Neuron

Viewpoint
A Theory of the Transition to Critical
Period Plasticity: Inhibition Selectively
Suppresses Spontaneous Activity
Taro Toyoizumi,1,3,4,* Hiroyuki Miyamoto,3 Yoko Yazaki-Sugiyama,3 Nafiseh Atapour,3 Takao K. Hensch,3,5
and Kenneth D. Miller1,2
1Center for Theoretical Neuroscience and Department of Neuroscience, College of Physicians and Surgeons
2Kavli Institute for Brain Science
Columbia University, 1051 Riverside Drive, New York, NY 10032, USA
3RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
4Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 226-8503, Japan
5Center for Brain Science, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA*Correspondence: taro.toyoizumi@brain.riken.jp
http://dx.doi.org/10.1016/j.neuron.2013.07.022

SUMMARY

What causes critical periods (CPs) to open? For the
best-studied case, ocular dominance plasticity in primary visual cortex in response to monocular deprivation (MD), the maturation of inhibition is necessary
and sufficient. How does inhibition open the CP?
We present a theory: the transition from pre-CP to
CP plasticity arises because inhibition preferentiallysuppresses responses to spontaneous relative to
visually driven input activity, switching learningcues from internal to external sources. This differsfrom previous proposals in (1) arguing that the CP
can open without changes in plasticity mechanisms
when activity patterns become more sensitive tosensory experience through circuit development,and (2) explaining not simply a transition from noplasticity to plasticity, but a change in outcome of
MD-induced plasticity from pre-CP to CP. More
broadly, hierarchical organization of sensory-motor
pathways may develop through a cascade of CPs
induced as circuit maturation progresses from‘‘lower’’ to ‘‘higher’’ cortical areas.

INTRODUCTION
Many brain circuits and functions across species exhibit developmental critical periods (CPs) or sensitive periods, during which
they become sensitive to particular changes in experience or
activity patterns (Hensch, 2004; Knudsen, 2004). Critical periods
can occur as a sequence of sensitivities to increasingly more
complex aspects of sensory or sensorimotor experience(Brainard and Doupe, 2000; Werker et al., 2009; Hernandez
and Li, 2007; Scott et al., 2007), suggesting a corresponding
developmental sequence of CPs from lower to higher brain
areas. The question of the neural mechanisms underlying CPs

is a fundamental one for more generally understanding both
cortical development and the development of perceptual andmotor abilities.
A paradigmatic and best-studied example of a CP is oculardominance (OD) plasticity in primary visual cortex (V1) in
response to visual deprivation of one eye (Wiesel and Hubel,
1963; Espinosa and Stryker, 2012). During the CP, but not
before, monocular deprivation (MD) yields a strong shift in
cortical responsiveness toward the nondeprived eye (OD plasticity) and an associated decrease in acuity of deprived-eye
vision (Fagiolini et al., 1994; Prusky and Douglas, 2003). This shift
involves an initial decrease of deprived-eye and later homeostatic increase of nondeprived-eye response (Mrsic-Flogel et al.,2007; Kaneko et al., 2008).
The opening of this CP is triggered by the maturation ofcortical inhibitory circuitry (Hensch et al., 1998; Hanover
et al., 1999; Fagiolini and Hensch, 2000; Iwai et al., 2003;
Hensch, 2005; Di Cristo et al., 2007; Katagiri et al., 2007;
Sugiyama et al., 2008; Kanold et al., 2009; Southwell et al.,2010). Mice in which inhibition remains immature, either due
to a lack of the synaptic isoform of glutamic acid decarboxylase (GAD65-KO) or to dark-rearing from birth, fail to enter theCP (Fagiolini and Hensch, 2000; Morales et al., 2002; Iwaiet al., 2003; Katagiri et al., 2007). However, focal administration in V1 of diazepam, a benzodiazepine agonist that enhances inhibitory transmission, initiates OD plasticity in theseanimals (Hensch et al., 1998; Fagiolini and Hensch, 2000;
Iwai et al., 2003; Kanold et al., 2009). Similarly, wild-type(WT) mice enter a CP prematurely upon early diazepam treatment (Fagiolini and Hensch, 2000; Fagiolini et al., 2004) ormolecular interventions that promote early GABA circuit development (Hanover et al., 1999; Di Cristo et al., 2007; Sugiyama
et al., 2008).
How does maturation of inhibition initiate a CP? One possibility
is that inhibition specifically ‘‘turns on’’ some form of plasticity.
Inhibition has been suggested to alter plasticity rules by changing the composition of N-methyl-D-aspartate (NMDA) receptors(Kanold et al., 2009) and/or by enabling long-term depression (LTD) (Choi et al., 2002). However, this issue remains
Neuron 80, 51–63, October 2, 2013 ª2013 Elsevier Inc. 51

NeuronSpontaneous/Visual Ratio Controls CP Onset

controversial because activity-dependent plasticity like thatseen in CP animals is also observed in juvenile animals before
the CP (Fox, 1995; Kuhlman et al., 2010; Chun et al., 2013) andin GAD65-KO mice (Hensch et al., 1998; and see further evidence presented in Discussion). Alternatively, without sufficientinhibition, many input patterns might activate the postsynaptic
cell equally well so that there is no activity-dependent competition between them. Inhibitory maturation might then intensify
competition among input patterns to initiate the CP (Hensch2005, Kuhlman et al., 2010). These proposals are all challenged
by the fact that MD before the CP also induces plasticity. During
the week before the CP, MD of the contralateral eye retards theongoing development of precision in both eyes’ retinotopic
maps and decreases the magnitude of responses to each eye
without inducing a significant OD shift, that is, without appreciable change in the relative response to the two eyes (Smith
and Trachtenberg, 2007). Thus, it is the outcome rather thanthe existence of experience-dependent plasticity that changes
at CP onset. The CP specifically reflects a time when OD andvisual acuity through a deprived eye becomes sensitive to MD,
whereas other aspects of visual development are sensitive to
MD prior to the CP.
In this study, we use the CP for OD plasticity in V1 as a model
system to propose the theory that CPs open when there is aswitch in the predominant learning cues from internally driven
spontaneous activity to externally driven evoked activity withoutchanges in plasticity rules. Unlike all previous models of CP
opening of which we are aware (Kuhlman et al., 2010; Kanold
and Shatz, 2006; Hensch, 2005; Kanold et al., 2009; Choiet al., 2002; Yazaki-Sugiyama et al., 2009; Sugiyama et al.,2008), our theory explains not simply an opening of experience-dependent plasticity but the change in this plasticity thatis associated with the transition from pre-CP to CP in V1.
Furthermore, this transition can occur strictly through alterationsin activity patterns, with no requirement for changes in the rules
governing synaptic modification. We examine this theory using acomputational model and test key predictions in freely behaving
mice.
The basic workings of our theory can be understood purely
conceptually:
1. Spontaneous activity is always equal between the twoeyes, whereas visually-evoked activity is unequal if one
eye is closed. Thus, if spontaneous input sufficiently predominates over visual in driving cortical activity, MD will
not induce OD plasticity; while if visual input contributes
sufficiently, OD plasticity can occur.
2. The maturation of inhibition can suppress the contributionof spontaneous relative to visually-evoked activity andthus open the CP for OD plasticity. Our experiments
confirm that inhibitory maturation relatively suppresses
spontaneous activity specifically when it causes CP onset.
3. Closing one eye greatly broadens the spatial correlations
between inputs from that eye (Faguet et al., 2009). Broader
correlations yield less retinotopic refinement of receptive
fields (RFs) under Hebbian (correlation-based) mechanisms of synaptic plasticity. This slowing of retinotopic
refinement also decreases the peak synaptic strengths
52 Neuron 80, 51–63, October 2, 2013 ª2013 Elsevier Inc.

of both eyes’ relative to normal development underhomeostatic plasticity. Thus, broadening of deprived-eye
spatial correlations can explain the effects of MD during
the pre-CP.
This conceptual theory leaves open the quantitative issue of
whether (1) and (3) can occur simultaneously: that is, whether
visual activity can contribute sufficiently during the pre-CP toslow retinotopic refinement and weaken both eyes’ inputs, but
not sufficiently to allow OD plasticity. We use simple computational and mathematical models to demonstrate that (1) and (3)can occur simultaneously and more generally that the theory
can reproduce multiple aspects of both pre-CP and CP behavior.
We examine the models to, determine the elements responsible
for each aspect of the outcome, arriving at a robust conceptual
picture of the key elements required to explain pre-CP and CP
behavior independent of model details. The main behaviors predicted by our theory arise cohesively at three very different levelsof description: the conceptual level; in a computational biologicalmodel; and in a simpler model analyzed mathematically (Supplemental Analysis, S1A available online). This supports the robustness of our findings, which we argue in the Discussion may apply
to cortical development more generally.
RESULTS
Modeling Framework
Here, we describe the main features of the model; full details andfurther discussion of parameter dependencies are in the Supplemental Computational and Experimental Procedures, S2A–S2C.
We model a single pyramidal neuron in V1 that receives input
from lateral geniculate nucleus (LGN) neurons driven by eacheye (Figure 1A). We neglect interactions between V1 cells
because a simpler model suffices to understand how our theory
can explain experimental observations. Each eye’s inputs are
uniformly spaced in a two-dimensional retinotopic space. The
density of contralateral-eye input neurons is about twice thatfor ipsilateral-eye inputs, as in mice (e.g., Hensch et al., 1998;
Hofer et al., 2006). We assume synapses from LGN are modified
by Hebbian plasticity, which is driven by correlations between
postsynaptic and presynaptic activity (Hebb, 1949; Markram
et al., 1997; Bi and Poo, 1998) and therefore is sensitive to thecorrelation structure of the input (e.g., Miller 1990; Dayan and
Abbott 2001; Gerstner and Kistler 2002), and by homeostatic
plasticity, which regulates the overall activity level and stabilizes
learning (Turrigiano et al., 1998; Maffei et al., 2004; Mrsic-Flogel
et al., 2007; Ibata et al., 2008; Kaneko et al., 2008).
We model the firing rate of each input neuron as the sum of two
Gaussian random variables: the spontaneous component andthe visually-evoked component. The spontaneous component
represents baseline input statistics in the absence of a visual
stimulus, which by definition are unchanged by visual stimuli.
We separately model these two components to demonstratethat acute enhancement of inhibition is sufficient to suppress
the contribution to Hebbian plasticity of the spontaneouscomponent relative to that of the visually-evoked component.
We assume that the spontaneous component is always active
but relatively weak. The visually-evoked component contributes

NeuronSpontaneous/Visual Ratio Controls CP Onset

A

B

pre-CPCP

Figure 1. Basic Elements of the Model(A) Model architecture: a cortical neuron receives
excitatory input from 28 3 28 contralateral inputs
and 20 3 20 ipsilateral inputs It also receives
thresholdthresholdinhibitory input from nearby cortical neurons,
inhibition
which are not explicitly modeled. The excitatory
cortical neuron
y mysynaptic strengths are subject to activity-depeninput spontaneous visual
spontaneous visual
nearby neurons
synaptic weights wident plasticity.
(B) Hypothesis of how the maturation of inhibitionC
initiates the CP for OD. Visual input is twice asstrong as, but only 1/10 as frequent as, spontacontra
between−eye
ipsi
10
10
10
neous input. During the pre-CP, the spontaneousxi
spont
visual, NR
input drives the cortical cell, so the contribution ofvisual,MD
10
10
10
the rare visual input to plasticity is relatively small.
10
10
10
At CP onset, maturation of inhibition subtracts
equally from all responses, causing greater pro10
1010contralateral input
Ipsilateral input
portional weakening of spontaneous than visual10
1010
input and shifting many responses to spontaneous
input below the threshold for Hebbian plasticity.
10
10
10This makes visual input the primary driver of
10
10
10
plasticity.
−1
0
1
−1
0
1
−1
0
1
distance
distance
distance(C) Input correlations (covariance) in simulations
are modeled as Gaussian functions of the distance
between input retinotopic positions. At a given distance, inputs from the same eye (‘‘contra’’ or ‘‘ipsi’’) are more correlated than those from opposite eyes(‘‘between-eye’’). Under normal rearing (NR), the visually-evoked covariance (gray curves) is four times as strong as, but retinotopically as precise as, thespontaneous input (blue curves). During MD to the contralateral eye, the closed-eye and between-eye visual covariances (red curves) are reduced in amplitude
and are retinotopically broadened because of the blurring of the visual stimulus through the closed eyelid.

covariance

outputmaturationof inhibition

2

2

2

1

1

1

0

0

0

−1

−1

−1

−2

−2

−2

−3

−3

−3

−4

−4

−4

only 10% of the time, but when activated is twice as strong as the
spontaneous component under normal-rearing (NR) conditions(Figure 1B). The infrequent visual contribution instantiates theidea that visual stimuli with orientation, position, and other features appropriate to visually drive a given neuron are present
only infrequently in natural stimuli (Vinje and Gallant, 2000). For
each component, pairs of inputs are correlated with a strengththat decreases with their retinotopic separation and, for a given
separation, is stronger for inputs from the same eye than from
opposite eyes: (Ohshiro and Weliky, 2006; Weliky and Katz,
1999) (Figure 1C). Under NR, spontaneous- and visually-evokedcomponents have the same spatial and interocular structure,with within-eye correlations twice as strong as between-eye.
LGN spontaneous activity has strong between-eye correlations
due to drive to LGN by cortical feedback (Weliky and Katz,
1999; Huberman et al., 2008). The identical spatial structure of
the two components, assumed for simplicity, is supported byexperiments finding almost identical shape (although not necessarily amplitude) of spatial covariance with or without visual stimulation in LGN (Ohshiro and Weliky, 2006) and V1 (Fiser et al.,2004). The structure of between-eye LGN correlations in visually-evoked activity has not been characterized and so wastaken for simplicity to be the same as in spontaneous activity.
To model MD, in which the visual stimulus reaches the contralateral eye only after penetration through the closed eyelid, we
assume the visual component of the closed-eye is blurred(Faguet et al., 2009) and weakened by half. Such blurring is
known to influence V1 development (White et al., 2001; Akerman
et al., 2002) and has been suggested to underlie the slowing of
retinotopic development by MD but not monocular inactivation(MI) during the pre-CP (Smith and Trachtenberg, 2007). To model
MI induced by pharmacologically inactivating retinal activity or
enucleating one eye, we set the visual component from the inac-

tivated eye to zero. We ignore MI-induced changes in spontaneous activity for simplicity, although MI alters burstiness of
responses and increases correlations in LGN spontaneous
activity (Weliky and Katz, 1999; Linden et al., 2009).
The experience-dependent refinement of the retinotopic map
during the pre-CP was measured by intrinsic imaging as adecrease in map scatter. Scatter was defined as the averageover pixels of the difference between a pixel and its near neighbors in their preferred retinotopic positions (Smith and Trachtenberg, 2007). To model this, we assume that decreased scattercorresponds to decreased RF sizes in single neurons. A larger
RF will be flatter near its peak. In the presence of measurement
noise, flatter RFs will produce larger map scatter. Therefore, we
simply equated map scatter with RF size in the model. Of course,aligning RF peak locations among nearby cortical neurons may
also reduce scatter. We ignore this factor because addressing
it requires modeling multiple, recurrently connected cortical cells
and perhaps plasticity of intracortical synapses. This introducescomplexities and arbitrary modeling choices that are not needed
to illustrate the workings of our theory. However, we note that, if
intracortical interactions lead nearby RFs to receive correlated
inputs, then broader spatial correlations in LGN inputs should
yield more widely scattered RF peaks of nearby cells as wellas larger RFs. Thus, if we were to model recurrent interactions
and the movements of RF peaks, we would expect qualitatively
the same effects on map scatter as we find in the simpler model
examined here.
Modeling of a Cortical Neuron and Plasticity Rule
The firing rate y of the output neuron is taken to be a thresholdP
linear function of the inputs: y = ½ N
i = 1 wi xi  my + . Here, []+ is alinear rectification function, [x]+ = x if x R 0, = 0 otherwise;
wi S 0 is the synaptic strength from, and xi the firing rate of,
Neuron 80, 51–63, October 2, 2013 ª2013 Elsevier Inc. 53

NeuronSpontaneous/Visual Ratio Controls CP Onset

B

NR−C

C
NR−I

0.02

−
0.01

I
−
0

0
−1

0.02

I
−
0

50 100time

0position

1

0.8
0.7

CBI

position

0.03
C
MD−I

0.55
0.5

0.01

−

−

E
NR−C
NR−I
MD−C
MD−I
MI−I

0.02

C

MD−C

NR−C
NR−I
MD−C
MD−I

0.03

0

50 100MDsynaptic strength

position

0.03

D

0.04

0.5

0

0.4
0

0.4

2

0.3

100

4
3

0.35

1

NR
MD
50
time

6
5

0.45

0.6

0.01

8
7

responseNR−

receptive field width

A

0.25
0

50
time

100

0
0

50
time

100

Figure 2. Model Activity-Dependent Plasticity during the Pre-CP: Activity-Dependent Retinotopic Refinement without OD Plasticity(A) Left panels: development of synaptic strengths (color) over time during the pre-CP under NR (top) and MD (bottom; C closed from time 0). Final strengths areinsensitive to initial conditions, which are identical in the two cases. Upper and lower halves show C and I strengths, respectively, over a one-dimensional (1D)
section of retinotopic positions through the center of the 2D RFs (note, there are more C than I input neurons). Right panels: final 2D RFs of both eyes. Under NR,
retinotopic refinement occurs with little OD competition. Under MD, there is less retinotopic refinement and both eyes’ strengths weaken relative to NR, but again
there is little OD competition.
(B) One-dimensional sections of the final synaptic strengths at time 100 under NR (black) and MD (red) (solid, C; dashed, I).
(C) Contralateral bias index (CBI) (see Supplemental Computational and Experimental Procedures, S2D), which ranges from 0 to 1 for responses varying frompurely driven by I to purely driven by C; the CBI settles to 0.73 under NR (black) and 0.68, only slightly lower, under MD (red), reflecting denser input from C than I.
(D and E) RF width (D) and the response magnitude (E) (defined in Supplemental Computational and Experimental Procedures, S2D) versus time. C, contralateraleye; I, ipsilateral eye; solid lines, C; dashed lines, I; black, NR; red, MD; green, monocular inactivation (MI) of C.
See also Figure S1.

the ith presynaptic neuron; and m is the strength of inhibition
driven by a nonspecific pooling of neighboring neurons. The
instantaneous activity averaged over neighboring neurons isapproximated by the long-term averaged firing rate, y, of thepostsynaptic neuron, assuming spatially homogeneous averagefiring rates. For simplicity, we model the maturation of corticalinhibition as an immediate increase of the inhibitory strength mat the onset of the CP for OD (Di Cristo et al., 2007; Fagiolini
et al., 2004; Katagiri et al., 2007; Kuhlman et al., 2010; Sugiyama
et al., 2008). Instead, if a gradual increase of inhibition(Morales et al., 2002) were modeled, it would progressively
open the CP by increasing the magnitude of the OD shift under
MD with increasing inhibitory strength (e.g., see Figure 3E for
the steady-state OD shift versus inhibition level).
The instantaneous change in synaptic strengths, wi, ismodeled as the sum of a Hebbian term and a homeostatic
term. The Hebbian term is proportional to a measure of correlation of pre- and postsynaptic firing, hxif(y)i  ahxii hf(y)i, where h i
is a short-term average, a = 1.001 is a constant slightly larger
than 1 to force a competitive learning outcome by suppressing
synapses with small input-output correlation, and f(y) = y  q
for y > q, f(y) = 0 otherwise. q is a threshold level of postsynaptic
activity required for Hebbian plasticity. We generally take q =2 Hz but also consider varying q. The homeostatic term is proportional to wi ðy0  yÞ, with y0 = 1.2 Hz. Here, y is the long-term
average of the postsynaptic firing rate. This term scales synaptic
strengths to bring y toward the set-point firing rate y0. The proportionality to wi causes the homeostatic term to multiplicatively
scale synaptic strengths. Such homeostatic scaling has been
54 Neuron 80, 51–63, October 2, 2013 ª2013 Elsevier Inc.

experimentally observed (Turrigiano et al., 1998; Stellwagen
and Malenka, 2006) including in V1 of developing animals in vivo
(Desai et al., 2002; Mrsic-Flogel et al., 2007; Kaneko et al., 2008).
Our choices of a thresholded input/output function (the []+function in the expression for y) and subtractive inhibition ensurethat inhibition suppresses spontaneous activity relative to visually-evoked activity, as illustrated in Figure 1B. Subtraction of a
fixed amount removes a greater proportion of the smaller spontaneous input than of the larger visual input and will also cause agreater fraction of spontaneous than of visual input to lie below
the spiking threshold. Alternatively, inhibition might reduce all
responses by an equal factor (e.g., Rothman et al., 2009). We
choose a neuronal model in which inhibition changes the relative
response strengths in order to explore our theory that such achange underlies CP opening. We will experimentally test theidea that inhibition acts in this way at CP opening. The function
f in the plasticity rule further suppresses the contribution of
weak spontaneous activity to Hebbian plasticity. We will explore
how varying the threshold q influences developmental outcome.
The exact form of f is not important: e.g., f could be a smoothfunction that enhances Hebbian modifications at high postsynaptic firing rates instead of suppressing such modifications at
low rates (see Supplemental Computational and Experimental
Procedures, S2C).
Simulations of Visual Cortical Development
We simulated pre-CP and CP plasticity under this model. Figure 2
shows activity-dependent development of retinotopy (asmeasured by RF size), OD, and response strength during theNeuronSpontaneous/Visual Ratio Controls CP Onset

pre-CP, beginning at eye opening (time zero). The initial RF profile was chosen relatively broad. Under the NR condition (Figure 2A, top, and black lines in Figures 2B–2E), the neuron’s RF
narrowed (synaptic strength became more concentrated near
the RF center, Figures 2A and 2D) due to the spatial correlationsof both the spontaneous and the visually-evoked activity components. This refinement is a typical outcome of competitive Hebbian plasticity, which tends to wire together a closely correlatedgroup of neurons onto a given postsynaptic cell. Despite theretinotopic competition, there was relatively little between-eye
competition, as reflected in the comparable strengths of thetwo eyes’ final synaptic strengths (Figure 2B). This wascontrolled by the relative magnitude of between-eye correlations
versus within-eye correlations for both the spontaneous andvisually-evoked activity, with relatively weaker between-eye correlations yielding stronger interocular competition. The contralateral bias index (CBI), which measures the bias toward thecontralateral eye, reached slightly above 0.7 (Figure 2C), primarily reflecting the two times denser input from the contralateraleye (Gordon and Stryker, 1996).
Homeostatic plasticity stabilized the Hebbian plasticity, which
otherwise was unstable. The multiplicative homeostatic scaling
yielded a bell-shaped synaptic strength profile, because it
more strongly suppressed stronger synapses. In contrast, stabilization by a hard upper bound on synaptic strengths pushes all
synapses to near the maximal or minimal allowed values (e.g.,Song et al., 2000; Miller and MacKay, 1994).
Under MD of the contralateral eye initiated at time zero during the pre-CP, the refinement of the RF was decreased
compared to the NR condition (Figures 2A, 2B, and 2D) but
with little change in OD (Figure 2C). The refinement decreased
due to the broadly correlated residual visual stimulus throughthe deprived eyelid (Faguet et al., 2009). Despite a dramaticchange in the distance over which visual inputs were correlated, the RF widened only by 30% (Figure 2D), because
the contribution of the frequent spontaneous input to plasticity
was greater than that of the rare visually evoked input during
the pre-CP.
The widening of the RF, in turn, reduced the maximum synaptic strength of each eye under the approximate conservation of
total synaptic strength enforced by the homeostatic term (Figure 2E). The total synaptic strength was roughly preserved
because the reduction in input activity induced by MD was small
given the dominant spontaneous input. MD did not induce muchchange in OD because the frequent input from spontaneous
activity produced almost equal overall levels of input from both
the deprived and open eyes (Figure 2C). The CBI under MD
was again about 0.7, despite similar synaptic strengths fromboth eyes, because of the denser input from the contralateraleye. Under MI (blockade of all visually-evoked activity) of thecontralateral eye, the ipsilateral RF did not show the full widening
and peak reduction seen under MD (Figures 2D and 2E), qualitatively in agreement with experiments (Smith and Trachtenberg,2007). This was due to the absence of the broadly-correlated
residual visual activity that remains during MD, as suggested
by Smith and Trachtenberg (2007).
Initiation of MD after the opening of the CP has a very different
effect (Figure 3). We continued the NR condition from Figure 2,changing the inhibitory strength m from 0 to 5 at time 100
to represent CP opening. As discussed above (Figure 1B),
the increased inhibition suppressed the spontaneous input
component more strongly than the visually-evoked component.
Homeostatic plasticity compensated for the inhibition-induced
loss of activity by scaling up synaptic strengths to approximately
preserve the long-term average of the postsynaptic firing rate,
largely maintaining the reduced spontaneous-to-visual activity
ratio. The reduced ratio, together with the threshold that suppresses Hebbian modifications under small postsynaptic activity, reduced the contribution to plasticity of spontaneous activity
relative to visually-evoked activity, rendering the neuron moresensitive to changes in visual statistics. As a result, MD, initiatedat time 150, caused a strong OD shift toward the open (ipsilateral) eye (Figures 3A–3C), as well as greater broadening of theipsilateral synaptic strength profile than was seen during the
pre-CP (deprived-eye RF width decreased by about 50%, Figures 3B and 3D; see Discussion).
The amount of OD shift depended on parameters—thestrength of inhibition and the Hebbian threshold—in a seemingly
complex way, but underlying this is a simple dependence of the
OD shift on the degree to which visually-evoked versus spontaneous activity contributed to Hebbian plasticity. Figure 3E plots
the final CBI after MD as a function of the spontaneous-to-visualratio of the mean firing rate (the rate without visual input divided
by the rate with visual input) at various levels of inhibition and of
the Hebbian threshold. For any given threshold, a lower spontaneous-to-visual ratio of firing rates, (due to stronger inhibition)
increased the OD shift. As threshold increased, the ratio of firing
rates that could produce a strong OD shift increased. Thisis because the spontaneous component did not contributeto plasticity when its rate fell below the Hebbian threshold(Figure 1B).
The many curves for different thresholds in Figure 3E allcollapse onto a single curve if the CBI is plotted against the
spontaneous-to-visual ratio of the gains for Hebbian plasticity(Figure 3F; see Supplemental Computational and Experimental
Procedures, S2C and S2D for a mathematical definition of thegain). Under the current set of parameters, the gain is equivalent
to the frequency of events that drive Hebbian plasticity, i.e., thepercentage of time the output firing rate exceeds the Hebbian
threshold. That is, across parameters, the CBI is simply determined by the relative contributions of spontaneous and visual
activity to Hebbian plasticity. This is the key, robust predictionof the model. A mathematical analysis of the Hebbian component also confirms that this is the key quantity for predicting
the magnitude of the OD shift (see Supplemental Computational
and Experimental Procedures, S2C).
In Supplemental Analysis, S1 and Figure S1, we present a
simplified mathematical model for which we can obtain an analytic expression for the weights that develop. In the simplified
model, postsynaptic activity is a simple linear sum of theweights times the input rates, and Hebbian plasticity is proportional to the covariance of pre- and postsynaptic activity
without the nonlinearity used above and with a = 1. The homeostatic plasticity rule and the statistics of excitatory input to thecortical neuron are unchanged. Rather than modeling subtractive inhibition, spike threshold, and Hebbian threshold as
Neuron 80, 51–63, October 2, 2013 ª2013 Elsevier Inc. 55

NeuronSpontaneous/Visual Ratio Controls CP Onset

A

B

C

D

E

F

Figure 3. Model Activity-Dependent Plasticity during the CP: Activity-Dependent Retinotopic Refinement and OD Plasticity
The simulation was continued from the final state of Figure 2 under NR and CP was modeled as mature inhibition (increase of m from 0 to 5 at time 100).
(A) Development under NR to time 150 and MD, initiated at time 150. Now, MD causes a strong shift to the open ipsilateral (I) eye. Panels are as in Figure 2A. Note,
homeostatic plasticity rapidly increased synaptic strengths after inhibitory strengthening.
(B) One-dimensional sections of the synaptic strengths at time 150 under NR (black) and at time 200 under MD (red) (solid: contra; dashed: ipsi). Conventions as in
Figure 2B.
(C) Comparison of the time courses of the contralateral bias index (CBI) under MD for CP (m = 5, solid-line) and pre-CP (m = 0, dotted-line) conditions. Maturationof inhibition yields about a 5-fold greater change in CBI. Note that the final CBI for the CP under MD indicates only slight ipsilateral advantage (CBI = 0.45): thesummed synaptic strength for each eye is similar, although I has stronger strengths, because of denser input from contralateral (C) eye.
(D) The RF width and the response magnitude versus time as in Figures 2D and 2E (solid, C; dashed, I).
(E and F) Final CBI versus the spontaneous-to-visual ratio of firing rates (E) and the ratio of gains (F) under various levels of the inhibitory strength (varied from 0 to10) and the Hebbian threshold (varied from 1 to 3 Hz; shown with different colors). Black crosses indicates parameters used in (A)–(D).
See also Figure S1.

above, we simply assume that, in the absence of visual drive,
postsynaptic activity is suppressed a certain fraction of thetime by inhibition, where this fraction increases with inhibitory
strength. This simpler model reproduces the main results ofour theory: in the pre-CP, MD yields broader RFs anddecreases both eyes’ inputs strengths with minimal change in
OD; while in the CP, MD yields strong OD shifts (and alsobroader RFs). The analytical expression for the weights showsthat these are all robust outcomes, with one exception. The
decrease in both eyes’ weights induced by MD in the pre-CP
involves a tradeoff between the decreased input activity, which
reduces the weights overall, homeostatic plasticity, which increases the weights, and the spatially broader input activity,
which spreads the weights and thus reduces the peak weightunder homeostatic plasticity. Thus, relatively strong reduction
in input activity, relatively weak homeostasis, and relatively
broad correlations all favor reduction in the two eyes’ weights,
while with different sets of these three choices the open eye’s
weights or both eye’s weights can instead be increased by
MD in the pre-CP. The model also predicts that the final steadystate of the synaptic strengths is insensitive to the initial synaptic strengths, which was generally true in the previous simulations. The ability of the simple model to reproduce the results
demonstrates their overall robustness, showing that the results
depend on the overall framework of Hebbian plus homeostatic
plasticity and an inhibition-induced reduction in the spontaneous-to-visual activity ratio, but not on the details of how inhibition causes this reduction.
56 Neuron 80, 51–63, October 2, 2013 ª2013 Elsevier Inc.

Spontaneous-to-Visual Activity Ratio in Freely BehavingMice
We have shown that a suppression of the spontaneous-to-visual
activity ratio induced by the maturation of inhibition is sufficient
to explain the transition from pre-CP to CP plasticity. To test
the prediction that such suppression occurs, we recorded extracellularly with head-mounted tetrodes from putative pyramidalcells in V1 of freely behaving adult mice (Supplemental Computational and Experimental Procedures, S2E). All experiments
were performed under the approval of the local animal care/review committees of RIKEN Brain Science Institute. We studied
both WT and GAD65-KO animals. The GAD65-KO mousemodels a pre-CP state and will enter a CP even in adulthood
upon chronic strengthening of inhibition by administration ofdiazepam (Fagiolini and Hensch, 2000). We also studied adult
GAD65-KO mice that had previously been exposed to chronic
diazepam for 6 days around postnatal day (P) 28 (KOc), which
had caused them to enter and then exit the CP. These mice,
like adult WT mice, are in a post-CP state in which plasticity is
no longer triggered by administration of diazepam (Fagiolini
and Hensch, 2000; Iwai et al., 2003).
Acute strengthening of inhibition by diazepam lowered thespontaneous-to-visual activity ratio specifically in those animals
in which this treatment, when maintained over days, opens theCP. We measured both sensory-evoked responses driven by
light-emitting diode (LED) flashes and baseline firing rates (Figures 4A and S2). Average firing responses to LED flashes oftypical multi-unit activity from fully mature WT and GAD65-KO

NeuronSpontaneous/Visual Ratio Controls CP Onset

A

B

baseline

Figure 4. Tetrode Recording from Freely
Behaving Adult Mice

cumulative dist.

ratio of firing rates

Spontaneous-to-visual activity ratio is a physiological correlate of OD plasticity as predicted by
the model.
(A) Experimental setup: periodic LED flashes (1 s)were presented to freely behaving adult mice
while putative pyramidal cells were recorded
from V1.
(B) Average firing response to LED flashes ofKOtypical multi-unit activity from WT (top) andGAD65-KO mice (bottom) before (left) and after(right) acute administration of diazepam. Greenbars mark stimulus duration.
(C) Summary of the spontaneous-to-visual ratioof firing rates before and after acute administration of diazepam (+Dz) in different mouseCD
3
1
groups. Diazepam decreased the spontaneousn.s.
n.s.
**
to-visual ratio of GAD65-KO mice but did not
2.5
0.8
significantly change the ratios of adult WT
mice or GAD65-KO mice chronically treated2with diazepam earlier in life (KOc). Box-and0.6
whisker plots show 25th to 75th percentiles
1.5(box), median (red line in box), the range of data
0.4
falling outside the 25th to 75th percentiles by1
WTup
to 1.5 times the distance from 25th to 75th
KO
0.2
0.5
percentile (whiskers) and outliers outside thisKOcrange (red crosses).
KO+Dz
0
0
(D) Cumulative distribution of spontaneous-to0
0.5
1
1.5
2
WT
WT+Dz KO
KO+Dz KOc KOc+Dz
visual ratios of firing rates of various mouse
ratio of firing rates
groups. Ratios of WT (black) mice were smaller
than those of GAD65-KO (red) mice. Acute diazepam (green) decreased the ratio of GAD65-KO mice far beyond adult WT levels. In KOc mice (blue), the visual response ratio had returned to a level
similar to naive GAD65-KO mice, but no longer responded acutely to the drug as in adult WT mice (C).
See also Figures S2 and S3.

mice are shown in Figure 4B before and after acute administration of diazepam (see Figure S2 for firing rates of all isolated
recorded cells under all treatment and stimulus conditions). We
then computed the spontaneous-to-visual ratio of firing rates(baseline firing rate divided by LED-evoked firing rate; Figure 4C).
In agreement with the model’s prediction, acute enhancement ofinhibitory transmission by diazepam suppressed the spontaneous-to-visual ratio in GAD65-KO mice (p = 0.002 Mann-Whitney test; 9 mice, n = 68 cells), in which diazepam opens the CP(Fagiolini and Hensch, 2000). Surprisingly, diazepam did not alter
the spontaneous-to-visual ratio in animals in which diazepam nolonger opens plasticity (Iwai et al., 2003), neither in adult wildtype (WT) mice (p = 0.6 Mann-Whitney test; 3 mice, n = 21 cells)
nor in KOc mice (p = 0.1 Mann-Whitney test; 2 mice, n = 18 cells).
To compare activity ratios across different animal groups,
additional cohorts were recorded without acute diazepam treatment (c.f., Figure S2, showing all firing rates for all animals inall conditions). In Figure 4D, the cumulative distributions of
the spontaneous-to-visual ratios of firing rates are shown fordifferent mouse groups. WT mice (11 mice, n = 66 cells) exhibited
significantly smaller ratios than the GAD65-KO mice (p = 0.003,
Mann-Whitney test; KO, 21 mice, n = 112 cells), consistent withthe pre-CP state of the GAD65-KO mice. Acute administration ofdiazepam in the GAD65-KO mice (9 mice, n = 68 cells) reduced
their ratios to levels smaller than in adult WT mice (p = 0.03,
Mann-Whitney test). WT mice also showed significantly smaller

ratios than in KOc mice (p = 0.02, Mann-Whitney test; KOc, 21
mice, n = 127 cells), whose ratios were not significantly different
from those of naive GAD65-KO mice (p = 0.6, Mann-Whitneytest). Thus, diazepam only transiently suppresses ratios in
GAD65-KO mice to open the CP, after which the ratios in KOc
animals return to their pre-CP levels. This finding was unexpected, but could reflect the enduring weak inhibition due tothe persistent lack of GAD65.
One caveat to these results is that we have somewhat overestimated the spontaneous-to-visual ratio. We overestimatedspontaneous activity, which we took as the baseline activity inthe absence of LED stimulation. Animals experience naturalvision under these conditions, so infrequently effective visual
drive was added to the spontaneous. Moreover, an LED flash
is not an optimal visual stimulus for visual cortical cells, i.e., weunderestimated visually evoked activity. However, assuming
linear addition of LED-evoked and other visual responses tospontaneous firing rates, the spontaneous-to-visual ratio estimated here shows a monotonic relation to the underlying true
ratio (see Supplemental Analysis, S1B). Our primary aim is tomake qualitative rather than quantitative predictions, due tothe simplicity of our models. Nonetheless, the measured ratiochanges, corrected for overestimation using reasonable parameter values and applied to the simple plasticity model we havepresented here, account quantitatively for the changes in plasticity in the majority of cells (Figure S3).
Neuron 80, 51–63, October 2, 2013 ª2013 Elsevier Inc. 57

NeuronSpontaneous/Visual Ratio Controls CP Onset

DISCUSSION
In this study, we have presented a hypothesis for the origin of
CPs that explains the diverse effects of visual plasticity in a unified manner. The transition from developmental plasticity seenprior to and during the CP in V1 arises through inhibitioninduced shifts in cortical activity, with no additional requirement
for changes in plasticity rules. The key idea is that the maturation of inhibition that initiates the CP reduces the ratio of
spontaneous-to-visual activity. In simulations and analysis, we
showed that changes in the ratio suffice to account for defining
features of pre-CP and CP plasticity: MD during the pre-CP
retards retinotopic refinement of RFs and weakens both eyes’
inputs with little OD change (Smith and Trachtenberg, 2007);
MD during the CP produces a large OD change (Gordon andStryker, 1996). The work supports the more general idea thatCPs may open when the dominant learning cue switches frominternal to external sources, without requiring changes in plasticity rules.
In our extracellular recordings from mature, awake-behaving
mice, we directly tested the hypothesis that the maturation of
inhibition that opens the CP decreases the spontaneous-to-visual activity ratio. Consistent with our theory, diazepam potently
decreased the spontaneous-to-visual activity ratio in naive
GAD65-KO mice, in which it opens the CP (Fagiolini and Hensch,2000). Diazepam did not alter this ratio in WT or KOc animals, inwhich the CP had already opened and closed (Iwai et al., 2003).
Furthermore, animals entering the CP—GAD65-KO mice treatedwith diazepam—exhibited significantly lower spontaneous-tovisual activity ratios than either pre-CP mice (untreated
GAD65-KO mice) or post-CP mice (WT or KOc).
We are proposing that maturation of inhibition induces a transition in the dominant input driving cortical activity, from spontaneous to visually-driven. This fits with a more general picture thatbrain circuits self-organize based on innate activity and molecular cues during an early stage and more finely tune later in development according to stimuli from the external world (Katz andShatz, 1996; Feller and Scanziani, 2005), but with the caveatthat experience also contributes during the pre-CP. Before theCP, spontaneous activity in V1 and its inputs plays fundamentalroles in the cortical acquisition of retinotopic organization (Canget al., 2005; Cang et al., 2008), synaptic strengthening and pruning (Hooks and Chen, 2006), OD segregation (Huberman et al.,2006), and orientation selectivity (Frégnac and Imbert, 1984;
Chapman and Stryker, 1993; Crair et al., 1998; Chapman andGödecke, 2000; White et al., 2001, Fagiolini et al., 2003). After
CP opening, the heightened sensitivity of visual development
to sensory experience is manifest not only in the opening of
OD plasticity, but also in the dependence on visual experienceof (1) the maintenance and further development of orientation
selectivity, which in the pre-CP are independent of experience(Frégnac and Imbert, 1984; Crair et al., 1998; White et al.,2001), and (2) at least in rodents, the binocular matching oforientation preference, which occurs during the CP (Wanget al., 2010). Spontaneous activity plays a similar role in theorganization of tonotopic maps in the auditory system before
the onset of hearing (Leao et al., 2006; Moody and Bosma,
2005; Tritsch et al., 2007).
58 Neuron 80, 51–63, October 2, 2013 ª2013 Elsevier Inc.

Mechanisms of CP Initiation
No previous model of CP induction has addressed the fact that
MD-induced plasticity is present before the CP but differs inquality from CP plasticity. The alternative models that bear theclosest resemblance to ours also suggest that inhibition opens
the CP by changing activity patterns, but focus on changes
that could open interocular competition under spike-timing
dependent plasticity (STDP). Kanold and Shatz (2006) argued
that excess spontaneous activity of cortical origin (as opposedto activity driven by the spontaneous component of LGN input,
as we consider) will decorrelate postsynaptic spiking from presynaptic inputs, causing STDP to strengthen less active inputs
relative to more active inputs. Reduction of cortical spontaneous
activity by inhibition causes STDP to instead strengthen more
active relative to less active inputs, allowing the OD shift caused
by MD. The model requires fine tuning of the level of pre-CP
cortical spontaneous activity so that pre-CP MD causes nochange in OD. Kuhlman et al. (2010) argued that inhibitory postsynaptic potentials (IPSPs) follow excitatory postsynaptic potentials (EPSPs) with a short time delay, so that stronger inhibitiontightens the temporal window within which inputs must fire
together to drive postsynaptic spikes and yield potentiation
under STDP. The more temporally coherent, visually-driven
open-eye inputs would therefore have an advantage over more
temporally dispersed deprived-eye inputs. Inhibitory maturation
would increase this advantage sufficiently that the initiallyweaker open eye could out-compete the deprived eye. Hensch(2005) suggested that weak inhibition could prevent interocularcompetition by allowing weaker as well as stronger inputs todrive backpropagating action potentials and so be strengthened
by STDP.
There have been many suggestions that molecular changes inplasticity mechanisms open the CP. These proposals again
would not account for the transition from pre-CP to CP plasticity,
but this does not preclude contributions of molecular mechanisms. Gene and protein expression patterns develop in amanner dramatically dependent upon visual input (Tropea
et al., 2006; Majdan and Shatz, 2006; Lyckman et al., 2008).
Several studies suggest that plasticity rules do not change during the pre-CP to CP transition (Fox, 1995; Hensch et al., 1998;
Kuhlman et al., 2010; Chun et al., 2013), as we have assumed,
but others report changes in plasticity rules (Jiang et al., 2007).
One molecular proposal focuses on the switch in the predominant subunit composition of NMDA receptors from NR2B to
NR2A, which occurs over the first 5 postnatal weeks and isboosted within hours by visual experience and attenuated orreversed by visual deprivation (Flint et al., 1997; Quinlan et al.,1999a, 1999b; Roberts and Ramoa, 1999). It has been argued
that inhibitory maturation might open the CP by triggering thischange in NMDA receptors: GAD65-KO mice show reduced
NMDA receptor subunit NR2A levels and correspondingly slower
NMDA currents, which are rescued by diazepam application(Kanold et al., 2009). However, while NR2A may modulate OD
plasticity (Fagiolini et al., 2003; Cho et al., 2009), it is not requiredfor opening or normal closing of the CP (Fagiolini et al., 2003).
There have been conflicting reports about cortical LTD in
GAD65-KO mice. One study reported a defect of cortical LTD
in these animals that could be rescued by chronic diazepam

NeuronSpontaneous/Visual Ratio Controls CP Onset(Choi et al., 2002). However, our earlier studies (Hensch et al.,1998) found clear synaptic depression following low-frequency
stimulation (LFS) in the same GAD65-KO mouse line of Kashet al. (1997) studied by Choi et al. (2002). Failure to observe
LTD in these fragile animals could be an artifact of poor general
nutrition or stress due to housing or slice recording conditions(Artola et al., 2006; Kash et al., 1997; Nishie et al., 2012).
To resolve this issue, we have revisited it in a second GAD65KO mouse line (of Asada et al., 1996) using a two-pathway paradigm (Kirkwood and Bear, 1994; Renger et al., 2002) in which
standard V1 coronal slices from GAD65-KO mice were cut vertically from white matter to layer 4 (Supplemental Computational
and Experimental Procedures, S2F). One ‘‘test’’ input is first
determined to be independent of a ‘‘control’’ input across the
cut by linearly additive responses and a lack of paired-pulseinteractions when stimuli are applied at short intervals (<50 ms)
to each side. The test pathway showed clear LTD in response
to LFS, while the unconditioned control input remained stable(Figure S4A). This confirms both the independence of the twopathways and the good health of our slice preparations for theduration of the recording. In a separate series of experiments,
repeated LFS to one pathway saturated LTD at comparable
levels in WT and GAD65-KO mice (Figure S4B). Thus, our data
now confirm robust LTD in two lines of GAD65-KO mice.
Other factors might play a role in CP initiation. In this study, we
focus on plasticity of synapses from excitatory feed-forward
inputs onto excitatory cells, but several recent reports also suggest a role for rapid plasticity of excitatory synapses onto inhibitory neurons (Gandhi et al., 2008; Yazaki-Sugiyama et al., 2009;
Kameyama et al., 2010; Aton et al., 2013; Kuhlman et al., 2013)
and inhibitory output plasticity (Maffei et al., 2006). Further
studies are required to determine the contributions made byeach of these factors and the dependencies between them.
While multiple mechanisms may potentially contribute to therange of phenomena in pre-CP and CP plasticity, we find it striking that the model we propose can account for both stages in a
simple, unified way.
Experimental Tests
The most direct test of the mechanism we propose would involve
directly manipulating the spontaneous-to-visual activity ratio. If
this can switch plasticity between pre-CP and CP regimes
without altering other hypothesized plasticity factors, it would
demonstrate both that our proposed mechanism is sufficientand that alternative factors are not necessary to explain this shift.
This might be achieved by optogenetic techniques (Knöpfel
et al., 2010; Wyatt et al., 2012) or by genetically engineered
reduction/increase in potassium currents to increase/decrease
spontaneous activity, respectively (Mizuno et al., 2007).
Correlative tests could be performed by measuring activity
patterns across experimental manipulations that change plasticity regime and/or measuring plasticity regime across manipulations that alter activity patterns. This would most directly relate
to our theory if conducted before or during the animal’s initial CP.
While reopening of the CP in adult animals might occur bydifferent mechanisms than initial CP opening, as further discussed below, it would also be interesting to examine spontaneous-to-visual activity ratio in a variety of paradigms of adult

CP reactivation (Morishita and Hensch, 2008; Bavelier et al.,2010). These include inhibitory precursor cell transplants (Southwell et al., 2010), perineuronal net removal or Otx2 reduction(Beurdeley et al., 2012), Nogo receptor deletion (McGee et al.,2005), caloric restriction (Spolidoro et al., 2011), dark exposure,fluoxetine administration, and environmental enrichment (Morishita and Hensch, 2008; Bavelier et al., 2010). A particularly
tractable example is the impact of cholinergic enhancement,
which has long been appreciated to improve neuronal signalto-noise (Sato et al., 1987; Parkinson et al., 1988), and which
rescues amblyopia in adulthood (Morishita et al., 2010).
Our model predicts that OD shifts should be observed during
the pre-CP if LGN input from one eye were mostly blocked, as
this would eliminate both visually-evoked and spontaneous input
from that eye (note that blockade of activity in one eye, rather
than in LGN, is unlikely to suffice, as substantial spontaneous activity remains in the inactivated eye’s LGN layers; Weliky andKatz 1999). Such OD plasticity in the pre-CP would demonstratethat spontaneous activity in the LGN is preventing an OD shiftduring the pre-CP and that developmental changes in other factors from pre-CP to CP are not necessary for OD plasticity.
Another prediction is that dark-reared animals, in which CP
onset (Fagiolini et al., 1994) and the maturation of inhibition (Morales et al., 2002; Katagiri et al., 2007) are delayed until light
exposure, should have larger, pre-CP-like spontaneous-to-visual ratios that should rapidly reduce to CP-like levels upon visual experience. Consistent with this, previous experiments (Benevento et al., 1992; Gianfranceschi et al., 2003) reported larger
spontaneous-to-visual activity ratios in dark-reared animals thanin age-matched controls.
Points the Theory Does Not Address
We do not address the mechanisms through which inhibition
suppresses the spontaneous-to-visual activity ratio in some circumstances but not others. In GAD65-KO mice, in which manyneurons showed low baseline firing rates after acute diazepam
administration (Figure S2), our suggestion that subtractive
inhibition and spike threshold cause suppression seems likely.
It is possible that the operating mode of parvalbumin-positivebasket cells that initiate the CP (Hensch, 2005) might subsequently change so that inhibition mainly divides input after the
CP (Atallah et al., 2012). In any case, our theory only requiresthat the maturation of inhibition that starts the CP suppress theratio, as we observed experimentally, regardless of mechanism.
Due to suppression of spontaneous input during the CP, ourmodel predicts that the increase of RF size in MD animals relative
to NR animals is much stronger during the CP than in the pre-CP.
Such relative expansions of RF size are experimentally observed
after long-term (Pizzorusso et al., 2006) but not brief (Gordon andStryker, 1996) MD. The simple learning rule we used in this paper
may not capture the time course of changes in RF size, which
may involve mechanisms beyond those we modeled such as
anatomical changes. These may have much longer time courses
than our simple model predicts.
Implications for Adult Plasticity and Disease
Our theory addresses only the opening of the CP. It does notaddress its ending, to which many factors may contribute
Neuron 80, 51–63, October 2, 2013 ª2013 Elsevier Inc. 59

NeuronSpontaneous/Visual Ratio Controls CP Onset(Bavelier et al., 2010; Hensch, 2005; Morishita et al., 2010; Harauzov et al., 2010; Pizzorusso et al., 2006; Maya Vetencourt
et al., 2008; Southwell et al., 2010). As such, it does not makepredictions about the evolution of the spontaneous-to-visualratio after CP opening. Nonetheless, the fact that this ratio wasincreased in post-CP animals relative to CP animals raises thepossibility that an increased ratio could be one factor in CP
closure. The fact that acute inhibitory strengthening did not lower
the ratio in post-CP animals, in which inhibitory strengthening
does not open a CP, is also consistent with this idea.
Evidence that inhibition can alter the spontaneous-to-visual
activity ratio, which in turn can alter plasticity, comes from neurological disorders. Mice experiencing early hypoxia-ischemia
display reduced parvalbumin expression, increased spontaneous-to-visual ratio (due to smaller visual responses), andless OD plasticity (Failor et al., 2010). In senescent monkeys,
the ratio decreases under administration of GABA or muscimoland increases under bicuculline (Leventhal et al., 2003). Rett
syndrome may provide a particularly informative case study. In
the MeCP2-KO mouse model, there is virtually no spontaneous
activity in V1 (Dani et al., 2005) and correspondingly low spontaneous-to-visual activity ratios, relative to WT animals, as well as
parvalbumin cell hyperconnectivity and a loss of visual acuity(Durand et al., 2012). These results are suggestive that hyperinhibition and too low a spontaneous-to-visual ratio can also bedeleterious to normal developmental plasticity. Notably, thisratio, parvalbumin cell connectivity and visual acuity can all be
restored to CP levels by dark-rearing or by genetic deletion of
NR2A receptors (Durand et al., 2012). Thus, optimization of
spontaneous-to-visual activity ratio may be essential for defining
different CP stages. Artificially adjusting spontaneous firing rates
might then offer potential therapeutic or learning strategies when
specific types or properties of developmental plasticity aredesirable.
General Implications for Cortical Development
Many sensory and sensorimotor systems exhibit sequences ofcritical or sensitive periods that progress from simpler to increasingly more complex aspects of experience (Cang et al., 2005;
Hensch, 2004; Brainard and Doupe, 2000; Werker et al., 2009;
Hernandez and Li, 2007; Scott et al., 2007). In the auditory system, for example, a cascade of CPs induces plasticity for tonotopy, bandwidth tuning, binaural integration, directionality,
phoneme discrimination, audio-visual matching, and semantic
and syntactic development (Barkat et al., 2011; Insanally et al.,2009; Popescu and Polley, 2010; Werker et al., 2009; Robinson,
1998; Lenneberg, 1967). An appealing hypothesis is that thesesequences of CPs may correspond to sequences of reductionsin the spontaneous-to-evoked activity ratio along hierarchies ofcortical areas (Felleman and Van Essen, 1991; Sharpee et al.,2011), switching the factors most strongly driving learning ineach area from internally to externally generated (Tritsch et al.,2007; Moody and Bosma, 2005; Katz and Shatz, 1996; Hooks
and Chen, 2006). Sequences of reductions in activity ratio may
in turn involve sequential maturation of key GABA circuits(Condé et al., 1996), which might arise through sequential activity-dependent maturation of perineuronal nets. These nets
induce maturation of inhibition upon binding Otx2 (Beurdeley
60 Neuron 80, 51–63, October 2, 2013 ª2013 Elsevier Inc.

et al., 2012), which is delivered both from retina to LGN to V1 (Sugiyama et al., 2008) and across cortex from the choroid plexus(Spatazza et al., 2013). It will be interesting to test whether acascade of reduced spontaneous-to-evoked ratios across areas
coincides with cascades of CPs, whether maturation of inhibition
is implicated both in these CP onsets and in corresponding ratiochanges, and whether postponement of these ratio changes
postpones CPs.
In sum, the co-occurrence of a CP with the switching of thedominant learning cue from internal to external may be a generalprinciple governing brain development.
SUPPLEMENTAL INFORMATION
Supplemental Information includes Supplemental Computational and Experimental Procedures, Supplemental Analyses, and four figures and can be foundwith this article online at http://dx.doi.org/10.1016/j.neuron.2013.07.022.

ACKNOWLEDGMENTS
We would like to thank M. Eisele and X. Pitkow for helpful discussions and C.
Yokoyama for comments on the manuscript. T.T. was supported by The Japan
Society for the Promotion of Science (JSPS), Grant-in-Aid 1806772 for JSPS
fellows, The Robert Leet and Clara Guthrie Patterson Trust Postdoctoral
Fellowship, Bank of America, Trustee, The Special Postdoctoral Researchers
Program of RIKEN, and RIKEN Brain Science Institute. The Japan Science andTechnology Agency supported H.M. (PRESTO), Y.Y-S., and T.K.H. (CREST).
N.A. and T.K.H. were further supported by RIKEN Brain Science Institute.
K.D.M. was supported by R01-EY11001 from the National Eye Institute (NEI)
and by the Gatsby Charitable Foundation through the Gatsby Initiative in Brain
Circuitry at Columbia University. T.T. and K.D.M. developed the theory in discussion with Y.Y-S. T.T. simulated the model in discussion with K.D.M. H.M.
and T.K.H. designed the tetrode experiment. H.M. recorded the data. T.T.
and H.M. analyzed the experimental data. N.A. provided the two pathway
LTD experiment. T.T., T.K.H., and K.D.M. wrote the manuscript.
Accepted: July 10, 2013
Published: October 2, 2013
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