 |
INTRODUCTION |
Understanding the functional organization of a central neural network requires an analysis of the relevant synaptic input that modulates neuronal activity during network function. It has been difficult to study in detail the synaptic information transfer during activity in central vertebrate networks because the necessary experimental conditions and normal network activity are often unobtainable together. We are interested particularly in the functional organization of the central pattern generator (CPG) networks of the mammalian spinal cord because they have the ability, even in reduced preparations, to produce locomotor rhythms that closely resemble those seen in intact animals (Grillner 1981
; Rossignol 1996
). Such preparations may allow the type of detailed analysis necessary to examine relevant synaptic information transfer in an operating central vertebrate network. We are presently far from understanding these CPG networks because the creation, flow, and cellular integration of the network signals are understood poorly. An important step to understand the mammalian CPG function is to analyze how synaptic signals are used to transmit the cyclic locomotor-related signal. Fast synaptic input, i.e., synapses with ionotropic receptors, could drive cyclic locomotor activity by modulation of the sign (excitatory or inhibitory) and by modulation of the frequency and/or the amplitude of the individual synaptic events. Earlier studies have examined the composition of synaptic inputs driving locomotor activity in vertebrate motoneurons by inverting the inhibitory synaptic potentials, either by filling motoneurons with chloride or by hyperpolarizing them. The locomotor CPG network was found to control motoneuron activity by alternating fast excitatory and inhibitory synaptic events during fictive locomotion (Cazalets et al. 1995
; Jordan 1983
; Perret 1983
; Russell and Wallen 1983
). Thus there is a reciprocal activity in presynaptic inhibitory and excitatory neurons to drive the motoneurons to fire rhythmically. In this study, we analyzed the fast synaptic signals received by interneurons in the rat spinal cord that were rhythmically active in phase with ongoing fictive locomotion. We asked if the collaboration between excitatory and inhibitory inputs that drives motoneurons was responsible for cyclic activity in a higher level of the motor pathway, interneurons possibly participating in the spinal locomotor CPG. Alternatively, other forms of synaptic signaling could be dominant here. For example, mutual reciprocal inhibition dominates the synaptic signaling in many invertebrate motor CPGs (Marder and Calabrese 1996
; Mulloney and Perkel 1988
), and a similar organization has been suggested for the rhythm-generating kernel in the mammalian spinal CPGs (Brown 1911
; Jankowska et al. 1967
; Lundberg 1979
; Pearson and Collins 1993; see also Kiehn et al. 1997
for a review).
No matter what the sign of the synaptic inputs, they can influence rhythmicity in the postsynaptic membrane potential either by modulation of the amplitude and/or the frequency of the postsynaptic currents (PSCs). Because little is known about the relative importance of PSC frequency and amplitude modification in functioning networks, we also examined this aspect of the synaptic inputs driving rhythmically active interneurons. If PSC amplitude modification mediates the rhythmic signal, mechanisms for such modulation, like facilitation and depression, will be important to study. The average PSC amplitude from a population of synapses also could be modulated by a preferential use of synapses with the appropriate strengths, if a range of synaptic strengths were available. This latter possibility is intriguing because the amplitude distributions of postsynaptic currents or potentials in most mammalian neurons reported so far, do indeed show a broad range of amplitudes (see for example Bekkers et al. 1990
; Clamann et al. 1985
; Mendell and Henneman 1970
). On the other hand, if frequency modification of synaptic input is the dominating mechanism in the control of postsynaptic activity, then the factors controlling presynaptic firing rates are important to study.
We used the isolated spinal cord from neonatal rats to examine the synaptic inputs responsible for locomotor activity in spinal interneurons. Under the influence of certain neuro-active compounds, this preparation produces a locomotor-like rhythmic activity that can be monitored as bursts of action potentials in the ventral roots (Cazalets et al. 1992
; Cowley and Schmidt 1994
; Kiehn and Kjaerulff 1996
; Kudo and Yamada 1987
; Smith and Feldman 1987
). This preparation is well suited for studies of network activity at a cellular level because spinal interneurons in the lumbar region can be recorded from with whole cell patch electrodes (Kiehn et al. 1996
).
We concentrated our analysis on rhythmically active neurons located in the intermediate gray matter and close to the central canal because, based on activity dependent dye-labeling studies in cat (Carr et al. 1995
) and neonatal rat (Kjaerulff et al. 1994
) and spinal cord lesion studies (Kjaerulff and Kiehn 1996
), these areas are thought to be important for locomotion. The majority of cells in these areas in the neonatal rat show rhythmic locomotor activity apparently driven mainly by synaptic input rather than by intrinsic bursting/plateau properties (Kiehn et al. 1996
). A detailed analysis of how the network uses the synapses for information transfer has been hampered previously by the fact that the individual synaptic events have been difficult to distinguish from each other during ongoing network activity. By voltage clamping the soma, we were able to gather recordings of clearly distinguishable fast synaptic currents that the cells receive and also reduce any contribution of active membrane properties to the rhythmic locomotor related activity.
 |
METHODS |
The methods for preparing the isolated neonatal rat spinal cord, inducing rhythmic locomotor-like activity from the cord, and recording the activity extracellularly from the ventral roots and from interneurons intracellularly, using tight-seal whole cell recordings have been described previously in detail (Kiehn and Kjaerulff 1996
; Kiehn et al. 1996
). Below follows a brief description of these procedures.
Preparation
Neonatal rats (0-3 days old) were anesthetized deeply with ether, decapitated, and the spinal cord extending from C1 to L6 including the ventral and dorsal roots was removed. For better access to spinal interneurons, the cord was split midsagittally in most experiments from T13 to L6, and the dorsal side of the cord was discarded. The preparation was transferred to a recording chamber, pinned down, and superfused with oxygenated (95% O2-5% CO2) Ringer solution of the following composition (in mM): 128 NaCl, 4.7 KCl, 25 NaHCO3, 1.2 KH2PO2, 1.25 MgSO4, 2.5 CaCl2, and 20 glucose (pH 7.4) at room temperature. Locomotor activity was induced by bath application of N-methyl-D-aspartic acid (NMDA, 6-7 µM) in combination with 5-hydroxytryptamine (5-HT, 4-20 µM). The drugs were obtained from Sigma (St. Louis, MO) or RBI (Natick, MA).
Recording
We combined ventral root recordings with intracellular tight-seal whole cell recordings (Blanton et al. 1989
; Edwards et al. 1989
) from rhythmically active spinal interneurons located around the central canal and in the intermediate gray matter. These are the areas that activity-dependent labeling and lesion studies suggest are involved in rhythm-generation in mammals (Carr et al. 1995
; Kjaerulff and Kiehn 1996
; Kjaerulff et al. 1994
). Activity in the L2 and L5 ventral roots, corresponding to leg flexor and extensor activity, respectively (Cowley and Schmidt 1994
; Kiehn and Kjaerulff 1996
), was recorded with suction electrodes. Activity in interneurons was recorded with patch electrodes (5-10 M
) pulled from 1.5 mm borosilicate glass without filaments. Whole cell voltage or current recordings were made with either Axopatch 1-D or Axoclamp 2B amplifiers (Axon Instruments; Foster City, CA). The signals were filtered at 2-5 kHz and digitized at 5 kHz. Series resistance was followed throughout the experiments and was usually <25-30 M
. The pipette solution contained (in mM) 130 potassium gluconate, 10 N-2-hydroxyethylpiperazine-N
-2-ethanesulfonic acid, 0 or 4 NaCl, 10 ethylene glycol-bis(
-aminoethyl ether)-N,N,N
,N
-tetraacetic acid, 1 CaCl2, 4 ATP-Mg, and 0 or 0.3 GTP-Li. The pH was adjusted to 7.3 with KOH. Intracellular potentials were not corrected for liquid junction potentials (Neher 1995
).
Synaptic detection algorithm
As previously described (Raastad et al. 1996
), we used a computer algorithm to detect putative PSCs. In short, an event was considered a PSC candidate if the current deviated from baseline noise more than a specified value within 2 ms. This detection value was set initially so that many events were considered PSC candidates, relative to the final number accepted (see below). An alpha function was fit to the initial 10 ms of the current, to evaluate if the shape resembled a PSC (with an abrupt start, fast rise time and slower decay). The function used was f(t) = amp*
*exp(
*t), where amp is the peak amplitude in picoamps, t is time, and
the factor characterizing the time course, and the squared deviation between the function and the original data were minimized. The function was defined only for positive values of t, and the current started at t = 2 ms. The function was fit only to the initial 10 ms of the single PSC because other PSCs and noise often contaminated the PSC tail.
Two measures were used to accept or reject the PSCs: the value of the squared deviation and the peak value of the PSC. These two values were used as detection thresholds set in an interactive (manual) procedure, so that <10% of the outward PSCs detected at depolarized membrane potentials (
40 to
50 mV) were detected at hyperpolarized potentials (
60 to
75 mV). This suggested that the outward events were indeed inhibitory PSCs (IPSCs) (Raastad et al. 1996
). This was one of our main criteria for accepting the detected events as PSCs and not noise. Other criteria included visually confirming that the detected events had a shape expected of PSCs and noting that the time courses were different between the outward and inward events (Raastad et al. 1996
), which is often the case for inhibitory and excitatory PSCs (Jones and Westbrook 1996
). All these features should characterize synaptic events and not noise.
The parameters of the fitted function were stored together with the time of the start of the function relative to the locomotor period they appeared in. The start of a locomotor period was defined as the onset of a ventral root burst. PSC charge was calculated as the integral of the fitted function, numerically integrating until the amplitude was <1/100 of the peak value.
 |
RESULTS |
Interneurons receiving cyclic locomotor-related information
Concomitant recordings extracellularly from spinal cord motor roots and intracellularly from cells located close to the central canal or in the intermediate gray matter often showed interneurons with rhythmic membrane potential modulation that correlated with rhythmic ventral root activity (Kiehn et al. 1996
). For example, Fig. 1A (bottom) shows 10 s of rhythmic ventral root activity (L2) during pharmacological induction of a locomotor rhythm with NMDA and 5-HT, whereas Fig. 1A (top) shows an interneuron firing out of phase with the ventral root activity. We cannot conclude that this neuron is a component of the spinal network producing locomotor activity, but it obviously received timing information from the locomotor network. The cyclic, locomotor-related information that the interneuron received appears to be mediated, at least partly, by fast synaptic events. These synaptic events can be better seen under voltage clamp, which reduces their duration and makes them stand out as fast outward and inward transients, probably representing IPSCs and excitatory PSCs (EPSCs), respectively (Fig. 1B, top).

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| FIG. 1.
A: 10 s of an intracellularly recorded membrane potential from an interneuron in segment L2 (top) and the concomitant recording of spikes in the L2 ventral root (bottom). Membrane potential is modulated in correlation with the spike activity in the root. B: same cell recorded in voltage-clamp mode shows postsynaptic currents (PSCs) as fast transients in positive and negative directions from a modestly fluctuating baseline. There are more positive transients during high root activity, and more negative transients during low root activity. C: 20 PSCs collected by a computer algorithm (see METHODS) from periods with high root activity (left collection) and 20 PSCs collected during low root activity (right).
|
|
The time course of both IPSCs and EPSCs is well described by an alpha-function, and we used this function to identify putative PSCs (see METHODS and Raastad et al. 1996
). For example, Fig. 1C shows PSCs detected from the cell in Fig. 1 during low and high root activity. Twenty PSCs were detected during high root activity (first half), and 20 were detected during low root activity (second half). It is obvious that both IPSCs and EPSCs occurred during both activity phases of this interneuron. Most synaptic events detected had a time course expected from unitary PSCs, with an abrupt start, fast and smooth rise time, and a slower decay. This suggests that they were due to synchronized transmitter release from one or more release sites in individual presynaptic neurons.
We first will show that the detected synaptic events actually contained cyclic temporal information phase locked to the cyclic output from the ventral roots. Furthermore, we will show that the detected PSCs actually were a significant part of the cyclic information that a cell received, because the original cyclic signal was reduced significantly in amplitude when these PSCs were subtracted digitally from the current trace. This verification of the detected PSCs as information carriers allowed us to analyze, finally, how the cyclic information was built by the PSCs.
Identification of PSCs giving locomotor-related information
If the detected events carried cycle-related information, one would expect to reduce the magnitude of the total cyclic information a cell received by subtracting these events from the original current trace. An alpha function was fitted to putative PSCs, like those in Fig. 1C, and the area (charge) of the function was subtracted for each identified PSC over several locomotor cycles. This procedure enabled us to test whether a significant proportion of the cyclic information could be accounted for by the detected PSCs. Figure 2A shows 10 s of rhythmic root activity (top) and the simultaneously recorded intracellular current (marked I) when the soma was clamped at
45 mV. All accepted fits obtained from this trace were added at their appropriate time positions to form what we call the analytic current trace (Fig. 2A, II). When the analytic current trace was subtracted from the original trace, the result (Fig. 2A, I
II) was a trace with less markedly transient events, suggesting that we identified PSCs that contributed to the rhythm related information. We call the last trace in Fig. 2A (I
II) the residual current trace. It contains what was left in the original trace after subtraction of the detected PSCs and contains undetected and rejected PSCs, the residual of poor PSC fits, and perhaps rhythmic modulation of the whole cell current that is not mediated by fast synaptic transmission.

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| FIG. 2.
A, top: rhythmic activity in ventral root L2 while the voltage clamp recording (marked I) from an interneuron in the same segment shows inward and outward PSCs as fast transients. Computer algorithm detected fast events with time courses as expected from PSCs (see METHODS). Each event with a peak amplitude above a certain threshold was fitted with an alpha-function. Best fit was added to a line at the time position where it was detected, giving line II, which we call the analytic current trace. When the analytic current trace (II) was subtracted from the original current trace (I), a residual current trace was obtained (I II). This trace may contain undetected PSCs and current modulations that were slower than the fast PSCs. B: cyclic locomotor-related signal in each of the 3 traces above was estimated by calculating the average signal over many cycles (see METHODS for details). Each cycle was divided in 10 equal time bins, and the average current in each of these bins is shown as a bar with standard error as a vertical line. Original current trace contained an obvious cyclic signal when averaged over 20 cycles (left). Analytic trace, composed of identified PSCs that are analyzed in this article, also contained a cyclic signal (middle). Residual current trace also contained a cyclic signal, showing that we detected only a part of the synaptic events that the cell received.
|
|
The cyclic signal in an intracellular current trace is more obvious when the signal is averaged over many locomotor cycles. For this illustration, in Fig. 2B, each cycle was divided into 10 time bins, and the average and SE of the current in each bin was calculated, in this case over 20 cycles. The cycle was defined as the period from the start of the root burst to the start of the next burst, determined by eye from the root recording. The resulting values are expressed as histograms with SE given as vertical lines on top of the bars. The lower amplitude of the averaged signal in the residual current trace as compared with the original suggests that the detected events (the analytic current trace) contained important cyclic information that was similar to the original trace but of lower amplitude (~45% of the original in this case).
We examined whether there was significant cycle-related information in the original and the analytic current traces by testing for a significant difference between the integral (charge) of the first and second halves of their cyclic signals using a Student's t-test. All 16 experiments included in the following analysis showed a significant (P < 0.05) cyclic signal in all traces. Furthermore, there was a significant reduction in the amplitude of the original trace after subtraction of the analytic trace (P < 0.05). This means that, despite not accounting for all the locomotor-related information, the identified PSCs were an important contribution to this information. We now can examine the composition of this subset of the cyclic information driving the locomotor-related activity of spinal interneurons.
Decomposition of the synaptic information
We next examined the relative contributions of the excitatory and inhibitory synaptic events to the generation of cyclic information. For the rest of the analysis, we arranged the intracellular locomotor-related cycles so that the most inhibitory (positive) phase was always in the first half of the cycle (Fig. 3A, left). We can distinguish three ways the PSCs can build a cyclic signal. The simplest possibility is to transfer the cyclic signal by EPSCs or IPSCs alone. Either a relative increase in the amount of excitatory charge or a relative decrease in the amount of inhibitory charge could make the second half of the cycle more excitatory than the first half. A second possibility is that both the inhibitory and the excitatory charges together form the cyclic signal. This would mean that the excitatory charge increased and the inhibitory charge decreased in the second compared with the first cycle half. A third possibility is that the amount of both inhibitory and excitatory charge increases simultaneously in one cycle half. In this case, the main cyclic signal would be dominated by either the EPSCs or the IPSCs, whichever contributed the largest charge difference between the two halves of the cycle. The less dominating charge in that case would counteract the main signal. The following analysis will demonstrate that all three possibilities are realized in the cyclic information-driving rhythmically active interneurons.

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| FIG. 3.
A: detected PSCs that together give a cyclic signal (left) can be separated into a signal from excitatory (EPSCs; middle) and inhibitory (IPSCs; right). Magnitude of the signal was estimated by subtracting the total charge during the second half of the cycle (marked b) from the charge in the first half (marked a). B: calculation of the contribution from excitatory and inhibitory charge is illustrated with an idealized cycle where the most excitatory half (b) is subtracted from the most inhibitory half (a). This difference is equal to the charge difference given by IPSCs plus the charge difference given by EPSCs [IPSC(a-b) + EPSCs(a-b)]. This sum is normalized to 1.0 to compare different experiments. C: normalized cyclic signal is divided into a contribution from EPSCs ( ) and IPSCs ( ). Most cells received a contribution from both IPSCs and EPSCs.
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To analyze the relative contribution of inhibitory relative to excitatory charges to the cyclic signal, we divided the detected PSC populations in two groups according to the sign of the charge (Fig. 3A, middle and right). In the example experiment of Fig. 3A, EPSCs increased their charge contribution, and IPSCs decreased their contribution during the second (most excitatory) phase of the cycle relative to the first (most inhibitory) phase of the cycle (Fig. 3A). Thus in this cell, both IPSCs and EPSCs changed their activity during the cycle to produce the locomotor-related information. In addition, all experiments showed a background activity of both IPSCs and EPSCs that did not contribute to the cyclic signal. The background activity is here defined as the lowest level of activity present in either the first or second part of the cycle-related signal. Our methods do not allow us to determine whether the background activity was caused by the same neurons contributing to the rhythmic signal or by a separate, unmodulated population of neurons.
The net contribution (the signal) from one type of charge during a cycle can be expressed as the difference in this charge between the two cycle halves. This can be written as a formula in which the size of the cyclic signal is defined as the difference between the integral (the charge, including the sign) of the first (a) and second halves (b) of the cycle (Fig. 3B). The charge of the total signal (a-b) then can be separated into the charge difference caused by EPSCs [EPSCs(a-b)] plus the charge difference caused by IPSCs [IPSCs(a-b)]. To compare different experiments, the sizes of the EPSC and IPSC contributions were normalized with respect to the original signal so that EPSCs(a-b) +IPSCs(a-b) = 1.0.
We then can distinguish between the three possibilities mentioned above by graphically representing the charge differences as shown in Fig. 3C. The first possibility above was demonstrated in three cells where either excitatory (cells 3 and 4) or inhibitory (cell 15) charge alone transferred cyclic information (Fig. 3C,
and
). The second possibility, that EPSCs and IPSCs both contributed to the cyclic signal was seen in most interneurons (cells 5-14) and is indicated in the figure by a combination of white and black bars. The third possibility, a simultaneous increase in EPSCs and IPSCs, also was present. This is seen as a negative signal contribution from IPSCs (cells 1 and 2) or EPSCs (cell 16). For cells 1 and 2, this means that there was more inhibitory charge in the second compared with the first half of the cycle. Because inhibitory charge is positive, a-b becomes negative when there is more inhibitory charge in b compared with a. There was, however, a much larger excitatory charge difference in cells 1 and 2, with more excitatory charge in b compared with a, making the sum of the two signals positive (and equal to 1.0 because of the normalization). For cell 16, the negative EPSC contribution means that there was less excitatory charge in the second compared with the first half of the cycle. Because excitatory charge is negative, a-b becomes negative when there is less excitatory charge in b compared with a. The main signal, however, was dominated by the inhibitory charge difference, and the EPSCs resulted only in a reduction of the IPSC-mediated signal. We can conclude from the above analysis that most rhythmically active interneurons received cyclic information through a reciprocal regulation of the activity of both excitatory and inhibitory inputs.
The amount of inward and outward charge modulating cyclic activity theoretically could be due to changes either in the frequency or the amplitude of the synaptic events or a combination of these factors. We estimated, therefore, the modulation of amplitude and frequency for EPSCs and IPSCs during the locomotor cycles.
PSC frequency and amplitude contributions to the cyclic signal
The relative changes in synaptic charge comparing the first and the second cycle halves must be due to changes either in the frequency or the average size of the PSCs or both these factors. The total charge contribution can be calculated as the average charge of the PSCs multiplied by their frequency, illustrated in Fig. 4A for the EPSCs and IPSCs from Fig. 3A. In this experiment, the average size (charge) and the number of PSCs occurring within 10 equal time bins during an average cycle show that the size changes very little, whereas there is an obvious modulation in the frequency of PSCs during the cycle.

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| FIG. 4.
A: cyclic signal given by EPSCs (top histograms) can be divided into a contribution from modulation of the average PSC size (charge) multiplied by their frequency. Same is done for the IPSCs (bottom histograms). During the average cycle, there is no detectable modulation of the average size but an obvious modulation of the frequency of PSCs. B: relative change in average charge and frequency from the first halves to the second halves of the cycles was estimated for all experiments. The results are given as a circle around the mean with a vertical line giving the standard error. , differences from 0 with P < 0.05. For the frequency estimates, the vertical line gives the standard deviation in a binomial population with N equal to the sample size. In those cases, where either EPSCs or IPSCs did not give a significant contribution to the signal, the cell is marked only with a vertical line on the unit line. We can see a more obvious modulation of the frequency of PSCs than of their average size.
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The relative change in frequency and amplitude was estimated separately for IPSCs and EPSCs for all experiments by dividing the frequencies and the average size in the second halves of the cycles with the frequencies and sizes in the first halves. In Fig. 4B, we see that the change in PSC frequency was more pronounced than the change in PSC size. The average relative change in frequency (given as mean ± SE) was 36 ± 8% for the EPSCs and 30 ± 5% for the IPSCs. The average change in size was 12 ± 5% for the EPSCs and 7 ± 3% for the IPSCs. The frequency modification was therefore, on average, three to four times larger than the estimated amplitude modification. The changes both in frequency and size were significant in most cases (P < 0.05 marked with filled circles). These results suggest that frequency modification was considerably (3-4 times) more important than amplitude modification for transmission of the cyclic locomotor-related signal.
To obtain an estimate for the range of PSC amplitudes that was included in this analysis, we also calculated the 0.1-0.9 percentile of the absolute PSC amplitudes. In all experiments, the largest 10% of the PSCs were more than four times larger than the smallest 10%.
 |
DISCUSSION |
In this study, we have recorded from rhythmically active interneurons located in the intermediate gray matter and around the central canal in the isolated neonatal rat spinal cord during transmitter-induced locomotion. Neurons in these areas probably are involved in locomotor rhythm generation (Carr et al. 1995
; Kjaerulff and Kiehn 1996
; Kjaerulff et al. 1994
). The cyclic modulations of interneuron membrane potential correlated with the locomotor associated ventral root activity. The neurons, therefore, received synaptic information from the network generating the rhythm and therefore may have been a part of a locomotor CPG.
We extracted a subpopulation of synaptic events from these neurons that also contained a cyclic signal. The identified PSCs were, indeed, a part of the population of PSCs that transmitted the cyclic information because there was less cyclic information left when the identified events were subtracted. To our knowledge, this is the first example of identified populations of unitary PSCs that carry information from a mammalian network producing a motor output. Such data open the possibility to analyze how the synaptic events built the cyclic signal.
Detection of PSCs
The majority of detected events are probably the result of transmitter release from individual release sites or release from several release sites synchronized by individual presynaptic neurons (unitary PSCs) because of the following observations: 1) the shape was characterized by an abrupt onset, fast and smooth rise time, and a slower decay, similar to unitary synaptic currents in other central neurons; 2) the outward currents were reduced greatly in amplitude, and therefore not detected, at
65 mV compared with
45 mV (see METHODS and Raastad et al. 1996
); and 3) the detected events formed a cyclic signal that was locked to the root activity, which would not be expected from noise or other electrical processes in nerve cells. This last argument strongly suggests that most PSCs appeared in response to presynaptic spikes and not because of spontaneous vesicular release, which would not be expected to build a regular signal.
Characteristics of the whole population of PSCs would be detectable also in this subpopulation if it was representative for the whole population. It is, therefore, important to be aware of the detection criteria. The PSCs were identified based on their shape and amplitude. Because we used an amplitude threshold for detection, there is probably a part of the PSC-population that is smaller than the detected PSCs and therefore not included in the analysis. Amplitude and frequency modification restricted to these small PSCs would therefore not be included in our analysis. The amplitudes of the detected PSCs covered, however, most of the amplitude range of the complete population because the largest 10% were at least four times larger than the smallest 10% in all experiments. This means that even if the smallest, undetected PSCs had amplitudes close to zero, the detected events still covered 4/5 of the amplitude range, giving the opportunity to study both large and relatively small PSCs.
Contribution from IPSCs and EPSCs to the rhythmic signal
In most of the interneurons from which we recorded, the cyclic locomotor related signal was mediated by an inversely related contribution from both EPSCs and IPSCs (Fig. 3). This alternating dominance between EPSC and IPSC activity, or push-pull drive (Russell and Wallen 1983
), is similar to how the rhythmic network drives the motoneurons in lamprey (Russel and Wallen 1983) and mammals (Cazalets et al. 1995
; Jordan 1983
; Perret 1983
).
Because the neurons recorded from are situated in an area that is particularly active during locomotion (Carr et al. 1995
; Kjaerulff et al. 1994
) and also seems to be necessary for the generation of the rhythm (Kjaerulff and Kiehn 1996
), they actually may be a part of the mammalian locomotor CPG. It is therefore interesting to compare our findings to investigations of and theories for CPG function. Many motor CPGs in invertebrates are dominated by rhythmic synaptic influence from IPSCs (Marder and Calabrese 1996
). In these animals, the basic building blocks of the CPGs are half-center oscillators based on reciprocal inhibitory connections. A similar mutually inhibitory half-center organization has been found in mammalian CPGs generating respiration (Bianchi et al. 1995
) and has been proposed for the kernel of the spinal network generating mammalian locomotion (Brown 1911
; Jankowska et al. 1967
; Pearson and Collins 1993; see Kiehn et al. 1997
for a review). Theoretical studies also show that, in a half-center model, reciprocally inhibitory connections in combination with tonic excitation and/or intrinsic membrane properties are sufficient to generate both alternating and synchronous rhythmic activity (Perkel and Mulloney 1974
; Sharp et al. 1996
; Wang and Rinzel 1992
).
Thus the alternating rhythmic contribution from both EPSCs and IPSCs found in the present investigation gives the opportunity for a more complex signaling than that seen in or hypothesized for the CPGs mentioned above. We, however, will emphasize that these data are not necessarily at odds with the mutually inhibitory half-center theory for the CPG. First, we may not have recorded from the cells that actually create the rhythm; they just may receive information from the rhythmic network, like, for example, motoneurons do. If the kernel of the CPG consists of relatively few neurons, it would be unlikely that the neurons we have recorded from are representative for the CPG function. Second, not all our neurons showed an alternation between excitatory and inhibitory contributions to the cyclic signal, leaving the possibility that portions of the network actually could give rhythmic information through either IPSCs or EPSCs alone.
PSC frequency and amplitude modification
Although significant modulation in both average amplitude and frequency were seen in the cyclic information transfer, frequency modification of the PSCs was contributing more than amplitude modification to the creation of the cyclic locomotor-related signal. The interpretation of the observed changes in PSC frequency depends on several unknown presynaptic factors. If the synapses in this network typically have few release sites combined with low release probability at the individual sites (as seen, for example, in the hippocampus: Arancio et al. 1994
; Gulyàs et al. 1993
; Hessler et al. 1993
; Rosenmund et al. 1993
), the increased frequencies of PSCs simply could be due to an increase in release probability. If the increased frequencies were the result of increased presynaptic spike activity, the additional PSCs could come from the same cells that caused PSCs during low frequencies, or they may come from cells that were active only during the high-activity half of the cycles. This is an important distinction for the identification of cells that contribute to the rhythmic behavior, and this could be illuminated by studying spike frequency modification in extracellular single-unit recordings during locomotion.
The interpretation of the significant amplitude modification in some experiments is ambiguous because an increase in frequency could affect the probability for superimposition of more than one PSC. A certain fraction of the detected PSCs are probably the summed effect of more than one unitary PSC, and the frequency of such multiunit PSCs probably would increase with increasing frequency. Another possibility is that the increases in mean PSC amplitude represent a true facilitation, with increased release probability at multiquantal synapses. Even if we accept a contribution from amplitude modification to creation of the cyclic signal, this contribution was modest and sometime lacking. This observation suggests that mechanisms for amplitude modification, like synaptic depression, desensitization and facilitation, are not very important for the information transfer at these cells. It also suggests that the large range of amplitudes that was available was not used very efficiently to code the cyclic signal. The absolute value of the largest 10% of the PSC amplitude populations was always more than four times larger than the smallest 10%. One possibility is that the differences in PSC amplitudes were caused by systematic differences between the synaptic connections, for example in their position on the dendritic tree, or in the number of contacts, release sites, or channels. The different amplitudes in this case would be controlled by different presynaptic cells, and, theoretically, give the opportunity to transfer information by a preferential use of the large EPSCs in periods with increased excitation, and large IPSCs for the inhibitory periods. A variability in amplitude, however, also could arise because of stochastic properties of the individual synapses. Such intrasynaptic variability could be due to a variable number of quanta released at synapses with few release sites, each with a low release probability, or from a variability even at the individual release sites (Bekkers et al. 1990
; Raastad et al. 1992
). A large stochastic variability in the signal from the individual cells, as seen, would reduce the opportunity to use the different amplitudes to code a signal. A stochastic amplitude variability at the individual synapses therefore could be one reason why the large amplitude range is not used efficiently to code the cyclic signal.
These results cannot be translated directly into the spiking behavior of the cells because the analysis is based on the cyclic modulation of currents. The integration of synaptic currents and the translation into spike trains may give the amplitude and frequency modification different weights compared with what we have found. If, for example, the time constant is too fast to result in integration of the individual PSCs, moderate changes in the frequency of events probably would have little effect on the firing behavior, and the amplitude of the individual PSCs could be more important. This is certainly a possibility for the neurons we are studying, because we found that relatively few average PSCs per second (8-300, Raastad et al. 1996
) were needed to account for the cyclic signal. With the opportunity to break down the cyclic locomotor-related signal into some of its synaptic elements, as we have described in this article, the preparation is well suited to study how the synaptic code is translated into a spike code.