# Spontaneous Plasticity of Multineuronal Activity Patterns in Activated Hippocampal Networks

Authors: Atsushi Usami; Norio Matsuki; Yuji Ikegaya
Journal: Neural Plasticity
 (2008)

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## Abstract

Using functional multineuron imaging with single-cell resolution, we examined how hippocampal networks by themselves change the spatiotemporal patterns of spontaneous activity during the course of emitting spontaneous activity. When extracellular ionic concentrations were changed to those that mimicked in vivo conditions, spontaneous activity was increased in active cell number and activity frequency. When ionic compositions were restored to the control conditions, the activity level returned to baseline, but the weighted spatial dispersion of active cells, as assessed by entropy-based metrics, did not. Thus, the networks can modify themselves by altering the internal structure of their correlated activity, even though they as a whole maintained the same level of activity in space and time.


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## Body

## 1. Introduction

The brain is an enormously complex system that is composed
of diverse types of neurons interacting with one another through topologically
defined networks. A fundamental feature of neuronal networks is
plasticity, that is, its functional connectivity undergoes an
activity-dependent modification, and this change persists over time. Experimentally,
plasticity is usually induced by artificial repetitive stimulation, such as
tetanic stimulation, low-frequency stimulation, or repeated coactivation of
presynaptic and postsynaptic neurons [1–8]. In the intact
brain, however, neuronal networks spontaneously modify themselves, depending on
sensory-evoked or internally generated activity. Such intrinsically occurring
plasticity is poorly understood, compared to artificially induced “conventional”
synaptic plasticity.In this study, we addressed how neuronal networks undergo
plastic changes while they generate internal activity, by using functional
multineuron calcium imaging (fMCI), an optical recording technique with
calcium-sensitive fluorescent indicator to monitor action potentials from large
neuron populations [9–18]. Unlike
electrophysiologic techniques, including extracellular unit recordings, fMCI
allows us to detect not only the activity of active neurons but also the
silence of nonactive neurons (cf. single-unit or multiunit recordings cannot
determine how many neurons are silent). Thus, fMCI can more comprehensively
capture the pattern of multineuronal activity in a local network.We sought to examine how the inner structure of spontaneous activity
changes at the network level following transiently enhanced spontaneous
activity. This idea has originated from our recent findings that briefly
increased levels of spontaneous activity induce long-lasting plasticity of
synaptic transmission in hippocampal CA3 region without any
electrophysiological stimulation [19]. These findings suggest that active
networks update their internal states in the course of generating spontaneous
activity. In this previous study, however, artificial electric pulses were
applied to afferent fibers as “test” stimulation to monitor the strength of
synapses, and it remains to be addressed how internally generated activity itself
is modified by spontaneous activity.In the present study, therefore, we have designed a series
of experiments without any artificial stimulation in order to examine the
effect of enhanced levels of spontaneous activity on the spatiotemporal pattern
of spontaneous activity. To this end, we have introduced a simple entropy-based
metrics, that is, a modified measure of the so-called Shannon index (SI), which is widely used in
the field of ecology for measuring the extent of diversity of species living in
a region [20]. With this new metrics, spontaneously occurring network plasticity
can be detected, although other conventional parameters, such as active cell
numbers and spike rates, cannot capture the network plasticity.


## 2. Materials and Methods

### 2.1. Materials

Oregon Green 488 BAPTA 1-AM
and Pluronic F-127 were obtained from Invitrogen (Carlsbad,
Calif, USA). Cremophor EL and
d,l-2-amino-5-phosphonopentanoic
acid (AP5) were obtained from Sigma
(St. Louis, Mo, USA). The stock solutions were stored at −20°C and
diluted immediately before use.


### 2.2. Slice Culture Preparations

Hippocampal
slice cultures were prepared from postnatal day 7 Wistar/ST rats (SLC, Shizuoka, Japan)
as previously described [21], according to The University of Tokyo guidelines
for laboratory animal care and safety. Briefly, rat pups were chilled and
decapitated. The brains were rapidly removed and cut into horizontal 300-μm-thick slices using a DTK-1500 microslicer (Dosaka, Kyoto, Japan) in
aerated, ice-cold Gey’s balanced salt solution (Invitrogen, Gaithersburg, Md,
USA) supplemented with 25 mM glucose. Entorhino-hippocampal stumps were
cultivated on Omnipore membrane filters (JHWP02500, Millipore, Bedford, Mass, USA)
that were laid on O-ring plastic disks (Hazai-Ya, Katsushika-ku, Tokyo). Cultures were fed with 1 mL of 50%
minimal essential medium, 25% Hanks' balanced salt solution (Invitrogen), 25%
horse serum (Cell Culture Laboratory, Cleveland, Ohio, USA), and antibiotics in
a humidified incubator at 37°C in 5%
CO2. The medium was changed every 3.5 days.


### 2.3. fMCI

fMCI was performed as previously described [18]. Briefly, slices at day 8-to-12 in
vitro were washed three times with artificial cerebrospinal fluid (ACSF),
bubbled with 95%O2 and 5% CO2, consisting of (mM): 127
NaCl, 26 NaHCO3, 1.5 KCl, 1.3 KH2PO4, 1.4 MgSO4,
2.4 CaCl2, and 10 glucose. They were transferred into a 35-mm dish
filled with 2 mL of dye solution and incubated for 60 minutes in a humidified
incubator at 37°C in 5%
CO2. The dye solution was ACSF containing 10 μl of 0.1% OGB-1 AM/DMSO, 2 μl of 10% Pluronic F-127/DMSO, and 2 μl of 5% Cremophor EL/DMSO. After being washed,
slices were incubated at room temperature at least for 30 minutes in fresh ACSF.
They were transferred to a recording chamber perfused with 32°C ACSF at a rate
of 1.5–2.0 mL/min. After
10 minutes, the activity was imaged from the hippocampal CA3 region for 5 minutes.
Then the extracellular solution was replaced with physiologic ACSF (pACSF)
consisting of (mM): 127 NaCl, 26 NaHCO3, 3.3 KCl, 1.24 KH2PO4,
1.0 MgSO4, 1.0 CaCl2, and 10 glucose [17, 19, 22, 23]. After 15 minutes, the extracellular solution
was replaced with normal
ACSF. As a result, calcium transients evoked by spontaneous spike activity were
constantly recorded for 30 minutes in total. Images (16-bit intensity, 512×512
pixels, 742×742 μm2) were captured at 10 frames/s with a Nipkow-disk
confocal unit (CSU22, Yokogawa Electric, Tokyo, Japan), cooled CCD camera (iXon
DV887DCS-BV; Andor Technology, Belfast, UK), upright microscope (Eclipse FN1; Nikon, Tokyo, Japan), water-immersion
objective (16×,0.80 numerical aperture, CFI75LWD16XW; Nikon, Tokyo, Japan),
and image acquisition software (SOLIS; Andor Technology, Belfast, UK).
Fluorophores were excited at 488 nm with an argon-krypton laser (10–15 mW,
641-YB-A01; Melles Griot, Carlsbad, Calif, USA) and
visualized through a 507 nm long-pass emission filter. Spike-triggered calcium
signals were semiautomatically detected with custom-written software in Visual
Basic Version 6.0 (Microsoft, Seattle, Wash, USA) [12] and
inspected by eye.


### 2.4. Electrophysiological Recordings

Loose-patch-clamp recordings were performed with glass
pipettes filled with pACSF to record extracellular
single-unit
activity. Recordings were carried out with Axopatch 700B amplifiers (Molecular Devices, Union City, Calif, USA), and signals were
low-pass filtered at 1 kHz, digitized at 10 kHz, and analyzed with pCLAMP
version 10.0 (Molecular Devices).


### 2.5. Data Analysis of Multineuronal Activity

SI quantifies the dispersion of components in a histogram [20]. SI is defined as(1)SI=−Σi(kiK)log2(kiK), where K is the total number of components, and ki is the number of components in the ith bin. This definition of diversity
is conceptually equivalent to Shannon’s entropy.
Because SI is very sensitive to K and the bin size, SI has often been normalized
with the maximal value and other standard values to compare groups [24, 25].
Here we normalized SI with the maximal (SImax) and minimal values (SImin) that
can be taken. SImax and SImin were
obtained through data shuffle with maintaining K and bin. SImax is
given when components are as evenly redistributed as possible, whereas SImin is
given when components are as biased as possible (Figure 2(a)). Then normalized
SI (NSI) is defined as
(2)NSI=SI−SIminSImax−SImin.
Thus, it takes a value from 0
to 1, with higher values being more dispersive. Unless otherwise specified, the NSI value was
used to quantify the dispersion of activity in a multineuronal spike train. The
dispersion was evaluated in two scopes, that is, the vertical (spatial) and horizontal
(temporal) projections in a rastergram, which were referred to herein as NSIcell and NSItime, respectively. A 1-minute
window was placed at a given time and shifted at a step of 30 seconds to scan
the temporal dynamics of the NSIs
(Figure 2(c)).


## 2.1. Materials

Oregon Green 488 BAPTA 1-AM
and Pluronic F-127 were obtained from Invitrogen (Carlsbad,
Calif, USA). Cremophor EL and
d,l-2-amino-5-phosphonopentanoic
acid (AP5) were obtained from Sigma
(St. Louis, Mo, USA). The stock solutions were stored at −20°C and
diluted immediately before use.


## 2.2. Slice Culture Preparations

Hippocampal
slice cultures were prepared from postnatal day 7 Wistar/ST rats (SLC, Shizuoka, Japan)
as previously described [21], according to The University of Tokyo guidelines
for laboratory animal care and safety. Briefly, rat pups were chilled and
decapitated. The brains were rapidly removed and cut into horizontal 300-μm-thick slices using a DTK-1500 microslicer (Dosaka, Kyoto, Japan) in
aerated, ice-cold Gey’s balanced salt solution (Invitrogen, Gaithersburg, Md,
USA) supplemented with 25 mM glucose. Entorhino-hippocampal stumps were
cultivated on Omnipore membrane filters (JHWP02500, Millipore, Bedford, Mass, USA)
that were laid on O-ring plastic disks (Hazai-Ya, Katsushika-ku, Tokyo). Cultures were fed with 1 mL of 50%
minimal essential medium, 25% Hanks' balanced salt solution (Invitrogen), 25%
horse serum (Cell Culture Laboratory, Cleveland, Ohio, USA), and antibiotics in
a humidified incubator at 37°C in 5%
CO2. The medium was changed every 3.5 days.


## 2.3. fMCI

fMCI was performed as previously described [18]. Briefly, slices at day 8-to-12 in
vitro were washed three times with artificial cerebrospinal fluid (ACSF),
bubbled with 95%O2 and 5% CO2, consisting of (mM): 127
NaCl, 26 NaHCO3, 1.5 KCl, 1.3 KH2PO4, 1.4 MgSO4,
2.4 CaCl2, and 10 glucose. They were transferred into a 35-mm dish
filled with 2 mL of dye solution and incubated for 60 minutes in a humidified
incubator at 37°C in 5%
CO2. The dye solution was ACSF containing 10 μl of 0.1% OGB-1 AM/DMSO, 2 μl of 10% Pluronic F-127/DMSO, and 2 μl of 5% Cremophor EL/DMSO. After being washed,
slices were incubated at room temperature at least for 30 minutes in fresh ACSF.
They were transferred to a recording chamber perfused with 32°C ACSF at a rate
of 1.5–2.0 mL/min. After
10 minutes, the activity was imaged from the hippocampal CA3 region for 5 minutes.
Then the extracellular solution was replaced with physiologic ACSF (pACSF)
consisting of (mM): 127 NaCl, 26 NaHCO3, 3.3 KCl, 1.24 KH2PO4,
1.0 MgSO4, 1.0 CaCl2, and 10 glucose [17, 19, 22, 23]. After 15 minutes, the extracellular solution
was replaced with normal
ACSF. As a result, calcium transients evoked by spontaneous spike activity were
constantly recorded for 30 minutes in total. Images (16-bit intensity, 512×512
pixels, 742×742 μm2) were captured at 10 frames/s with a Nipkow-disk
confocal unit (CSU22, Yokogawa Electric, Tokyo, Japan), cooled CCD camera (iXon
DV887DCS-BV; Andor Technology, Belfast, UK), upright microscope (Eclipse FN1; Nikon, Tokyo, Japan), water-immersion
objective (16×,0.80 numerical aperture, CFI75LWD16XW; Nikon, Tokyo, Japan),
and image acquisition software (SOLIS; Andor Technology, Belfast, UK).
Fluorophores were excited at 488 nm with an argon-krypton laser (10–15 mW,
641-YB-A01; Melles Griot, Carlsbad, Calif, USA) and
visualized through a 507 nm long-pass emission filter. Spike-triggered calcium
signals were semiautomatically detected with custom-written software in Visual
Basic Version 6.0 (Microsoft, Seattle, Wash, USA) [12] and
inspected by eye.


## 2.4. Electrophysiological Recordings

Loose-patch-clamp recordings were performed with glass
pipettes filled with pACSF to record extracellular
single-unit
activity. Recordings were carried out with Axopatch 700B amplifiers (Molecular Devices, Union City, Calif, USA), and signals were
low-pass filtered at 1 kHz, digitized at 10 kHz, and analyzed with pCLAMP
version 10.0 (Molecular Devices).


## 2.5. Data Analysis of Multineuronal Activity

SI quantifies the dispersion of components in a histogram [20]. SI is defined as(1)SI=−Σi(kiK)log2(kiK), where K is the total number of components, and ki is the number of components in the ith bin. This definition of diversity
is conceptually equivalent to Shannon’s entropy.
Because SI is very sensitive to K and the bin size, SI has often been normalized
with the maximal value and other standard values to compare groups [24, 25].
Here we normalized SI with the maximal (SImax) and minimal values (SImin) that
can be taken. SImax and SImin were
obtained through data shuffle with maintaining K and bin. SImax is
given when components are as evenly redistributed as possible, whereas SImin is
given when components are as biased as possible (Figure 2(a)). Then normalized
SI (NSI) is defined as
(2)NSI=SI−SIminSImax−SImin.
Thus, it takes a value from 0
to 1, with higher values being more dispersive. Unless otherwise specified, the NSI value was
used to quantify the dispersion of activity in a multineuronal spike train. The
dispersion was evaluated in two scopes, that is, the vertical (spatial) and horizontal
(temporal) projections in a rastergram, which were referred to herein as NSIcell and NSItime, respectively. A 1-minute
window was placed at a given time and shifted at a step of 30 seconds to scan
the temporal dynamics of the NSIs
(Figure 2(c)).


## 3. Results

Hippocampal slice cultures were incubated with Oregon  green 488
BAPTA-1AM, and the fluorescent intensity from the cell bodies of CA3 neurons was
measured at 10 frames per second with spinning-disk confocal microscopy. Because
action potentials, as assessed by loose-patch-clamp recordings, were reflected
in transient rises in somatic fluorescent signal (Figure1(a)), we were able to
reliably reconstruct the time series of action potentials from calcium fluorescent
traces. Spontaneous activity was recorded from, on average, 157 ± 57 neurons
(mean ± SD of 17 slices), ranging from 101 to 350 neurons.Figure 1
Physiologically relevant ionic conditions induce a reversible increase in spontaneous activity of hippocampal
CA3 neurons in organotypic cultures. (a) Simultaneous loose patch-clamp recording
and time-lapse imaging of somatic calcium signal from a hippocampal CA3 neuron
loaded with Oregon green 488 BAPTA-1AM. The timings of action potentials can be
reconstructed from the onset timings of individual calcium rise events. (b)
Representative example of a change in CA3 network activity following 15-min replacement
of normal extracellular solution with pACSF. Each dot represents a single-calcium transient.
(c) pACSF-induced change in the percentage of active cells to the total number
of cells monitored. (d) pACSF-induced change in the mean event frequency per cell.
Open and closed circles indicate group without (N=5 slices, Control) and with pACSF replacement (N=8 slices, pACSF), respectively. Data
are means ± SEMs.Diagram of entropy-based metrics to capture network activity patterns. (a) Shannon
index (SI) is calculated with the Shannon entropy equation and normalized with its maximum
and minimum values that can be taken through data shuffle with keeping the
total number of events constant, as shown in the schematics. (b) SI can take
different values depending on the pattern of activity, even when the numbers of
activity events and active cells are invariant. (c) Example of the normalized SI (NSI)
in multineuronal spike trains. A 1-minute window (shadow area) was placed at
any given time on a rastergram. Two histograms were made by projecting the dataset
to the vertical (right) and horizontal axes (bottom), so that NSIs evaluate the event dispersion in
terms of space and time (NSIcell and NSItime, resp.).
(a)(b)(c)In conventionally used “control” ACSF, only about 5% neurons
were spontaneously active (Figure1(b)). The ionic composition of this standard
ACSF is different from that of in vivo circumstances, because it was designed to
reduce the level of spontaneous neuronal activity in slice preparations [26–33]. Indeed, when
slices are perfused with solution with physiologically relevant ionic
conditions (pACSF), the activity level is known to increase [17, 19, 22, 23].
In our preparation as well, more neurons
became spontaneously active in pACSF (Figure 1(b)). After 15 minutes, pACSF was
replaced again with control ACSF, and the activity level was returned to the pre-pACSF
baseline within 5 minutes.Data of 8 slices are summarized in Figures1(c) and 1(d). Perfusion
with pACSF increased the number of active cells (c) and the mean activity rate (cell-1·min-1) (d). The increases in the activity level were
statistically significant, whereas there was no significant difference between the
pre-pACSF period (−5–0 minute) and post-pACSF period (20–25 minutes) (Tables
1 (a) and 1(b)). In control experiments without pACSF perfusion, the level of spontaneous
activity was kept stable throughout 30 minutes of optical imaging (Figures 1(c)
and 1(d), Tables 1(a) and 1(b), N=5
slices). This indicates that photodamage and photobleaching are negligible in
our fMCI experiments [17].Table 1
Statistics of pACSF-induced plasticity of spontaneous network activity. The
percentage of active cells (a), the mean event frequency (b), NSIcell (c),
and NSItime (d) are shown as
the average values from −5 to 0 minute (before), from 10 to 15 minutes (middle),
and from 20 to 25 minutes (after) after treatment of pACSF. (Control, N=5; pACSF, N=8; pACSF+AP5, N=4). *P<.05, **P<.01 post-hoc Tukey-Kramer multiple comparison test after one-way
ANOVA.Was the effect of pACSF truly reversed? We sought to examine
whether or not the history of the transiently enhanced activity is registered
in some form of network activity. To this end, we introduced SI, new parameters to capture the
diversity of the spatiotemporal pattern of network activity (Figure2). Note that
even though both the numbers of activity and active cells are unchanged, SI can take different values, reflecting
the pattern of activity (Figure 2(b)). For data comparison, we normalized SI into NSI (see methods). We used two NSI,
that is, NSIcell and NSItime. NSIcell reflects the spatial dispersion of calcium events
across neurons, that is, NSIcell becomes smaller when more events occur in a few specific neurons. On the other
hand, NSItime reflects the
degree in temporal decorrelation of calcium events, that is, NSItime becomes smaller when
more events occur in synchrony across neurons (Figure 2(c)).pACSF perfusion led to an increase inNSIcell (Figure 3(a)). This indicates that neurons in
the network participated more uniformly in spontaneous activity. On the other
hand, pACSF caused a decrease in NSItime (Figure 3(b)), that is, neurons became to exhibit more synchronized activity. In
general, NSIcell and NSItime may display a
tradeoff change, that is, the coincident NSIcell increase and NSItime decrease
as observed here seem to be plausible because network synchronization inevitably
recruits a more number of neurons at a given time. In other words, pACSF-enhanced
network activity was accompanied by an increase in synchronicity. Surprisingly,
however, the NSIcell increase
was maintained even after pACSF washout, whereas the NSItime decrease reverted to the pre-pACSF baseline
within 5 minutes. This is intriguing, at least in two points, (i) a
dissociation between NSIcell and NSItime parameters,
and (ii) plasticity in the form of network activity patterns without a change
in the activity level. Incidentally, these phenomena were not detected with
another normalization of SI, in which SI was divided simply by the mean SI in the pre-pACSF period (Figures 3(c)
and 3(d)).Figure 3
pACSF induces an NMDA-dependent persistent increase inNSIcell, but not NSItime. The
same data as Figures 1(c) and 1(d) and data of perfusion with pACSF in the
presence of 50 μM AP5 were
analyzed with the NSIcell (a), NSItime(b), %SIcell (c), and %SItime(d) indices
(Control, N=5; pACSF, N=8; pACSF+AP5, N=4). (a) and (b) pACSF-induced increase in NSIcell persisted after replacement with normal ACSF, an effect that was prevented by
AP5, whereas pACSF-induced increase in NSItime was recovered to the pre-pACSF baseline level after pACSF washout. (c) and (d) show
that neither %SIcell nor %SItime detected pACSF-induced
plastic changes. Data are means ±SEMs.This plasticity was dependent onN-methyl-D-aspartate (NMDA)
receptor activity, because pACSF-increased NSIcell did not persist in the presence of 50 μM AP5, an NMDA receptor antagonist (Figures 3(a)
and 3(b), N=4 slices). In control
experiments without pACSF perfusion, NSIcell and NSItime were unchanged
for 30 minutes (Figures 3(a) and 3(b), N=5 slices).We summarize all statistically analyzed data in Table1. Three
groups were compared: control group (N=5 slices), pACSF replacement groups without (N=8 slices) and with AP5 (N=4 slices). Four parameters, that is, the percentage of active cells to the
total cells monitored (Table 1(a)), the mean event frequency per minute per
cell (Table 1(b)), NSIcell (Table 1(c)), and NSItime (Table 1(d)), were extracted from three periods, that is, the pre-pACSF period (−5 to 0
minute relative to the start time of 15-minute perfusion with pACSF), the middle-pACSF
period (10 to 15 minutes), and the post-pACSF period (20 to 25 minutes). The
data were assessed with one-way analysis of variance (ANOVA) and
post-hoc
Tukey-Kramer multiple comparison test.


## 4. Discussion

In a previous study using whole-cell patch-clamp recordings,
we demonstrated that CA3 pyramidal neurons respond to pACSF perfusion by
emitting spontaneous oscillatory activity and displaying bidirectional long-lasting
synaptic modification [19]. The direction and amount of the plasticity varied
depending on the patterns of spontaneous activity shown by the neuron and the locations
of synapses, and thus pACSF-induced plasticity was diverse across neurons and
experiments. In the previous study, however, synaptic responses were monitored
with artificial electric stimulation, that is, bulk activation of presynaptic
axon fibers, and spontaneous activity and plasticity were recorded from at most
two neurons at once. Thus, it remained unclear how pACSF-induced enhancement of
spontaneous activity affected the pattern of intrinsic network activity. In the
present study, we described that pACSF-enhanced activity induced a change in the
pattern of spontaneous activity toward an increase in NSIcell. In these experiments, we did not use any electric
stimulation at all; both activity and plasticity were spontaneously generated
by the CA3 network, and the active networks reported the occurrence of plasticity
through their own activity.We underline that without NSI, it was difficult to detect plasticity in spontaneous activity. SI is an information theory-based
metrics that has been widely used to assess the number and relative abundance
of animal and plant species in ecology [20]. This measure was recently introduced
to quantify the heterogeneity of GABAergic synaptic and cellular populations [34, 35]. A change in SI in peak
conductance in injected inhibitory postsynaptic currents is associated with the
firing rates of CA1 pyramidal neurons. Furthermore, an increase in SI of interneuron populations is linked
to a decrease in network coherence, even when population variance remained
unchanged. Thus, SI is a useful scalar
to assess diversity in various experimental data. In our experimental systems,
however, SI is susceptible to the
total number of cells, the frequency of calcium transients, movie length, and bin
size. For example, cases in which 10 or 15 data points are distributed in the
10×10 and 6×6 squares are shown in Figure 4. Note that the maximal and minimal SI values are different among these
cases. Thus, SI cannot be directly
compared between different datasets, especially with different numbers of
neurons monitored or different levels of network activity. To overcome this
problem, we normalized SI so as to be
independent of these factors. In addition, as NSI ranges from 0 to 1, it is mathematically tractable. Using NSI, we succeeded in quantifying the
pattern of network activity and thereby detecting plasticity hidden at the
network level.The range of Shannon Index (SI)
changes depending on the number of data points and the bin size. As
examples, when 10 (left) and 15 (right) blocks are distributed in the 10×10 (top)
and 6×6 (bottom) squared spaces, the maximal and minimal SI values are shown in each case.
(a)(b)(c)(d)There are mainly two types of synaptic plasticity, NMDA
receptor-dependent and NMDA receptor-independent plasticities [36]. Plasticity in NSIcell was the former, because it did not occur in the presence
of the NMDA receptor antagonist AP5. NMDA receptor-independent plasticity, such
as L-type calcium channel-dependent, calcium-permeable AMPA receptor-dependent,
and metabotropic glutamate receptor-dependent plasticity observed in pyramidal
cells and interneurons [37–42], may occur during
pACSF perfusion. Given that the post-pACSF activity level in the presence of AP5 was higher than
the pre-pACSF level, NMDA receptor-independent plasticity may counteract NMDA
receptor-dependent plasticity and balance the degree of spontaneous activity
after pACSF washout.


## 5. Conclusions

Plasticity in the brain is essential in processing and
storing information so that animals can act against changing environments in a
predictive manner. The major form of neuronal plasticity is embodied through activity-dependent
modification of synaptic connectivity or strength. Various forms of synaptic plasticity
have been described at glutamatergic and GABAergic synapses in terms of direction,
magnitude, duration, receptor and molecular mechanisms, and triggering stimuli.
Many studies on synaptic plasticity, however, have focused on the behavior of monosynaptic
transmission or single neurons by ignoring their complex dynamics in a network,
or otherwise on the averaged response of a local network to stimulation by
ignoring the detailed dynamics of individual neurons. Moreover, in these
studies, extremely artificial stimuli, such as electrical or chemical
stimulation, have been used to induce synaptic plasticity. In this scope, our
experiments are designed to utilize fMCI to monitor network activity with
single-cell resolution, use spontaneous activity as plasticity-triggering
stimulation, and monitor the pattern of spontaneous activity. By introducing NSI, a new parameter to quantify event diversity, we found that a
network experiencing transiently enhanced spontaneous activity modifies the
inner structure of its spontaneous activity. Transiently enhanced activity
caused a decorrelation of preexisting spontaneous activity, which might serve
as a sparse memory trace decodable in downstream neuronal systems. Importantly,
even though such dramatic plasticity occurred in the entire network, the observed
level of spontaneous activity was preserved over time. We interpret this persistent
increase in NSI as a reconfiguration of network dynamics, where a trace of the
elevated activity period has remained, despite the alleged decrease in network
activity. Therefore, this novel form of plasticity at the network level is
featured by “homeostasis-like” scaling properties and may help avoid network
hyperexcitability and hypoexcitability.



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*Source: 108969-2008-07-14.xml*