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Neuron
Review
Rapid Neocortical Dynamics:Cellular and Network Mechanisms
Bilal Haider1 and David A. McCormick1,*1Department of Neurobiology, Kavli Institute for Neuroscience, Yale University School of Medicine, 333 Cedar Street, New Haven,CT 06510, USA*Correspondence: [email protected] 10.1016/j.neuron.2009.04.008
The highly interconnected local and large-scale networks of the neocortical sheet rapidly and dynamicallymodulate their functional connectivity according to behavioral demands. This basic operating principle ofthe neocortex is mediated by the continuously changing flow of excitatory and inhibitory synaptic barragesthat not only control participation of neurons in networks but also define the networks themselves. The rapidcontrol of neuronal responsiveness via synaptic bombardment is a fundamental property of corticaldynamics that may provide the basis of diverse behaviors, including sensory perception, motor integration,working memory, and attention.
IntroductionTypical diagrams of the neocortical sheet suggest a static,
compartmentalized structure in which signals travel in a well-
prescribed manner, flowing across and within layers in a
manner dictated predominantly by patterns of anatomical
connectivity. While these relatively hard-wired pathways
certainly establish routes for information transfer within cortical
networks, it is the dynamic, moment-to-moment fluctuations in
activity traversing these pathways that determine the functional
connectivity of cortical networks. Since an individual cortical
neuron receives input from tens of thousands of other cortical
neurons, and in turn projects its output activity patterns to
thousands of recipient neurons, the influence of one neuron
(or group of neurons) upon another depends critically upon
the activity state of all of the neurons that are interconnected
with the cells or networks under consideration. In other words,
although there is a strong anatomical bias to neuronal interac-
tions, exactly which neuronal subpopulations actively commu-
nicate at any particular moment in time (i.e., functional connec-
tivity) depends upon the state of activity in the network itself
and can change rapidly to meet behavioral demands. Although
this framework of functional cortical connectivity is by no
means new (Hebb, 1949; Lorente de No, 1938), it nonetheless
has motivated a considerable number of studies that collec-
tively have yielded insights into the subtle spatial and temporal
dynamics of local recurrent excitatory and inhibitory cortical
networks (e.g., see Douglas and Martin, 2007a; Gilbert and
Sigman, 2007; Thomson et al., 2002). Through such work, it
is becoming clear that a basic operation of the cerebral cortex
is to control the flow of neuronal communication by transiently
linking specific groups of neurons (subnetworks) together
by dynamic modulation of neuronal responsiveness. These
rapid changes in responsiveness (over milliseconds to
seconds) occur largely through alterations in ongoing synaptic
activity generated by local, as well as long-range, neuronal
interactions and may underlie changes in functional connec-
tivity that enable the flexibility of sensory-motor behaviors at
these same timescales.
The purpose of this review is to provide a brief overview of
rapid (milliseconds to seconds) network dynamics in the cortex
that may be mediated by changes in neuronal responsiveness
and interactions resultant from synaptic bombardment. Many
excellent reviews have been written on other aspects of cortical
dynamics, including the roles of synaptic plasticity, intrinsic
membrane properties, neuromodulators, and changes in
anatomical connectivity (Alvarez and Sabatini, 2007; Bean,
2007; Caporale and Dan, 2008; Kerchner and Nicoll, 2008; Lli-
nas, 1988; Luo et al., 2008; Magee and Johnston, 2005; Malenka
and Bear, 2004; McCormick, 1992; Sjostrom et al., 2008). Here,
we begin by considering the main cellular and network mecha-
nisms that may rapidly alter the responsiveness and functional
connectivity of cortical neurons and examine the predictions of
computational models regarding the impact of synaptic
barrages on neuronal input-output relations in vivo. We will
then review experimental work demonstrating that rapid alter-
ations in excitatory and inhibitory synaptic barrages generated
within specific subnetworks mediate fast changes in cortical
excitability and information flow. We will take these insights
and evaluate how such state changes via synaptic bombard-
ment affect the sensory response properties of cortical neurons
in vivo and attempt to link the described cellular and network
mechanisms to recent evidence for cortical activity states
observed in awake and behaving animals. We will conclude by
outlining some key experiments dissecting rapid changes in
functional connectivity that may be testable in the near future
with newly emerging techniques. Our overall synthesis suggests
that concerted changes in synaptic activity in local networks may
serve as the key mechanism for determining not only action
potential rate and timing in single neurons but also serve as
a context of past and present network activity, linking ensembles
of neurons together in a behaviorally relevant fashion. The
dynamic control of neuronal interactions through synaptic
barrages generated in the local network may be a unifying prin-
ciple underlying diverse phenomena such as attention,
sensory-motor integration, working memory, and the sleep-
wake cycle.
Neuron 62, April 30, 2009 ª2009 Elsevier Inc. 171
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A B Figure 1. Rapid Cortical Network DynamicsAre Mediatedby Modulation ofNeuronalGain(A) Schematic of various forms of response modu-lation. Input gain (dashed gray) shifts the thresholdand saturation points without altering the ratio ofinput to output. Multiplicative gain increases (red)or divisively decreases (blue) the responsivenessat all levels of input by a similar percentage andresults in a change in the slope of the input-outputfunction.(B) Schematic diagram illustrating five sets ofneurons or neuronal groups that are anatomicallyinterconnected (double-headed arrows), with themiddle group (1) receiving an input. If neuronalgroup2 has high gain, then groups 1, 2, and 3 exhibitenhanced functional connectivity (dashed arrows),with activity flowing easily between groups 1 and3. Likewise, if group 5 has low gain, then the flowof information and interactions between groups 1,4, and 5 will be limited.Note that the responsiveness(gain) within and among neuronal groups is deter-mined by local recurrent excitation and inhibition.
Modulating Responsiveness Dynamically ReconfiguresFunctional ConnectivityNeurons in the neocortex are interconnected directly with each
other and indirectly through other interposed neurons. Conse-
quently, a major mechanism by which neocortical subnetworks
are rapidly formed and broken is through the modulation of
neuronal excitability (Figure 1). Of course, changes in nearly
any property of the cortical network will affect neuronal respon-
siveness and network interactions. Classic examples include
changes in ionic concentrations and currents (Bean, 2007),
synaptic plasticity (Dan and Poo, 2006), and the actions of neuro-
modulators (McCormick, 1992). Here, we will focus on rapid
changes in network dynamics (milliseconds to seconds) and
the subsequent changes in synaptic barrages in recipient
neurons as a possible mediator of response modulation, since
network reconfiguration at this timescale is likely to be of utmost
importance to active waking behavior.
Let us first consider the relationship of neuronal discharge to
the amplitude of incoming synaptic barrages. The relationship
between neuronal response magnitude as a function of input
amplitude, or input-output curve, generally exhibits a sigmoidal
shape such that increasing the amplitude of weak inputs causes
a gradual increase in action potential response, while increasing
the amplitude of intermediate-sized inputs results in a steep
increase in spike response. Larger inputs typically elicit response
saturation (Figure 1A). To alter neuronal responsiveness to an
input, this whole curve may move leftward or rightward along
the input axis, altering the threshold and response saturation
points, while preserving the shape of the input-output function
(Figure 1A, dashed lines). Alternatively, the input-output curve
may shift up or down along the output axis, changing the sensi-
tivity of the neuron and the maximal response rate. Finally, the
ratio of response magnitude relative to input amplitude may
change by a constant gain factor (i.e., stretch along the output
axis), producing a multiplicative (Figure 1A, red) or divisive
(blue) change, thus varying the slope of the input-output curve.
Although all of these alterations represent changes in neural
responsiveness, the last, a multiplicative change of the input-
output curve, is typically referred to as gain modulation (Cardin
172 Neuron 62, April 30, 2009 ª2009 Elsevier Inc.
et al., 2008; Chance et al., 2002; McAdams and Maunsell,
1999; Salinas and Abbott, 1996; Treue and Martinez Trujillo,
1999).
How might cortical neurons rapidly alter the responsiveness,
or gain, of other cortical neurons? The major mechanism by
which neurons influence one another is through the generation
of action potentials that are visible in target structures as post-
synaptic potentials. Bombardment of neurons by synaptic
potentials strongly influences neuronal excitability by controlling
membrane potential, membrane conductance, and membrane
potential variance. Together, these factors ultimately control
not only spike number but also spike timing (Chance et al.,
2002; Ho and Destexhe, 2000; Kuhn et al., 2004; Rudolph and
Destexhe, 2003; Shu et al., 2003a). In other words, patterns of
synaptic bombardment control, on a moment-to-moment basis,
the probability of spike generation in the recipient neurons.
These changes in spike probability are highly dynamic in time
and provide ‘‘windows of opportunity’’ for interactions of cells
with coactivated groups of cortical and subcortical neurons
(Figure 2). These variations in neuronal interactions through
synaptic bombardment occur at multiple timescales, from hours
in the case of the sleep-wake cycles to milliseconds in the case
of higher-frequency fluctuations in membrane potential (Buzsaki
and Draguhn, 2004). The changing form of ongoing synaptic
barrages continuously determines whether or not a neuron
responds to incoming synaptic inputs, particularly those of small
to medium amplitudes (Figure 1A), as typically found between
single cortical neurons. If the modulated neuron or group of
neurons is interposed between other cells, then its level of
responsiveness will in turn determine the degree to which the
other neurons interact (Figure 1B). When the window of opportu-
nity becomes relatively short (within the integration time constant
of a single neuron, e.g., window 3 in Figure 2), then the precise
timing of spikes may play an important role in the interactions
within and between cortical networks.
Models of Rapid Response ModulationModulation of the input/output function lies at the heart of rapid
changes in cortical functional connectivity. Could rapid changes
Neuron
Review
in membrane potential, conductance, and variance during
different behavioral states underlie the well-known changes in
neuronal responsiveness occurring in cortical neurons accord-
ing to context and behavior? This question has been explored
through several computational studies (Azouz, 2005; Chance
et al., 2002; Compte et al., 2003b; El Boustani et al., 2007; Ho
and Destexhe, 2000; Kuhn et al., 2004; Murphy and Miller,
2003; Prescott and De Koninck, 2003; Rudolph and Destexhe,
2003). These studies have revealed that there are several mech-
anisms for modulation of neuronal responsiveness, the mix
of which can cause a multiplicative, or nearly multiplicative,
increase in neuronal gain. In the absence of significant levels of
membrane potential variance (also called ‘‘noise’’), tonic depo-
larization increases neuronal responsiveness by shifting input-
output curves along the input axis (Figure 1A; change input
offset) (Cardin et al., 2008; Chance et al., 2002; Compte et al.,
2003b; Ho and Destexhe, 2000; Shu et al., 2003a). A similar
effect can be obtained by decreases in membrane conductance
in the absence of membrane potential noise (Chance et al., 2002;
Ho and Destexhe, 2000; Shu et al., 2003a). These lateral shifts in
the input-output curve result in a large enhancement of the
responsiveness to small inputs and a moderate or only small
enhancement of larger inputs (dashed gray lines in Figure 1A).
If the input range is restricted to only the initial portions of the
Figure 2. Synaptic Bombardment in Active Cortical Networks In VivoIllustrated are simultaneous recordings of the extracellular local field potential(LFP, top), the multiple unit activity (MU, middle), and intracellular membranepotential (bottom) from a cortical pyramidal cell during the generation of oneUp state (bordered by two Down states). Note that the Up state is associatedwith a marked increase in local network activity, depolarization of the neuron(�25 mV), a marked increase in membrane potential variance (SD of Up =2.5 mV), and the presence of higher-frequency (gamma, �40 Hz) oscillationsin the LFP. Periods of depolarization mediated by network activity provide‘‘windows of opportunity’’ based upon increased neuronal gain and may beof long (example 1), medium (example 2), or short (example 3) duration. Thus,various frequency components of synaptic activity interact to initiate actionpotentials. Inset expands window 3, where the depolarizing half (�12.5 ms)of one full LFP oscillation cycle provides a short temporal window for theintegration of both excitatory and inhibitory postsynaptic potentials (PSPs).
input-output curve, then depolarization or decreases in conduc-
tance (in the absence of membrane potential variance) can result
in what appears to be a change in gain (e.g., Figure 1A; portions
where dashed gray overlaps with solid red or blue). It is thus crit-
ical to determine the input-output relationship over a wide range
of subthreshold as well as saturating inputs. In real neurons
in vivo, the membrane potential of neurons exhibits substantial
rapid variations, during all behavioral states, owing to fluctua-
tions in synaptic bombardment (Figure 2) (Steriade et al.,
2001). The addition of membrane potential variance causes
neurons to discharge in a probabilistic fashion across a wide
range of synaptic barrage amplitudes (Ho and Destexhe, 2000;
Shu et al., 2003a). By providing a variable level of depolarization,
membrane variance can actually enhance the probability that
subthreshold inputs become suprathreshold, thus enhancing
the responsiveness to weak inputs (Chance et al., 2002; Ho
and Destexhe, 2000; Shu et al., 2003a). Various combinations
of changes in membrane potential, membrane conductance,
and membrane potential variance can be utilized to result in
several types of shifts in neuronal responsiveness, including
changes in the slope of the input-output function (Ayaz and
Chance, 2009; Chance et al., 2002; Ho and Destexhe, 2000;
Shu et al., 2003a). Indeed, it has been suggested that increases
in neuronal gain can be achieved through a simultaneous
decrease in membrane conductance and variance, such as
could occur from the concerted withdrawal of barrages of
synaptic potentials, the composition of which is perfectly
balanced so as to not depolarize or hyperpolarize the neuronal
membrane potential (Chance et al., 2002). Such a model predicts
that increases in neuronal gain are associated with decreases in
local network activity.
A consequence of membrane potential variance is that the
average frequency of firing in response to an input can follow
a power law function [output = (input)x; Figure 3A] (Miller and
Troyer, 2002). This power law relationship between membrane
potential and firing rate is not merely a theoretical possibility; it
is often observed in cortical neurons in vivo when the cells are
synaptically driven with sensory stimulation (Anderson et al.,
2000; Priebe and Ferster, 2008; Sanchez-Vives et al., 2000)
(Figure 3B). Such a relationship between membrane potential
and firing rate has broad and important implications for how
any tonic changes in membrane potential (such as from a neuro-
modulatory input) affect the gain of these cells (e.g., Disney et al.,
2007; Thurley et al., 2008). For instance, depolarization of
cortical neurons by a few millivolts, as occurs with barrages of
synaptic potentials, will result in only a small increase in activity
at the lower end of the membrane potential-firing rate relation-
ship but a much larger increase in the firing rate at the high
end of this relationship (Figures 3A and 3B). The resulting larger
increase in firing rate with higher levels of input (Figures 3A and
3B) can result in a percent enhancement that is quite similar
(but not identical) at each point of the input-output curve
(Figure 3C). In other words, over the range of voltages in which
natural synaptic activity activates action potentials in cortical
neurons, the presence of membrane potential variance induces
a nonlinear relationship between membrane potential and firing
rate. The presence of this power law behavior can cause simple
depolarization to nonetheless appear as a multiplicative-like
Neuron 62, April 30, 2009 ª2009 Elsevier Inc. 173
Neuron
Review
A B C
D E F
Figure 3. Modulation of Neuronal Gainthrough an Interaction of MembranePotential and Intrinsic Nonlinearities inInput-Output Transformation of CorticalNeurons(A) In the absence of neuronal variance, neuronsrespond to depolarization with an abrupt increasein firing rate once threshold has been reached(black). However, in the presence of membranepotential variance, the average relationshipbetween firing rate and membrane potential canexhibit a power law (red).(B) Intracellular recordings of cortical neuronalresponses to visual stimuli in vivo have revealed,on average, a power law relationship betweenmembrane potential and firing rate.(C) In the presence of a power law relationshipbetween membrane potential and firing rate,changes in either excitatory or inhibitory back-ground conductance that depolarize or hyperpo-larize neurons (±2–5 mV), respectively, can resultin multiplicative-like changes in the input-outputrelationship of cortical neurons.(D) Depolarization induced through the intracel-lular injection of current results in a multiplica-tive-like change in the contrast response functioncurve of visual cortical neurons.(E) Scaling the hyperpolarized curve from (D) witha constant gain factor reveals a multiplicative-likegain change. (F) Depolarization that occurs spon-taneously owing to synaptic bombardment in vivocan also result in multiplicative-like increases inneuronal responsiveness of the contrast responsefunction.(B) Adapted by permission from MacmillanPublishers Ltd: Nature Neuroscience (Priebe et al.,2004), copyright 2004; (C) modified from Murphyand Miller (2003); (D) modified from Sanchez-Viveset al. (2000); (E) modified from Haider et al. (2007).
increase in gain (Murphy and Miller, 2003). These results suggest
that multiplicative-like gain modulation to sensory stimuli could
be obtained simply by depolarizing neurons, through the release
of neuromodulators (McCormick, 1992), the alteration of intrinsic
ionic conductances (Sanchez-Vives et al., 2000), or by barrages
of synaptic activity (Haider et al., 2007; Ho and Destexhe, 2000;
Shu et al., 2003a).
In summary, tonic changes in membrane potential in the pres-
ence of membrane potential variance—resulting from the high
variability of synaptic barrages—can result in rapid, nonlinear
changes in neuronal responsiveness and gain. Membrane
potential variance causes neurons to respond in a probabilistic
manner as opposed to all-or-none threshold elements, and
increasing depolarization can have large effects on the average
input-output relations of cortical neurons. As we will see, exper-
imental interrogation of synaptic barrages in vivo during active
cortical states strongly suggests that such a multiplicative-like
enhancement of responsiveness can indeed occur by transient
epochs of synaptic depolarization.
Local Network Activity Dominates the MembranePotential of Cortical Neurons In VivoCortical neurons receive the great majority of their synaptic
inputs from neurons within the local network (i.e., <1 mm distant)
(Binzegger et al., 2004). Consequently, the activity of the local
network will have a dominating, but not exclusive, influence on
174 Neuron 62, April 30, 2009 ª2009 Elsevier Inc.
the membrane potential of nearby cells. This is important since
investigations of the effects of changes in membrane potential,
membrane conductance, and membrane potential variance
demonstrate that the primary determinant of the probability of
action potential generation is the level of membrane potential
depolarization (Shu et al., 2003a). The closer a neuron is to action
potential threshold (usually around�53 mV for cortical pyramidal
neurons) the more likely it is to fire upon stimulation, all else being
equal. In the absence of any synaptic input, the membrane
potential of a cortical neuron in vivo is markedly hyperpolarized
(e.g., between �80 and �65 mV, depending on layer, cell type,
and state of the animal) and dominated by K+ conductances
(Pare et al., 1998). Under these conditions, the typical cortical
neuron is �10–25 mV below action potential threshold. Depola-
rizing a typical cortical pyramidal cell that has an input resistance
of 50 MU by 10 mV with a balanced barrage of excitatory and
inhibitory synaptic potentials would result in approximately
a 30% increase in membrane conductance (Shu et al., 2003a).
Although this increase in conductance by itself would decrease
neuronal responsiveness, the increase in excitability provided
by the depolarization is six times greater and therefore swamps
the conductance effect (Shu et al., 2003a). Of course, it should
be kept in mind that increases in membrane conductance can
themselves change the spatiotemporal integrative properties of
cortical neurons (Bernander et al., 1991; Borg-Graham et al.,
1998; Holt and Koch, 1997). In general, however, balanced
Neuron
Review
barrages of excitatory and inhibitory synaptic potentials (dis-
cussed in detail later) have their effects on action potential
discharge largely through determining the membrane potential
level of the postsynaptic neuron.
How many neurons must discharge to cause a typical cortical
pyramidal cell to fire? The average monosynaptic excitatory
postsynaptic potential (EPSP) from one cortical neuron to
another is on the order of 0.5 mV, far too small to elicit an action
potential in a quiescent neuron (Markram et al., 1997; Thomson
et al., 2002). A back-of-the-envelope calculation suggests that
on average approximately 50–100 excitatory synaptic potentials
(along with balanced inhibitory potentials) would need to be
received every 10 ms to keep the typical cortical pyramidal cell
depolarized by the 20 mV between resting membrane potential
and firing threshold. Since cortical neurons receive inputs from
approximately 3,000–10,000 other neurons (Binzegger et al.,
2004; Larkman, 1991), this indicates that even a low average
level of action potential activity (e.g., 0.1–1 Hz) in the presynaptic
network could provide this maintained depolarization, depend-
ing also upon the level of activation of inhibitory networks. This
average firing rate is well within the range of cortical neurons
active in the waking state (Beloozerova et al., 2003; Shoham
et al., 2006).
It is important to keep in mind that synaptic potentials arrive
throughout the dendrites, soma, and initial segment of the
axon and interact in a spatially and temporally complex manner,
the details of which are beyond the scope of this review, although
this important issue has received considerable attention (Larkum
et al., 2004; London and Hausser, 2005; Magee, 2000; Major
et al., 2008; Milojkovic et al., 2005; Murayama et al., 2009;
Nevian et al., 2007; Rudolph and Destexhe, 2003; Williams,
2004). Since nearly all action potentials that are responsible for
synaptic communication between cortical neurons are initiated
in the axon initial segment (Kole et al., 2008; McCormick et al.,
2007; Shu et al., 2007; Stuart et al., 1997; Yu et al., 2008), the
fluctuations as they appear at the level of the soma and axon
initial segment are of particular importance. Luckily, owing to
its large size, the soma is by far the most common place to
record the intracellular membrane potential fluctuations of
cortical neurons. For our present purposes, we propose that
the precise and rapid control of somatic membrane potential,
conductance, and variance is achieved through control of
concerted fluctuations in local network activity that can be
engaged by local and distant cortical, as well as subcortical,
neurons. Importantly, long-range connections are overwhelm-
ingly excitatory, but synapse onto both local inhibitory and excit-
atory neurons (McGuire et al., 1991), which are themselves
densely interconnected with one another. Experimental investi-
gations into the influence of synaptic bombardment on neuronal
responsiveness in local cortical networks have largely focused
on ‘‘spontaneous’’ cortical activity, of which the slow oscillation
is perhaps the best-characterized example.
The Cortical Slow Oscillation: A Model Systemto Investigate Neuronal Responsiveness and Excitatory-Inhibitory Interactions during Active Cortical StatesHow can we begin to examine the synaptic mechanisms of rapid
changes in the state and sensory responsiveness of single
cortical neurons and their extended interactions within an active
cortical network? Studies at multiple levels and in many systems
indicate that one key consequence of dense, local connectivity is
that the cortex exhibits spontaneous, persistent activity in the
absence of any external stimulation (for review, see Destexhe
et al., 2003; Major and Tank, 2004). This spontaneous, or
ongoing, activity is present during active behavior, quiet resting,
sleep, anesthesia, and even in cortical slices in vitro. However,
the spatial and temporal patterns of spontaneous activity in the
cortex differ markedly across these different conditions.
Studies in anesthetized cat visual cortex indicate that both
extended and local cortical regions exhibit dynamic activity
correlations varying over tens to hundreds of milliseconds.
Importantly, local regions that exhibit the strongest correlation
of excitability correspond to regions of cortex that exhibit similar
stimulus preferences, e.g., for the orientation of a drifting bar of
light (Arieli et al., 1996; Kenet et al., 2003). If a network is stimu-
lated appropriately and enters into an excitable state, temporally
coincident responses may be transmitted throughout this tran-
siently interconnected group of neurons, facilitating their interac-
tion (Abeles et al., 1995; Fujisawa et al., 2008; Harris, 2005;
Rutishauser and Douglas, 2008; Tiesinga et al., 2008). These
studies indicate that local cortical networks have a strong
tendency to transiently enter into stable states of enhanced
excitability that dynamically interact with sensory stimulation.
There is reason to believe, as we will discuss, that network acti-
vation in the waking state may exhibit at least some characteris-
tics similar to those described during anesthesia (Abeles et al.,
1995; Fujisawa et al., 2008; Poulet and Petersen, 2008; Steriade
et al., 2001). What are the cellular mechanisms that allow local
cortical networks to rapidly switch into and out of these semista-
ble and excitable states?
In the cortex, perhaps the most well-studiedpatternof recurrent
network activity is that of the slow (<1 Hz) oscillation, which is
characterized by a rhythmic alternation between activated and
deactivated states (Figure 2). The cortical slow oscillation has
been used to investigate a diverse array of cortical network
dynamics, ranging from sensory processing (Haider et al., 2007;
Hasenstaub et al., 2007; Lampl et al., 1999; Petersen et al.,
2003; Sachdev et al., 2004), short-term synaptic plasticity
(Crochet et al., 2005; Reig et al., 2006; Shu et al., 2006), commu-
nication between brain areas (Hahnetal., 2007), and the dynamics
of local recurrent networks (Cossart et al., 2003; Destexhe et al.,
2003; Haider et al., 2006; Luczak et al., 2007; Sanchez-Vives
and McCormick, 2000). This experimental work has also inspired
a series of computational studies (Compte et al., 2003b; Destexhe
et al., 2007; El Boustani et al., 2007; Holcman and Tsodyks, 2006;
Parga and Abbott, 2007; Rudolph et al., 2005).
Examination of the cortical slow oscillation in anesthetized and
naturally sleeping animals has yielded considerable insight into
the basic mechanisms of rapid changes in the state of cortical
networks, stable propagation of these activity states, and the
synchronization of activity across cortical regions. We propose
that insights gained from these studies provide useful and test-
able predictions about the mechanisms underlying rapid
changes in functional connectivity of cortical networks during
behavior. The cortical slow oscillation was first described
in studies of anesthetized rat auditory (Metherate and Ashe,
Neuron 62, April 30, 2009 ª2009 Elsevier Inc. 175
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1993) and cat association cortex in vivo (Steriade et al., 1993b),
where rhythmic oscillations of the local field potential (LFP),
approximately once every second or two, were seen mirrored
in the membrane potential of nearby neurons (e.g., Figure 2).
This rhythmic cycle of depolarization and hyperpolarization
survived thalamic lesions and transection of the corpus callosum
(Steriade et al., 1993a) and even occurs in slices of many cortical
regions (Cossart et al., 2003; Sanchez-Vives and McCormick,
2000; Shu et al., 2003a, 2003b), indicating that the core mecha-
nisms of its generation exist in local cortical circuits. Similar
epochs of depolarization and hyperpolarization were found in
striatal and corticostriatal neurons of anesthetized rodents,
where it results from synaptic bombardment originating in
cortical networks (Cowan and Wilson, 1994; Stern et al., 1997).
Importantly, this type of activity occurs not only in anesthesia
but also naturally during slow wave sleep (SWS) (Destexhe
et al., 1999; Steriade et al., 2001) and may even occur during
quiet waking (Petersen et al., 2003; Poulet and Petersen,
2008). The activation and withdrawal of persistent activity during
neocortical Up and Down states has been compared to pharma-
cologically induced network activity (Tahvildari et al., 2008),
spontaneous network activity in organotypic cortical cultures
(Blackwell et al., 2003; Johnson and Buonomano, 2007),
and subthreshold depolarizing events in cortical dendrites
(Major et al., 2008; Milojkovic et al., 2005). While the ‘‘Up/
Down’’ terminology may be convenient for comparison of
grossly similar cortical dynamics, the mechanisms of generation,
time course, and consequences for neural responsiveness are
dramatically different in these varying depolarizing-hyperpolariz-
ing phenomena (for review, see Major and Tank, 2004) and
different still from the pattern of activity observed in striatal
neurons (Wilson and Kawaguchi, 1996). Our perspective is that
the cortical Up state, or active state of the slow oscillation, as
originally identified, is a synaptically generated, rhythmic oscilla-
tion of cortical origin that is seen simultaneously in the somatic,
dendritic, and proximal axonal membrane potential of single
neurons and in the local cortical network, as typically reflected
in the local field potential (Haider et al., 2006; Kerr et al., 2005;
Lampl et al., 1999; Sanchez-Vives and McCormick, 2000; Shu
et al., 2006; Steriade et al., 1993b; Waters and Helmchen, 2006).
Examination of the slow oscillation reveals that the transitions
between semistable states are rapid, occurring in approximately
50–100 ms (Figure 2). During the activated period, the membrane
potential of an individual cortical neuron is characterized by
depolarization of 15–25 mV, a modest increase in membrane
conductance, and irregular action potential firing driven by irreg-
ularity in the depolarizing potentials that reach action potential
threshold (Compte et al., 2003a, 2003b; Softky and Koch,
1993; van Vreeswijk and Sompolinsky, 1996). This firing irregu-
larity is also an important hallmark of neuronal activity in awake,
behaving animals (Compte et al., 2003a; Shadlen and Newsome,
1998). Many studies have demonstrated that activity in the local
network generates the depolarized and moderately variable (SD
of 2–3 mV) membrane potential of the Up state (Anderson et al.,
2000; Destexhe et al., 2003; Haider et al., 2006; Petersen et al.,
2003; Sanchez-Vives and McCormick, 2000; Shu et al., 2003b)
through an interaction of inhibitory and excitatory synaptic
potentials (Haider et al., 2006; Hasenstaub et al., 2005; Miura
176 Neuron 62, April 30, 2009 ª2009 Elsevier Inc.
et al., 2007; Shu et al., 2003b; van Vreeswijk and Sompolinsky,
1996; Vogels and Abbott, 2009). Long-range connections play
a key role in the communication, propagation, and synchroniza-
tion of this locally generated activity (Timofeev et al., 2000;
Volgushev et al., 2006).
Response Modulation in Active Cortical Networks:Excitatory-Inhibitory Interactions and TimingThe flow of synaptic activity through the cortex, which is highly
nonlinear and only partially hierarchical, is dependent on several
factors, including the amplitude of the particular synaptic event
in question (i.e., the ‘‘signal’’) in relation to the membrane poten-
tial and spike threshold, as well as the timing of the synaptic
event in relation to other synaptic potentials (i.e., the ‘‘back-
ground’’). As we have discussed, the nonlinear transformation
of membrane potential depolarization to spike output will take
place in each of the neurons in an ensemble, partially deter-
mining the ensuing pattern of synaptic activity and shaping
how each neuron interacts with the evolving barrage of synaptic
potentials. Neurons that maintain enhanced excitability will
continue to interact and remain in the ensemble, while those
neurons that are refractory (or actively inhibited) will transiently
fall out of ensemble membership. Given these complex proper-
ties of network dynamics, it becomes critical to understand the
mechanisms by which the amplitude-time course of the
membrane potential of cortical neurons is controlled and how
this interacts with synaptic barrages that might be considered
‘‘signals.’’ This key question has recently been addressed using
the cortical slow oscillation as a model, since it is characterized
by alternating periods of neuronal quiescence and recurrent
network activity. This property of the slow oscillation allows the
investigator to examine the influence of background, or sponta-
neous, synaptic barrages on, for example, responses to sensory
evoked synaptic potentials constituting ‘‘signals.’’
Intracellular recordings of synaptic activity in vivo during Up
states of the slow oscillation, as well as during sensory-evoked
responses, reveal that cortical networks operate through
a remarkable balance of synaptic excitation and inhibition
(Haider et al., 2006; Higley and Contreras, 2006; Marino et al.,
2005; Shu et al., 2003b; Wehr and Zador, 2003). In general, as
synaptic excitation to a cortical region increases, so does the
level of inhibition, owing to the highly recurrent nature of intra-
cortical excitatory and inhibitory networks (Binzegger et al.,
2004; Douglas and Martin, 2007b; Haider et al., 2006; Kapfer
et al., 2007; Murphy and Miller, 2009; Sanchez-Vives and
McCormick, 2000; Shu et al., 2003b; Silberberg and Markram,
2007; van Vreeswijk and Sompolinsky, 1996; Vogels and Abbott,
2009). This general balancing of excitatory and inhibitory inputs
to cortical neurons and networks must be generated in large
part locally, since long-range inhibitory connections are rare in
the neocortex. Intracellular analysis of sustained activity in
cortical networks as seen during Up states reveals that while
the general level of membrane potential is set by the excit-
atory-inhibitory balance and the intensity of synaptic barrages,
the moment-to-moment variations of membrane potential are
determined by rapid fluctuations in the timing of excitatory,
and especially inhibitory, synaptic potentials. In other words,
spike generation on a short timescale is determined by rapid
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departures from a precise excitatory-inhibitory balance, which
lies on top of a stable depolarization that is mediated by a more
prolonged excitatory-inhibitory balance (Gabernet et al., 2005;
Hasenstaub et al., 2005; Higley and Contreras, 2006; Kapfer
et al., 2007; Okun and Lampl, 2008; Pouille and Scanziani,
2001; Rudolph et al., 2007; Vogels and Abbott, 2009). This is
important because there are generally two synaptic components
underlying action potential timing in cortical neurons. The
broader, less temporally precise component is generated
through recurrent excitatory and inhibitory interactions that set
the basal—yet variable—membrane potential of the neuron.
These broad changes in excitability are visible preferentially at
lower frequencies (<10 Hz). In addition to this component, rapid,
higher-frequency (30–100 Hz) fluctuations in inhibitory and excit-
atory synaptic potentials determine the precise probability and
timing of action potential generation, even to the millisecond level
(Cardin et al., 2009; Hasenstaub et al., 2005; Higley and Contre-
ras, 2006; Mainen and Sejnowski, 1995; Nowak et al., 1997;
Pouille and Scanziani, 2001). Importantly, it is the higher-
frequency components of membrane potential fluctuations that
most strongly influence the precise timing of action potentials
(Mainen and Sejnowski, 1995; Nowak et al., 1997) and have
thus been proposed to play an important role in determining func-
tional connectivity among populations of cortical neurons (Sali-
nas and Sejnowski, 2001; Womelsdorf et al., 2007).
These results emphasize the interactions of different temporal
bandwidths and ‘‘windows of opportunity’’ in cortical processing
(Figure 2; see also Canolty et al., 2006; Sirota et al., 2008). Depo-
larizing a cortical neuron by 15–25 mV from true resting potential
to firing threshold may require the summed efforts of prolonged
(e.g., seconds or longer) depolarizations mediated by ongoing
recurrent network activation (e.g., window 1, Figure 2), in addi-
tion to shorter-duration (e.g., tens to hundreds of ms) events
that are brought about through temporary coactivation of
a subpopulation of cortical neurons resulting in rapid changes
in the excitatory/inhibitory balance (e.g., windows 2 and 3,
Figure 2). One advantage to such a framework of temporarily
increased recurrent excitation balanced with inhibition is that
the network can maintain a dynamic range that is close to spike
threshold, and thereby is capable of responding rapidly to small
and fast fluctuations in ensemble dynamics (Kapfer et al., 2007;
Murphy and Miller, 2009; Pouille and Scanziani, 2001; Rudolph
and Destexhe, 2003; Silberberg and Markram, 2007; Swadlow,
2002; Vogels and Abbott, 2009; Wilent and Contreras, 2005). A
concomitant function of this fast-responding, balanced regime
is an ongoing check on runaway excitation that is present in
pathological states, such as cortical seizure activity (McCormick
and Contreras, 2001) and perhaps even during disordered infor-
mation flow characteristic of higher cognitive dysfunctions
(Aghajanian, 2009; Vogels and Abbott, 2009).
The chief advantage of this relatively balanced activity is its
dynamic nature: momentary excesses of excitation, or rapid with-
drawal of inhibition, may transiently depolarize the membrane
potential and lead to enhanced spike generation. The time frame
of these imbalances of excitation or inhibition may be as little as
only a few milliseconds, as evidenced by regular-spiking (RS)
pyramidal neuron firing leading fast-spiking (FS) inhibitory neuron
activity by�4 ms during fast network activation; this temporal lag
has also been confirmed by direct measurement of EPSPs and
IPSPs in nearby pairs of pyramidal neurons (Hasenstaub et al.,
2005; Okun and Lampl, 2008). Moreover, studies in behaving
animals suggest that a time window equal to the period of the
gamma oscillation (�10–30 ms) may be a critical constraint for
the dynamic coupling of interacting neural populations (for
review, see Fries et al., 2007; Harris, 2005). In support of this
crucial temporal window, the membrane potential trajectory
preceding sensory evoked spikes is often composed of a rapid
hyperpolarization-depolarization sequence occurring within
�20 ms (around 50 Hz), presumably mediated by a dynamic inter-
action of inhibitory and excitatory neurons and synaptic poten-
tials (Hasenstaub et al., 2005; Nowak et al., 1997; Poulet and
Petersen, 2008). An important consequence of a hyperpolarizing
potential followed by a rapid depolarizing potential is a lower
spike threshold and a more rapid action potential response
(Azouz and Gray, 2003). Furthermore, this time window of
10–30 ms is also on the same order as the membrane integration
time for cortical pyramidal cells in vivo during spontaneous
activity, further constraining the summation of synaptic barrages
(Leger et al., 2005). Therefore, through multiple mechanisms, this
narrow temporal window dictates how effectively the network
responds to the timing of synaptic potentials (Azouz, 2005; Singer
and Gray, 1995). Accordingly, intracellular studies have revealed
that facilitation of spiking responses during increased levels of
network activity is accompanied by a heightened sensitivity to
rapid depolarizations (indicative of synchronized synaptic
barrages), due to a shorter membrane time constant and accu-
mulation of sodium channel inactivation (Azouz and Gray, 1999;
Henze and Buzsaki, 2001; Nowak et al., 1997). Together these
observations suggest that rapid, balanced interactions between
excitatory and inhibitory circuits in active local networks dictate
neuronal sensitivity to synchronized synaptic inputs.
The excitatory-inhibitory interactions leading to such precise
moments of enhanced excitability have also been examined
in vitro, where spike-triggered averages of randomly fluctuating
artificial excitatory and inhibitory conductance stimuli revealed
that the withdrawal of inhibition may be at least as important
as increased excitation for the production of action potentials
(Hasenstaub et al., 2005; Piwkowska et al., 2008; Tiesinga
et al., 2008). Analytical methods have also suggested that
withdrawal of inhibition may be critical for production of spikes
in vivo (Azouz and Gray, 2008; Rudolph et al., 2007). Further,
when synaptic conductances are pharmacologically separated
in vivo, it is observed that there is more variance in the inhibitory
rather than the excitatory conductances, mirroring the observed
increase in power at higher frequencies in IPSPs over EPSPs
during network activation (Haider et al., 2006; Hasenstaub
et al., 2005). These results strongly suggest that fast variations
in membrane potential that define windows of neuronal excit-
ability depend critically upon distinct subnetworks of inhibitory
neurons and their interactions with locally and distantly projec-
ting excitatory cells.
Of the wide variety of inhibitory interneurons in the cerebral
cortex (Markram et al., 2004), the activity of fast-spiking (FS)
interneurons, especially basket and chandelier cells, is of partic-
ular relevance to the control of rapid variations in membrane
potential at the soma and proximal axon, since FS interneurons
Neuron 62, April 30, 2009 ª2009 Elsevier Inc. 177
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synapse predominantly onto the soma, axon initial segment, and
proximal dendrites of cortical pyramidal cells (Buzsaki and Chro-
bak, 1995; Csicsvari et al., 2003; Penttonen et al., 1998; Peters,
1984). Not only are the synaptic terminals of these cells well-
placed to control the spike output of cortical pyramidal neurons,
but many of the physiological aspects of FS interneurons allow
these cells to communicate activity across a broad range of
temporal frequencies. FS interneurons generate short-duration
action potentials and are capable of discharging at high frequen-
cies with little spike frequency adaptation (McCormick et al.,
1985; Nowak et al., 2003). The responses of their membranes
to the injection of broad-band fluctuations show less decrement
of higher frequencies in comparison to other cell types (Destexhe
et al., 2001; Hasenstaub et al., 2005), a fact that results in part
from the shorter membrane time constant of FS interneurons
(Cruikshank et al., 2007). FS interneurons are better able to main-
tain synaptic transmission at higher frequencies than RS pyra-
midal cells (Galarreta and Hestrin, 1998; Varela et al., 1999),
and they receive kinetically faster EPSPs from presynaptic excit-
atory cells (Thomson et al., 2002). FS interneurons also form
direct electrical synapses with one another, enhancing their
synchronization (Connors and Long, 2004). FS interneurons
in vivo discharge with a wide range of frequencies, including
synchronization to gamma (30–80 Hz) oscillations (Cardin
et al., 2007; Csicsvari et al., 1999; Hasenstaub et al., 2005; Pent-
tonen et al., 1998), and are characterized by a marked sensitivity
to the activation of afferent inputs (Azouz et al., 1997; Cruikshank
et al., 2007; Hirsch et al., 2003; Jones et al., 2000; Nowak et al.,
2007; Swadlow et al., 1998). Finally, direct and temporally
precise activation of FS neurons through genetically encoded
light-activated channels results in the specific enhancement of
higher-frequency oscillations in cortical networks (Cardin et al.,
2009). Together, these results indicate that the cortical FS inter-
neuron network is responsible for conveying broad-band
frequency fluctuations in postsynaptic targets, thereby control-
ling the probability and timing of action potential generation in
pyramidal neurons that contribute to both local network interac-
tions as well as more distant cortico-cortical communication.
All of these results together suggest that rapid modulation of
functional connectivity across cortical areas is critically regulated
by local inhibitory subnetworks responsible for precise spike
timing in nearby pyramidal neurons and thus proper control of
information flow. The cortex contains numerous subtypes of
interneurons, in addition to the FS subtype, that collectively
exhibit a rich repertoire of local network excitatory and inhibitory
interactions, the details of which are only beginning to be re-
vealed (Fuentealba et al., 2008; Kapfer et al., 2007; Kawaguchi,
1997; Klausberger et al., 2003; Markram et al., 2004; Silberberg
and Markram, 2007). Similarly, subclasses of cortical pyramidal
cell can be distinguished based upon their anatomical connec-
tions and morphologies (DeFelipe et al., 2002), intrinsic electro-
physiological properties (Connors and Gutnick, 1990; Douglas
and Martin, 2007a; McCormick et al., 1985; Nowak et al., 2003),
and responses to neurotransmitters (Gil et al., 1997; McCormick,
1992). As with interneurons, these properties are nonrandomly
distributed, yielding strong correlations between all of these
measures (Nelson et al., 2006). Of particular relevance to our
discussion is the finding that connections between cortical
178 Neuron 62, April 30, 2009 ª2009 Elsevier Inc.
neurons are also highly nonrandom, with preferred pathways
and subcircuits appearing to be the rule rather than the exception
(Petreanu et al., 2009; Sawatari and Callaway, 2000; Song et al.,
2005).
The distribution of single excitatory postsynaptic potential
amplitudes between cortical neurons is highly skewed, with
a small but significant fraction exhibiting large values (e.g.,
several mV; Feldmeyer et al., 1999; Markram et al., 1997). During
any moment in time, the pathways that exhibit large amplitudes
may be considered preferred, while those eliciting small-ampli-
tude responses (or failures) are nonpreferred, with a more
moderate range of synaptic strengths in between these two
extremes. The occurrence of bidirectional connectivity among
nearby pyramidal neurons is substantially higher than chance,
as is the probability that a third neuron forms a functionally con-
nected ‘‘triplet’’ with these two interconnected neurons (Song
et al., 2005). Moreover, the strength of synaptic connections
between neurons within the input layer 4 may be quite specific
and restricted to only those neurons that share the same feed-
forward inputs from the thalamus (Feldmeyer et al., 1999). The
feed-forward transfer of information from input layer 4 neurons
to superficial layer neurons is also highly specific; intercon-
nected neurons in layer 2/3 also share inputs from common
pools of layer 4 neurons. However, these pools of feed-forward
inputs are not shared if the neurons in layers 2/3 are not con-
nected to one another (Feldmeyer et al., 1999; Shepherd and
Svoboda, 2005; Yoshimura et al., 2005). Thus, nearby pyramidal
neurons form highly specific connections that establish unique,
but flexible, cortical subnetworks.
Of particular importance to our discussion is the finding that,
regardless of the connectivity relationships among pairs of
neurons, superficial layers receive equipotent global excitatory
synaptic inputs from deep layers and equipotent global inhibitory
input from within a layer (Yoshimura et al., 2005). This raises the
intriguing possibility that nearby and spatially overlapping
neuronal populations that are preferentially interconnected
may be modulated cohesively by more global excitatory and
inhibitory background activity. Moreover, the amplitude of post-
synaptic potentials between cortical pyramidal neurons may be
facilitated by depolarization of the somatic membrane potential
(Shu et al., 2006), adding an additional ‘‘analog’’ mechanism of
enhanced interactions among functionally coactive subnetworks
of pyramidal neurons. All of these studies together suggest that
the functional connectivity among neurons in preferred subnet-
works could be rapidly modulated by synaptic barrages resulting
from low levels of sustained activity across the larger neural pop-
ulation, as we have described. What is the evidence that ongoing
changes in global excitatory and inhibitory background activity
levels may rapidly modulate responsiveness to specific
‘‘signals,’’ such as sensory evoked synaptic potentials?
Rapid Modulation of Sensory Responsivenessin Recurrent Networks In VivoIt is essential to efficient behavior that the responsiveness of
cortical neurons and networks be rapidly modulated according
to changes in context or shifts in behavioral demands. In the
visual cortex, several studies have examined how ongoing
changes in spontaneous activity (as measured, for example, by
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the EEG) affect visual response properties (e.g., Haider et al.,
2007; Livingstone and Hubel, 1981; Worgotter et al., 1998).
Perhaps the simplest andmost instructive example for our discus-
sion is a study in which the sensory response during the lack of
network activity (during the Down state of the slow oscillation)
was compared with sensory responses elicited when the cortical
network was strongly activated (during the Up state) (Haider et al.,
2007). These experiments clearly showed that the spiking
response to short duration (50 ms) visual stimulation of the recep-
tive field was enhanced, more than two-fold, when primary visual
cortical networks were active versus silent. Importantly, this
enhancement during the Up state was continuously modulated
by ongoing variations in network activity levels. Comparing visual
responses elicited during different levels of the activated (Up)
state revealed that the input-output curve underwent a multiplica-
tive-like increase in gain (of up to 60%) with depolarization of the
recorded neuron induced by increases in local network activity
(Figure 3F). Responses to the intracellular injection of synaptic
conductance-like stimuli in these same neurons also revealed
response enhancement, indicating that the increase in respon-
siveness was due to the neuronal membrane potential level and
not purely the result of alterations in presynaptic activity (Haider
et al., 2007; Shu et al., 2003a). In fact, the dominant factor deter-
mining the enhanced cortical response in this case was the level
of membrane potential depolarization, not the magnitude of
the sensory-evoked synaptic potential, since direct depolariza-
tion of visual cortical neurons also caused a multiplicative-like
increase in neuronal gain (Figure 3D) (Sanchez-Vives et al.,
2000). Could such simple mechanisms underlie response
enhancement as observed in behaving animals?
Perhaps the most well-known example of cortical response
enhancement due to cognitive state is found in tasks engaging
attention. Attentional signals have been proposed to arise from
higher cortical areas (‘‘top-down’’) that facilitate the interactions
between neurons in lower cortical areas that encode object
features that are to be detected (Hahnloser et al., 2002; Miller
and Cohen, 2001; Reynolds and Chelazzi, 2004). Attentional
modulation of neuronal responsiveness is a powerful example
of interareal gain control and has been particularly well studied
in the visual system (Desimone and Duncan, 1995; Fries et al.,
2001; Lee and Maunsell, 2009; Martinez-Trujillo and Treue,
2004; Maunsell and Treue, 2006; McAdams and Reid, 2005;
Reynolds and Heeger, 2009; Reynolds et al., 2000). Spatial
attention can rapidly increase neuronal response amplitude by
up to 40% or more, with results that are consistent with both
a shift along the input axis (e.g., contrast gain enhancement
[Reynolds et al., 2000]) as well as multiplicative response gain
(Martinez-Trujillo and Treue, 2004; Williford and Maunsell,
2006; see also Lee and Maunsell, 2009; Reynolds and Heeger,
2009). Although intracellular examination of the synaptic mecha-
nisms producing these alterations is not yet possible in behaving
animals, in vitro recording, computational models, and intracel-
lular recording studies in anesthetized animals in vivo all suggest
that response facilitation may occur through simple depolariza-
tion of membrane potential via balanced excitatory and inhibitory
synaptic bombardment.
Can small amounts of depolarization have a behaviorally rele-
vant effect? The firing rate of cortical neurons is rather sensitive
to membrane potential, increasing by an average of three to
seven spikes/s per mV of depolarization (depending on the posi-
tion along the input-output curve, Figure 1A) (Carandini and
Ferster, 2000; Haider et al., 2007; Sanchez-Vives et al., 2000).
These results suggest that the ongoing variations in membrane
potential that occur in the waking state, which cover a range of
�10 mV, remarkably similar to the range of the Up state (Steriade
et al., 2001), may have a very strong, multiplicative-like effect on
neuronal responsiveness. Indeed, studies of behaving primates
occasionally show increases in spontaneous activity of a few
spikes/s upon engagement of selective attention, even in the
absence of visual stimuli (Luck et al., 1997; Reynolds et al.,
2000; Sundberg et al., 2009). We suspect that attention may
result in a slight depolarization of cortical neurons in the attended
sensory dimension (which may be spatial location, form, color,
motion, etc.), a mechanism that should also be accompanied
by an increase in background activity levels. Since depolariza-
tion of %1 mV may cause a significant increase in neuronal
responsiveness, in both excitatory and inhibitory neurons (e.g.,
three to seven spikes/s; Carandini and Ferster, 2000; Contreras
and Palmer, 2003), we suspect that gain modulation in subnet-
works of excitatory and inhibitory neurons engaged during
behavior may be achieved by moderate average changes in
the membrane potential, ranging from submillivolt up to several
millivolts. These small, rapid changes in membrane potential
are distinct from those mediated by classical Up and Down
states, although they may use similar network mechanisms.
How is membrane potential rapidly modulated in individual
neurons along with changing background activity levels? All of
our observations thus far suggest a basic mechanism for
sensory response facilitation: a balanced mixture of excitatory
and inhibitory synaptic bombardment tonically depolarizes
target neurons. Importantly, as long as the reversal potential of
this mixture of excitatory and inhibitory barrages is more positive
than the resting membrane potential, the neuron will rapidly
depolarize. This depolarization will be counteracted by the acti-
vation of intrinsic K+ conductances and facilitated by the activa-
tion of the persistent Na+ current (Bean, 2007; Llinas, 1988;
Waters and Helmchen, 2006). This net synaptic depolarization
in the presence of a noisy baseline can yield a nonlinear, multipli-
cative-like increase in neuronal gain (Murphy and Miller, 2003).
This enhanced activity is subsequently disseminated within a
coactivated ensemble of neurons, both local and distant (Fig-
ure 1B). The increased responsiveness of these coactivated
neurons may result in facilitated interactions between the
members, resulting in a competitive advantage over other
groups of neurons. Thus, moderate amounts of depolarization
in subgroups of local cortical neurons may have a powerful,
network-wide effect upon responsiveness and provide the
main cellular mechanism for rapidly altering functional connec-
tivity in cortical networks. The facilitating interactions may then
allow the activity of coactivated neurons to temporarily increase
their discharge in a ‘‘pop-out’’ effect employing ‘‘winner-take-
all’’ or ‘‘attractor’’ dynamics. Such dynamics are likely to underlie
diverse behavioral phenomena such as binocular rivalry, contex-
tual modulation, binary decision making, and working memory
(Albright and Stoner, 2002; Ardid et al., 2007; Barraclough
et al., 2004; Gilbert and Sigman, 2007; Hahnloser et al., 2002;
Neuron 62, April 30, 2009 ª2009 Elsevier Inc. 179
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Logothetis, 1998; Luna et al., 2005; Rutishauser and Douglas,
2008; Vogels et al., 2005; Wang, 2008; Wong et al., 2007). Since
the activity of neuronal populations is constantly changing and
influencing other populations, a continual and dynamic flow of
synaptic barrages provides the context upon which neuronal
signals interact. The moment-to-moment evolution of synaptic
activity not only links groups of neurons and cortical territories
together but also links the past to the present and future of
cortical network behavior (e.g., Harris, 2005).
Rapid Modulation of Membrane Potentialand Background Activity during WakingIn the preceding sections, we have detailed how specific proper-
ties of balanced excitatory and inhibitory synaptic barrages may
result in transient depolarizations that interact with nonlinear
spike transformation to modulate the gain and functional
connectivity of cortical networks. What is the evidence that the
mechanisms revealed in anesthetized animals and in slices
in vitro are present in awake animals? Intracellular recordings
in waking rodents, cats, and monkeys indicate that neuronal
activity in the awake cortex exhibits an activated baseline state,
similar to that of the Up state of slow wave sleep (Chen and Fetz,
2005; Crochet and Petersen, 2006; Lee et al., 2006; Steriade
et al., 2001). Indeed, the transition from slow wave sleep to
waking is associated with a loss of Down states, giving the
impression that the waking state is a prolonged Up-like state
that merely lacks rhythmic hyperpolarization or deactivation
(Steriade et al., 2001). These findings suggest that under-
standing the cellular mechanisms of the Up state of slow wave
sleep and anesthesia may yield clues about the basic mecha-
nisms of the baseline waking state (Destexhe et al., 2007).
Interwoven with this baseline activation, there are rapid modu-
lations of neuronal responsiveness and activity, in relation to
behavioral demands and cortical processing (Brecht et al.,
2004b; Ferezou et al., 2006; Fries et al., 2007; Pesaran et al.,
2002; Poulet and Petersen, 2008; Singer and Gray, 1995). The
baseline membrane potential of the waking animal exhibits
a steady depolarization such that it is typically within a few milli-
volts of firing threshold, but variations of up to several millivolts
rapidly occur around this baseline level (Chen and Fetz, 2005;
Crochet and Petersen, 2006; Lee et al., 2006; Steriade et al.,
2001). There are further important variations on this theme of
sustained activation or depolarization during waking. First, the
waking cortex exhibits a broad range of states that correlate
with the general level of arousal (e.g., drowsiness, quiet waking,
alert, attentive, active behavior, etc. [e.g., Bezdudnaya et al.,
2006]) as well as the specifics of the behavioral task at hand.
Second, the level of depolarization and synaptic activity is likely
to vary dramatically between different cell types and layers,
since extracellular recordings in awake animals reveal large
differences in spontaneous and evoked discharge rates between
excitatory and FS inhibitory cells, as well as in cells across
cortical lamina (Beloozerova et al., 2003; Brecht et al., 2004b;
de Kock et al., 2007; Houweling and Brecht, 2008; Mitchell
et al., 2007).
As mentioned, a key difference between slow wave sleep and
active waking is that the synchronized and prolonged hyperpola-
rizing epochs similar to Down states appear to be relatively rare,
180 Neuron 62, April 30, 2009 ª2009 Elsevier Inc.
especially during active behavior. In addition, while the transi-
tions between Up and Down states of slow wave sleep and anes-
thesia exhibit broad propagation and synchrony, the occurrence
and synchronization of faster oscillations of membrane potential
and action potential activity of the attentive and waking brain are
much more restricted to select populations of neurons (Destexhe
et al., 1999; Fujisawa et al., 2008; Poulet and Petersen, 2008;
Tsujimoto et al., 2008).
In the inattentive or quiet resting state, the situation may be
different. Recent whole-cell recordings and voltage-sensitive
dye imaging of somatosensory cortex in quietly resting rodents
indicate that large regions of cortex exhibit spatiotemporally
correlated waves of hyperpolarization and depolarization at
1–5 Hz, in some ways similar to Up and Down states (Crochet
and Petersen, 2006; Poulet and Petersen, 2008). These large,
slow oscillations of membrane potential are highly synchronized
between neighboring neurons and thus generate slow oscilla-
tions in the local field potential (Poulet and Petersen, 2008).
During active behavior (i.e., vibrissal whisking), the amplitude
of these waves is dramatically reduced and is replaced with
membrane potential dynamics that more closely resemble those
previously observed in awake primates and cats (Crochet and
Petersen, 2006; Ferezou et al., 2007; Poulet and Petersen,
2008). Interestingly, action potentials in individual cortical
neurons during active whisking of the vibrissa are each preceded
by a large (5–10 mV) fast depolarizing, presumably synaptic,
event. In contrast to the slow fluctuations of the quiet waking
state, this fast synaptic event during active behavior does not
occur throughout the local network (Poulet and Petersen,
2008). This is expected if the action potential activity of individual
cells is driven by unique combinations of relatively sparse, but
synchronized, discharge within the large numbers of presynaptic
cortical neurons that influence the recorded cell (DeWeese and
Zador, 2006; Houweling and Brecht, 2008). We hypothesize
that each action potential is initiated by the interaction of multiple
synaptic components: one which collectively sets the general
level of membrane potential properties (particularly the level of
depolarization) and another which occurs in a well-defined
subnetwork of cortical neurons, the synchronized activity of
which determines the precise timing of the action potential.
How likely is it that the maintained increase in neuronal firing
occurring during waking has the same basis as the maintained
increase in activity associated with the Up state of the slow oscil-
lation? These two periods of activity are similar in that they both
are associated with a highly variable pattern of action potential
generation (Compte et al., 2003a; Hasenstaub et al., 2005; Shad-
len and Newsome, 1998), which is indicative of underlying varia-
tion in excitatory and inhibitory synaptic interactions (Mainen and
Sejnowski, 1995; McCormick et al., 2007; Shadlen and News-
ome, 1998; Vogels and Abbott, 2009). In addition, both changes
in persistent activity in waking animals and transitions into and
out of Up states can be very rapid, within 50–100 ms (Figure 2).
Finally, cortical Up states are typically associated with high-
frequency LFP oscillations (Figure 2) (Buzsaki and Draguhn,
2004; Destexhe et al., 2007; Hasenstaub et al., 2005) as is often
seen during increases in cortical activity in behaving animals
(Csicsvari et al., 2003; Fries et al., 2001, 2007; Pesaran et al.,
2008; Singer and Gray, 1995). In contrast to persistent cortical
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activity in the waking animal, however, cortical Up states occur
nearly simultaneously throughout all or nearly all neurons in the
local network (Contreras and Steriade, 1995; Haider et al.,
2006; Hasenstaub et al., 2005; Lampl et al., 1999; Volgushev
et al., 2006), while persistent activity in the waking state can be
much sparser, with neighboring neurons capable of behaving
relatively independently (Genovesio et al., 2005). In particular,
intracellular recordings in awake animals suggests that single
spikes in a given neuron are preceded by relatively large-ampli-
tude synaptic potentials that are not shared or visible in the
membrane potential of nearby neurons (Chen and Fetz, 2005;
Poulet and Petersen, 2008). Similarly, the simultaneously
recorded activity across large populations of neurons may be
highly independent and exhibit significant trial-to-trial variability
(Greenberg et al., 2008; Kerr et al., 2007; Ohki et al., 2005). If
nearby neurons receive relatively unique constellations of strong
connections amidst a sea of weaker ones, it becomes possible
that highly specific and sparse transmission of sensory
responses occurs even if background activity levels are shared
(Olshausen and Field, 2004). In this manner, small differences
(<1 to a few millivolts) in synaptic potentials, riding on top of a
depolarized baseline membrane potential, would produce
nonlinear increases in spike output, as we have already detailed,
and facilitate the temporary interaction of specific pathways. We
suggest that although similar cellular mechanisms may be
involved in both awake and anesthetized or sleeping cortical
dynamics, network interactions in awake behaving animals are
less synchronized as compared to sleeping or anesthetized
conditions, restricting interactions to select subnetworks of
neurons that vary with the behavioral task at hand. We presume
that this parcellation of cortical networks in behaving animals
depends upon the rapid interaction of cortico-cortical excitatory
and local inhibitory subnetworks, utilizing similar though not
identical mechanisms as those observed in anesthetized and
sleeping animals.
Cortical Network Dynamics during Behavior: NeuralVariability Reflects Changes in Functional ConnectivityThe mechanisms that enable local cortical networks to quickly
change their responsiveness through recurrent interactions
have strong implications for rapid cortical dynamics in awake,
behaving animals. As we have seen, recurrent networks in the
cerebral cortex may control the amplitude, variability, and timing
of action potential responses, so as to provide appropriate
control over the spatiotemporal flow of neuronal activity elicited
by the rapidly changing demands of behavior. These issues
have been addressed in vivo with a variety of preparations,
most notable of which are recent investigations of trial-to-trial
response variability and contextual modulation of response
amplitude and timing.
Repeated presentation of a sensory stimulus, or performance
of a mental process or task, usually involves significant trial-
to-trial variability (Shadlen and Newsome, 1998). Although
portions of this variability arise from the inability to precisely
control experimental variables (such as eye movements), a large
part of it derives from spontaneous fluctuations in the activity
and state of the cortical network (Arieli et al., 1996; Fox et al.,
2007; Gur et al., 1997). A case in point: spontaneous variations
in firing of motor cortical neurons during a delay period spanning
stimulus delivery and ensuing motor command (i.e., when the
movement was being planned) are highly correlated with the
subsequent variability in velocity of the instructed movement
(Churchland et al., 2006a). These firing rate variations were
moderate (tens of spikes per second) and therefore may be
reflective of small to moderate (<2–4 mV) changes in membrane
potential. Indeed, if depolarization of neurons was responsible
for increases in neuronal responsiveness, it may also explain
the decrease in reaction time that is associated with trials in
which the patterns of spikes (within the spike train) exhibited
less variability (Churchland et al., 2006b). Sustained depolariza-
tion should cause spikes to occur earlier and more frequently
(since the membrane potential is closer to threshold) and should
also decrease long interspike intervals. These effects of depolar-
ization should enhance neuronal interactions and decrease
behavioral reaction times. Such results support our conjecture
that trial-to-trial variability is indicative of alterations in functional
connectivity, as determined by the interplay of ongoing patterns
of synaptic activity with the anatomical connections of the
cortex, and illustrate that changes in background activity reflect
changes in behavioral performance.
While it is not yet routinely possible to directly examine
membrane potential dynamics underlying variations in functional
connectivity in awake, behaving animals, it is possible to examine
changes in functional connectivity by performing multiple single-
unit recordings. Alterations in functional connectivity that are
driven by behavioral context have been recently demonstrated
in the medial temporal area (MT) of the monkey visual system (Co-
hen and Newsome, 2008). Pairs of neurons in this motion-sensi-
tive visual area changed the degree of their correlated activity
during the display of noncoherent ‘‘noisy’’ stimuli depending on
whether the animal was attempting to detect motion in the direc-
tion shared by the receptive field properties of the two neurons, or
in an orthogonal, nonshared direction (Cohen and Newsome,
2008). This change in correlated activity that depended upon
behavioral context indicates that single cells were able to rapidly
and flexibly participate in different ensembles of interacting
neurons, even in the presence of identical visual stimuli. Modula-
tion of neuronal interactions by internal activity states has also
been demonstrated in simultaneous ensemble recordings from
>100 rat prefrontal cortical neurons while animals ran a maze
for a food reward. These experiments showed that putative
monosynaptic influences appeared in spike cross-correlation
histograms transiently and only during specific portions of the
task (Fujisawa et al., 2008) (Figures 4A and 4B). Subsequently,
diagrams of functionally interconnected networks of cells re-
vealed large, distributed functional neuronal ensembles in the
medial prefrontal cortex, in which the participating members
change according to the behavioral performance of the animal
and the spike history of the neuron and network (Figures 4C
and 4D). These recordings also support the idea that the cortex
operates through a relatively sparse code in which individual
neurons discharge relatively rarely (average firing rate of <1 Hz),
but with transient and marked increases in firing occurring in
response to activation of particular patterns of network activity
tied to unique behavioral states (Olshausen and Field, 2004; Sho-
ham et al., 2006). Such rapid alterations in prefrontal network
Neuron 62, April 30, 2009 ª2009 Elsevier Inc. 181
Neuron
Review
A
B
C
D
Figure 4. State-Dependent Modifications ofFunctional Connectivity during Behavior(A) Short-latency (putative monosynaptic) neuronalinteractions between simultaneously recordedputative pyramidal (red) and inhibitory (blue) cellsin rat prefrontal cortex during the performance ofa maze task, as judged from extracellular record-ings and cross-correlation spike histograms.(B) For this pair, the strength of interaction varied asa function of spatial position in the maze, with thestrongest interactions apparent near the decisionpoint of the maze (middle).(C) Local network diagram constructed fromensembles of simultaneously recorded pyramidaland putative inhibitory neurons reveal similarshort-latency enhancement of monosynapticinteractions that are spatially dependent andwhose strength varies significantly with behavioraldemands. Interactions in red (1, 2) are significantlyenhanced on rightward turns during maze running.(D) Same ensemble of neurons upon leftward turnsduring maze running. Some functional connectionsthat are spatially modulated are present in bothdirections of running (2), while others appear (3)or disappear (1) during leftward turns, indicatingrapid changes in functional connectivity duringongoing behavior. Adapted by permission fromMacmillan Publishers Ltd: Nature Neuroscience(Fujisawa et al., 2008), copyright 2008.
dynamics could readily communicate with specific motor cortical
ensembles to control the time of action (Narayanan and Laubach,
2006).
Although the cellular mechanisms underlying transient periods
of functional connectivity cannot be examined with extracellular
recordings, several possibilities exist, including synaptic facilita-
tion and depression, or network interactions involving the large
number of interposed neurons that are not recorded. For
example, the facilitation of a monosynaptic excitatory interaction
between two neurons may be achieved through the rapid
depolarization of both the pre- and postsynaptic cells by a third
neuron (or neurons), allowing the transient appearance of
this monosynaptic connection in the extracellularly recorded
spike-rate histograms during restricted portions of behavior.
Keep in mind that this depolarization could result from increases
in excitation, withdrawal of inhibition, or some balanced mixture
between the two. A mechanism relying upon depolarization of
interacting or interposed neuronal groups to rapidly facilitate
functional connections, in highly specific task-dependent
ways, implies that the patterns of depolarization in individual
cortical neurons selectively regulate how information flows
through the cortical network. This routing of information by
transient increases in net depolarizing synaptic bombardment,
or withdrawal of net hyperpolarizing synaptic activity, will occur
in nearby and often spatially overlapping neuronal populations.
Changes in synaptic bombardment could modulate overlapping
circuits differentially owing to differences in the timing of
synaptic potentials received by these networks, differences
in the amplitude of synaptic interconnections, or differences
in intrinsic properties of the constituent neurons, as we have
discussed. Of course, these possibilities are not mutually exclu-
sive and will require well-formulated hypotheses and experi-
ments to elucidate the underlying mechanisms in behaving
animals.
182 Neuron 62, April 30, 2009 ª2009 Elsevier Inc.
Future Directions for Examinationof Rapid Cortical DynamicsThe functional analysis of neuronal circuits has always depended
upon the application of some variation of three basic techniques:
record, stimulate, or lesion. Current investigations into the
dynamics of cortical networks are no exception, although recent
advances in all three of these approaches have greatly increased
the finesse of the experimenter. Ideally, to fully investigate
cortical network dynamics, an investigator would have detailed
information on the connectivity pattern and activity state of all
the cells involved, as well as the ability to rapidly manipulate
and monitor any of the constituent cells during natural behavior.
The densely interconnected and ever-changing activity of intact
cortical networks makes this ideal very far from practical reach
now, and maybe forever. However, the application of current
technologies, as well as those that are just being developed,
promises to yield information that, while not all encompassing,
is certainly toward this ideal.
Monitoring the activity of multiple interconnected cortical
neurons is now possible through either arrays of extracellular
recording electrodes (for review, see Buzsaki, 2004) or the bulk
application of Ca2+-sensitive dyes (Garaschuk et al., 2006; Kerr
et al., 2005; Ohki et al., 2005; Sato et al., 2007). Combining the
latter with mice in which particular subpopulations of neurons
are genetically labeled with fluorescent proteins may yield valu-
able information on the dynamic interactions of these identified
cell types with other subpopulations of cortical neurons or during
sensory stimulation (Sohya et al., 2007). This approach is being
developed to study the dynamics of neural activity in vivo, using
two-photon microscopy (Svoboda and Yasuda, 2006), and could
be readily applied to active cortical slices in vitro (Sanchez-Vives
and McCormick, 2000). One serious limitation to this technique
is the relatively poor time resolution, primarily owing to the
slow kinetics of neuronal Ca2+ levels (Helmchen et al., 1996).
Neuron
Review
Unfortunately, many of the most interesting and relevant
cortical dynamics occur at a rate that is more rapid than can
be currently resolved using internal Ca2+ levels as a reporter.
These limitations can be overcome with the use of voltage-
sensitive dyes, but when applied extracellularly these suffer
from nonspecific staining of all (including nonneuronal) cortical
tissue and therefore are unable to reveal single-cell dynamics
(Grinvald and Hildesheim, 2004; Homma et al., 2009). While
combined extracellular or intracellular recording and imaging
may yield precise temporal and/or membrane potential data,
this will necessarily be limited to a small number of cells and
requires a suitable preparation and significant effort and exper-
tise on the part of the experimenter. Nonetheless, the combina-
tion of imaging and traditional electrophysiological recording
methods will certainly be advantageous for the monitoring
of cortical network dynamics. Important questions to be
answered include: What is the network basis of large, fast
synaptic events that give rise to action potentials? Do action
potentials arise from the activation of a unique combination of
sparse yet reliable neurons in restricted subnetworks, or do
spikes mainly arise from distributed and heterogeneous patterns
of activity? If there are repeating patterns, do they occupy
succinct spatial and temporal bandwidths? How do these two
different components interact to give rise to sensory-evoked
action potentials?
On the side of stimulation and lesion techniques, it is now
possible, using optogenetic techniques, to selectively depolarize
or hyperpolarize the membrane potential of selected subgroups
of neurons with the application of light (Boyden et al., 2005;
Zemelman et al., 2002; Zhang et al., 2007). Channelrhodopsin
and halorhodopsin are two light-sensitive and genetically encod-
able proteins that depolarize or hyperpolarize the membrane
potential and can be delivered to subpopulations of neurons
through the use of cell-specific promoters, retroviral delivery, or
in utero electroporation (Cardin et al., 2009; Gradinaru et al.,
2007; Huber et al., 2008; Nagel et al., 2003; Petreanu et al.,
2007). This methodology has already been used to optically stim-
ulate neural populations that drive perception and behavior (Dou-
glass et al., 2008; Huber et al., 2008) and has also been used to
change global brain state (Adamantidis et al., 2007). It is likely
that the expression of these light-sensitive channels will be under
increasingly more precise genetic control (e.g., the Cre-lox
system), allowing investigators to make use of the ever increasing
variety of mice that express Cre in specific subpopulations of
neurons (Cardin et al., 2009; Luo et al., 2008). The resulting
expression of light-sensitive channels/transporters in subpopula-
tions of neurons will allow investigators to control the membrane
potential of these cells specifically and noninvasively. This tech-
nique could be used to examine the influence of changes in
membrane potential of a particular subclass of neurons (e.g.,
FS interneurons) on the operation of the network (Cardin et al.,
2009). The targeting of light-sensitive channels/transporters to
particular locations in neurons (e.g., axon initial segment, apical
and basal dendrites) would also be useful for examining the role
of each of these substructures in cortical network dynamics
(Gradinaru et al., 2007). One general question that could be ad-
dressed is: Does subthreshold manipulation of membrane poten-
tial, conductance, and variance in subnetworks and/or substruc-
tures of pyramidal neurons confer a competitive advantage such
that these cells exhibit enhanced network interactions, e.g.,
during bistable sensory perception or binary decision making?
As all techniques have their advantages and limitations, signifi-
cant difficulties to these optogenetic strategies remain, particu-
larly in the titration of expression level needed for subthreshold
or suprathreshold activation, the cell-type specificity of channel
expression, as well as the toxicity or compensatory changes
induced by specific levels of protein expression or ionic disrup-
tion. Careful researchers will always keep in mind that any lesion
or stimulation technique suffers from problems of interpretation
due to, for example, rapid compensatory mechanisms.
As always, constrained and testable hypotheses on the mech-
anisms of cortical dynamics are of utmost value to the experi-
menter. One key hypothesis put forth in our discussion is that
behaviorally relevant changes in neuronal excitability should be
associated with changes in membrane potential, conductance,
and variance. As we have emphasized throughout this review,
the interaction of known (e.g., sensory) ‘‘signals’’ with changes
in background activity (‘‘noise’’) are particularly important for
the rapid modulation of neuronal responsiveness. For example,
increases in neuronal excitability associated with shifts in atten-
tion may be mediated by either depolarization of cortical neurons
through changes in ongoing barrages of synaptic activity (Haider
et al., 2007; Murphy and Miller, 2003) or by decreases in
membrane potential variance and conductance owing to
decreases in ongoing synaptic barrages (Chance et al., 2002).
Both of these hypotheses are testable with intracellular and/or
whole-cell recordings in awake behaving animals, a technique
that is thankfully starting to move out of the shadows as a party
trick and into the limelight as a serious tool for the neurophysiol-
ogist (Brecht et al., 2004a; Chen and Fetz, 2005; Houweling and
Brecht, 2008; Lee et al., 2006; Petersen et al., 2003; Poulet and
Petersen, 2008; Timofeev et al., 2001).
Finally, our understanding of feed-forward (i.e., thalamic) acti-
vation of sensory cortical networks is relatively well-understood
compared to our understanding of the mechanisms by which
long-range and feed-back connections activate cortical circuits.
This is of crucial importance to our understanding of rapid
cortical dynamics, for it is the distributed activity traversing
long-range connections across multiple cortical areas that
underlies the unity and diversity of behavior. We suspect that
long-range synaptic connections have the same specificity and
‘‘preferred’’ functionality as observed in local cortical subnet-
works, and these properties may allow, for example, specific
subnetworks of neurons to perform local versus long-range
computations (Le Be et al., 2007; Petreanu et al., 2007). In
combination with the above-mentioned techniques, it may
become possible to selectively perturb and reroute cortical
activity during behavior and functionally manipulate higher-level
perception, e.g., altering motion discrimination by manipulating
neuronal signals in primate MT or altering the memory of specific
visual cues by perturbing persistent activity in prefrontal cortex.
Given the pace of technological development and experimental
application in just the last 30 years since the first single-channel
patch-clamp recordings (Neher and Sakmann, 1976; Sakmann,
2006), this short list of dream experiments will likely come to
reality in the not-so-distant future.
Neuron 62, April 30, 2009 ª2009 Elsevier Inc. 183
Neuron
Review
ConclusionA fundamental task of cortical network operation is the dynamic
and rapid modulation of neuronal responsiveness according to
the ever-changing demands of behavior. Anatomical constraints
dictate that the majority of a single neuron’s activity is deter-
mined by fluctuations in local network activity levels, which
necessarily engage the unique recurrent properties of excitatory
and inhibitory subnetworks. We have outlined how the challenge
of rapid flexibility faced by the cortex could be solved by a simple
and general mechanism: modulation of neuronal membrane
potential through concerted synaptic bombardment in specific
excitatory and inhibitory subnetworks of functionally connected
neurons. This bombardment causes a noisy and elevated level of
depolarization that can enhance responsiveness rapidly and in
a multiplicative manner to a variety of inputs. Precise control of
membrane potential fluctuations via local network activity may
serve as the main mechanism to dynamically link one cortical
area to another, one cortical neuron to another, and even
substructures of individual neurons (e.g., dendritic branches)
together. Therefore, synaptic barrages operate in a holistic
manner: functionally associating and dissociating information
across vast cortical territory, while simultaneously modulating
interactions both between and within individual cortical neurons.
This fundamental operating principle of the cortex utilizes the
interaction of a highly recurrent local cortical network architec-
ture with intrinsic nonlinearities to rapidly and dynamically alter
neuronal responsive to incoming inputs. These enhanced local
network interactions in turn facilitate communication with distant
cortical neurons through long-range connections. By selectively
and temporally modulating the ongoing patterns of activity in
distal cortical regions, shared windows of excitability— deter-
mined by the biophysical rules of recurrent networks—serve to
transiently link one cortical area to another, facilitating network
computation and synchronous arrival of information in down-
stream targets. Our understanding of the rapid control of
synaptic barrages in local cortical networks has gained consid-
erably from the study of activated cortical states in a diversity
of preparations, from anesthetized, sleeping, and behaving
animals, to cortical slices in vitro and models in silico. A
continued dialog between these and other rapidly emerging
physiological and computational approaches will be necessary
to unravel the full details of local and large-scale network interac-
tions of the cerebral cortex.
ACKNOWLEDGMENTS
We thank past and present members of the McCormick lab, James Mazer, JonTouryan, Mark Laubach, and the anonymous reviewers for critical and helpfulcomments. This work was supported by the NIH, NINDS, NEI, and the KavliInstitute for Neuroscience.
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