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The Journal of Neuroscience, April 15, 2003, 23(8):3154
Variable Properties in a Single Class of Excitatory Spinal Synapse
David Parker Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, United Kingdom
ABSTRACT
TOP
ABSTRACT
Introduction
Although synaptic properties are specific to the type of synapse examined, there is evidence to suggest that properties can vary in individual synaptic populations. Here, a large sample of monosynaptic connections made by excitatory interneurons (EINs) onto motor neurons in the lamprey spinal cord locomotor network has been used to examine the properties of a single class of spinal synapse in detail.
The properties and activity-dependent plasticity of EIN-evoked EPSPs varied considerably. This variability occurred at convergent inputs made by several EINs onto single motor neurons. This suggests that it was an intrinsic network property and not simply related to differences between animals or experiments. The activity-dependent plasticity of EIN-evoked EPSPs could be negatively or positively related to the initial EPSP amplitude (P1 and P2 connections, respectively). This reflected the development of facilitation and depression from either small or large initial EPSPs.
To identify differences in presynaptic properties that could contribute to the synaptic variability, the quantal amplitude, release probability, number of release sites, and size of the available vesicle pool were examined. This analysis suggested that the variable amplitude and plasticity of EPSPs at P1 and P2 connections reflected an interaction between the release probability and the size of the available transmitter store.
There is thus significant functional variability in EIN synaptic properties. Synapses ranged from strong (evoked postsynaptic spikes) to weak (small depressing EPSPs). The selection of interneurons with different synaptic properties could provide an intrinsic mechanism for modifying excitatory network interactions and the locomotor network output.
Key words: interneuron; spinal cord; synaptic plasticity; neural network; motor neuron; lamprey
Introduction
The output of a neural network depends on the types of neurons it contains and the pattern of their synaptic connections ("network architecture"). To understand how a network output is generated, information is required on the cellular and synaptic properties of its component neurons (Selverston, 1980
; Getting, 1989
Activity-dependent synaptic plasticity is a ubiquitous phenomenon. Its analysis has focused primarily on long-term effects associated with development and learning and memory (Byrne and Kandel, 1996
I have begun to examine intrinsic activity-dependent synaptic plasticity in the lamprey locomotor network (Parker, 2000a
Here, a large sample of connections made by glutamatergic excitatory network interneurons (EINs) onto motor neurons has been used to examine the properties of this connection in detail. The results show considerable variability in the properties and plasticity of this single class of synapse. The variability was associated with differences in presynaptic release properties at different connections. The variability occurred at convergent inputs to single motor neurons. It thus cannot be accounted for by differences between animals or postsynaptic motor neurons, but instead represents an intrinsic property that could contribute to the patterning of the network output.
Materials and Methods
Adult male and female lampreys (Lamptera fluviatilis) were anesthetized with MS-222, and the spinal cord and notochord were removed. The spinal cord was isolated from the notochord and placed ventral side up in a Sylgard-lined chamber where it was superfused with Ringer's solution containing (in mM): 138 NaCl, 2.1 KCl, 1.8 CaCl2, 1.2 MgCl2, 4 glucose, 2 HEPES, 0.5 L-glutamine. The Ringer's solution was bubbled with O2, and the pH was adjusted to 7.4 with 1 M NaOH. The experimental chamber was kept at a temperature of 10-12°C.
Paired recordings were made from EINs and motor neurons using thin-walled micropipettes filled with 3 M potassium acetate and 0.1 M potassium chloride. Motor neurons were identified by recording orthodromic extracellular spikes in the corresponding ventral root after current injection into their somata. EINs were identified by their ability to elicit monosynaptic EPSPs in motor neurons (Buchanan, 1993
Single EPSPs were evoked at 0.1 Hz to examine basic synaptic properties. No activity-dependent plasticity occurred at this frequency. EIN spikes were evoked either by injecting 1 msec depolarizing current pulses of 10-60 nA or, where possible, on rebound from hyperpolarizing current pulses (2-5 msec, 1-5 nA) to avoid stimulation artifacts on the action potential. The plasticity of inputs during spike trains was examined by stimulating the presynaptic EIN at frequencies of 5, 10, and 20 Hz. These frequencies are within the range reported for interneuron spiking during network activity (Buchanan and Cohen, 1982
EPSP amplitudes were measured as the peak amplitude above the baseline immediately preceding the spike. At the frequencies used there was little summation of EPSPs during spike trains. The initial EPSP, the paired-pulse (PP) plasticity, and the plasticity over the 2nd-5th spikes (Train2-5), the 6th-10th spikes (Train6-10), and the 11th-20th spikes in the train (Train11-20) were measured. Paired-pulse plasticity was expressed as EPSP2/EPSP1, and plasticity over different regions of the spike train was expressed as EPSPTrain/EPSP1. Low-frequency-evoked EPSPs or the initial EPSPs in the trains were used to measure EPSP rise times, amplitudes, and half-widths. Where stimulation artifacts did not obscure action potentials, their rise times, peak amplitudes, and half-widths were also measured.
Statistical significance was examined using two-tailed paired or independent t tests or one-way ANOVA. When an ANOVA was used, a Tukey test was used for post hoc analysis of differences between groups. n in the text refers to the number of connections examined. Up to seven connections were examined in a single animal (n = 163 animals). All values given refer to mean ± SEM.
Results
Properties of low-frequency-evoked EPSPs
This analysis is based on a sample size of 278 monosynaptic EIN connections to motor neurons. In some connections (n = 26), polysynaptic excitatory and inhibitory inputs reliably followed the evoked EPSP. These connections have been examined separately and are not included in this analysis (my unpublished observations).
The mean amplitude of low-frequency-evoked EPSPs was 1.5 ± 0.05 mV (range, 0.32 to 4.2 mV) (Fig. 1A). The mean rise time was 3.7 ± 0.18 msec (range, 0.6-11.9 msec; n = 100) (Fig. 1B), and the mean half-width was 10.2 ± 0.4 msec (range, 3.4-27.5 msec; n = 100) (Fig. 1C). These rise times and half-widths resemble those of non-NMDA-mediated and mixed NMDA and non-NMDA-mediated EPSPs in the lamprey (Dale and Grillner, 1986
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Figure 1. The properties of EIN-evoked EPSPs. Histograms of EPSP amplitudes (A), rise times (B), and half-widths (C) are shown. Di, Dii, An example of an electrical connection between an EIN and a motor neuron. Notice that the chemical component varies, but the electrical component is constant. The chemical but not electrical component was blocked by cadmium (200 µM). E, An example of a connection in which EIN stimulation at 20 Hz consistently evoked spikes in the postsynaptic motor neuron. F, The relationship of the coefficient of variation to the initial EPSP amplitude. Each symbol represents a single connection.
In a small proportion of connections (n = 8), EIN-evoked EPSPs reliably evoked spikes in the postsynaptic motor neuron (Fig. 1E). All cells were held at resting potentials of between
65 and
Although their basic properties varied, a constant property of EIN inputs to motor neurons was that they were very reliable; EPSP failures were absent in normal Ringer's solution (my unpublished observations). Although EPSPs did not fail, the amplitude of successive low-frequency-evoked EPSPs could fluctuate. The coefficient of variation (CV) (SD/mean) of low-frequency-evoked EPSPs ranged from 0.06 to 0.94 (n = 169). EPSPs of >1.5 mV had lower CVs (0.14 ± 0.05) than EPSPs of <1.5 mV (0.27 ± 0.07), although the variability, particularly of small EPSPs, weakened the relationship between the EPSP amplitude and CV (r2 = 0.12).
Plasticity over spike trains
In a previous analysis of the activity-dependent plasticity of EIN inputs to motor neurons, EPSPs usually depressed over spike trains (Parker and Grillner, 2000
There were four responses over spike trains: depression, facilitation, biphasic, and unchanged (no significant plasticity over the train) (Fig. 2). As in the previous analysis, depression was the usual effect at 20 Hz, where it was seen in 103 of 225 connections (46%). The depression reached a plateau at ~60% of control between the 10th and 20th spikes in the train. Facilitation occurred in 61 connections (27%). It developed to its maximum level of ~160% of control by the second spike in the train. It was maintained at this level over Train6-10, but some recovery (between 10-20%) occurred later in the train. Biphasic responses (n = 33 of 225) consisted of an initial facilitation to ~120% of control over Train2-5 and depression to 75% of control over Train10-20. There was no significant plasticity in 26 connections (12%).
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Figure 2. The plasticity of EIN inputs over trains of 20 spikes at 5-20 Hz. Graphs show summed data at depressing (A), facilitating (B), biphasic (C), and unchanged connections (D). Insets on the graphs show examples of each type of plasticity over the first five spikes during a 20 Hz spike train.
Depression was marginally the most frequent effect at 10 Hz (34% depressed, 33% unchanged, 18% facilitated, 15% biphasic; n = 134). Facilitation developed to 160% of control by the second spike, with modest recovery occurring again later in the train. Biphasic connections facilitated to ~130% of control before depressing over Train6-10 to reach a plateau of 70% of control.
At 5 Hz, 52% of connections depressed, 27% were unchanged, 13% facilitated, and 8% were biphasic (n = 97). Peak facilitation to 150% of the initial EPSP was reached by the third spike in the train (Fig. 2B). Biphasic connections facilitated to 110% of control before depressing over Train6-10 to reach depression of 60% of control.
The same type of plasticity was usually evoked when single connections were examined at each frequency (n = 69 of 95). Relatively consistent features in connections where the plasticity differed at different frequencies were facilitation at 10 and 20 Hz but depression or no plasticity at 5 Hz (n = 6), and depression at 20 Hz but unchanged responses at 10 and 5 Hz (n = 4).
Significant depression or facilitation occurred over Train2-5 (p < 0.05) (Fig. 2A,B). The plasticity could thus develop during network activity and influence the patterning of the network output (Buchanan and Cohen, 1982
The initial EPSP amplitude was significantly larger at depressing connections (1.66 ± 0.11 mV) than at facilitating (1.08 ± 0.1 mV), biphasic (1.10 ± 0.13 mV), or unchanged connections (0.96 ± 0.08 mV; p < 0.05; one-way ANOVA). Differences in EPSP rise times and half-widths were not associated with different forms of plasticity (p > 0.05; ANOVA). Activity-dependent changes in rise time and half-width were studied in 23 pairs in which EPSPs were free of stimulus artifacts. Because depression was the usual effect (n = 18 of 23), only depressing connections were examined. The half-width and rise time were usually unchanged over spike trains (n = 14 of 18), but in four connections the half-width was reduced at 20 Hz (data not shown).
Convergence and divergence
These results show that monosynaptic EIN-evoked EPSPs in motor neurons differ in their properties and plasticity. This is a common finding at spinal and supraspinal synapses (Koerber and Mendell, 1991
In a previous analysis using triple intracellular recordings, divergent inputs from a single EIN to two motor neurons were rare (n = 2 of 21) (Parker and Grillner, 2000
In contrast to divergence, convergent EIN inputs from two or more EINs to a single motor neuron were common. Up to seven EINs could connect to a single motor neuron (n = 44). This does not necessarily reflect the maximum number of convergent inputs a motor neuron can receive, but the number found before a motor neuron recording was lost. There were varying patterns of convergent inputs. The initial EPSP amplitude and plasticity over the train could differ (Fig. 3Ai,Aii); both the initial EPSP amplitude and plasticity could be the same (data not shown); the initial EPSP amplitude could differ, but the plasticity over the train was the same (Fig. 3Bi,Bii); the initial EPSP amplitude was similar, but the plasticity differed (Fig. 3Ci,Cii); and both the initial EPSP amplitude and plasticity could differ, but in this case the different plasticity equalized the input during the spike train (Fig. 3Di,Dii).
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Figure 3. The properties of convergent EIN inputs to single motor neurons. The graphs and traces show inputs made by two different EINs onto a single motor neuron. Ai, Aii, Two convergent inputs to a single motor neuron in which the initial EPSP amplitude and plasticity differed. Bi, Bii, Convergent inputs in which the same type of plasticity developed from different initial EPSP amplitudes. Ci, Cii, Convergent inputs that evoked different plasticity from two EPSPs with similar initial amplitudes. Di, Dii, Convergent inputs where EPSPs with different initial amplitudes and plasticity resulted in the input equalizing during the spike train.
The initial amplitude and plasticity of convergent EIN inputs to single motor neurons can thus vary. This suggests that the presynaptic EIN and not the postsynaptic motor neuron essentially determined the properties of the synapse. Importantly, the properties of convergent inputs to single motor neurons show that the synaptic variability is not simply related to differences between animals, experiments, or inputs to different classes of motor neurons (Rovainen, 1979
Locus of plasticity
Although postsynaptic voltage, AMPA receptor desensitization, or properties or the presynaptic action potential do not contribute to the plasticity of EIN inputs (Parker, 2000b
2 of the second EPSP should be reduced relative to the CV
The PP CV
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Figure 4. Examination of the locus of plasticity using the CV
An increased CV
As synapses are examined in an intact network, evoked inputs can occur on a certain amount of background synaptic noise. In quantal analyses this noise reduces the EPSP variability and could thus also account for the increased CV
The influence of the initial EPSP amplitude on plasticity
The activity-dependent plasticity of EIN inputs thus appears to be mediated presynaptically. Presynaptic influences on plasticity are often associated with differences in the initial release probability (Zucker, 1989
Overall, plasticity was negatively related to the initial EPSP amplitude, the relationship expected of a release probability-dependent mechanism (Fig. 5A) (Zucker and Regehr 2002
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Figure 5. The relationship of PP plasticity (EPSP2/EPSP1) to the initial EPSP amplitude. A, Graph showing PP plasticity at different frequencies. For clarity only PP plasticity with values of <2.5 is shown. B, The relationship of PP plasticity to the initial EPSP amplitude is shown at 20 Hz, when connections that show an inverse relationship between initial EPSP amplitude and plasticity (P1 connections, ) are plotted separately to connections that have PP plasticity positively related to EPSP amplitude (P2 connections,
). Notice that above 2 mV the EPSP was not related to PP plasticity in either type of connection. The inset shows sample traces of paired-pulse plasticity from P1 and P2 connections at 20 Hz.
The low r2 values reflected the development of facilitation and depression from both small and large initial EPSPs. To examine further the relationship between the initial EPSP amplitude and plasticity, connections in which the plasticity was positively related to the EPSP amplitude (i.e., small depressing and large facilitating connections) were removed from the grouped data and plotted separately. Only PP responses were examined. At 20 Hz, 110 of 252 pairs (44%) were removed (Fig. 5B). Of these, 74 (67%) were EPSPs of >1 mV that facilitated. The remainder were small (<1 mV) depressing EPSPs. At 10 Hz, 86 pairs were removed and plotted separately (44%). Of these, 76 (88%) were facilitating EPSPs of >1 mV (data not shown). At 5 Hz, 76 pairs were removed and plotted separately (39%), of which 69 (91%) were large initial EPSPs that facilitated (data not shown).
Separating the connections in which plasticity was positively or negatively related to the initial EPSP amplitude not surprisingly resulted in higher r2 values over the different regions of the train (P1, 0.28 ± 0.03, range, 0.16-0.42; P2, 0.39 ± 0.09, range, 0.06-0.77). However, with EPSPs of >2 mV there was a reduced range of plasticity and little influence of the initial EPSP amplitude over a 2 mV range in both groups (Fig. 5B). The different responses at connections in which plasticity was negatively or positively related to the initial EPSP amplitude presumably reflect differences in the synaptic properties at these connections. For convenience, in the subsequent analysis connections in which plasticity was negatively related to the initial EPSP amplitude (i.e., small facilitating and large depressing EPSPs) will be termed P1, and those in which there was a positive relationship (small depressing and large facilitating EPSPs) will be termed P2.
Variance-mean analysis of synaptic properties
The basic properties and plasticity of EIN-evoked EPSPs thus varied, suggesting that EINs do not form a functionally homogenous interneuron population. The plasticity appears to mostly be mediated presynaptically. Differences between connections should thus be reflected in differences in their presynaptic release properties. These properties are usually examined using a quantal analysis. However, this analysis can be difficult to apply at central synapses (Korn and Faber, 1992
Release probability was altered by changing Ringer's solution calcium levels or by using cadmium to block calcium entry. In the best cases (n = 3), two low (50 and 75%) and two high (150 and 200%) calcium Ringer's solutions could be used before a recording was lost, but usually only 50 and 200% calcium Ringer's solution could be used (n = 26). At least 100 EIN-evoked EPSPs were evoked at a frequency of 0.2 Hz under each condition (Fig. 6A). Stimulation at this frequency did not usually evoke any plasticity (Fig. 6B), but if this occurred (n = 3) the connection was not included in the analysis.
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Figure 6. Variance-mean analysis of EIN synaptic transmission. A, Traces showing 100 EIN (top traces) or reticulospinal (bottom traces)-evoked EPSPs (evoked at 0.2 Hz) in control and in high- and low-calcium Ringer's solution. B, Graph showing EPSP amplitudes over the stimulation train in different calcium levels ( , control;
, low calcium). C, Plot of the EPSP variance against the EPSP mean in control and in high- and low-calcium Ringer's solution. Di-Diii, Histograms of the qw, Nmin, and pw of EIN and reticulospinal (RS) inputs to motor neurons. Histograms showing the quantal amplitude (qw) (Ei), minimum number of release sites (Nmin) (Eii), and release probability (pw,) (Eiii) at facilitating and depressing EIN connections.
The mean quantal amplitude (qw) was 0.12 ± 0.01 mV (range, 0.06-0.22 mV; n = 29) (Fig. 6Di). This is less than half the amplitude of TTX-resistant miniature EPSPs (mEPSPs) (range, 0.3-0.55 mV) (Parker, 2000c
The pw ranged from moderate (0.32) to relatively high values (0.73). However, the evidence suggests a wider range of pw than this. First, in some cases the relationship of the EPSP mean to variance was linear (n = 14). In these cases only qw can be determined (Clements and Silver, 2000
To determine the role of synaptic properties in the plasticity of EIN inputs, the qw, pw, and nmin were compared at depressing and facilitating connections. The qw was 0.08 ± 0.01 mV at depressing connections and 0.10 ± 0.04 mV at facilitating connections (Fig. 6Ei). The nmin was 23.5 ± 2.1 at depressing connections and 28.3 ± 10 at facilitating connections (Fig. 6Eii). The pw was 0.67 ± 0.1 at depressing connections and 0.32 ± 0.08 at facilitating connections (Fig. 6Eiii). Only pw differed significantly between depressing and facilitating connections (p < 0.05), suggesting that, as in other systems (Zucker, 1989
For comparison with EIN-evoked EPSPs, glutamatergic inputs from reticulospinal axons to motor neurons were also examined using a V-M analysis (Fig. 6A,Di-Diii) (n = 9). The qw of reticulospinal inputs was 0.14 ± 0.04 mV (range, 0.03-0.23; n = 9). This is a mean and range similar to that of the EINs and thus suggests that reticulospinal axons are not the source of larger TTX-resistant mEPSPs (see above). The pw was less than the EINs (0.38 ± 0.025; range, 0.33-0.49; n = 6). In three connections the V-M relationship was linear, again suggesting release probabilities of <0.3. However, high-calcium Ringer's solution potentiated reticulospinal-evoked EPSPs in every case (n = 9 of 9), suggesting a reduced upper range of pw compared with the EINs. Finally, the mean number of release sites (nmin) was greater than at the EINs (85 ± 56; range, 12-250; n = 6), and consequently the mean quantal content (m = nminpw) was larger (32; range, 5-95). With a quantal amplitude of 0.14 mV, evoked EPSP amplitudes could theoretically range from 0.7 to 13.3 mV. This is a much greater range than EIN EPSPs. It is consistent with the observation that reticulospinal inputs typically have larger amplitudes than EIN-evoked EPSPs, although the theoretical upper range is greater than that observed experimentally (observed range, 0.57-8.5 mV) (my unpublished observations).
Number of available synaptic vesicles
The difference in pw at facilitating and depressing connections could account for the negative relationship between the initial EIN-evoked EPSP amplitude and plasticity at P1 connections. A high pw will release a large proportion of the available transmitter store. This will result in a large initial EPSP that could subsequently depress because of transmitter depletion. A low initial pw will release less transmitter. It will thus evoke a smaller initial EPSP but could provide the potential for a subsequent increase in pw and facilitation over the train (Zucker, 1989
The release probability acts on or is influenced by the available synaptic resources (Schikorski and Stevens 1999
d/q(Vo
), where Vo is the initial EPSP amplitude,
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Figure 7. The releasable vesicle pool size in EINs. A, Graph showing the depression of an EIN-evoked EPSP in a single connection, illustrating the parameters used to estimate the available vesicle numbers. B, Histogram showing vesicle numbers in all depressing connections and in P1 and P2 connections. The estimated releasable vesicle pool was significantly larger at P1 than at P2 connections.
With the quantal amplitude of 0.12 mV estimated from the V-M analysis, the mean number of available vesicles at these connections was 257 ± 92 (range, 34-954; n = 57) (Fig. 7B). There was no significant difference between the number of available vesicles calculated from depression at 5, 10, or 20 Hz (p > 0.05; one-way ANOVA). This is expected, because the stimulation frequency should not affect basal vesicle numbers. However, the model assumes the absence of activity-dependent replenishment, which may not be the case at connections between EINs and motor neurons (Parker, 2000b
The number of available vesicles varied 30-fold between different connections. To determine whether this variability was related to functional differences in synaptic properties, the number of vesicles was compared at depressing P1 and P2 connections (Fig. 7B). Depressing P1 connections had significantly more vesicles (374 ± 70; p < 0.05; n = 38) than depressing P2 connections (175 ± 33; n = 19). Although there was a twofold difference in the size of the available vesicle pool, in connections where a V-M analysis was also performed the pw was similar at both types of connection (P1, depressing pw = 0.63, n = 3; P2, depressing pw = 0.56, n = 4).
Discussion
This study shows variable properties at an individual synapse in the lamprey spinal cord. The variability occurred at convergent inputs onto single motor neurons and is thus an intrinsic network property.
Small sample sizes in previous analyses prevented synaptic variability from being examined. Large sample sizes are thus required if synaptic properties are to be examined in detail. Although possible at interneuron connections onto motor neurons, this will be difficult at connections between network interneurons where stable recordings are rare.
The analysis has been performed only in quiescent preparations. It is possible that activity-dependent synaptic properties are altered during network activity, for example, as a result of rhythmic presynaptic inputs (Alford et al., 1991
EPSP properties and plasticity
EIN-evoked EPSPs are reliable and essentially never fail. However, EPSP amplitudes, rise times, half-widths, and plasticity differed between connections. These properties were weakly correlated, suggesting a potential continuum of functional properties that could influence the strength and integration of synaptic inputs. Further variability is suggested by the presence of electrical components in some connections and the ability of some EINs to reliably evoke postsynaptic spikes.
For plasticity to influence network activity it must occur over Train2-5 (Buchanan and Cohen, 1982
Plasticity mechanisms
Depression and facilitation could develop from small and large initial EPSPs. Plasticity could thus be negatively or positively related to the initial EPSP amplitude (P1 and P2 connections, respectively). Despite the weak relationship between the initial EPSP amplitude and plasticity in the grouped data, release probability could account for the properties of P1 and P2 connections.
The measured pw ranged from 0.3 to 0.73, although there is evidence to suggest that it extends above and below this range (Hanse and Gustafsson, 2001
Release probability alone cannot account for the positive relationship between the EPSP amplitude and plasticity at P2 connections, where facilitating (presumed low release probability) connections have large initial EPSPs and depressing (presumed high release probability) connections have small initial EPSPs. The difference between P1 and P2 connections appears to reflect an interaction between release probability and the available transmitter store. The size of the available vesicle pool was examined using the model of Wang and Zucker (1998)
Interacting influences on network synaptic transmission
An interaction between the release probability and the available vesicle pool thus presynaptically influences the EPSP amplitude and plasticity. There is also evidence that suggests that postsynaptic properties could also influence the integration of synaptic inputs. For example, the CV
Network interneurons generate repetitive bursts of spikes during swimming. As a result, the replenishment of transmitter stores will also influence transmission over repetitive spike bursts. The summed depolarization over a train of five spikes, the upper number of spikes during a locomotor burst (Buchanan and Cohen, 1982
Presynaptic influences during an episode of network activity will thus reflect an interaction between the release probability, the size of the available transmitter store, and the efficiency of transmitter replenishment. For example, decreasing the release probability by modulating calcium entry will reduce the initial EPSP amplitude and consequently reduce depression caused by depletion. Increasing the release probability could release a larger proportion of the available transmitter store, which will result in an increase in the initial EPSP amplitude and enhance depression caused by depletion. Because replenishment appears to be coupled to calcium entry and transmitter release (Parker, 2000b
Function of interneuron variability
Synaptic properties vary at synapses made between different classes of interneurons in the lamprey spinal cord (Parker, 2000a
The properties of synapses made between different classes of network interneurons could contribute to the specific role that these neurons have in patterning network activity. The variability at individual connections could instead provide an intrinsic mechanism for modifying the network output (Aradi and Soltesz, 2002
FOOTNOTES Received Dec. 2, 2002; revised Jan. 22, 2003; accepted Jan. 28, 2003.
This work was supported by a Royal Society University Research Fellowship and grants from the Biotechnology and Biological Sciences Research Council and Wellcome Trust.
Correspondence should be addressed to David Parker, Department of Zoology, Cambridge University, Downing Street, Cambridge CB2 3EJ, UK. E-mail: djp27{at}cam.ac.uk.
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[Abstract/Free Full Text] .- Parker D (2000c) Presynaptic and interactive peptidergic modulation of reticulospinal synaptic inputs in the lamprey. J Neurophysiol 83:2497-2507
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[Abstract/Free Full Text] .- Parker D, Grillner S (2000) The activity-dependent plasticity of segmental and intersegmental synaptic connections in the lamprey spinal cord. Eur J Neurosci 12:2135-2146
