Previous Article | Next Article 
The Journal of Neuroscience, February 1, 2002, 22(3):1081-1097
Simulations of the Role of the Muscarinic-Activated
Calcium-Sensitive Nonspecific Cation Current
INCM in Entorhinal Neuronal Activity during
Delayed Matching Tasks
Erik
Fransén1,
Angel A.
Alonso2, and
Michael E.
Hasselmo3
1 Department of Numerical Analysis and Computer
Science, Royal Institute of Technology, S-100 44 Stockholm, Sweden,
2 Department of Neurology and Neurosurgery, Montreal
Neurological Institute and McGill University, Montreal, QC H3A Canada,
and 3 Department of Psychology, Program in Neuroscience,
and Center for Biodynamics, Boston University, Boston, Massachusetts
02215
 |
ABSTRACT |
Entorhinal lesions impair performance in delayed matching tasks,
and blockade of muscarinic cholinergic receptors also impairs performance in these tasks. Physiological data demonstrate that muscarinic cholinergic receptor stimulation activates intrinsic cellular currents in entorhinal neurons that could underlie the role of
entorhinal cortex in performance of these tasks. Here we use a network
biophysical simulation of the entorhinal cortex to demonstrate the
potential role of this cellular mechanism in the behavioral tasks.
Simulations demonstrate how the muscarinic-activated calcium-sensitive
nonspecific cation current INCM could provide a cellular mechanism for features of the neuronal activity observed during performance of delayed matching tasks. In particular, INCM could underlie (1) the maintenance of
sustained spiking activity during the delay period, (2) the enhancement of spiking activity during the matching period relative to the sample
period, and (3) the resistance of sustained activity to distractors.
Simulation of a larger entorhinal network with connectivity chosen
randomly within constraints on number, distribution, and weight
demonstrates appearance of other phenomena observed in unit recordings
from awake animals, including match suppression, non-match enhancement,
and non-match suppression.
Key words:
delayed match to sample; delayed non-match; stellate
cells; pyramidal cells; medial entorhinal cortex; afterhyperpolarization; working memory; biophysical modeling; computer
simulation; nonspecific cationic current
INCM
 |
INTRODUCTION |
Lesions of the entorhinal and
perirhinal cortices impair performance in delayed non-match to sample
(DNMS) tasks in both non-human primates (Zola-Morgan et al.,
1993
; Leonard et al., 1995
) and rats
(Otto and Eichenbaum, 1992
). In delayed non-match to
sample tasks, stimuli are presented sequentially, and animals must
respond to a particular stimulus if that stimulus does not match the
previously presented stimulus. The role of the entorhinal cortex in
these tasks may involve activation of muscarinic cholinergic receptors, because performance in delayed matching tasks is impaired by systemic injections of muscarinic cholinergic antagonists (Bartus and
Johnson, 1976
; Penetar and McDonough, 1983
).
Encoding of stimuli in a recognition memory task is impaired by local
infusions of scopolamine into the perirhinal (and entorhinal) regions
but not by infusions into the dentate gyrus or inferotemporal cortex
(Tang et al., 1997
), and microdialysis shows a 41%
increase in acetylcholine levels in the perirhinal cortex during
performance of this task (Tang and Aigner, 1996
). Data
from slice preparations of entorhinal cortex demonstrate a possible
cellular mechanism for cholinergic modulation of entorhinal memory
function. In physiological recordings from layer II principal cells in
slice preparations (Klink and Alonso,
1997a
,b
),
application of the cholinergic agonist carbachol causes long-term
depolarizations, which have been termed plateau potentials. If neurons
generate an action potential during cholinergic modulation, because of
cholinergic depolarization or current injection, these neurons show
sustained spiking activity. Modeling presented here demonstrates how
plateau potentials and sustained spiking activity could arise from
muscarinic cholinergic activation of a calcium-sensitive nonspecific
cation current INCM for which in
vitro physiological data have been obtained (Klink and Alonso, 1997a
,b
; Magistretti et al., 2001
) (J. Magistretti, L. Ma, M. H. Shalinsky, and A. A. Alonso, unpublished
observations). A current causing afterdepolarization also
appears in prefrontal cortical neurons, but shows voltage dependence
(Andrade, 1991
; Haj-Dahmane and Andrade,
1996
, 1998
).
Simplified models of short-term memory capacity have been inspired by
these data (Lisman and Idiart, 1995
; Jensen and
Lisman, 1996
,
1998
).
The INCM could provide a potential
mechanism for the selective maintenance of spiking activity in a subset
of entorhinal cortical neurons for working memory function in
behavioral tasks. Unit recording in awake behaving animals demonstrates
specific patterns of spiking activity occurring during performance of
delayed matching tasks (Suzuki et al., 1997
;
Young et al., 1997
). These include stimulus-selective
spiking activity during the delay period, enhancement of the spiking
response to stimuli that match the previously presented sample
stimulus, and resistance of sustained activity to distractors.
Simulations presented here demonstrate how these phenomena could
directly arise from INCM in individual
entorhinal neurons. In addition, a number of studies have shown match
suppression, in which spiking activity is weaker in response to a
stimulus that matches the previously presented sample (Suzuki et
al., 1997
; Young et al., 1997
), as well as
non-match enhancement and non-match suppression. Simulations of a large network with connectivity chosen randomly within constraints on number,
distribution, and weight demonstrate how these additional phenomena can
arise from network interactions of neurons with the intrinsic
properties provided by the INCM current. This work has been published previously in abstract form
(Fransén et al.,
1999b
,
2000
).
 |
MATERIALS AND METHODS |
The GENESIS simulation package (Bower and Beeman,
1995
) was used to model intrinsic properties of neurons in
layer II of the entorhinal cortex. Separate compartmental biophysical
simulations were developed for layer II stellate neurons, layer II
pyramidal neurons, and layer II interneurons. The models used
Hodgkin-Huxley representations of a range of intrinsic currents that
underlie membrane potential changes in these neurons. The simulations
presented here focus on how acetylcholine causes sustained
depolarization and spiking activity in the pyramidal cells, and how
these phenomena might underlie sustained activity and match enhancement
during performance of delayed non-match to sample tasks (Young
et al., 1997
). Additional network simulations demonstrate that
inclusion of inhibitory interneurons and inclusion of both
stimulus-specific and nonspecific cells provides potential mechanisms
for match suppression and non-match enhancement and suppression
(Suzuki et al., 1997
; Young et al.,
1997
).
 |
Cell and network models |
In these simulations, the entorhinal cortex layer II stellate
and pyramidal cells (Klink and Alonso, 1997c
) and
interneurons (Jones and Buhl, 1993
) have been reduced to
equivalent cylinder models.
Entorhinal layer II pyramidal cells
The properties of entorhinal pyramidal cells were simulated with
biophysical models containing multiple compartments, with an emphasis
on the calcium-sensitive nonspecific cation current INCM. The compartmental structure of these
simulations is shown in Figure 1. The
pyramidal cell is composed of six compartments, one representing the
soma, three representing the apical dendrite, one representing a basal
dendrite, and one representing all but one basal dendrite lumped
together, to constitute the main "load" to the soma. The proximal
of the apical compartments, the basal dendrite, and the lump
compartment are all connected to the soma. The lengths and cross
sections of the three apical dendrite compartments were adjusted to
give the dendrite a length constant of 2 (sealed-end condition). The
compartment profiles are found in Table
1. Simulations with just a soma
compartment and its conductances showed that dendritic compartments
were not necessary to obtain robust spiking activity during delay
periods in the cell. However, these dendritic compartments were
important for matching a range of features in the data, including spike
shape, afterhyperpolarization shape, and spike-frequency accommodation,
as well as providing a more realistic attenuation of excitatory
synaptic input.

View larger version (31K):
[in this window]
[in a new window]
|
Figure 1.
Schematic representation of the compartmental
models used in the simulation of the intrinsic properties of entorhinal
neurons, including pyramidal cells (top), stellate cells
(middle), and interneurons (bottom). The
dimensions of each component of the model are summarized in the
figure.
|
|
Passive properties. Simulations of the passive membrane
properties of the cell used the standard equivalent circuit
representation for each compartment (Bower and Beeman,
1995
).
The passive parameters are as follows:
RM, 5.0
m2;
RA, 1.0
m;
CM, 0.01 F/m2. The
value of the membrane reversal potential Em
depends on contributions from leakage Na+ and
leakage Cl
currents. (The K+
leak current is explicitly represented as a separate current.) One may
also view synaptic background activation with slow kinetics (e.g., NMDA
and GABAB) as part of the leakage current. Note that because the K+ current is represented separately,
its conductance should be added to the value of
RM given above when making comparisons with other data.
Active properties of pyramidal cells. The simulations
included multiple currents underlying the active properties of the
membrane, including both currents sensitive to changes in membrane
potential and currents sensitive to intracellular calcium
concentration. Equations describing the currents can be found in the
section on ionic currents; the respective conductances are found in
Table 4. The pyramidal cell model includes the following membrane
currents listed in the section on ionic currents (with the appropriate subsection listed in parentheses): the Na+ (Na) and
K+ (Kdr) currents
responsible for fast action potentials, a high-threshold Ca2+ current (CaL), a
calcium-dependent K+ current
(KAHP), a fast calcium- and
voltage-dependent K+ current
(KC), a potassium leak current
(Kleak), a persistent-type Na current
(NaP), a noninactivating muscarinic K+ current
(KM), and the muscarinic activated,
nonspecific Ca2+-sensitive cationic current
INCM (NCM). The compartment where spikes
are initiated (the soma in this case) has Na and K currents with faster
kinetics [Na(soma) and K(soma)], based on previous work (Traub
et al., 1994
). In the experimental preparation, there are
indications of a T-type Ca current (Bruehl and Wadman, 1999
), but because these simulations result in relatively
depolarized membrane potentials, this current was not included. To
evaluate the ionic mechanisms of delay activity, additional simulations were performed with individual currents deleted.
Stellate cells
The properties of stellate cells were simulated with biophysical
simulations containing multiple compartments. These simulations were
developed for previous work on the mechanisms of subthreshold membrane
potential oscillations in stellate cells (Fransén et al.,
1998
; Dickson et al., 2000
) (E. Fransén, Alonso, and M. E. Hasselmo, unpublished
observations). The compartmental structure of these simulations
is shown in Figure 1. The stellate cell is composed of seven
compartments. One compartment represents the soma; one compartment
represents the initial segment; three compartments connected in
succession represent the primary, secondary, and tertiary segments of a
single principal dendrite; and two connected compartments represent all
remaining dendrites lumped together. The addition of the separate
initial segment compartment differs from the pyramidal cell. The
lengths and cross sections of the three principal dendrite compartments
were adjusted to give the dendrite a length constant of 2 (sealed-end
condition). The compartment profiles are found in Table
2.
Passive properties. Simulations of the passive membrane
properties of the cell used the same properties described above for pyramidal cells.
Active properties of stellate cells. Simulations of stellate
cells included most of the same active currents included in the simulations of pyramidal cells, with adjustments in parameters to
account for some of the differences in the intrinsic properties of
these neurons. The KAHP current is
stronger in pyramidal cells than in stellate cells, and the
KM current is not present in stellate cells. As
a consequence of the difference in conductance amplitudes,
spike-frequency adaptation is stronger in pyramidal cells than in
stellate cells. The stellate cells include a
hyperpolarization-activated nonspecific cation current
Ih that is not included in the pyramidal cell
models. Equations describing the currents can be found in the section
on ionic currents; the respective conductances are found in Table 5.
The stellate cell model includes the following currents described in
the section on ionic currents (with the appropriate subsection in
parentheses): the Na+ and K+
currents responsible for fast action potentials (described in sections
Na and Kdr), a high-threshold
Ca2+ current (CaL), a
calcium-dependent K+ current
(KAHP), a fast calcium- and
voltage-dependent K+ current
(KC), a potassium leak current
(Kleak), a persistent-type Na current
(NaP), and a hyperpolarization-activated nonspecific cation current
Ih (H).
Interneurons
The interneuron is modeled to replicate the basic
properties of a fast-spiking nonadapting type cell. The interneuron is
composed of six compartments, with one representing the soma, three
representing a principal dendrite, and two representing all but one of
the dendrites lumped together. The interneuron model does not have the
separate initial segment compartment. The compartment profiles are
found in Table 3.
Passive properties. Simulations of the passive membrane
properties of the cell used the same properties described above for stellate cells.
Active properties. The interneuron model has the
Na+ and K+ currents responsible
for fast action potentials (Na and Kdr),
a high-threshold Ca2+ current
(CaL), a calcium-dependent
K+ current (KAHP), and
a potassium leak current (Kleak). The
KAHP was set at very weak values, consistent
with the absence of spike-frequency accommodation in these neurons. The
compartment where spikes are initiated (the soma in this case) has Na
and K currents with faster kinetics [Na(soma) and K(soma)], as in previous models (Traub et al., 1994
). Ionic conductances
are found in Table 6.
Modeling of synaptic interactions
Synaptic currents in a postsynaptic neuron were activated by the
membrane potential in the associated presynaptic neuron crossing a
threshold value of
0.025 V. After a conductance delay of 2 msec, a
synaptic current was initiated in the postsynaptic cell with a dual
exponential time course (Bower and Beeman, 1995
).
Synaptic contacts on the cells were either of a mixed AMPA/kainate and
NMDA type or of a mixed GABAA and GABAB type.
Equations describing the currents can be found in the section on
synaptic currents; the respective parameters are found in Table 7.
The simulations included a conductance-based noise source. This
represents potential effects of channel noise (White et al., 1998
) or synaptic noise on actual neural function. The noise
was generated from a Poisson process and was placed on the proximal lumped dendritic compartment.
Network models
The interaction of different cell types was analyzed in network
simulations of different sizes. Many of the interactions of different
cell types could be captured in small circuit simulations including six
simulated neurons (see Fig. 6). These simulations included two neurons
representing input to entorhinal cortex layer II, two simulated layer
II pyramidal cells, one simulated layer II stellate cell, and one
simulated layer II interneuron. One of the simulated pyramidal cells
received input from only one input neuron, representing the
odor-specific responses observed during unit recording (Young et
al., 1997
). The other simulated pyramidal cell received input
from both input neurons, representing the odor nonspecific cells
observed during recordings (Young et al., 1997
). The
input neurons did not contact the inhibitory interneuron, but the
pyramidal cells, stellate cell, and interneuron were all interconnected
with synaptic properties summarized in Table 7.
Larger-scale network simulations explored the interaction of larger
numbers of neurons during several stages of a delayed matching task.
They show how a single network of cells, with connectivity chosen
randomly within constraints on number, distribution within a local
neighborhood, and weight, can display many of the unit response types
reported in experimental data (Suzuki et al., 1997
; Young et al., 1997
). See the section on network topology.
Computational methods
Biophysical simulations were developed using the GENESIS
simulation package (Bower and Beeman, 1995
). The
Crank-Nicholson (Hines, 1984
) method for numerical
solution to differential equations was used (with modifications). A
time step of 150 µsec was used for the simulations.
 |
Ionic currents |
Voltage-dependent conductances were modeled using a
Hodgkin-Huxley type of kinetic model. The following reversal
potentials were used: Na+, +55 mV;
K+,
75 mV; Ca2+, +80 mV;
Ih,
20 mV; and
INCM, 0 mV.
Na, Kdr. The Na+
current responsible for fast action potentials had kinetics taken from
a model of hippocampal pyramidal cells (Traub et al.,
1991
, 1994
). The
pyramidal cell and the interneuron have Na and K currents with faster
kinetics [described in a separate section below and labeled
Na(soma) and Kdr(soma)] on the
compartment where spikes are initiated (Traub et al.,
1994
). Both the Na as well as the K current were shifted +5 mV
from the model by Traub et al.
(1991
, 1994
) to
make the spiking threshold more positive (around
50 mV). The
spatial distribution and maximal
conductance of all currents on the different
compartments are found in Tables 4-6. The maximal conductances were
adjusted to match experimental data (Alonso and Klink,
1993
) on the action potential rate of depolarization
(Na+) and rate of repolarization
(K+) as well as spike threshold, amplitude, and
duration of action potentials. The currents used the following
equations:
Na.
gate exponent = 2;
gate exponent = 1.
Na(soma).
gate exponent = 3;
gate exponent = 1.
Kdr.
gate exponent = 2.
Kdr(soma).
gate exponent = 4.
CaL. The high-threshold Ca2+ current was
modeled according to previous models (Traub et al.,
1994
). The maximal conductance was set to the same value as in
previous work (Traub et al., 1994
).
gate exponent = 2.
KAHP. The calcium-dependent K+
(afterhyperpolarization) current was modeled according to previous
models (Traub et al., 1991
), with the slope set at 30 and the saturation set at 30 (arbitrary units). The maximal conductance
was adjusted to match the slow afterhyperpolarization (sAHP) depth in
experimental data (Alonso and Klink, 1993
).
gate exponent = 1.
KC. The fast calcium- and voltage-dependent
K+ current was modeled according to previous work
(Traub et al., 1991
). The maximal conductance was
adjusted to match the fast afterhyperpolarization (fAHP) depth and
Ca-dependent spike repolarization rate (Alonso and Klink,
1993
).
gate exponent = 1.
KM. The slowly activated voltage-dependent
K+ current was modeled according to Bhalla
and Bower (1993)
. The maximal conductance was adjusted to match
the length of the suprathreshold plateau after a spike in the presence
of a Ca block (Alonso and Klink, 1993
).
gate exponent = 1.
H. The hyperpolarization-activated nonspecific cation current
Ih was modeled according to previous work
(Dickson et al., 2000
) (Fransén Alonso, and
Hasselmo, unpublished observations). The maximal conductance was
adjusted to comply with voltage-clamp data as well as current-clamp
data on the "sag" produced by Ih (Dickson et al., 2000
).
gate exponent = 1;
gate exponent = 1.
NaP. The persistent-type slowly inactivating Na+
current was modeled according to experimental data (Magistretti
et al., 1999
) for the steady-state activation and inactivation
and kinetics of inactivation and for the reversal potential, and
according to McCormick and Huguenard (1992)
for the
kinetics of activation and the exponents of the activation rates
m and h. The maximal conductance was adjusted to
the conductance of Ih to allow subthreshold oscillations to develop.
gate exponent = 1;
gate exponent = 1.
Kleak. The Kleak conductance
was considered to be linear and uniformly distributed with a reversal
potential of: Erev =
0.075 V.
NCM. The nonspecific Ca2+-dependent cationic current
was modeled using a framework similar to the calcium-dependent
K+ current found in previous work (Traub et
al., 1991
). Time constants for the
INCM were derived by replicating
experimental data (Klink and Alonso,
1997a
,b
). Note
that these experimental data were fitted by modifying the total
kinetics of both the calcium diffusion and the
INCM current to replicate experimental traces.
Here we focus on the Ca-sensitive component of
INCM. Recent evidence suggests that
INCM also has a calcium-insensitive component, but this is not explicitly modeled in our simulations. The
resting potential of our simulations, before any input has been
presented, corresponds to a resting state with cholinergic modulation
that would include conductance contributions attributable to the
calcium-insensitive component. The maximal conductance was adjusted to
produce spiking frequencies similar to those observed during delay
activity and during match enhancement in recordings of single units in
awake rats performing a delayed non-match to sample task (Young
et al., 1997
).
gate exponent = 1.
 |
Ca2+ buffering |
The Ca2+ diffusion and buffering was modeled
according to previous techniques (Traub et al., 1991
;
McCormick and Huguenard, 1992
). To take into account the
differences in distances and diffusion constants for the calcium
influencing each of the different currents, the calcium kinetics was
modeled separately for each case, consistent with separate calcium
compartments and reaction pathways within the cell. In addition, until
calcium-clamp data exist on the individual currents, it is not possible
to separate the kinetics of the calcium concentration from the kinetics
of the channel itself. Therefore, the models of the concentration and
of the individual currents should be seen as a unit.
Following the convention used in Traub et al. (1991)
,
the calcium concentration has arbitrary units. Because the calcium
concentration is converted to an effect on rate parameters of the
calcium-sensitive channel, the absolute concentration of calcium can be
arbitrary, although we have tried to keep the magnitude in a range
similar to millimolar to enable comparisons. For the calcium related to the calcium-dependent K+ current, the diffusion rate
constant of 0.1 sec was set to give a spike-frequency adaptation rate
according to Alonso and Klink (1993)
. The minimal
[Ca2+]i was set to 5.0 × 10
3.
For the fast calcium- and voltage-dependent K+
current (KC), the calcium values were 0.5 msec and 5.0 × 10
6, respectively, and the
related values for the non-specific Ca2+-dependent
cationic current were 1.333 sec and 1.0 × 10
5, respectively. This was determined by tuning
the channel to replicate data obtained during blockade of calcium
influx by Klink and Alonso (1993)
. The changes in
the concentration of calcium used for KAHP above were too slow to effectively represent
KC, so different dynamics were necessary.
Similarly, the slower changes in INCM relative
to KAHP required use of the slower calcium
dynamics for INCM described above. In addition,
the conversion factor,
, from charge density to concentration for
each component and compartment is found in Tables 4-6. Because this
conversion factor converts channel current to calcium concentration,
valence is implicitly addressed by using current.
 |
Synaptic currents |
Synaptic conductances between neurons were modeled with an
function (Bower and Beeman, 1995
):
gsyn = (A × gmax)/(
d
r) (e
t/
d
e
t/
r), where
gmax is the peak synaptic conductance,
r is the rising time constant,
d is the decaying time constant,
gsyn is the synaptic conductance at time
t, and A is a scaling constant set to yield a
maximum conductance of gmax.
For the NMDA current, the conductance was multiplied with the magnesium
block conductance described previously (Zador et al., 1990
): gMg = 1/(1 + 0.018e
60 V).
Before determining the synaptic conductance values, the relative
proportions of the various components were fixed according to the
following experimental data: The NMDA component has the same
postsynaptic potential (PSP) height as AMPA at
72 mV (Alonso et al., 1990
). GABAA is 70% of GABAB
at
66 mV (Gloveli et al., 1999
).
The synaptic conductances were adjusted so that firing rates would
resemble those observed in recordings of entorhinal units from rats
performing a delayed non-match to sample task (Young et al.,
1997
) for the various parts of an experiment (i.e., sample, delay, and test). Note that firing rates
were matched to firing rates observed in rats, but for
phenomena of match as well as non-match
enhancement and non-match suppression,
the relative changes in firing rates were adjusted to match
those observed in monkeys
(Suzuki et al., 1997
). The values of
r and
d can be
found in Table 7. The values of gmax can
be found in Tables 8-11.
 |
Network topology |
Smaller example networks were used to illustrate the fundamental
features of connectivity that could result in specific phenomena observed in unit recordings of entorhinal neurons during delayed matching tasks. These simple example networks used the basic
connectivity summarized in Figure 6, with synaptic conductances found
in Tables 8-10. Table 8 shows the synaptic conductances for
Figure 7, Table 9 shows conductances for Figures 8 and 9A,
and Table 10 shows conductances for Figure 9B.
To demonstrate that these simple interactions could easily be obtained
with connectivity chosen randomly within constraints on number,
distribution, and weight, a larger network simulation was developed. As
shown in Figure 10, the larger network consists of one population of 12 input cells representing association cortices projecting into the
entorhinal cortex, one population of 30 stellate cells, one population
of 18 pyramidal cells, and one population of 12 interneurons. The
relative proportion of EC cells was determined according to
experimental findings (Alonso and Klink, 1993
), although these estimates may be subject to differences in the probability of
sampling different types of neurons.
The input cells were divided into three equal groups representing three
stimuli (A, B, and C). A did not overlap at all
with B or C, but B and C
had moderate overlap. The existence of a connection was determined
randomly within a window of possible connections. The connection
strengths from one of the inputs to either the stellates or the
pyramidal cells had a uniform value for a central set of connections
and decreasing values for neurons at the sides of the central
projection, to provide fully activated cells as well as weakly
activated cells. The input cells contacted both stellates and pyramidal cells.
The stellate cells, pyramidal cells, and interneurons connected to the
other cell types according to a localized scheme. Geometrically, each
population was uniformly spaced on a line of unit length. A pair of
presynaptic and postsynaptic cells from two cell populations corresponded if they occupied the same position within the line. A
presynaptic cell was first randomly selected and a postsynaptic cell
was thereafter randomly selected within a window centered around the
corresponding cell in the postsynaptic type. The resulting percentage
of connectivity between individual populations of cell types is shown
in Figure 10, and the total number of connections between different
populations is shown in Table 12. Additionally, as a control, the
number of connections, window width, and conductance values were varied
to test the sensitivity of the model.
 |
RESULTS |
Single-cell mechanisms for sustained spiking activity
The compartmental biophysical models of entorhinal layer II
pyramidal cells were used to simulate intracellular recording data from
this type of cell obtained in brain slice preparations of the
entorhinal cortex (Alonso and Klink, 1993
; Klink and Alonso, 1993
). The parameters of the intrinsic currents listed
in Materials and Methods were adjusted to simulate specific examples of
the intrinsic properties observed during these intracellular
recordings. As shown in Figure 2,
simulations were tuned to replicate the properties of spike shape and
spike-frequency accommodation observed in the experimental data in the
absence of cholinergic modulation. This figure follows the same time
course for stimulation that is shown in Figure 10A of
Alonso and Klink (1993)
.

View larger version (15K):
[in this window]
[in a new window]
|
Figure 2.
Spike-frequency accommodation in a simulated layer
II pyramidal cell during membrane depolarization in response to a 265 msec simulated current injection. This cell contains a wide range of
voltage- and calcium-dependent currents. The figure displays the
response properties of the neuron in the absence of cholinergic
modulation.
|
|
In physiological recordings from pyramidal cells in brain slice
preparations (Klink and Alonso,
1997a
,b
),
application of the cholinergic agonist carbachol causes long-term
depolarizations, which have been termed plateau potentials. These
plateau potentials appear to arise from muscarinic receptor activation
of a nonspecific cation current INCM
(Magistretti, MA, Shalinsky, and Alonso, unpublished
observations). This nonspecific cation current has a sensitivity
to intracellular calcium concentration changes caused by calcium influx
through voltage-gated calcium channels (CaL) (Magistretti et al.,
2001
). Experimental data show that the muscarinic-activated,
calcium-sensitive, nonspecific cation currents have the potential to
cause sustained repetitive spiking activity in single neurons
(Klink and Alonso, 1997b
). The combined cholinergic
muscarinic receptor activation of the INCM current and block of potassium currents
IKAHP and IKM brings the cell closer to its firing threshold. If the neuron then
discharges, the spike-associated calcium influx potentiates INCM, leading to additional
depolarization. The spiking necessary to induce sustained firing can
also be induced by synaptic input or current injection.
As shown in Figure 3, computational
modeling of this current demonstrates potential mechanisms for the
generation of sustained spiking activity in single neurons. The
self-sustained generation of spiking activity results from the
following cycle: (1) each action potential causes an influx of calcium
through voltage-sensitive calcium channels; (2) the inflow of calcium
replenishes the pool of calcium acting to enhance the activity of the
calcium-sensitive INCM current; and (3)
the sustained activation of the INCM
current depolarizes the cell sufficiently to cause generation of
another action potential, thereby repeating the cycle.

View larger version (30K):
[in this window]
[in a new window]
|
Figure 3.
Simulation of delay activity and match enhancement
in a pyramidal cell during a delayed matching task attributable to
cholinergic enhancement of the INCM
current. Traces show responses to two 600 msec current injections with
a 2400 msec delay between injection. A, Generation of
sustained spiking activity during the delay period. At the start of the
simulation, muscarinic activation of the
INCM current causes subthreshold
activation. During an initial 600 msec current injection, spike
generation causes an influx of calcium through voltage-sensitive
calcium channels. This calcium influx activates the calcium-sensitive
INCM, causing generation of an additional
spike well after the end of the current injection. This causes
additional calcium influx which causes additional
INCM activation and additional
depolarization, resulting in sustained spiking behavior. The response
to a subsequent 600 msec current injection shows considerably greater
spiking activity than the response to the first current injection.
B, Generation of match enhancement. Even without
spiking during the delay period, the activation of the
INCM by the initial spiking activity
persists long enough that a residual depolarization at the end of the
delay period results in greater spiking activity during the second
current injection. C, Without cholinergic activation of
INCM the response of the cell is similar
during the two current injection periods. D, Timing of
current injection during sample and test periods. E,
Sustained spiking during the delay period with
INCM in the range of saturation, which
causes acceleration to asymptotically approach a lower stable firing
rate. F, Interaction of the simulated pyramidal cell from
A with feedback inhibition also allows a stable firing
frequency during sustained spiking activity. In this simple example, a
single inhibitory interneuron responds to pyramidal cell spiking
activity and causes feedback inhibition that maintains spiking activity
at a moderate frequency during the delay period. (At the end of the
delay, the neuron still shows match enhancement in response to the
second current injection.)
|
|
Delay activity and match enhancement
This intrinsic cellular mechanism could underlie certain aspects
of the neuronal firing activity observed in the entorhinal cortex
during performance of a continuous DNMS (cDNMS) task with odors in rats
(Young et al., 1997
). In particular, the cellular mechanism in single neurons could account for the spiking phenomena observed during the performance of these tasks, including (1) delay
activity, and (2) match enhancement (Young et al.,
1997
). The term delay activity refers to the stimulus-selective
activity induced by a sample stimulus that can be maintained after the stimulus ends and throughout the delay period until the next stimulus is presented. The term match enhancement refers to trials in which an
individual neuron generates more spikes in response to a test stimulus
that matches the preceding sample stimulus than were generated in
response to the same test stimulus when it did not match the preceding
sample stimulus.
Cholinergic modulation of INCM could
provide a mechanism for stimulus-selective delay activity and match
enhancement. If there is an increase in acetylcholine levels during
performance of the task, then the spiking activity induced in a set of
neurons by a specific sensory stimulus (i.e., a single odor) can be
maintained because of the repetitive activation mechanism described
above, as shown in Figure 3. In these simulations, we replicated the time course of different components of the behavioral cDNMS task used
during in vivo unit recordings from the entorhinal cortex (Young et al., 1997
). The task is simulated with a 600 msec current injection representing afferent input to the entorhinal
cortex during sniffing of one odor during the stimulus period of the task, followed by a 2400 msec delay period with no current injection, and followed by a 600 msec current injection representing afferent input to the entorhinal cortex during sniffing of the same odor during
the next stimulus period (corresponding to a match condition). (Fig.
3D illustrates the timing of current injection.)
The trace in Figure 3A shows how a neuron responds after
simulated cholinergic activation of the
INCM current. A suprathreshold input
causes spiking during the stimulus period, which causes calcium influx
through voltage-sensitive calcium channels. This calcium influx causes
additional activation of the calcium-sensitive
INCM current, which depolarizes the
membrane sufficiently to cause another action potential even after the
end of the depolarizing current injection. Each subsequent spike causes
sufficient depolarization to induce another spike, allowing
self-sustained spiking activity throughout the delay period until the
next stimulus is given. When the second presentation of the same
stimulus occurs, the cell is already depolarized by INCM activation, such that it responds to
the "match" stimulus with considerably greater spiking than the
initial response to that same stimulus. A comparison of this match
period with the preceding stimulus period corresponds to the match
enhancement observed previously (Young et al., 1997
).
Thus, INCM provides an intrinsic mechanism
appropriate for both the sustained delay activity and the match
enhancement phenomenon. Between the spiking activity produced by the
current injection and the spikes produced by the
INCM depolarization, there may be an interval without spikes. This interval also appears before sustained spiking in experimental data from brain slice preparations, as seen in
Figure 5A.
The top example in Figure 3A shows match enhancement after
delay activity, but neurophysiological studies indicate that many neurons show match enhancement even without delay activity
(Young et al., 1997
). The trace in Figure 3B
shows that match enhancement can be observed in the simulation even in
the absence of sustained delay activity. This could provide another
neurobiological mechanism for performing matching that does not depend
on maintaining activity during the delay. In this example, the spiking
activity during the initial depolarization does not cause sufficient
activation of INCM to induce additional
spiking after the end of the initial afferent input. However, the
subthreshold depolarization attributable to
INCM persists during the delay period, causing enough residual depolarization that a new afferent input at the
end of the delay period causes generation of a larger number of spikes.
Thus, match enhancement can occur without delay activity. This
subthreshold depolarization decays with a time constant of 3.6 sec,
which is sufficiently slow enough to mediate match enhancement after a
3 sec interval. This suggests that match enhancement after longer
delays without spiking results from input from neurons showing
persistent activity or from longer-term synaptic changes. Figure
3B may be compared with in vitro experimental
data in Figure 5B showing stronger responses to a second
identical current injection even when there is no delay activity. The
trace in Figure 3C shows what happens without cholinergic
enhancement of INCM. In this case, the
neuron will spike during the initial stimulus period, but the absence
of INCM activation prevents the neuron
from showing sustained depolarization and spike generation during the
delay period. In addition, the absence of a depolarization during the
delay prevents the neuron from generating increased spiking activity
during the match stimulus. Thus, it fires the same number of spikes in
response to the match stimulus that it did in response to the previous
input. In this framework, cholinergic activation of the
calcium-sensitive INCM is important to the
intrinsic mechanisms for sustained activity and match enhancement. This
is consistent with data showing that muscarinic cholinergic antagonists
cause impairments of behavioral performance in delayed match to sample
tasks (Bartus and Johnson, 1976
; Penetar and
McDonough, 1983