-mediated excitation, but the distractor pattern neurons suffer in addition from the fact that a reduction in gAMPA also affects the external inputs to these neurons.
Stability did not increase monotonically with the enhancement of GABAA conductances but, in the present set of simulations, peaked at the 100% condition (Fig. 5A). Hence, whether an increase in GABAA conductances enhances or reduces stability might depend on the magnitude of the parameter shift. Besides a possible role in stability, an increase in GABAA conductances concurrently with the other DA-induced parameter changes was a necessary prerequisite for proper functioning of the network model. Without an increase in gGABAA along with the other DA-induced alterations, the network rapidly and almost inevitably pushed itself into one of the high-activity states within the first 2 s after the network was started, even without an external stimulus (Fig. 6A). (For this reason, it was not possible to obtain a stability estimate for the GABA 0%-condition in Fig. 5A.) Figure 6B quantifies this phenomenon for different DA levels. For a 50% DA shift in GABAA currents, spontaneous jumps were still very likely to occur, but they never happened in any of the simulations run with the 100 and 150% DA shifts in gGABAA (Fig. 6B). Thus a major function of the DA-induced increase in GABAergic currents may be to maintain control over the network behavior and to keep the network responsive to the correct stimulus conditions. Self-induced high-activity states also occurred occasionally with the 150% shifts in INaP or gNMDA alone, emphasizing the need for suppression of this phenomenon at high-DA levels.
|
A reduction in gHVA tended to increase
stability in the present model (Fig. 5B). To examine how a
reduction in gHVA might increase
stability, trains of sub- or suprathreshold EPSPs of different
amplitudes were evoked in single-model neurons, and the change in EPSP
amplitude or firing frequency upon
gHVA reduction was assessed. In the
subthreshold range and at low firing rates, IHVA amplified EPSPs in the present
model neurons (consistent with findings by Seamans et al.
1997) such that neurons with low firing rates suffered from a
reduction in gHVA (not shown). In contrast, high-frequency firing led to strong
Ca2+ accumulation (Helmchen et al.
1996
) and consequently to strong activation of
Ca2+-dependent K+ currents
like IC in the present model. Hence at
constantly high firing rates, gHVA
reduction actually could produce a slight increase in firing rate (as
supported by data from Schwindt and Crill 1997
) (not
shown). Thus a gHVA reduction might
differentially affect target pattern and nontarget pattern neurons in
the network.
A reduction in gKS had almost no
effect on stability in the present model in the range over which it was
varied. It enhanced subthreshold EPSPs and initial responses but
increased firing at frequencies >15 Hz only slightly. To determine
whether this was due to the particular
gKS kinetics or to its comparatively low contribution to the total membrane conductance in the cells modeled
here, simulations were run with
gKS,max increased 2.5-fold (compared
with the baseline condition). In these simulations, stability decreased
remarkably (mean Fcrit 38 Hz),
indicating that in neurons with an initially quite strong
gKS, its reduction indeed could have a
profound stabilizing effect.
In conclusion, DA-induced alterations in the persistent Na+, and the NMDA and AMPA synaptic currents monotonically increased stability over the range of parameters tested here. A reduction in IHVA might have a stabilizing effect by mainly reducing EPSP sizes in background neurons while reducing Ca2+-dependent K+ currents in foreground neurons. DA-induced alterations in GABAA conductances could prevent spontaneous switches into high-activity states that might otherwise interfere with proper working-memory function.
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The present paper investigated the possible functional
implications of the dopaminergic modulation in the PFC in a network of
compartmental model neurons. In the model network, DA strongly enhanced
task-related high-activity states while suppressing background activity. Through these differential effects, the robustness of delay
period activity, which presumably reflects goal-directed processing,
with respect to afferent stimuli interfering with the present network
state was highly increased. Several ionic conductances might contribute
to these differential effects of DA. Most importantly, the DA-induced
enhancements of INaP and NMDA currents
had a much larger impact for highly active neurons embedded in a
recurrent network than for neurons in a low state of activity that did
not participate in the active pattern. A DA-induced reduction in N-type
Ca2+ currents also might enhance stability
because it mainly leads to diminished amplification of EPSPs during low
activity (Seamans et al. 1997), whereas it mainly causes
a reduction in Ca2+-activated
K+ currents at higher firing frequencies
(Schwindt and Crill 1997
). Finally, a reduction of AMPA
conductances might contribute to stability by diminishing mainly the
impact of afferent inputs on nontarget pattern neurons.
In the context of the activity-enhancing effects of DA, the function of the DA-induced enhancement of GABAA currents might be to readjust the overall level of network activity so that the network does not automatically get pushed into high-activity states by spontaneous activity. Such spontaneous "pop-outs" would be detrimental in a working-memory context where specific patterns have to be activated and held active in a task-related manner.
Role of DA in working memory and goal-directed behavior
Stability of neural activity encoding goal-related information
including goal-directed movements is an essential prerequisite of any
goal-directed behavior that extends over time. The PFC generally is
believed to underlie goal- and memory-guided behavior as well as the
temporal organization and planning of behavior (Cohen et al.
1997; Dehaene et al. 1999
; Fuster
1989
; Goldman-Rakic 1995
; Kesner et al.
1994
; Koechlin et al. 1999
; Milner and
Petrides 1984
). If the contents of working memory are wiped out
by interfering stimuli or response tendencies, goal-directed behavior
will fail (Funahashi et al. 1989
; Fuster 1973
,
1989
). Thus PFC circuits should be equipped with neural
mechanisms that ensure the stability of delay activity in the presence
of interfering stimuli. Miller et al. (1996)
have shown
that prefrontal neurons indeed can maintain delay-activity even if
stimuli intervene between a cue and a matching target stimulus. On the
other hand, prefrontal patients and animals, which presumably lack such
a mechanism, suffer from an inability to plan and temporally organize
their behavior, from a high distractibility, and from failures to
inhibit interfering or well-learned response tendencies (Dias et
al. 1997
; Fuster 1989
; Iversen and
Mishkin 1970
; Kesner et al. 1994
; Milner
and Petrides 1984
; Seamans et al. 1998
;
Zahrt et al. 1997
).
It also has been shown that dopaminergic midbrain neurons become active
at the onset of working-memory tasks (Schultz et al. 1993), that DA levels in the PFC increase during working memory (Watanabe et al. 1997
), and that optimal stimulation of
dopaminergic receptors is essential for proper working-memory
performance in rats (Murphy et al. 1996a
,b
;
Seamans et al. 1998
; Zahrt et al. 1997
),
birds (Durstewitz et al. 1999b
;
Güntürkün and Durstewitz 2000
),
monkeys (Arnsten et al. 1994
; Murphy et al.
1996a
,b
; Sawaguchi and Goldman-Rakic 1994
), and
humans (Luciana et al. 1998
; Müller et al.
1998
). In addition, it has been shown in vivo that DA in the
PFC specifically increases the firing rate of neurons active during the
delay, cue, and response periods of working-memory tasks and that this
enhancement is primarily due to D1-receptor stimulation
(Sawaguchi et al. 1988
, 1990a
,b
). On the basis of this
evidence, it has been proposed that DA in the context of working-memory
performance might stabilize neural representations coding for
goal-related information, and it has been demonstrated in a network of
simple leaky-integrator units that the DA-induced shifts in the
parameters of several currents could in principle fulfill this function
(Durstewitz et al. 1999a
).
The network simulations presented here confirm and extend the findings
from the simpler model in several important ways. First, the simpler
model investigated previously had the advantage that many effects of DA
could be analyzed and understood relatively easily, but it lacked
biological detail in many respects. Therefore, to confirm that the
effects of DA were not an artifact of the simple model architecture,
the present network model used highly realistic PFC compartmental model
neurons with Hodgkin-Huxley-like membrane kinetics that could reproduce
in vitro whole cell recordings, and it was tuned to exhibit in
vivo-like behavior. The present results show that the DA-induced
stability of neural representations also obtains in this biologically
more realistic model and that the underlying mechanisms observed in the
simple model also occurred in the realistic model. In both models, a
DA-induced enhancement of INaP led to
higher activity of the foreground neurons that resulted in stronger
inhibition of the background neurons through feedback inhibition.
Moreover, a reduction in AMPA currents in both models resulted in
higher stability primarily via reduction of the afferent input strength
(Durstewitz et al. 1999a).
Second, the previous model did not provide a satisfying explanation for
the role of the DA-induced increase in GABAergic currents. The present
simulations suggest that the increased GABAergic inhibition might be
essential in rescaling the overall level of activity and in keeping the
high-activity states of the network under the control of the inputs.
Third, the previous model did not differentiate between AMPA and NMDA
conductances but collapsed them into a single, short-lasting,
voltage-independent excitatory efficiency. Mounting evidence, including
recent experiments in our laboratory, suggests that AMPA and NMDA
currents are differentially affected by DA in the PFC (Cepeda et
al. 1992; Kita et al. 1999
; Moore et al. 1998
; Seamans et al. 1999
; Zheng et al.
1999
), as they are in the striatum (Cepeda and Levine
1998
; Cepeda et al. 1993
; Levine et al.
1996
). Hence an important finding of the present study is that
both a reduction of the fast, voltage-independent AMPA conductances
(consistent with our previous analysis) and an enhancement in NMDA
conductances result in increased stability.
Finally, both IKS and
IHVA reduction might have a
stabilizing effect under certain conditions:
IHVA via its double role in amplifying
EPSPs (Seamans et al. 1997) and activating
Ca2+-dependent K+ currents
(Schwindt and Crill 1997
) and
IKS in neurons where this current
makes a sufficiently high contribution to the total membrane conductance. A reduction in IHVA also
might be especially effective for stronger attractors firing at higher
frequencies (because of the high Ca2+ influx, see
RESULTS), whereas a reduction in
IKS might have its strongest impact on
weak attractors firing at comparatively low frequencies (because
IKS increasingly inactivates at higher
frequencies). A reduction in N-type Ca2+
conductances furthermore might enhance stability by diminishing especially synaptic inputs to distal (upper layer) dendritic sites where mainly fibers from other association and higher sensory areas
terminate (Durstewitz et al. 1999a
; Yang and
Seamans 1996
).
In conclusion, the present study provides additional support for the
notion that DA might change the integrative properties of single
neurons and prefrontal networks in a way that allows them to maintain
goal-related delay activity for longer periods of time and to protect
working-memory representations from being wiped out by minor
interfering events. Hence when an animal actively pursues a
behaviorally important goal, high prefrontal DA levels might ensure
that the behavior of the animal remains directed toward that goal. On
the other hand, under conditions where a specific goal has not yet been
selected or where a goal has been reached and hence goal-directed
behavior should be terminated, high DA levels in the PFC might be
counterproductive and might cause perseveration and behavioral
stereotypies (Le Moal and Simon 1991; Roberts et
al. 1994
; Waddington and Daly 1993
; Zahrt
et al. 1997
). Finally, it should be noted that DA causes a
relative increase in stability but does not ensure absolute robustness (see Fig. 4). In general, the robustness of a goal-related
representation in the PFC might reflect the importance or behavioral
relevance of the current goal state.
Behavioral performance and optimal DA levels
Behavioral evidence suggests that there might be an optimal level
of DA or of DA receptor stimulation in the PFC (Arnsten et al.
1994; Murphy et al. 1996a
,b
; Zahrt et al.
1997
; but see Luciana et al. 1998
;
Müller et al. 1998
). The present study suggests that working-memory deficits resulting from DA receptor blockade (Sawaguchi and Goldman-Rakic 1994
; Seamans et al.
1998
) or diminished DA input into the PFC (Brozoski et
al. 1979
; Simon et al. 1980
) might be a direct
consequence of the largely reduced stability of prefrontal
representations and processes under these conditions.
On the other hand, working-memory deficits resulting from supranormal
stimulation of dopaminergic receptors in the PFC (Murphy et al.
1996a,b
; Zahrt et al. 1997
) might arise on many
different levels. For example, they might result from interactions of
the PFC with other areas, e.g., because very high, sustained activity levels in the PFC trigger premature motor responses (Durstewitz et al. 1999a
). Another possibility is that at supranormal DA
levels, prefrontal representations become so stable that they persist even across trials, thus causing response perseveration and behavioral stereotypies as observed after, e.g., apomorphine injections
(Durstewitz et al. 1999b
; Le Moal and Simon
1991
; Waddington and Daly 1993
). Perseveration
errors indeed seem to account for most of the diminished working-memory
performance after supranormal DA receptor stimulation (Zahrt et
al. 1997
).
On the single-neuron level, an optimal DA level could result from
different DA-dependent parameters exhibiting different
dose-response-curves. For example, the DA-induced increase in GABAergic
currents might saturate at higher levels than other DA-induced changes,
resulting in a relative increase of the contribution of inhibition at
very high levels of DA receptor stimulation that causes stability to decline again at these levels. A decrease in stability with an increase
in GABAA conductances not accompanied by likewise
changes in other DA-modulated parameters indeed occurred in our network simulations (see Fig. 5A, 150% shift in
gGABAA). This interpretation also is
supported tentatively by the finding of Williams and
Goldman-Rakic (1995) that supranormal DA receptor stimulation,
at least in the vicinity of the soma where mainly GABAergic inputs
converge, might diminish delay activity.
Experimental predictions of the model
One way to test the predictions of the model in vivo is to place
monkeys into a working-memory task with intervening stimuli as employed
by Miller et al. (1996) and to inject locally into the
PFC different concentrations of DA receptor antagonists while recording
from PFC neurons. This should render delay-activity in the PFC more
vulnerable to interference by intervening stimuli so that
delay-activity should collapse after one of the intervening stimuli. In
vivo experiments that test predictions of the model with regard to the
separate contributions of DA-modulated synaptic conductances are also
conceivable. A partial blockade of NMDA conductances should lead to a
reduction in or a premature cessation of delay activity and should
increase the vulnerability of delay activity with respect to
interfering stimuli. Whether, in contrast, a partial blockade of AMPA
receptors enhances robustness, might be more difficult to test because,
depending on the relation between prefrontal DA level and AMPA channel
modulation, at optimal DA levels a further reduction in AMPA currents
might not have a large enough effect on stability. Finally, a partial
blockade of GABAA conductances also should
diminish working-memory performance, although it actually might enhance
delay activity, because spontaneous activity in the PFC would be highly
increased (causing the activation of irrelevant representations) and
representations might persist between trials.
Another prediction of the present study is that DA in the PFC
differentially affects task-related high-activity states in a
working-memory context versus low-activity states occurring spontaneously outside any task context. This differential modulation was an intrinsic property of the voltage dependencies and kinetic properties of the conductances affected by DA and thus was related to
the stability-enhancing effect of DA. There is some indirect evidence
for this hypothesis: Sawaguchi et al. (1988, 1990a
,b
) found that DA strongly increases task-related activity in PFC neurons
during working memory, whereas, in contrast, other researchers (Ferron et al. 1984
; Godbout et al. 1991
;
Mantz et al. 1988
; Pirot et al. 1992
,
1996
) found that stimulation of the ventral tegmental area or
local DA application in the PFC caused a transient suppression of
activity in anesthetized animals outside a behavioral context. Thus the
observations in the latter studies might correspond to the suppression
of spontaneous activity that occurred in the network model in the high
DA condition. However, the animals in these studies were anesthetized
with ketamine, which blocks NMDA conductances and thus one of the major
excitatory effects of DA. Therefore more direct empirical tests
are needed to demonstrate that DA within the same animals
suppresses spontaneous but enhances task-related activity or at least
enhances the latter more than the former.
Our results are also consistent with the finding that the subjective
reward magnitude of an expected stimulus or situation, i.e., the
behavioral importance of a current goal state, is correlated with the
firing rate of prefrontal neurons, including those neurons that exhibit
delay or anticipatory activity (Tremblay and Schultz 1999; Watanabe 1996
). Because the behavioral
importance (expected reward magnitude) of a stimulus also is reflected
in the firing of dopaminergic midbrain neurons (Ljungberg et al.
1992
; Schultz and Romo 1990
; Schultz et
al. 1993
), these findings support the idea that DA enhances
delay activity and increases the robustness of representations encoding
goal-related information. In this sense, DA in the PFC might be
interpreted as a signal that instructs the PFC to hold active and
protect currently incoming information and preparatory processes that
might be important in obtaining future rewards. The overall
stabilization might depend on the importance of the goal and the
expected magnitude of the reward.
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ACKNOWLEDGMENTS |
---|
We especially thank M. Eisele and D. Needleman for a thorough reading of this paper and for many helpful remarks.
D. Durstewitz was supported by a grant from the Deutscher Akademischer Austauschdienst (DAAD) within the Hochschulsonderprogramm III. J. K. Seamans was supported by a fellowship of the Natural Sciences and Engineering Research Council of Canada.
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FOOTNOTES |
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Address for reprint requests: D. Durstewitz, Salk Institute for Biological Studies, Computational Neurobiology Laboratory, 10010 N. Torrey Pines Rd., La Jolla, CA 92037.
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 13 September 1999; accepted in final form 21 November 1999.
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REFERENCES |
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