cies. When both stimuli were presented within the same period, neurons in the network attenuated their responsiveness to the more
and their balance play an active role in generating selective gain control. The observation that selective adaptation arises naturally in a
Selective adaptation to stimuli with different features is seen in a
ticular the frequency of stimuli is one property that can induce
selective changes in the sensitivity of the response to those stim-
uli. This phenomenon has two components: a decline of neuro-
nal responses with time as a result of repetitive presentations of a
of the response to rarely presented stimuli. It has been named
“stimulus-specific adaptation” in the context of visual stimuli
and auditory cortex (Ringo, 1996; Ulanovsky et al., 2003), “mis-
match negativity” in the context of the event-related potentials
field (Tiitinen et al., 1994; Naatanen et al., 2001), and
ing in the visual cortex (Dragoi et al., 2000). Neurophysiological
correlates to the phenomenon are observed using brain imaging
et al., 2002), electroencephalography (Picton, 1992; Tiitinen et
al., 1994; Naatanen et al., 2001), and intracranial single neuron
recordings (Saul and Cynader, 1989; Dragoi et al., 2000, 2001;
Sengpiel and Bonhoeffer, 2002; Ulanovsky et al., 2003). Regard-
less of the exact modality or cortical location, it seems that the
various neuronal structures implicated in this phenomenon
1997), extracting the common invariant input patterns, while
remaining sensitive to rare features. It has been suggested that
selective adaptation is a network phenomenon rather than a sin-
gle neuron property (Ringo, 1996; Abbott et al., 1997; Dragoi et
al., 2000; Naatanen et al., 2001; Sengpiel and Bonhoeffer, 2002),
and attention has focused on synaptic depression in this context
In the last several years, specific cortical structures have been
suggested as generators of this phenomenon; however, given the
ubiquitous nature of selective adaptation in perception, it is rea-
sonable to expect the existence of general network-level princi-
ples that underlie this phenomenon. The present experimental
research focuses on the level of neuronal populations in large
networks of cortical neurons, seeking an analog of selective ad-
aptation at this level and a characterization of the underlying
mechanisms that may support such network behavior. The ex-
perimental system is built of random ex vivo networks of cortical
evant cortical in vivo features are conserved in these ex vivo net-
well as synaptic and cellular level plasticity (for review, see
trodes (see Fig. 1A), ex vivo networks can be interrogated by
Jimbo, 1999; Shahaf and Marom, 2001; for review, see Marom
and Shahaf, 2002) show that such site-specific stimuli induce
pathway-specific (rather then neuron-specific) changes in re-
sponsiveness (potentiation or depression), which may last for at
least several minutes. Moreover, the observed changes are at the
fine structure of the spike trains and correlations between activ-
ities that are not necessarily obvious when one looks at global
tute for Psychobiology in Israel (S.M.), the Minerva Foundation (S.M.), and the United States–Israel Binational
TheJournalofNeuroscience,October15,2003 • 23(28):9349–9356 • 9349
for studying interactions between pathways. The present study
explores mechanisms of selective adaptation by focusing on in-
teractions between activation pathways evoked by rare and fre-
quent stimulation at two different sites.
Cultured networks. Cortical neurons were obtained from newborn rats
within 24 hr after birth, following standard procedures as described pre-
Fig. 1A). The cultures were bathed in MEM supplemented with heat-
gentamycin (10 ?g/ml) and maintained in an atmosphere of 37°C, 5%
CO2and 95% air in a tissue culture incubator as well as during the
recording phases. Experiments were performed during the third week
after plating, thus allowing functional and structural maturation of the
neurons (Marom and Shahaf, 2002).
Electrophysiology. We used arrays of 60 Ti/Au/TiN electrodes, 30 ?m
Reutlingen, Germany). The insulation layer (silicone nitride) was pre-
1060, MultiChannelSystems) with frequency limits of 1–5000 Hz and a
gain of 1024? was used. The B-MEA-1060 was connected to MCPPlus
variable gain filter amplifiers (Alpha-Omega, Nazareth, Israel) for fur-
dedicated eight-channel stimulus generator (MultiChannelSystems).
Data were digitized using two, parallel 5200a/526 analog-to-digital
boards (Microstar Laboratories, Bellevue, WA). Each channel was sam-
pled at a frequency of 24 kilosample/sec and prepared for analysis using
ically in the range of 10–20 ?V) were defined separately for each of the
recording channels before the beginning of the experiment. All results
on the basis of data extracted from voltage traces using the principal
component spike-sorting technique (Abeles and Goldstein, 1977) (Al-
phaSort, Alpha-Omega). To minimize confusion between spike sources
from each electrode. In single stimulation site experiments (see Fig. 5),
data without spike sorting are presented; sample experiments were sub-
jected to spike sorting, and no qualitative differences in the results were
ity to induce a reliable, reverberating electrical activity in response to a
stimulus parameters for the two sites in each experiment were identical.
The first 3 msec after a stimulus are not included in the analyzed data to
exclude stimulus artifacts.
Selective adaptation experiments (two-site stimulation). Seven experi-
six experiments. In one experiment we did not observe selective adapta-
lasted 15 min; 162 stimuli were delivered through the frequently stimu-
at 1/5 and 1/50 sec?1, respectively. Stimulation frequencies for the two
inactivation, and on the lower end, not to evoke such response decline
(see Fig. 5). In our networks, this range amounts to ?1/10 sec?1at the
frequently stimulated site and ?1/10 sec?1at the rarely stimulated site.
that it does not cause a marked decline of responsiveness in all networks
tested. The higher stimulation frequency (1/5 sec?1) was chosen arbi-
trarily to be one order of magnitude higher. Stimulation order was set to
experiments presented here, experiments with other frequencies were
conducted (1/2–1/20 sec?1; 1/3 and 1/30 sec?1) with similar results.
which the networks were stimulated at identical frequencies (1/5 and
1/50 sec?1) from the two sites were performed (see Fig. 7A,B).
kinetics of response inactivation (see Fig. 5). Three to six networks were
used for each stimulation frequency examined (an average of ?30 active
monitored for at least 15 min to ensure stability of the activity.
Synaptic blockade experiments. To explore the mechanism of selective
adaptation, eight experiments with specific synaptic blockers were per-
formed. The synaptic blockers used were as follows: (1) 2-amino-5-
phosphonovalerate (APV), 8–10 ?M. One experiment was performed
using a single stimulation source and the other using the standard selec-
tive adaptation protocol; (2) 6,7-dinitroquinozaline-2,3-(1H,4H)-
dione) (DNQX), 6–10 ?M, and (3) bicuculline, 0.5–1.5 ?M. Higher con-
centrations of synaptic blockers caused either a complete block of
propagated activity in the networks (DNQX and APV) or hyperactivity
with long-lasting bursts (?2 sec) in response to stimulation and unreli-
in DMSO and diluted in bath application ?2000-fold.
using either a 5 msec (see Fig. 2) or 10 msec (see Fig. 3) time bin. Popu-
lation PSTH (PPSTH) is the sum of all PSTHs obtained for all sorted
spike sources in the network (36 units in the network shown in Figs. 2B
and 3). Responsiveness is defined as the number of spikes detected in a
two-site stimulation experiments, are averaged over the first six stimuli
delivered and the final six stimuli delivered from each site, respectively.
Accordingly, Figure 4B shows only units that responded at least once to
change in average network responsiveness for each time point is calcu-
lated from the number of spikes evoked by five consecutive stimuli,
normalized to the initial average responsiveness.
Figure 1 shows the experimental setup and paradigm. Each net-
work was stimulated at two different sites (Fig. 1A, right): rela-
relatively rare stimuli (1/50 sec?1) were delivered from site B. In
each experiment, the network was stimulated continuously with
180 stimuli, of which 162 were delivered from the frequently
B). The ratio between the stimulation times of the two sites was
set to be 9:1, that is, every nine stimuli from site A were followed
by one stimulus from site B. A single stimulus evoked a network
reverberating response lasting several tens of milliseconds, de-
tectable by the recording electrodes. There are 60 recording elec-
trodes in this array (Fig. 1A, right, gray dots). A voltage trace
recorded from one such electrode in response to a stimulus de-
livered at site A is shown in the top trace of Figure 1B (concate-
recording electrodes underwent a principal-component spike-
extracted spike shape is shown in the bottom trace of Figure 1B.
This analysis procedure produced activity from several tens of
identified spike sources (“units”) for each network. As observed
by others (Jimbo et al., 2000; Shahaf and Marom, 2001; Marom
and Shahaf, 2002), a typical response to stimulation in a random
The early phase, which terminates within ?20 msec, mostly re-
flects direct activation of a distinct subset of the neurons in the
network whose identity is determined by the stimulating elec-
trodes location. The late “reverberating” phase, which may last
hundreds of milliseconds, involves the propagation of signals
9350 • J.Neurosci.,October15,2003 • 23(28):9349–9356Eytanetal.•SelectiveAdaptationinCorticalNetworks
two phases seem to reflect the biphasic AMPA–NMDA nature of
the glutamate synapses in the culture and its dynamics (Jimbo et
The results obtained from one network are shown in Figure 2.
Figure 2A shows the average response of an identified unit to a
single frequently delivered (gray) and rarely delivered (black)
stimulus. The response is presented in the form of a PSTH of
instantaneous firing rate as a function of time after stimulus de-
livery. Figure 2A shows a comparison of the PSTHs of a single
unit, at the beginning of the experiment (dashed lines) and after
to stimuli coming from the frequently stimulated site markedly
decreased, whereas responses to stimuli coming from the rarely
stimulated site increased. Figure 2B shows the same phenome-
non, represented at the population level (PPSTH), summing fir-
ing rates over all the identified spike sources (units) extracted
from the network.
The changes in the PSTHs shown in Figure 2 are reversible.
This is exemplified in Figure 3. Within 15 min of stimulation in
given loci of frequently and rarely stimulating electrodes, the
network becomes hypersensitive to the rare and hyposensitive to
the frequent (left panel). Then, the network is left unstimulated
for 15 min, after which the differences in sensitivity to the two
sources diminish (middle panel). Thereafter, the frequent and
rare stimuli loci are reversed, and stimuli are delivered for an
additional 15 min. At the end of this last session, the responses
have reversed accordingly (right panel). This reversal of changes
in the PSTH was observed in an additional culture and a total of
The ability to differentiate between rare and frequent, at the
individual neuron level, is presented in Figure 4 (summarizing
results from six experiments obtained from four different net-
decreases, whereas responsiveness of these same units to the
this graph represents an identified spike source (i.e., one unit).
Responsiveness is defined as the number of spikes detected in a
delivered from each site, respectively.
Most of the units respond preferentially to the rare stimuli at
the end of the experiment, regardless of their initial ratio of re-
sponsiveness. We used the rare-to-frequent ratio of responsive-
ness as a quantitative measure of this effect. As can be seen in
Figure 4B, at the beginning of the experiment (x-axis) many of
the units responded similarly to both stimulation sources (ratio
observed in identified spike sources from several experiments
(182 units, six experiments).
The decrease in responsiveness of the network to the frequent
stimulation occurs regardless of the presence of the rare stimula-
tion. Figure 5A shows the slow changes in network response to a
continuous train of pulses from one source, at different frequen-
cies. Each point represents a normalized average response to five
see Fig. 5 legend). As the stimulation frequency increases, the
network shows a stronger decrease on a time scale of a few min-
utes. In some networks, several cycles of response inactivation
and partial recovery (“escapes”) are observed, as reflected in
Figure 5 for frequencies above 0.1 sec?1. Figure 5, B and C,
represented schematically by gray (1/5 sec?1) and black (1/50 sec?1) circles. B, A typical
Experimental setup and paradigm. A, Experimental setup; a network of cortical
Selective adaptation; single unit and population. A, PSTHs for the unit whose
Eytanetal.•SelectiveAdaptationinCorticalNetworks J.Neurosci.,October15,2003 • 23(28):9349–9356 • 9351
shows traces from individual networks
of changes in response to stimulation at
1/3 and 1/5 sec?1, respectively. The
mechanism underlying these partial es-
capes from inactivation awaits further
response to frequent stimulation can arise
tic changes, or a combination of both. In
our experiments, the changes in respon-
siveness of single units are not an intrinsic
property of the unit but depend on the
stimulus identity (Fig. 4A). We have not
detected single identified units that signif-
icantly decrease their responsiveness to
rare stimuli despite the marked attenua-
tion in the response of these same units to
the frequent stimulation. This suggests that inactivation in our
experiments is mostly a result of short-term synaptic depression
(Varela et al., 1997; for review, see Zucker and Regehr, 2002).
To pinpoint the excitatory synaptic resource undergoing de-
pression, we performed several experiments with application of
specific NMDA and AMPA receptor blockers (APV and DNQX,
respectively). Although both caused a dose-dependent decrease
in observed responsiveness to stimulations, selective blockade of
NMDA-mediated transmission had a greater effect on the later
average firing rate of the network within the early phase (3–15
msec after the stimulation) was 1292 ? 282 spikes per second
(mean ? SD) in control conditions and 1368 ? 607 spikes per
second after the addition of APV. In contrast, within the late
phase of the response (50–150 msec) we measured 1885 ? 1064
spikes per second before and 54 ? 216 spikes per second after
exposure to APV (8–10 ?M). Data are obtained from two net-
under each condition.
Note that the reverberating response to stimulation is depen-
depression in frequently activated pathways.
the presence of frequent stimulation. This is evident from the
data presented in Figure 6. Figure 6A shows the distribution of
the change in responsiveness to rarely delivered stimuli in the
uli. Apparently, the presence of the frequent stimulation mark-
edly skews the distribution toward increased responsiveness
(2.35 ? 3 as compared with ?0.44 ? 1.8); the two distributions
are significantly different ( p ? 0.001; Wilcoxon Rank Sum test;
n ? 442; six experiments in the presence of frequent stimulation
siveness to rarely delivered stimuli in the presence (black circles)
and absence (gray squares) of frequently delivered stimuli are
shown. Note that the increase in sensitivity takes several minutes
Figure 7, A and B, shows the results of a control experiment in
which the two sites are stimulated at the same frequency: 1/50 or
1/5 sec?1. Under such conditions, the response of units to fre-
quently delivered stimuli from both sites decreases significantly
(Fig. 7A). In contrast, the responsiveness of units to rare (1/50
sec?1) stimuli from both sites (Fig. 7B) remains relatively stable
or even decreases but certainly does not increase, as seen in the
above experiments in which rare stimuli were delivered in the
presence of frequent ones (compare with Figs. 4A, 6A).
Several pieces of evidence indicate that response inactivation
to the frequent stimuli plays a role in the enhancement of sensi-
tivity to the rare. First, the time course of inactivation at a stim-
ulation frequency of 1/5 sec?1is similar to the time course of
5 and 6B. It takes the network a few minutes to develop the
differentiation between the rare and the frequent stimuli, which
is in the same order of time it takes the network to enter an
inactivated phase. Second, in those experiments in which en-
hanced sensitivity to the rare was observed (six of seven; see Ma-
terials and Methods), we find a significant negative correlation
between the change in responsiveness to the frequently stimu-
responses to stimuli from what was the frequent stimulation site in the left panel. The right panel shows that switching the
response to the frequently stimulated site and an average increased response to the rarely
stimulated site. In A, final versus initial responsiveness to the rarely (black circles) and fre-
quently (gray squares) stimulated sites is shown for each unit (182 units). Responsiveness is
site are statistically significant ( p ? 0.001; one-sided Student’s t tests). In B, the ratio of
ning (x-axis) and end ( y-axis) of the experiment. Although the average initial rare/frequent
Selective adaptation for each unit is composed of two components: decreased
9352 • J.Neurosci.,October15,2003 • 23(28):9349–9356 Eytanetal.•SelectiveAdaptationinCorticalNetworks
the rarely stimulated site (Fig. 7C) (r ? ?0.694; p ? 0.001; cal-
culated from all experiments and from all units; n ? 182). In
other words, the greater the decrease in the response to the fre-
response to the frequently delivered stimuli, we were also unable
to detect an increase in sensitivity to the rarely delivered stimuli
(one of seven experiments; see Materials and Methods).
As shown above, the enhanced response to the rarely stimu-
lated site is dependent on the presence of frequent stimulation.
To explore the possibility that GABAergic synaptic depression
plays a role in the enhancement, we performed selective adapta-
tion experiments in the presence of partial blockade of the fast
GABAergic transmission by bicuculline. Figure 8A depicts the
final versus initial responsiveness to the rarely stimulated site in
squares; n ? 156; five experiments). It is evident that addition of
the rarely stimulated site. To quantify this effect we performed
tive adaptation experiments described above 72% of the units
increased their responsiveness (six experiments; 182 units; see
also Fig. 6A), in the presence of 0.5 ?M bicuculline we observed
an increase in 55% of the units (two experiments; 65 units;, p ?
0.01 compared with the fraction observed without bicuculline);
in the presence of 1.5 ?M bicuculline such an increase was ob-
served in only 36% (three experiments; 91 units; p ? 0.001 com-
pared with the fraction observed without bicuculline). The frac-
1.5 ?M bicuculline is strikingly similar to the fraction observed
under stimulation from only one site (at the “rare” frequency of
stimulation from one source at 1/50 sec?1(representing the sta-
ble alternative). Figure 8C shows the changes in responses of
these same units to the frequent stimulation source. In the pres-
ence of both 0.5 and 1.5 ?M bicuculline, the percentage of units
decreasing their responses is significantly higher than that ex-
source (53%; white bar). Note that the decreased responsiveness
observed is smaller compared with bicuculline-free conditions.
We have shown that the ability to adapt selectively to different
stimuli arises naturally in a random network of cortical neurons
developing ex vivo with no predefined specialized structure.
When stimulated at two sites, the neurons in the network ampli-
fied their response to the rarely delivered stimuli and attenuated
their response to the more frequent input.
The observation that the enhanced response to the rarely stimu-
lated site is dependent on the presence of frequent stimulation,
taken together with reported properties of the cortical inhibitory
cies. Each point is calculated from the number of spikes evoked by the entire network in re-
sponse to five consecutive stimuli normalized to unity at the beginning of the experiment.
siveness) in the presence (black; 2.35 ? 3) and absence (gray; ?0.44 ? 1.8) of another,
five consecutive stimuli, normalized to the initial average responsiveness of each entire net-
Eytanetal.•SelectiveAdaptationinCorticalNetworksJ.Neurosci.,October15,2003 • 23(28):9349–9356 • 9353
sub-network, led us to hypothesize a possible role for the inhibi-
tory cells in response enhancement. Because these cells form a
highly interconnected (by chemical and electric synapses) net-
to the excitatory activation pathways, which are relatively sensi-
tive to the site of stimulation, the inhibitory activation pathways
are not. This implies that the inhibitory sub-network is a com-
mon resource. Taken together with activity-dependent short-
term partial depression of fast GABAergic synapses (Galarreta
and Hestrin, 1998; Varela et al., 1999), a possible mechanism
underlying the enhancement of the response to the rarely stimu-
lated site is suggested: the activation of the frequently stimulated
site results in depression of both the excitatory and inhibitory
tory synapses tend to undergo deeper depression than inhibitory
ones, the balance between excitation and inhibition for the fre-
quently activated pathway is disrupted, resulting in a marked
reduced responsiveness; however, because most of the synapses
responsiveness. The results show an average decrease in responsiveness: ?2.2 and ?2.7
rare in the presence of frequent stimulation); the results show a slight decrease in average
to rare stimulation. The change in responsiveness to the rarely stimulated site [(Final) ?
change for the frequently stimulated site (n ? 182; calculated from all selective adaptation
rare stimuli seen in selective adaptation. A, Final versus initial responsiveness to the rarely
stimulated site, for each unit, in selective adaptation experiments performed either in the
the presence of 0.5–1.5 ?M bicuculline (black squares; n ? 156; 5 experiments). Initial and
final responsiveness are averaged over the first and last six delivered stimuli, respectively.
response to the rarely stimulated site at the end of 15 min of stimulation, as a function of
65 units); in the presence of 1.5 ?M bicuculline, 36% (3 experiments; 91 units). p values
lation, as a function of bicuculline concentration (gray bars). For comparison the same
quantity is shown for stimulation at a low frequency (1/50 sec?1) from one site only
(white bar), representing the null case of a relatively stable response: in the absence of
experiments; 65 units); in the presence of 1.5 ?M bicuculline, 74% (3 experiments; 91
units); stimulation at a low frequency (1/50 sec?1), 53% (5 experiments; 201 units). p
9354 • J.Neurosci.,October15,2003 • 23(28):9349–9356Eytanetal.•SelectiveAdaptationinCorticalNetworks
response to the rare stimulation source is increased on average.
The results shown above support this interpretation: addition of
tion of units that increase their responsiveness to the rare stimu-
lation source. Moreover, such a blockade of fast inhibitory syn-
frequent source. Such an interpretation is also compatible with
the dependency of the increased responsiveness to the rarely
stimulated site on another, more frequent site and with the neg-
ative correlation between the change in responsiveness to the
the same unit to the rarely stimulated site.
to focal stimuli is not a new observation (Maeda et al., 1998;
reported cases, however, such increased responsiveness was
achieved by transient tetanization (at the range of tens of cycles
per second). In the present study we show that selective potenti-
ation of responses to rare stimuli can be achieved, provided the
network is “primed” by higher (but not tetanizing) frequencies
from another site.
In our experiments, a decrease in response of the entire network
was observed when the network was stimulated at frequencies
higher than ?0.1 sec?1(Fig. 5). It is important to note that the
stimulation frequency is not synonymous with the neuronal fir-
ing frequency. Each stimulus evokes a reverberating network re-
sponse in which some of the neurons may fire at instantaneous
rates as high as several hundreds of spikes per seconds. As shown
in Figure 5, at stimulation frequencies lower than ?0.1 sec?1,
there is enough time for the effects of responses to relax, and no
shown here to induce declined network response are in accord
with reports by others, using this preparation and in the context
of stimulation from one site. For example, Maeda et al. (1998)
used a stimulation frequency of 1/15–1/30 sec?1to allow recov-
that the networks cannot follow periodic stimulation separated
frequencies ?1 sec?1the networks usually inactivate after a few
cellular excitability does not seem to be a key player in this net-
work phenomenon. The fact that we have not detected single
identified units that significantly decrease in responsiveness to
rare stimuli (Fig. 4) despite the response of those same units to
inactivation in our experiments is mostly a result of short-term
synaptic depression rather than cellular-level excitability reduc-
analyzed theoretically by Abbott et al. (1997).
The above interpretation of the data is supported further by
the observation that decreased responsiveness to the frequently
lesser extent. Under normal conditions the balance between ex-
citation and inhibition for the frequently activated pathway is
partially decreases the extent of response decline.
Placing the relevant kinetics underlying selective adaptation at
the level of the synapses entails that it is not the identity of the
units participating in the response that is important but the path
defined by the activated synapses. Because the stimulation fre-
quency from the two sources in our experiments is different,
population (Jimbo et al., 1999). Exemplifying this is Figure 9,
which shows the cumulative fraction of units responding to ei-
ther the rare or frequent stimulus as a function of time after
msec after a stimulus, the two different stimulation sources acti-
vate the same neurons. Thus the mechanism implying a role for
GABAergic synapses in response enhancement can be described
in terms of interaction between such activation paths. From Fig-
ure 9 we learn that each path may be roughly divided into two
segments; an early segment, which is unique to each of the paths,
exists. The fact that the frequently stimulated path is inactivated
suggests that the efficacy of signal propagation in one of its seg-
ments, or both, decreases. On the other hand, the fact that the
rarely stimulated path increases its responsiveness suggests that
the efficacy of signal propagation in one of its segments, or both,
increases. This suggests that the propagation of the rarely deliv-
ered signal is enhanced in the late common segment (but not in
the early unique segment) by the frequent stimulation. Indeed,
rare is mostly confined to the later phase of the response.
phenomenon of selective adaptation arises naturally in a sponta-
neously organizing network of heterogeneous neurons. When
stimulated at two sites, the network amplifies its responsiveness
the more frequent input. The differential change in responsive-
ness to the two stimulation sources occurs gradually over several
minutes (Fig. 6B) and is restricted to the later phase of the re-
sponse (Figs. 2A,B, 3). These features are reminiscent of the re-
line depicts the cumulative fraction of units that responded to either one of the stimulation
Eytanetal.•SelectiveAdaptationinCorticalNetworksJ.Neurosci.,October15,2003 • 23(28):9349–9356 • 9355
activitywasrecorded(SaulandCynader,1989;Ringo,1996;Dra- Download full-text
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underlying this phenomenon points to depression of the excita-
tory synaptic transmission at the base of the decreased response
to frequent stimulation. This is in accord with the interpretation
by Abbott et al. (1997). Depression of the fast GABAergic trans-
mission seems to underlie the enhanced response to the rare
stimulation source. Thus, excitatory synaptic depression, the in-
hibitory sub-network, and their balance play an active role in the
generation of this phenomenon. Taken together, these results
indicate that selective adaptation is an inherent feature of self-
organizing cortical networks, stemming from the properties of
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