| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
1Neural Information Processing Group, Berlin University of Technology, Berlin, Germany; 2Department of Neurophysiology, Ruhr-University Bochum, Bochum, Germany; and 3Institute of Higher Nervous Activity and Neurophysiology, Moscow, Russia
Submitted 16 December 2004; accepted in final form 8 March 2005
 |
ABSTRACT |
|---|
|
|
|
INTRODUCTION |
|---|
|
100 ms) is short-term plasticity, which has been studied in the rat neocortex for synaptic connections formed between different neuron types (Akaneya et al. 2003
; Galarreta and Hestrin 1998; Jia et al. 2004; Markram et al. 1998; Petersen 2002; Reyes et al. 1998; Thomson 1997; Wang and Kaczmarek 1998). The strength of these synaptic connections is changed depending on the activity history of the particular synapse. Mechanisms of the short-term synaptic changes include both release-dependent as well as release-independent components (reviewed in Zucker and Regehr 2002). Simple use-dependent models have been very successfully applied for numerical characterization of the presynaptic component of facilitation and depression at these synapses. It has been shown that models with few degrees of freedom are able to capture essential parts of synaptic dynamics and can in turn be easily interpreted in terms of the depletion of transmitter vesicles after the release and their subsequent replenishment (Abbott et al. 1997; Tsodyks and Markram 1997; Tsodyks et al. 1998; Varela et al. 1997, 1999). The preceding studies characterized the dynamic behavior of synaptic connections after long periods of rest or during low activity levels, that is, essentially without taking into account the history of the high-frequency pre- or post-synaptic activity on the time scale of several seconds. However, it is well known that synaptic dynamics in the neocortex indeed can be altered by a number of manipulations, including induction of long-term plasticity (Markram and Tsodyks 1996; Volgushev et al. 1997) or sensory deprivation (Reyes and Sakmann 1999). On the short time scale of seconds to dozens of seconds, adaptation has been shown to depress responses at the thalamocortical synapses but not at corticocortical synapses of rat somatosensory cortex (Chung et al. 2002). Thus synaptic dynamics can be dramatically influenced by the activity history of that particular synapse on both long- and short-term scales.
The effect of synaptic dynamics on cortical information processing has been investigated in a number of theoretical studies (Adorjan et al. 1999; Artun et al. 1998; Fuhrmann et al. 2002; Goldman et al. 2002). Depression of synaptic transmission in the afferent pathway was suggested as one of the mechanisms contributing to various cortical phenomena, including nonlinear summation, temporal phase shifts, contrast saturation, contrast adaptation or cross-orientation suppression (Carandini et al. 2002; Chance et al. 1998). In particular, it has been proposed that contrast adaptation might be due to a slow form of synaptic depression (Chance et al. 1998) or a slow change in neurotransmitter release probability (Adorjan et al. 1999) at the thalamocortical synapses. Another study (Adorjan et al. 2000) implicated intracortical depression in the optimal coding strategy for the representation of complex stimuli. Motivated by these experimental and theoretical studies we investigated the effect of "adaptation," which consisted of brief, 4-s intervals, of presynaptic activity on the dynamic characteristics of synaptic connections onto rat layer 2/3 pyramidal cells.
|
|
METHODS |
|---|
|
Slices
Slices of the visual cortex were prepared as described in detail elsewhere (Volgushev et al. 2004). Wistar rats (P25-P35, Charles River GmbH, Suzfeld, Germany) were anesthetized with ether and decapitated, and the brain was rapidly removed and put into an ice-cold oxygenated solution. Frontal slices of the visual cortex (350- to 400-µm thick) were cut with a vibrotome (Leica, VT 1000S, Nussloch, Germany). After the cutting, the slices were let to recover in an incubator for 
1 h at room temperature. The solution used during the preparation of the slices had the same ionic composition as the recording medium (see following text), except for L-glutamine.
Electrophysiological recordings
For recordings, a slice was put into a submerged chamber. The perfusion medium contained (in mM) 125 NaCl, 2.5 KCl, 2 CaCl2, 1.5 MgCl2, 1.25 NaH2PO4, 25 NaHCO3, 25 D-glucose, and 0.5 L-glutamine and was aerated with 95% O2-5% CO2 bubbles. All recordings were made at 32–34°C. Patch electrodes were filled with a solution containing (in mM) 127 K-gluconate, 20 KCl, 2 MgCl2, 2 Na2ATP, 10 HEPES, and 0.1 EGTA and had a resistance of 3–7 M
. Whole cell recordings were made from pyramidal neurons in layers II–III in slices of rat visual cortex. Pyramidal cells were selected under visual control using Nomarski optics and infrared videomicroscopy (Dodt and Zieglgansberger 1990). Reliability of the identification of the pyramidal cells has been proved in our previous work by labeling the recorded cells with biocytin and morphological reconstruction (Volgushev et al. 2000). Recordings were made with Axoclamp-2A (Axon Instruments) in voltage-clamp mode at holding potential between –75 and –85 mV, which was kept constant for the length of recording from one cell. Synaptic responses were evoked by electric shocks applied through bipolar stimulation electrodes located 0.5–1.5 mm below or lateral to the recording site (Fig. 1). We used low intensity of the stimulation, which was set to produce small postsynaptic responses (excitatory postsynaptic currents, EPSCs) without failures. The electrode signal was digitized at 10 kHz and fed into a computer (PC-486; Digidata 1200 interface and pCLAMP software, Axon Instruments). Data were processed off-line using custom written programs.
|
The following chemicals were obtained from Sigma (Deisenhofen, Germany): biocytin, EGTA, HEPES, K-gluconate, L-glutamine, Na2ATP. The remaining chemicals were from J. T. Baker B.V., Deventer, Holland.
Modeling use-dependent synaptic dynamics
To assess parameters of synaptic transmission, we fitted the EPSCs, evoked by repetitive stimulation at different frequencies with a phenomenological model of synaptic transmission (Abbott et al. 1997; Markram et al. 1998; Tsodyks and Markram 1997; Tsodyks et al. 1998). According to the model, a synapse contains a store R of immediately releasable vesicles, the resource. When an action potential arrives at the presynaptic terminal, it leads to a utilization of a fraction U of this store, and at the same time, to a temporal increase of the release probability U by a certain amount. The utilization U in the model has physiological meaning of release probability. In this manuscript we will use U in the equations describing the use-dependent model, as in the original account of the model (Tsodyks and Markram 1997), and p to denote release probability in the binomial release model. The released vesicles are replenished with the time constant 
rec, and release facilitation decays with the time constant fac, both processes are exponential. The released transmitter evokes a current in the postsynaptic neuron that is proportional to the number of released vesicles by a factor g (I = g R U).
Thus our model equations describing the synaptic dynamics are
 | (1) |
 | (2) |
 | (3) |
t between the nth and (n + 1)-th spikes
 | (4) |
 | (5) |
Binomial release model
In addition to assessing the release parameters from the response dynamics, we estimated changes in the release probability with the use of quantal analysis (Korn and Faber 1991; Redman 1990; Tarczy-Hornoch et al. 1999). The binomial model of release assumes that all n release sites contributing to the postsynaptically recorded EPSC have the same release probability p, release neurotransmitter independently from each other, have the synaptic vesicles of identical size, and, on arrival of an action potential to the presynapse, release either none or exactly one vesicle, which produces a postsynaptic effect of a quantal size q. The expectation (
) and the SD [std(EPSC)] of the evoked EPSC is then given by
 | (6) |
 | (7) |
 | (8) |
 | (9) |
; Voronin 1993). Stimulus protocol and data analysis
We have assessed parameters of synaptic transmission and their changes after an adaptation by fitting the phenomenological model of synaptic dynamics (Abbott et al. 1997; Tsodyks et al. 1998) to the postsynaptic responses evoked by stimuli at different frequencies. Our experimental protocol is schematically illustrated in Fig. 1. Test stimuli were applied in trains of 10 pulses at 5, 10, 20, or 40 Hz, either after an adapting stimulation or without adaptation. The response of a cell to one train of test stimuli is referred to as one sweep. For adaptation of synapses, we used the same stimuli as in the test trains but applied them for 4 s at 10, 25, or 40 Hz. Thus the higher frequencies led also to the higher number of adapting stimuli. The adaptation was followed by a 2-s interval without stimulation, before a train of test stimuli was applied. In one experiment, we applied test stimuli at three to four different frequencies without adaptation and after an adaptation, either with only one frequency or with one of the two different adapting frequencies. Different combinations of the preceding test and adaptation stimuli were presented intermingled, once in 75–90 s. The stimuli at two stimulation sites (Fig. 1A) were applied in an interleaved manner. Simulations, performed prior to the beginning of electrophysiological recordings, demonstrated that synaptic parameters can be assessed from the responses to three to four test frequencies (data not shown). Therefore for the further analysis we used synaptic connections for which responses to at least five presentations of three different test frequencies, without adaptation and after at least one adapting frequency, were collected. Of 56 synaptic connections, which fulfilled these requirements, 26 could be characterized for two different adaptation frequencies. The amplitudes of EPSCs were measured as the difference between the mean current within two windows of 1- to 5-ms width, one positioned immediately before the response and another one around the peak of the averaged EPSC or on the last portion of the rising slope (Volgushev et al. 2000). For each synaptic connection, the responses obtained with one given set of the adapting and test frequency were averaged and then normalized to the response evoked by the first stimulus in the test train. Our model Eqs. 4 and 5 were then fitted via a least squares method to these normalized responses, obtained with all available test frequencies. Specifically, we were looking for the parameters U, rec, and fac in Eqs. 4 and 5 that led to the best match between model prediction and measured averaged test responses in a least-square sense. As a fitting routine we used a Gauss-Newton method provided by the function nlinfit of the statistics toolbox of Matlab (Mathworks, Natick, MA). The maximum number of iterations was set to 500, the termination tolerance for the estimated coefficients as well as the residual sum of squares was chosen as 10–6. The Fig. 2A illustrates the averaged response traces (A1) and normalized EPSC amplitudes together with the best fit for one synaptic connection in the control condition (A2). To estimate the quality of the fits across the sample, we also calculated the root-mean-square (rms) error for each fit. Figure 2B shows the distribution of the rms error per pulse over all synaptic connections in the control (Fig. 2B1) and for all adaptation conditions and control (Fig. 2B2). The mean and median rms error per stimulus for the whole sample were 0.11 and 0.10, respectively, thus a response to a single stimulus could be predicted by the model with an average error of 11%. The rms error is a measure of the quality of the fit, and it shows how well the data are represented by the model. However, it does not by itself say anything about the reliability of the estimation of the free model parameters U, rec, and fac in Eqs. 4 and 5.
|
rec, and fac, we simulated the EPSC responses given by Eqs. 4 and 5 and then added to each EPSC a Gaussian noise with a coefficient of variation CV = 0.3, which is similar to the values found in the experiments. As in the experimentswe averaged five traces of simulated responses for each test frequency. Then we fitted the optimal parameters U, rec, and fac to these noisy EPSC responses and compared them to the true U, rec, and fac of the noiseless model synapse. The whole procedure was repeated 100 times for each set of synaptic parameters. The results of these simulations showed that estimation of the release probability U is highly reliable, with a median deviation of <7% from the true value of the U, regardless of the absolute values of the true release probability and facilitation time constant. In addition, the deviation of the estimated U from the true value decreased rapidly with increasing the recovery time constant used in the simulations. Estimation of the facilitation time constant appeared to be less reliable with a deviation of 
30% from the true value in the cases in which a combination of the high true release probability, long facilitation time constant, and short recovery time constant was used. The error in estimation of the facilitation time constant decreased rapidly with increasing the true recovery time constant. Estimation of the recovery time constant showed the strongest dependence on the initial settings used for the simulation of synaptic responses. For the set of true values of the release probability U > 0.2 and recovery time constants shorter than 1.5 s, the deviation of the estimated rec from its true value remained <15%. With the decreasing values of simulated release probability (U < 0.2), the median deviation of the estimated recovery time constant from the true value increased but stayed <35%. For the simulated synaptic responses with the combination of low U, long rec, and short fac, the estimation of the recovery time constant became unreliable with deviation from the true value increasing to >75%. In that latter parameter regime did not only increase the deviation from the true value, but we observed a number of cases with "diverging" (>103 s) recovery time constant as the optimal fit to the data. Thus for synapses with a long recovery process (rec > 3–5 s) and a low release probability (U < 0.2), we frequently (1–25% of all runs) could not attribute a finite recovery process to the synapse based on the optimal root-mean-square fit. Reversing the argument, if our recovery time constant estimate diverged, it was likely that the true recovery time constant was >3 s and the true release probability <0.2. Because under conditions of our experimental protocol the recovery processes lasting >3 s could not reliably resolved, all "diverging" recovery time constants in the following will be regarded as rec > 3s.
In the second approach, we investigated how variable are the estimations of synaptic parameters from repetitive measurements at the same synapse. We therefore increased the number of repetitions of test stimuli and recorded 10–12 sweeps of responses to each test frequency, in the control condition and after an adaptation. For each of the seven synaptic connections recorded with this protocol, we composed 20 random subsets of data, each subset including five randomly chosen sweeps of responses to each test frequency. It should be noted, that different random subsets are not mutually independent, and therefore the following procedure gives only a rough estimate of the true CV of the assessment of synaptic parameters by the model. These random subsets of sweeps were processed as described in the preceding text, and the optimal parameters U, rec, and fac were estimated. The quality of these fits was not different from the rest of the sample, as indicated by the similar values of the mean rms errors (0.11 vs. 0.11 for the rest of the sample). Next, we calculated the CV for the estimated U, rec, and fac for each of the seven synaptic connections. The CV gives an estimate of the reproducibility of the convergence of the model to the same set of optimal synaptic parameter values when different subsets of data from the same synapse are used. The lower the CV, the higher the reproducibility of convergence and thus the reliability of the estimation. The mean CV for the estimated release probability U, the recovery time constant rec and the facilitation time constant fac in control were 
= 0.10, 
= 0.13, and 
= 0.40, and after an adaptation the mean CV were 
= 0.15, 
= 0.17, and 
= 0.46.
Taken together, the results of this analysis show that our protocol gives a reliable estimation of the release probability U, with a low variability of the assessments obtained from the repeated measurements. The recovery time constant rec could also be reliably estimated in the above sample, but we expect from our theoretical analysis that this reliability would decrease substantially if the synapses had longer recovery time constants. The estimate of the facilitation time constant is less reliable and varies even between different measurements at the same synaptic connection.
|
|
RESULTS |
|---|
|
The dynamic parameters in the control condition without preceding adaptation were highly heterogeneous across the investigated synaptic connections. The distributions of the release probability U, the recovery time constant rec, and the facilitation time constant fac over the population of 56 synaptic connections are shown in Fig. 3. The release probability at these synapses varied between 0.04 and 0.57, with predominance of low values (Fig. 3A). The average release probability was 
= 0.21 ± 0.12 (median: 0.17). The distribution of the recovery time constant covered a wide range between 85 and >3,000 ms with most of the values <1,500 ms (39 of 56, 70%) but 14 values (25%) >3,000 ms. The facilitation time constant was on average fac = 32.9 ± 49.7 ms [median: 14.9 ms; range: 1–278.6 ms]. Altogether, the assessed values of these three synaptic parameters, as well as their large heterogeneity, are in line with previous studies of synaptic characteristics in rat visual cortex (Varela et al. 1997) or somatosensory cortex (Markram et al. 1998). The three synaptic parameters were not independent, but some of them were correlated. A negative correlation has been found between the release probability and the recovery time constant (r = –0.47; P < 0.0003; F statistic) and a positive correlation between the release probability and the facilitation time constant (r = 0.59; P < 2 · 10–6). Thus synapses with higher release probability had shorter recovery time constants and longer facilitation. No significant correlation was found between the facilitation and recovery time constants, rec and fac.
|
Adaptation led to marked changes in synaptic transmission. The most prominent effect, consistently observed after adaptation with any of the three frequencies (10, 25, or 40 Hz) was a reduction of the amplitude of the response to the first stimulus in the test train (EPSC1). The synaptic dynamics and assessed parameters of synaptic transmission expressed differential changes after a weak (10 Hz) and strong (25 or 40 Hz) adaptation. In the following text, we will first consider the EPSC1 amplitude changes and then describe separately changes in synaptic parameters after weak and strong adaptation.
Decrease of EPSC1 amplitude after adaptation
A typical example of the effect of a 10-Hz adaptation on synaptic transmission is illustrated in Fig. 4. The amplitude of the EPSC1 decreased after the adaptation to 
65% of the control value. The EPSC1 amplitude reduction is clearly seen in the averaged response traces (compare Fig. 4, A1 and B2) and is highly significant (P < 2 · 10–5;Wilcoxon nonpaired test). The reduction of the EPSC1 after adaptation was typical for our sample and occurred in the majority of synaptic connections. In the scatter plot in Fig. 4C, where the amplitude of the EPSC1 after the 10-Hz adaptation is plotted against the EPSC1 amplitude in the control condition, most of the points are located below the main diagonal. On average, 10-Hz adaptation led to a reduction of the EPSC1 amplitude to 75.5% ± 28.3% of the control value (median: 77.3; range: 32.4–164.6%; P < 9 · 10–5; Wilcoxon paired test). Stronger adaptation with 25 or 40 Hz led to a yet stronger decrease of the EPSC1 amplitude (Fig. 5A). After 25-Hz adaptation the first response amplitude dropped to 65.6% ± 23.3% (median: 63.0; range: 32.4–148.2%; P < 3 · 10–4) and after a 40-Hz adaptation to 53.5% (median: 54.9 ± 23.5; range: 9.3–93.5%; P < 3 · 10–6).
|
|
EPSC140,10) = median(EPSC140/EPSC10 – EPSC110/EPSC10 = –0.24 (mean: –0.31 ± 0.25; range: –0.89–0.06; P < 4 · 10–6; Wilcoxon paired test) for a comparison between 10- and 40-Hz adaptation. Thus at any given synapse, a stronger (higher frequency) adapting stimulation indeed led to a stronger reduction of the EPSC1 amplitude. Recovery of single EPSCs after adaptation
The decrease of the EPSC1 amplitude after adaptation was short-lasting and reversible, and the response amplitude recovered to the control value before the next test stimulus was applied (in 75–90 s). To investigate the time course of the recovery of the adaptation-induced decrease of the EPSC1, we performed an additional series of experiments, in which seven test pulses were applied at a low frequency (0.2 Hz) starting 2 s after the end of the adaptation (Fig. 6A1). In the example shown in Fig. 6A, adaptation with 10 Hz lead to only a moderate increase of the response amplitude, but adaptation with 40 Hz led to a marked decrease of the EPSC amplitude (Fig. 6A2). After both, 10- or 40-Hz adaptation, the response amplitude recovered to the control value after 10–20 s. To quantify this recovery process, we fitted a single exponential to the normalized EPSC amplitude responses
 | (10) |
and the time constant of this recovery process. When fitting the Eq. 10 to the data, the EPSC0 was constrained to be >0. Figure 6B shows the relationship between the decrease of the EPSC1 amplitude after adaptation to 25 or 40 Hz and the optimal fit of the recovery time constant . The average time constant of the recovery was = 7.1 ± 5.9 s (median: 5.2 s; range: 1.8–18 s), the correlation coefficient between the decrease of the EPSC1 and the time constant was r = –0.6 (P < 0.11; F statistics).
|
We assessed changes of the synaptic parameters after a weak adaptation with 10-Hz frequency relative to control in 29 synaptic connections. The fitting of control data and of the responses recorded after the adaptation was of comparable, in both cases high, quality. This is illustrated in the scatter in Fig. 4D, where the rms errors of the fits of control and adaptation data are plotted against each other. No significant difference was found in the median error between the fits of the responses recorded in control (median: 0.09) and after 10-Hz (median: 0.10) adaptation (P > 0.3; Wilcoxon nonpaired test).
In the example in Fig. 4, the control responses (Fig. 4A) were optimally fitted with U0 = 0.17, rec0 = 500.5 ms, and fac0 = 1 ms. After the adaptation, the best fit was obtained with U0 = 0.09, rec10 = 629.9 ms, and fac10 = 11.7 ms (Fig. 4B). In this example, the decrease in the release probability U10/U0 = 0.53 can reasonably well account for the reduction of the amplitude of the EPSC1 (mean: 0.64). A decrease of the release probability after a 10-Hz adaptation was typical for our sample as illustrated in Fig. 7A, in which for each synaptic connection, the release probability after the adaptation is plotted against the control value. A statistical analysis reveals a highly significant decrease of U in the population of measured connections [median(U) = median(U0 – U10) = 0.030; mean: 0.039 ± 0.060; range: –0.05–0.2; P < 2 · 10–3; Wilcoxon paired test). Moreover, the change in the release probability was significantly albeit weakly correlated with the change of EPSC1 amplitude after the adaptation (Fig. 7F, correlation coefficient: r = 0.45; P < 0.02; F statistic). These observations, which rely on the assessment of the release probability from the response dynamics, are corroborated by an independent estimation of the release probability changes with the coefficient of variation method. After the adaptation, the inverse squared coefficient of variation (CV–2) of the EPSC1 amplitude decreased significantly (P < 0.04; Wilcoxon paired test), which is indicative of the decreased release probability (Fig. 7D). A significant correlation between the change in the CV–2 and the change in the EPSC1 amplitude (r = 0.66; P < 1 · 10–4; F statistic, Fig. 7E) lends further support to the conclusion that the reduction of the EPSC1 amplitude after the adaptation is at least partially due to the decrease of the release probability. However, because the above correlations are weak, and for some synapses changes in EPSC1 and U or P clearly do not go hand in hand, other factors might have contributed to the decrease of the response amplitude after an adaptation. This topic will be elaborated further later in this text.
|
rec, and facilitation, fac, did not show consistent changes on the population level. It should be noted here that at some synaptic connections the best fit for recovery time constant was out of the range of its reliable estimation. The estimated recovery time constant was >3 s in both, control conditions and after 10-Hz adaptation in four synaptic connections. In three more synapses, the estimated rec was >3 s in control, and in five other synapses, rec became >3 s after 10-Hz adaptation. All these cases were excluded from the population analysis. For the remaining subpopulation of synaptic connections, in which the estimation of rec was reliable (rec < 3 s) both before and after 10-Hz adaptation (Fig. 7B), no significant changes of the recovery time constant were found: median(rec) = median(rec0 – rec10) = –7.9 ms; mean: 75.2 ± 450 ms; range: –783–1,092 ms; P > 0.6. The facilitation time constant fac also did not show significant changes on the population level [Fig. 7C, median (fac) = 0.5 ms; mean: 13.9 ± 34.5 ms; range: –21.7–108.3 ms; P > 0.15].
Despite the absence of a unidirectional trend in changes of facilitation and recovery time constants on the population level, individual connections often show a very different behavior before and after the adaptation and alter rec and/or fac quite dramatically. In Fig. 7, B and C, in which the values of rec and fac after the adaptation are plotted against the control values, such cases are represented by points, which are located well away from the main diagonal. Some connections were only weakly depressing in the control but displayed strong depression after an adaptation, or vice versa, as indicated by the increase or decrease of the recovery time constant, respectively. The facilitation time constant expressed most heterogeneous changes, whereby synaptic connections may be subdivided in three distinct groups with respect to the change of facilitation time constant (Fig. 7C). In most of the connections, the fac changes little (data points around main diagonal in Fig. 7C, 
), but in some, it changes from 1 ms, which corresponds to almost purely depressing synapses, to 10–20 ms, making synapses facilitating, or the other way round (data points next to the axes in Fig. 7C, 
, 
). To figure out if these groups exhibit special characteristics with regard to other parameters, we have segregated synaptic connections into three groups, one group that increases and one that decreases the facilitation time constant by more than a factor of six and one group in between (Fig. 7C, different symbols).
The separation of synaptic connections into these three subgroups neither revealed any group-specific pattern of parameter changes (Fig. 7, A–F) nor affected the significance of changes. We then related changes in each synaptic parameter after an adaptation to either changes in other parameters or to their values in the control. From all possible combinations, the only significant correlation was found between the change in release probability U and the change in the recovery time constant rec (r = –0.38; P < 0.06; F statistic). Thus a decrease of the release probability after an adaptation was often accompanied by longer recovery of the resources at the presynapse.
Synaptic changes after strong (25 or 40 Hz) adaptation
Changes of parameters of synaptic transmission after 25-Hz adaptation were assessed in 24 connections and after 40-Hz adaptation in 29 synaptic connections. The quality of the fits of the responses after 25-Hz adaptation was similar to the quality of fitting the control data (Fig. 8D1), and the median rms error in the two data sets showed no significant difference (median: 0.11; P > 0.25; Wilcoxon nonpaired test). Fitting of the responses recorded after 40-Hz adaptation was slightly inferior (Fig. 8D2) as indicated by a slightly higher median rms error (median: 0.13; P < 0.02).
|
rec0 = 839.4 ms, and fac0 = 18.6 ms for the control condition and U25 = 0.26, rec25 = 541.9 ms, and fac25 =1 ms after a 25-Hz adaptation. The amplitude of the EPSC1 decreased from 0.15 pA in control to 0.09 pA after an adaptation (P < 3 · 10–5).
Thus although the first response amplitude clearly decreased after the adaptation, no accompanying reduction of the release probability was detected by the model. This situation was typical for the effects of strong adaptation with either 25- or 40-Hz stimulation (Fig. 9, A and B). Despite significant decrease of the EPSC1 amplitude to 66 and 54% of the control, (25- and 40-Hz adaptation, respectively, see preceding text), no significant changes of the release probability over the population were found for either 25- or 40-Hz adaptation [25-Hz adaptation: median(U) = median (U0 – U25) = 0.004; mean: –0.011 ± 0.055; range: –0.122–0.074; P > 0.4; 40-Hz adaptation: median: –0.029; mean: –0.064 ± 0.197; range: –0.764–0.210; P > 0.1]. Furthermore, there was no significant correlation between the EPSC1 amplitude changes and the release probability changes after strong adaptation (Fig. 9, I and J). In contrast to the results of assessment of release probability by fitting response dynamics, the coefficient of variation method indicated a decrease of the release probability after strong adaptation (Fig. 9, G and H). The decrease in the median CV–2 was significant after both 25- and 40-Hz adapting stimuli (25 Hz: P < 6.7 · 10–4; 40 Hz: P< 3.8 · 10–4; Wilcoxon paired test). Moreover, the changes in the CV–2 and the changes in the EPSC1 amplitude after strong adaptation were significantly correlated (25 Hz: r = 0.58; P < 0.003; F statistic, Fig. 9K; 40 Hz: r = 0.39; P < 0.03, Fig. 9L). When comparing the results of the two methods, however, it is important to note that the coefficient of variation method relies on stronger assumptions.
|
rec < 3 s) both in the control and after the adaptation, the mean decrease of the recovery time constant after 25-Hz adaptation was 285.3 ± 293.3 ms (median: 333.7 ms; range: –256–967 ms; P < 0.003). After an adaptation with 40 Hz, the mean decrease of the recovery time constant was mean (rec10 – rec40) = 289 ± 534 ms (median: 143.6 ms; range: –588–1,699 ms; P < 0.008). Those synapses, for which optimal fits were obtained with recovery time constants >3 s, were excluded from the calculation of the above statistics. However, the higher frequency of occurrence of such synapses in control conditions than after an adaptation (7 in control, 2 after 25-Hz adaptation; 7 in control, 3 after 40-Hz adaptation) also points at shortening of the recovery time after a strong adaptation and thus reinforces the above conclusion.
For the facilitation time constant fac, no statistically significant changes on the population level for strong adaptation with either frequency were found (25-Hz adaptation: median: 0 s; mean: –33 ± 201 ms; range: –959.9–109.1 ms; P > 0.6; 40-Hz adaptation: median: –14.7 ms; mean: –66.1 ± 219.7 ms; range: –117.07–39.8 ms, P > 0.05).
Similar to the effects of weak adaptation, we observed three distinct subsets of connections with respect to change of the facilitation time constant. As after the weak adaptation, the connections with extreme changes of the facilitation did not express any specific pattern of changes of the other parameters after a strong adaptation, and their omission did not alter the significance of the parameter changes after adaptation. Analysis of the relation between changes of different parameters, reveal the only significant correlation between the change in release probability and the change in the facilitation time constant, which were positively correlated after both 25- and 40-Hz adaptation (r = 0.48; P < 0.02 and r = 0.72; P < 3 · 10–5, respectively, F statistic). This positive correlation is, however, difficult to interpret because neither of these two parameters alone showed significant changes after the strong adaptation. The change in any parameter after strong adaptation stimulus did not correlate significantly with any synaptic parameter in the control state.
To summarize, our analysis revealed the following changes in synaptic transmission after adaptation. 1) Both weak and strong adaptation led to a significant decrease of the EPSC1 amplitude, the stronger adaptation leading to a stronger decrease of the response amplitude. This decrease was short-lasting, and response amplitude recovered to the control level with a time constant between 2 and 18 s. 2) After weak adaptation, the decrease of the EPSC1 was accompanied by and correlated with the decrease in release probability, and CV–2, although some synapses clearly deviated from this rule. 3) After strong adaptation, despite an even stronger reduction of the EPSC1 amplitude, no change in release probability was revealed from the fits of synaptic dynamics, however, a significant decrease of CV–2 was still observed. 4) Both, weak and strong adaptation had very heterogeneous effects on facilitation and recovery time constants, and thus on the synaptic dynamics. But the only significant change on the population level was a decrease of the recovery time constant after strong adaptation.
Model extension
Inconsistency between the reduction of the EPSC1 amplitude and the absence of a detected decrease of the release probability after strong adaptation implies that other factors, not accounted for by the simple phenomenological model of synaptic dynamics, were in play. One obvious candidate mechanism here is depletion of the resources, which are not completely recovered after an adaptation. The observed recovery of the depressed EPSC1 amplitude to the control level with a time constant of several (2–18) seconds corresponds to the suggested time course of refilling of a ready-to-release pool of synaptic vesicles (Sudhof 2000; Zucker and Regehr 2002). To investigate if inclusion of that additional, slow recovery process may influence our estimations of the release parameters, we have extended the original three-parameter model. As a first step, we have included as an additional parameter the amount of resources, available at the beginning of the test train (R1)
 | (11) |
fac, rec) were optimized to get the best fit of the data. The best fits of this four-parameter model for U, fac, rec did not differ much from the best fits obtained with the original, three-parameter model. Although the four-parameter model did give better fits of the data, as indicated by the decrease of the rms error by 6.9% on the average (median: 5.8%), the optimal values for the release probability, facilitation, and recovery time constants were not significantly different from the respective values obtained with the three-parameter model. The average differences were, for estimations of the release probability U, 5.8% (median: 2.1%), for the recovery time constant, rec, 1.1% (median: 1.3%), and for the facilitation time constant, fac, 15.1% (median: 1.5%).
As the next step, we introduced a second recovery process, which describes slow recovery of the maximal amount of available resources after an adaptation. This maximal amount of resources, Rmax(t) recovers to the limit Rmax with a longer recovery time constant max. As initial conditions, we set Rmax(0) = R1 and Rmax > R1. This extended model is thus described by the following equations
 | (12) |
 | (13) |
 | (14) |
rec, and fac from Eqs. 4 and 5, three additional parameters R1, Rmax, and max have to be estimated in Eqs. 12–14
. Because fitting all six parameters cannot be done unambiguously, we have fixed the max = 7 s, which corresponds to the average recovery time constant of the EPSC amplitude after the adaptation, measured experimentally. This extended five-parameter model captured the changes of synaptic responses and their dynamics after both, a weak and a strong adaptation. The results of fitting the responses after the weak adaptation (10 Hz), showed a significant decrease of the release probability, which was correlated with the decrease of the EPSC1 amplitude (r = 0.39, P < 0.06, F statistics). Stronger correlations were found between the decrease of the EPSC1 amplitude on the one hand, and the decrease of the available resources R1 (r = 0.47, P < 0.009) or decrease of the product of U and R1 (r = 0.99, P < 10–10). Interestingly, after the strong adaptation, the extended five-parameter model still did not reveal a change of the release probability, or a correlation between the release probability change and the EPSC1 amplitude decrease (r = –0.07, P > 0.6 for 25-Hz adaptation; r = –0.13, P > 0.3 for 40-Hz adaptation). However, the decrease of the EPSC1 amplitude was significantly correlated with the decrease of estimated resources by the time of application of the first stimulus, R1 (r = 0.70, P < 2 · 10–4 for 25-Hz adaptation and r = 0.61, P < 6 · 10–4 for 40-Hz adaptation).
Furthermore, the optimal values for U, fac, rec estimated with the extended five-parameter model did not differ strongly from the estimations obtained with the three-parameter model. The average differences were, for estimations of the release probability U, 5.9% (median: 2.1%), for the recovery time constant, rec, 2.2% (median: 1.3%), and for the facilitation time constant, fac, 15.7% (median: 1.5%). Moreover, inclusion of the second recovery process did not yield superior fits as compared with the four-parameter model.
Taken together, comparison of the assessment of parameters of synaptic transmission and their changes after an adaptation with the help of the original three-parameter model and extended, four- and five-parameter models allows to draw the following conclusions. First, the estimation of U, fac, and rec is robust because extensions of the model did not lead to notable changes of the estimates of these three basic parameters. Thus the slow recovery of the EPSC1 after an adaptation did not exert significant influence on estimation of the parameters from responses to brief trains of test stimuli. Second, extended models allowed to quantify the contribution of the resource depletion to the adaptation induced changes of synaptic transmission. Finally, the extended models suggest differential contribution of the changes in release probability and resource depletion to the response changes after adaptation with different frequencies.
|
|
DISCUSSION |
|---|
|
Estimation of parameters of synaptic transmission in the neocortex
Before discussing the effects of adaptation on synaptic transmission and dynamics, we shall compare our assessments of the synaptic parameters to the published data. We have recorded small excitatory postsynaptic currents, evoked with the stimulation intensity set just high enough to produce responses without failures. Such weak stimuli, even if recorded in current-clamp mode, evoke postsynaptic responses that are well below the threshold of activation of voltage-gated conductances. Because we have used the same weak intensity of stimulation for both the test trains and the adaptation, we consider as highly unlikely the possibility of contribution of voltage-clamp errors to our results. We have used a modification of a phenomenological model of synaptic transmission (Tsodyks and Markram 1997; Varela et al. 1997) for fitting the synaptic responses, evoked by stimulation with a set of test frequencies. At synaptic connections between layer 5 pyramidal cells in somatosensory cortex, earlier studies reported values for the mean recovery time constants of 810 ms (Markram et al. 1998; Tsodyks and Markram 1997) and 760 ms (Fuhrmann et al. 2004). Applying a modified phenomenological model for the analysis of synaptic connections in rat barrel cortex, Finnerty et al. reported mean recovery time constants of 480–1,190 ms, depending on the group of recorded cells, the developmental history of an animal and experimental conditions (Finnerty and Connors 2000; Finnerty et al. 1999). The preceding results were obtained on synaptic connections involving single axons or few presynaptic fibers. In a complementary approach, which exploited both intracellularly recorded postsynaptic responses and field potentials, the use of dynamic models for larger populations of synapses has been validated (Varela et al. 1997). Further, the authors demonstrated the usefulness of models of dynamic synapses in prediction of cellular responses to more complex patterns of prolonged stimulation. Varela et al. have found that recovery from the depression could be best described by a bi-exponential process with time constants of several hundreds of milliseconds and several, 7–9, seconds (Varela et al. 1997). Sparse published data on time constants of facilitation at neocortical synapses show that at synapses between excitatory cells they are usually about a hundred of ms (Markram et al. 1998; Varela et al. 1997), but at synapses, which are formed by pyramidal cells onto interneurons, facilitation of release may last up to several hundreds of milliseconds, making these synaptic contacts highly susceptible for temporal summation (Markram et al. 1998). In the visual cortex synapses in control, we have found recovery time constants in the range of hundreds of milliseonds, with most recovery time constants <1.5 s, and facilitation time constants in the range from several milliseconds to 300 ms, with the mean of 33 ms. Application of an adapting stimulation revealed an additional, slower recovery process with the time constant of several seconds (mean: 7.1 s). These estimations are in good agreement with the preceding data.
Release probability at neocortical synapses is highly heterogeneous, the values reported so far covering almost the whole possible range. At synapses between layer 5 pyramidal cells in rat somatosensory cortex, possible values of release probability were between 0.025 and 0.9 (Markram et al. 1997). In the barrel cortex, at synaptic connections between layer 4 cells, the release probability was found to be between 0.125 and 0.9 (Feldmeyer et al. 1999). One study reported an exceptionally high release probability, averaged 0.8, at synapses formed by layer 4 stellate cells onto layers 2–3 pyramidal neurons in the barrel cortex (Silver et al. 2003). Our recent study of release probability at synaptic connections to layer 2–3 pyramidal cells in rat visual cortex with the use of MK-801, an open-channel blocker of the N-methyl-D-aspartate receptor-gated channels, revealed a skewed distribution of release probabilities, with predominance of values <0.2 and an average of 0.17 (Volgushev et al. 2004). In the present study, we found similar values of release probability in control, with the average of 0.21 and median of 0.17. Given the high degree of heterogeneity of synaptic connections in the neocortex, where even synapses formed by the same axon onto different postsynaptic cells may express differential dynamic properties (Markram et al. 1998; Reyes et al. 1998; Thomson and Deuchars 1994), these comparisons show that our assessments of parameters of synaptic transmission in control, without adapting stimulation, are in good agreement with the data published so far. Reliability of our estimations of synaptic parameters is further substantiated by the low errors of the fits, which were of comparable range for fits of the data obtained under different conditions of stimulation with and without adaptation and by the fact that an extension of the model by additional parameters did not lead to a notable change of the optimal release probability, facilitation, and depression time constants. Taken together, these results allow us to conclude that our method of estimation of these three basic characteristics of synaptic transmission is reliable and can be exploited for assessment of changes of synaptic transmission after an adaptation.
Changes of synaptic transmission after adaptation
In the whole organism, adaptation is expressed as a reduction of the response amplitude. Recent in vivo study directly related adaptation of the responses to repetitive sensory stimulation, to the changes of synaptic responses, evoked with electric stimuli (Chung et al. 2002). The authors demonstrated that in rat barrel cortex, adaptation to repetitive whisker stimulation is indeed accompanied by the reduction of the amplitude of the postsynaptic potentials evoked by electric stimulation of the thalamus. Thus the reduction of the response amplitude to repetitive activation of the synapses, either in vivo by sensory stimulation, or in vitro by applying electric shocks, does serve as a mechanism of adaptation. Our results show that this mechanism might also be involved in adaptation in the visual system, specifically at synapses in the visual cortex, where we have observed reduction of the amplitude of postsynaptic responses after an adapting stimulation. Possible changes of two parameters of the presynaptic release machinery may underlie the reduction of response amplitude after an adaptation: a decrease of the release probability and a decrease of the available synaptic vesicles or resources. Evidence in support of the reduced release probability as one of the reasons for the response amplitude decrease includes the results of our analysis of synaptic dynamics after 10-Hz adaptation, and estimations of the changes of the release probability with the coefficient of variation method. Supportive evidence comes also from the recent somatosensory cortex study in which authors report the decrease of the EPSC amplitude after a train of 20 pulses (Fuhrmann et al. 2004). The authors found that 600 ms after the adapting train, the response amplitude decreased to 43–84% of the control, depending on the adapting frequency and temperature. The decrease of the response amplitude was accompanied by the decrease of the release probability as indicated by the decrease of the inverse coefficient of variation and an increase of the skew of the distribution of response amplitudes (Fuhrmann et al. 2004). The similarity between the results obtained in the visual cortex and in the barrel cortex is further stressed by the similar magnitudes of the response reduction and by the similar dependence of the amplitude reduction on the adaptation strength. In both our data and results of Fuhrmann et al., the depression of responses was stronger after an adaptation with higher frequency. However, we observed a decrease of the release probability in association with the reduction of the response amplitude only after 10-Hz adaptation but not after 25 or 40 Hz, whereas the other study reported a decrease of the release probability after both 10- and 20-Hz adaptation. This apparent inconsistency might well be explained by the different adaptation protocols. We have adapted synapses with 4-s trains at 10, 25, or 40 Hz, whereas Fuhrmann et al. used trains of 20 pulses at 10 or 20 Hz. One possible effect of our adaptation with 25 or 40 Hz for 4 s may be a kind of augmentation, which is typical for synapses in different parts of the brain including the neocortex (e.g., Castro-Alamancos and Connors 1997; Fuhrmann et al. 2004; see Thomson and Deuchars 1994; Zucker and Regehr 2002 for review). The short-term increase of the release probability, associated with the augmentation, could have counteracted an adaptation-evoked suppression of the release.
One further presynaptic mechanism, which could be responsible for the decrease of the amplitude of postsynaptic responses after an adaptation, is the depletion of a ready releasable pool of synaptic vesicles. During synaptic transmission, the vesicles are released from the immediately releasable pool, which is refilled from the pool of readily releasable vesicles (see Sudhof 2000; Zucker and Regehr 2002 for review). At low rates of presynaptic activity, the size of the larger readily releasable pool does not change substantially, and the recovery is limited by the speed of the vesicle transfer from the readily releasable pool to the immediately releasable pool. This process occurs with a time constant of several hundreds of milliseconds. At high rates of presynaptic activity, the readily releasable pool also becomes depleted and recovery is now limited by the slow process of replenishment (which occurs with a time constant of several seconds) of the readily releasable pool. These two recovery processes are expressed as depression of synaptic transmission with two different, rapid and slow, time courses. Previous studies revealed a slow form of depression, which recovers with a time constant of seconds to tens of seconds also at neocortical synapses (Fuhrmann et al. 2004; Varela et al. 1997). Possible mechanism underlying this form of depression could be the slow replenishment of the readily releasable pool of synaptic vesicles, as suggested by the similarity of the time course of the slowly recovered depression at neocortical synapses, and the replenishment of the readily releasable pool at synapses in other structures (Sudhof 2000; Zucker and Regehr 2002). Our analysis of results with extended models, which took into account this slow recovery process, showed that the relative contribution of the depletion of vesicle pools to the decrease of the response amplitude increases with the adaptation strength. After a weak, 10-Hz adaptation, the reduction of the response amplitude could be accounted for by the reduced release probability with little contribution of the vesicle depletion. In contrast to that, strong adaptation with 25 or 40 Hz led to a significant depletion of the synaptic vesicles, which became the main factor of the response amplitude reduction. Moreover, this analysis demonstrates that the effects of adaptation on synaptic transmission could not be faithfully described by a simple, three-parameter model. Only more complex models, in which interaction between different pools of synaptic vesicles is taken into account, are capable to capture the main features of the response changes after an adaptation. Notably, the time course of the vesicle exchange between different pools is itself a dynamic variable because it can be accelerated by the high-frequency presynaptic firing. This had been demonstrated first for the calyx of Held synapses (Wang and Kaczmarek 1998), and recent study provides evidence for activity dependent acceleration of the vesicle recovery at the synapses in somatosensory cortex (Fuhrmann et al. 2004). Our results on the consistent decrease of the recovery time constant after strong adaptation suggest that a similar acceleration of the vesicle trafficking between different pools might occur also at synapses in the visual cortex. However, further specific experiments are required to clarify the precise time course of these processes at neocortical synapses.
Our study revealed highly heterogeneous changes of dynamic properties of different synaptic connections after an adaptation. Although adaptation led to changes of the transmission in most of the synapses, the effects vary considerably from one synaptic connection to the other. On the population level, only the decrease of the release probability after a weak adaptation and acceleration of the recovery after a strong adaptation reached significance level. In other cases, synaptic parameters could change even in the opposite directions at different synapses, which resulted in the absence of significant changes in the averaged data. For example, adaptation led to an almost complete disappearance of facilitation at some synapses, but at other synapses, which did not show facilitation in control, it may become apparent after an adaptation. Therefore apart from the decrease of the amplitude of postsynaptic responses, adaptation may produce also cell-specific or synapse-specific effects, which may be averaged out on the population level but could nevertheless result in a kind of re-distribution of activity within neural networks. A possible role, which these subtle tunings of network activity may play in sensory adaptation and, more generally in sensory processing, remains to be clarified.
|
|
GRANTS |
|---|
|
|
|
ACKNOWLEDGMENTS |
|---|
|
|
|
FOOTNOTES |
|---|
Address for reprint requests and other correspondence: O. Beck, Neural Information Processing Group, Berlin University of Technology, 10587 Berlin, Germany (sekr{at}ni.cs.tu-berlin.de)
|
|
REFERENCES |
|---|
|
Adorjan P, Piepenbrock C, and Obermayer K. Contrast adaptation and infomax in visual cortical neurons. Rev Neurosci 10: 181–200, 1999.[Web of Science][Medline]
Adorjan P, Schwabe L, Piepenbrock C, and Obermayer K. Recurrent cortical competition: Strengthen or weaken? In: Advances in Neural Information Processing Systems 12, edited by Müller K.-R. Cambridge, MA: MIT Press, 2000, vol. 12, p. 89–95.
Akaneya Y, Altinbaev R, Bayazitov IT, Kinoshita S, Voronin LL, and Tsumoto T. Low-frequency depression of synaptic responses recorded from rat visual cortex. Neuroscience 117: 305–320, 2003.[CrossRef][Web of Science][Medline]
Artun OB, Shouval HZ, and Cooper LN. The effect of dynamic synapses on spatiotemporal receptive fields in visual cortex. Proc Natl Acad Sci USA 95: 11999–12003, 1998.
Carandini M, Heeger DJ, and Senn W. A synaptic explanation of suppression in visual cortex. J Neurosci 22: 10053–10065, 2002.
Castro-Alamancos MA and Connors BW. Distinct forms of short-term plasticity at excitatory synapses of hippocampus and neocortex. Proc Natl Acad Sci USA 94: 4161–4166, 1997.
Chance FS, Nelson SB, and Abbott LF. Synaptic depression and the temporal response characteristics of V1 cells. J Neurosci 18: 4785–4799, 1998.
Chung S, Li X, and Nelson SB. Short-term depression at thalamocortical synapses contributes to rapid adaptation of cortical sensory responses in vivo. Neuron 34: 437–446, 2002.[CrossRef][Web of Science][Medline]
Dodt HU and Zieglgansberger W. Visualizing unstained neurons in living brain slices by infrared DIC-videomicroscopy. Brain Res 537: 333–336, 1990.[CrossRef][Web of Science][Medline]
Faber DS and Korn H. Applicability of the coefficient of variation method for analyzing synaptic plasticity. Biophys J 60: 1288–1294, 1991.[Web of Science][Medline]
Feldmeyer D, Egger V, Lubke J, and Sakmann B. Reliable synaptic connections between pairs of excitatory layer 4 neurons within a single "barrel" of developing rat somatosensory cortex. J Physiol 521: 169–190, 1999.
Finnerty GT and Connors BW. Sensory deprivation without competition yields modest alterations of short-term synaptic dynamics. Proc Natl Acad Sci USA 97: 12864–12868, 2000.
Finnerty GT, Roberts LS, and Connors BW. Sensory experience modifies the short-term dynamics of neocortical synapses. Nature 400: 367–371, 1999.[CrossRef][Medline]
Fuhrmann G, Cowan A, Segev I, Tsodyks M, and Stricker C. Multiple mechanisms govern the dynamics of depression at neocortical synapses of young rats. J Physiol 557: 415–438, 2004.
Fuhrmann G, Segev I, Markram H, and Tsodyks M. Coding of temporal information by activity-dependent synapses. J Neurophysiol 87: 140–148, 2002.
Galarreta M and Hestrin S. Frequency-dependent synaptic depression and the balance of excitation and inhibition in the neocortex. Nat Neurosci 1: 587–594, 1998.[CrossRef][Web of Science][Medline]
Goldman MS, Maldonado P, and Abbott LF. Redundancy reduction and sustained firing with stochastic depressing synapses. J Neurosci 22: 584–591, 2002.
Jia F, Xie X, and Zhou Y. Short-term depression of synaptic transmission from rat lateral geniculate nucleus to primary visual cortex in vivo. Brain Res 1002: 158–161, 2004.[CrossRef][Web of Science][Medline]
Korn H and Faber DS. Quantal analysis and synaptic efficacy in the CNS. Trends Neurosci 14: 439–445, 1991.[CrossRef][Web of Science][Medline]
Markram H, Lubke J, Frotscher M, Roth A, and Sakmann B. Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex. J Physiol 500: 409–440, 1997.
Markram H and Tsodyks M. Redistribution of synaptic efficacy between neocortical pyramidal neurons. Nature 382: 807–810, 1996.[CrossRef][Medline]
Markram H, Wang Y, and Tsodyks M. Differential signaling via the same axon of neocortical pyramidal neurons. Proc Natl Acad Sci USA 95: 5323–5328, 1998.
Petersen CC. Short-term dynamics of synaptic transmission within the excitatory neuronal network of rat layer 4 barrel cortex. J Neurophysiol 87: 2904–2914, 2002.
Redman S. Quantal analysis of synaptic potentials in neurons of the central nervous system. Physiol Rev 70: 165–198, 1990.
Reyes A, Lujan R, Rozov A, Burnashev N, Somogyi P, and Sakmann B. Target-cell-specific facilitation and depression in neocortical circuits. Nat Neurosci 1: 279–285, 1998.[CrossRef][Web of Science][Medline]
Reyes A and Sakmann B. Developmental switch in the short-term modification of unitary EPSPs evoked in layer 2/3 and layer 5 pyramidal neurons of rat neocortex. J Neurosci 19: 3827–3835, 1999.
Silver RA, Lubke J, Sakmann B, and Feldmeyer D. High-probability uniquantal transmission at excitatory synapses in barrel cortex. Science 302: 1981–1984, 2003.
Sudhof TC. The synaptic vesicle cycle revisited. Neuron 28: 317–320, 2000.[CrossRef][Web of Science][Medline]
Tarczy-Hornoch K, Martin KA, Stratford KJ, and Jack JJ. Intracortical excitation of spiny neurons in layer 4 of cat striate cortex in vitro. Cereb Cortex 9: 833–843, 1999.
Thomson AM. Activity-dependent properties of synaptic transmission at two classes of connections made by rat neocortical pyramidal axons in vitro. J Physiol 502: 131–147, 1997.
Thomson AM and Deuchars J. Temporal and spatial properties of local circuits in neocortex. Trends Neurosci 17: 119–126, 1994.[CrossRef][Web of Science][Medline]
Tsodyks M, Pawelzik K, and Markram H. Neural networks with dynamic synapses. Neural Comput 10: 821–835, 1998.[CrossRef][Web of Science][Medline]
Tsodyks MV and Markram H. The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc Natl Acad Sci USA 94: 719–723, 1997.
Varela J, Sen K, Gibson J, Fost J, Abbott LF, and Nelson S. A quantitative description of short-term plasticity at excitatory synapses in layer 2/3 of rat primary visual cortex. J Neurosci 17: 7926–7940, 1997.
Varela JA, Song S, Turrigiano GG, and Nelson SB. Differential depression at excitatory and inhibitory synapses in visual cortex. J Neurosci 19: 4293–4304, 1999.
Volgushev M, Kudryashov I, Chistiakova M, Mukovski M, Niesmann J, and Eysel UT. Probability of transmitter release at neocortical synapses at different temperatures. J Neurophysiol 92: 212–220, 2004.
Volgushev M, Vidyasagar TR, Chistiakova M, and Eysel UT. Synaptic transmission in the neocortex during reversible cooling. Neuroscience 98: 9–22, 2000.[CrossRef][Web of Science][Medline]
Volgushev M, Voronin LL, Chistiakova M, and Singer W. Relations between long-term synaptic modifications and paired-pulse interactions in the rat neocortex. Eur J Neurosci 9: 1656–1665, 1997.[CrossRef][Web of Science][Medline]
Voronin LL. On the quantal analysis of hippocampal long-term potentiation and related phenomena of synaptic plasticity. Neuroscience 56: 275–304, 1993.[CrossRef][Web of Science][Medline]
Wang LY and Kaczmarek LK. High-frequency firing helps replenish the readily releasable pool of synaptic vesicles. Nature 394: 384–388, 1998.[CrossRef][Medline]
Zucker RS and Regehr WG. Short-term synaptic plasticity. Annu Rev Physiol 64: 355–405, 2002.[CrossRef][Web of Science][Medline]
This article has been cited by other articles:
|
|
 |
|
  Y. Katz, J. E. Heiss, and I. Lampl Cross-Whisker Adaptation of Neurons in the Rat Barrel Cortex J. Neurosci., December 20, 2006; 26(51): 13363 - 13372. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Visit Other APS Journals Online |
TOP
ABSTRACT
INTRODUCTION
, the time of the end of the adapting train. B: the EPSC1 amplitude after an adaptation (ordinate) with 25 Hz (
