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The Journal of Neuroscience, February 1, 2000, 20(3):908-918
Secondary Nicotinic Synapses on Sympathetic B Neurons and Their Putative Role in Ganglionic Amplification of Activity
Paul Karila and John P. Horn Department of Neurobiology, University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania 15261
ABSTRACT
TOP
ABSTRACT
INTRODUCTION
The strength and number of nicotinic synapses that converge on secretomotor B neurons were assessed in the bullfrog by recording intracellularly from isolated preparations of paravertebral sympathetic ganglia 9 and 10. One input to every B neuron invariably produced a suprathreshold EPSP and was defined as the primary nicotinic synapse. In addition, 93% of the cells received one to four subthreshold inputs that were defined as secondary nicotinic synapses. This contradicts the prevailing view, which has long held that amphibian B neurons are singly innervated. More important, the results revealed that B cells provide the simplest possible experimental system for examining the role of secondary nicotinic synapses on sympathetic neurons. Combining the convergence data with previous estimates of divergence indicates that the average preganglionic B neuron forms connections with 50 ganglionic B neurons and that the majority of these nicotinic synapses are secondary in strength. Secondary EPSPs evoked by low-frequency stimulation ranged from 0.5 to 10 mV in amplitude and had an average quantal content of 1. Nonetheless, secondary synapses could trigger action potentials via four mechanisms: spontaneous fluctuations of EPSP amplitude, two-pulse facilitation, coactivation with other secondary synapses, and coactivation with a slow peptidergic EPSP. The data were used to formulate a stochastic theory of integration, which predicts that ganglia function as amplifiers of the sympathetic outflow. In this two-component scheme, primary nicotinic synapses mediate invariant synaptic gain, and secondary nicotinic synapses mediate activity-dependent synaptic gain. The model also provides a common framework for considering how facilitation, metabotropic mechanisms, and preganglionic oscillators regulate synaptic amplification in sympathetic ganglia.
Key words: activity-dependent modulation; bullfrog sympathetic ganglia; neuronal nicotinic receptors; nicotinic synapses; metabotropic synapses; synaptic integration; sympathetic nervous system
INTRODUCTION
Synaptic convergence is a basic determinant of neuronal integration, and it appears to follow a simple pattern in paravertebral sympathetic ganglia. Sympathetic neurons are generally innervated by one preganglionic axon that forms a strong nicotinic synapse and by a variable number of axons that form weaker nicotinic synapses (Dodd and Horn, 1983b
; Skok and Ivanov, 1983
One approach to the problem of convergence has stressed the developmental mechanisms that specify formation of appropriate synaptic connections between preganglionic and postganglionic neurons. Comparison of the superior cervical ganglion (SCG) in five mammalian species revealed that preganglionic to postganglionic convergence ranged from 4 to 15 and correlated with the number of primary dendrites on postganglionic neurons (Purves and Lichtman, 1985
Our approach to convergence focuses on its integrative consequences in functional subsets of sympathetic neurons, using anuran amphibians as the model. Convergence in paravertebral ganglia 9 and 10 of frogs and toads is lower than that in the mammalian SCG and appears to differ between secretomotor B neurons and vasomotor C neurons. B cells, which innervate cutaneous glands (Lang et al., 1975
We decided to examine the issue of secondary nicotinic synapses after Ivanoff and Smith (1995)
MATERIALS AND METHODS
Twenty-eight bullfrogs (Rana catesbeiana; 14-18 cm) of both sexes were cooled on ice for 30 min and killed by double pithing. Unilateral preparations of the paravertebral chain, including ganglia 7-10 and the associated spinal nerves, were isolated and pinned flat in a recording dish (Dodd and Horn, 1983a
1) at room temperature (20-22°C) with Ringer's solution (mM): 115 NaCl, 2 KCl, 1.8 CaCl2, and 4 HEPES, pH 7.2.
Intracellular recording. Neurons were impaled under visual guidance (40× water; Zeiss WL) with sharp microelectrodes filled with 3 M KCl (70-90 M
). In some experiments, electrodes were beveled to lower their resistance (15-35 M
During characterization of convergence, cellular leak resistance (Rleak) was measured at regular intervals and just before pulling out, when possible. To estimate Rleak, I-V curves were constructed, typically using 500 msec current pulses with 50 pA steps. After subtracting unbalanced electrode resistance, Rleak was taken as the slope of the I-V relation in the linear range between
60 and
Counting of nicotinic synapses. Synaptic inputs to B neurons were fractionated by varying the presynaptic stimulus intensity. Low-frequency stimulation (0.2 Hz) was used to avoid effects of facilitation and depression (Shen and Horn, 1995
Quantal analysis of secondary nicotinic synapses. Stimulus strength was adjusted to minimize the rate of transmission failure during selective activation of a single preganglionic axon (Allen and Stevens, 1994
2 statistics and accepted when the probability of its occurrence by chance was <0.05. For purposes of minimal stimulation and illustration (e.g., see Fig. 2), transmission failures were assessed by visual inspection of records. Estimates of X for quantal analysis include data from all stimulus trials. Elsewhere in the results, visually discriminated failures were removed from the data before calculating average secondary EPSP amplitudes.
RESULTS
Distribution of secondary nicotinic synapses
We begin by presenting examples of convergence and follow with grouped data. The first case illustrates a B neuron with two synaptic inputs (Fig. 1A). Supramaximal stimulation of the preganglionic B pathway evoked a primary EPSP that invariably triggered an action potential (Fig. 1B). During recovery of the action potential, the initial phase of the spike afterhyperpolarization was clearly distorted by the fast EPSP. This profile matches the classical picture of mononeuronal innervation by a single strong nicotinic synapse (Blackman et al., 1963a
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Figure 1. Identification of primary and secondary nicotinic EPSPs. A, Schematic for convergence of primary (1°) and secondary (2°) synapses on a sympathetic B neuron is shown. B, Presynaptic stimulation of the primary synapse evoked an invariably suprathreshold EPSP, which distorted the action potential afterhyperpolarization. C, Lowering presynaptic stimulus strength revealed subthreshold secondary EPSPs. Superimposed records illustrate the range of spontaneous fluctuations in EPSP amplitude. In this cell, the secondary EPSP occasionally crossed threshold. Inset, Spike afterpotentials triggered by the secondary synapse showed little sign of an EPSP. Vm =
Two examples of B neurons with higher levels of convergence are illustrated in Figure 2. In both cases, graded increases in presynaptic stimulus strength revealed multiple steps in the average EPSP amplitude and finally recruited a primary synapse that triggered an action potential. The neuron in Figure 2, A and B, had five inputs, the most observed in this study. They consisted of one primary and four secondary synapses. Three of the secondary EPSPs had similar latencies but differed in their average amplitudes and stimulus thresholds (Fig. 2A,B). The fourth secondary synapse was distinguished by its longer latency. Our final example is from a neuron with four inputs (Fig. 2C,D). In this case, the synapse labeled a had the lowest stimulus threshold, and its failure rate declined as stimulus intensity was increased (Fig. 2C,D). Adjusting the stimulus strength in this manner exemplifies the technique of minimal stimulation, which was used to minimize presynaptic action potential failures in subsequent release experiments. In cells with multiple secondary synapses, we attempted to resolve the time course and amplitude of individual components by subtracting averaged EPSPs representing different combinations of inputs (Fig. 2A,C). This approach will work when EPSP amplitudes are much smaller than the driving force on synaptic currents and summation is linear. However, subtraction revealed evidence in some cells of EPSPs with rounded peaks (Fig. 2C, input c) and the other signs of voltage-dependent currents (e.g., Fig. 1C). Because of the activation of these nonsynaptic currents, summation is likely to be nonlinear. Thus the subtraction approach provides only an approximate estimate of secondary EPSP components, and it could not be used to quantitate the size of individual responses.
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Figure 2. Examples of two B neurons with relatively high levels of synaptic convergence. One cell had five synapses (A, B), and the other had four synapses (C, D). A, C, Combined and resolved secondary EPSPs from each cell are depicted. Also shown are action potentials (right) initiated by the primary synapses. Each record is an average of 9-18 responses at 0.2 Hz. Individual components of the combined EPSP each had a distinct stimulus threshold or latency. Components of secondary EPSPs were resolved by subtraction. For example, input a in A was selectively evoked by stimuli of 0.48-0.53 V, inputs a + b were evoked by 0.55 V stimuli, and input b was resolved by subtracting a from a + b. B, D, Top, The relation between stimulus intensity and secondary EPSP amplitude illustrates the differences in threshold for individual secondary synapses. D, Bottom, The transmission failure rate decreased as stimulus intensity was increased.
The distribution of primary and secondary nicotinic synapses was analyzed in two groups of B neurons. In the first group (67 neurons; 14 frogs), the goal was to minimize sampling bias by including all neurons that were innervated and could generate action potentials >45 mV. All 67 neurons had one primary nicotinic synapse, 93% had at least one secondary nicotinic synapse, and 22% had two or three secondary synapses. On average, 2.2 ± 0.1 axons converged onto each neuron, and secondary EPSP amplitude (excluding failures) was 2.6 ± 0.3 mV (n = 37). For cells with >1 secondary input, only the EPSP with the lowest stimulus threshold was included in the averaged data. In this group, the resting membrane potential (Vm) was
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Figure 3. Impact of resting membrane properties on estimates of convergence and secondary EPSP amplitude. A, B, The number of secondary nicotinic synapses per neuron did not correlate with Vm (A) and Rleak (B). C, D, Secondary EPSP amplitude declined with decreases in Vm (C) and Rleak (D). Each point [ (A, C);
(B, D)] in the scatter plots represents data from one cell. Lines are drawn by linear regression.
In a second group of 32 neurons from 14 additional frogs, recordings were made using lower resistance beveled electrodes to reduce noise, and cells without well isolated secondary synapses were discarded. This group was similar to the first in that all cells received one primary synapse, and Vm =
Overall, the pattern of convergence that we observed in sympathetic B neurons was reminiscent of previous work on parasympathetic neurons in the frog cardiac ganglion, where 55% of the cells receive one to four secondary synapses in addition to a primary input (Dennis and Sargent, 1978
Quantal properties of secondary nicotinic synapses
Quantal content (m) and other release parameters provide valuable indices of synaptic function because they reflect synaptic strength and structure and they influence release dynamics. We therefore sought to measure the resting value of m during low-frequency stimulation in physiological [Ca2+] and to determine whether simple Poisson or binomial models could describe secondary EPSP amplitudes. Although spontaneous acetylcholine release is rare in sympathetic ganglia, we found seven neurons in which nerve stimulation evoked a secondary EPSP and asynchronous EPSPs (e.g., Fig. 4A). The average amplitudes of these events were similar (X = 0.99 ± 0.32 mV; x = 1.00 ± 0.13 mV). By making the assumption that the magnitudes of asynchronous EPSPs reflected quantal size at secondary synaptic release sites we could calculate quantal content for each cell by the direct method (m = 1.04 ± 0.31; range, 0.31-2.33). This sample of neurons was characterized by 2.7 ± 0.2 inputs per cell, Vm =
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Figure 4. Analysis of secondary EPSP amplitudes. A, Selected stimulus trials illustrate EPSPs evoked by 0.2 Hz stimulation. In most of the trials shown, each nerve stimulus evoked a short-latency synchronous EPSP and an asynchronous EPSP (*) whose latency was longer and variable. An arrow in the first trial marks a failure of synchronous transmission. B, In an amplitude histogram, the distribution of 154 asynchronous EPSPs recorded from the neuron in A shows a positive skew and an average amplitude (x) of 1.39 ± 0.02 mV. C, The amplitude histogram of synchronous EPSPs evoked from the same neuron (X = 2.67 ± 0.13; 341 trials) was broad and lacked discrete peaks at intervals corresponding to the average amplitude of asynchronous EPSPs. In this histogram, the peak near 0 mV corresponds to 131 transmission failures. D, The distribution of EPSP amplitudes from another B neuron in which synchronous responses (large graph) and asynchronous responses (Asynch; inset) were recorded. The thick solid line in the large graph was drawn from a binomial fit in which N = 4 and p = 0.56. In this cell, x = 0.6 ± 0.02 mV (n = 24), and X = 1.4 ± 0.04 mV (n = 240). Thin lines in the histogram were generated by the binomial-fitting procedure, and they represent baseline noise and the expected distribution of EPSP amplitudes attributable to zero to four quantal events. Negative values in the overall fit (thick line) were introduced by the baseline noise.
Estimating the probability of release (p) and the number of release sites (N) from distributions of EPSP amplitudes proved difficult. Figure 4A illustrates a series of trials in which most presynaptic nerve stimuli evoked a short-latency EPSP and also a longer-latency asynchronous EPSP. These examples are from the cell in which we observed the largest number of asynchronous events (154). In this instance, the amplitude distribution of asynchronous EPSPs had a clear positive skew (Fig. 4B). Unfortunately, asynchronous EPSPs were too infrequent to assess their amplitude distributions in the other six cells in which they were observed. In another approach, 2 mM Sr2+ was added during recordings from seven other B neurons (data not shown) to elevate the rate of asynchronous release during stimulation of a secondary synapse (average number of events = 320 ± 124). In every one of these cells, the amplitude distribution of asynchronous EPSPs had a positive skew similar to that seen for asynchronous events in normal Ringer's solution. Skewed distributions like those in Figure 4B and in the Sr2+-treated neurons are better described by a
function than a Gaussian function. This resembles previous descriptions of spontaneous release in this preparation (Blackman et al., 1963b
Histograms of synchronously evoked EPSP amplitudes were generally symmetric, without any sign of quantal peaks (Fig. 4C,D). The absence of peaks presumably reflects a smearing effect produced by the quantum's high coefficient of variation (Fig. 4B), a by-product of the
Excitatory action of secondary nicotinic synapses
The physiological impact of secondary nicotinic synapses depends critically on their ability to initiate action potentials. In 5 of 99 B neurons, spontaneous fluctuations of secondary EPSP amplitude during 0.2 Hz stimulation were sufficient on their own to cause firing (Fig. 1C). To examine further the excitatory role of secondary synapses, we measured the consequences of repetitive stimulation and summation.
Pairing of stimulus shocks at intervals <100 msec consistently facilitated secondary EPSP amplitude and reduced the rate of transmission failures, when tested in 18 neurons. Figure 5A illustrates the facilitation of subthreshold EPSP amplitude associated with interstimulus intervals of 10, 30, and 80 msec (repetition rate = 0.1 Hz). In grouped data (Fig. 5B), facilitation produced a maximal 3.2-fold increase in EPSP amplitude when stimuli were paired at 10 msec, the shortest interval examined.
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Figure 5. Paired-pulse facilitation of secondary EPSPs can enhance firing. A, The time course of facilitation in one neuron, as shown by superimposed records of paired EPSPs, at stimulus intervals of 10, 30, and 80 msec. Each trace is an average of 6-10 trials after removing responses that evoked action potentials. B, The time course of facilitation in grouped data. Paired-pulse ratios [(peak of second response)(peak of first response) ), 130 M
). The interstimulus frequency is the reciprocal of the paired-pulse interval.
Facilitation at a single secondary synapse also increased the probability of postsynaptic firing. In the case illustrated in Figure 5A, the first EPSP in each pair was always subthreshold. Delivery of the second stimulus 10 msec later evoked an action potential in 11 of 20 trials. The excitatory efficacy of the second EPSP declined in this cell to 20% at 30 msec and 0% at 80 msec. Facilitation may also operate over longer intervals to enhance postsynaptic firing. Figure 5C shows 15 trials from another cell in which the first EPSP of the pair triggered 1 action potential and the second EPSP, 80 msec later, triggered 10 action potentials. In general, the relation between the interstimulus interval and enhanced firing was quite variable between cells. The most effective interval ranged from 10 to 100 msec in the four cells compared in Figure 5D. In seven cells in which 0.2 Hz stimulation of a secondary synapse never initiated firing, pairing stimuli at a 20 msec interval caused the second EPSP to trigger action potentials in 29.0 ± 5.7% of trials. In this group, Vm =
Coactivation of two convergent secondary synapses was also found to trigger action potentials in B neurons (Fig. 6). In a group of nine cells, summation of two secondary EPSPs stimulated action potentials in 21.3 ± 2.8% of trials, whereas activation of one input produced action potentials in only 0.5 ± 0.5% of trials. In this group Vm =
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Figure 6. Summation of two secondary EPSPs can enhance firing. A, Resolved inputs from a neuron with two secondary synapses (top; a, b) and a primary synapse (bottom) are shown. B, Top, Selective stimulation of the lower threshold input (A; a) evoked an EPSP that never reached threshold. Bottom, Coactivation of inputs a + b triggered action potentials in 8 of 24 trials.
In the bullfrog, repetitive stimulation of the C pathway releases a luteinizing hormone-releasing hormone-like peptide, which diffuses to nearby B neurons and produces a slow EPSP lasting several minutes (Jan et al., 1979
0.05, two-tailed paired Student's t test). An example of the effect is shown in Figure 7A. It is interesting to note that some fast EPSPs were also inhibited in this experiment (Fig. 7B). This suggests that an inhibitory effect on the fast EPSP was again present, despite the fact that interaction with the peptidergic EPSP produced a net enhancement of action potential generation.
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Figure 7. Interaction between secondary EPSPs and the slow peptidergic EPSP can enhance firing. A, Chart record of fast EPSPs before and during a slow EPSP generated by stimulation of the preganglionic C pathway (*). The large fast responses are truncated action potentials. After stimulation of the slow EPSP there was a clear increase in the proportion of secondary EPSPs that triggered action potentials. B, Plot of subthreshold EPSP amplitudes showing a slight reduction in the size of nicotinic responses during the slow EPSP and an apparent increase in the failures of fast transmission.
A theory of ganglionic integration
The experimental results show that convergence typifies the normal synaptic input to secretomotor B neurons. The importance of this observation lies in the minimal nature of the convergence. Because frog B neurons receive so few secondary synapses, their properties can be resolved more easily than can those in homologous mammalian or avian ganglia. This simplicity has enabled us to demonstrate that interactions between minimal numbers of secondary synapses are sufficient to excite sympathetic neurons to threshold. The results thus frame an interesting question about the physiological role of secondary synapses in B neurons and elsewhere. Can a few weak nicotinic synapses contribute in any meaningful way to the integrated output of sympathetic neurons? The question is deceptively simple. Efforts to answer it in seemingly the most direct manner, by intracellular recording in vivo, have yielded equivocal results because of technical difficulties. In essence, such experiments are hampered by limitations of synapse identification, which will be discussed later, and by the fact that anesthesia depresses and disrupts sympathetic behaviors (Jänig, 1995
To construct the model, we first considered the total number of synapses formed by each preganglionic neuron. Organization of the preganglionic neural unit can be inferred from estimates of synaptic divergence and convergence. Preganglionic to postganglionic divergence (D) is 23 in the B system, on the basis of cell counts (Horn et al., 1987
1.2 in the B system. However, little is known about the coupling between primary and secondary synaptic connections. Are all synapses formed by one preganglionic pool of neurons, or do they arise from specialized subsets of preganglionic neurons having distinct patterns of connectivity? We assumed the simplest case, a uniform population of presynaptic neurons in which each cell forms both types of nicotinic synapses. The resulting preganglionic neural unit would then drive 50 ganglion cells [D × (n + 1)] through 23 primary synapses and 27 secondary synapses (Fig. 8A). The assumption of uniform synaptic connectivity also implies that each ganglionic neuron contributes equally to total postsynaptic activity. In other words the output of the entire circuit can be deduced by considering a single ganglion cell with n + 1 independent inputs.
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Figure 8. Schematic depictions of the preganglionic neural unit (A) and its consequences for ganglionic integration (B). A, The average preganglionic sympathetic B neuron forms 23 primary nicotinic synapses and 27 secondary nicotinic synapses on 50 B neurons in paravertebral ganglia 9 and 10. B, A model depicts the theoretical input-output relation between preganglionic and ganglionic activity. Preganglionic divergence sets the boundaries of synaptic amplification. The lower boundary for ganglionic output is defined by the divergence of primary nicotinic synapses. The upper boundary is set by the sum of primary and secondary divergence. Synaptic gain within these limits is regulated by preganglionic patterns of activity and by mechanisms that enhance or inhibit the strength of secondary nicotinic synapses. The modulatory mechanism can include presynaptic facilitation of transmitter release and effects mediated by the metabotropic actions of neurotransmitters.
Boundary conditions for postsynaptic output can be derived from two idealized extremes of presynaptic activity. The lower boundary is defined by the case in which all preganglionic neurons are synchronously activated at a constant frequency (fpre). This condition occurs when preganglionic nerves are repetitively stimulated with supramaximal shocks, a paradigm commonly used in studies of isolated ganglia (Jobling and Horn, 1996
t), will depend on the number of preganglionic neurons (Npre), the preganglionic divergence factor (D), and the preganglionic firing rate (fpre):
The upper boundary for ganglionic output can be derived by considering low-frequency asynchronous preganglionic activity, a condition that better describes physiological behavior in vivo (Ivanoff and Smith, 1995
(1) 0, primary and secondary EPSPs will always occur at different times because they arise independently from different neurons whose activity is asynchronous. Because secondary EPSPs evoked at low rates are subthreshold by definition, ganglionic output driven by low-frequency asynchronous activity should normally approximate the lower boundary described by Equation 1. However, if some form of modulation acted to enhance the strength of secondary synapses during asynchronous activity, then the postsynaptic firing rate would increase. Such a possibility is suggested by the observation that the slow peptidergic EPSP can convert fast EPSPs produced by a single secondary synapse from subthreshold to suprathreshold in strength (Fig. 7). In the upper limit for this type of effect, every secondary EPSP in addition to every primary EPSP would trigger an action potential. The upper boundary for ganglionic output Amax(
At the boundaries defined by Equations 1 and 2, anatomical divergence serves to amplify overall preganglionic activity in the B system by a factor ranging from 23 to 50 (Fig. 8B). It should be noted that Equations 1 and 2 also imply that nicotinic EPSPs do not trigger repetitive firing, a condition consistent with our experimental observations.
(2) We next evaluate how the interaction between secondary EPSPs can regulate ganglionic amplification between its boundaries. Temporal interactions arising from presynaptic facilitation (Fig. 5) and postsynaptic summation (Fig. 6) are each capable of initiating action potentials. One can therefore define a window of summation (tsum) as the time during which generation of two secondary EPSPs initiates an action potential with 100% certainty. As part of the model, we also propose that presynaptic facilitation and metabotropic modulation (e.g., slow EPSPs) each regulate the gain of ganglionic transmission by altering tsum (Fig. 8B). In this approach, ganglionic amplification can be estimated by calculating the number of coincidences between secondary EPSPs within a given temporal window. The probability of such coincidences can be predicted if one postulates that the generation of EPSPs is a random process, an idea with some experimental basis in mammalian ganglia (McLachlan et al., 1998
, the intervals between successive EPSPs will be exponentially distributed (Colquhoun, 1971
Given the previous assumption of preganglionic uniformity,
(3)
P(t
(4)
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Figure 9. Quantitative predictions based on a stochastic model of synaptic amplification. Calculations were performed for three levels of synaptic convergence. A, Convergence of 1.2 secondary synapses and one primary synapse reflects the average observed in our survey of B neurons. B, Convergence of three secondary synapses and one primary synapse mimics the maximal polyinnervation observed in individual B neurons (e.g., Fig. 2C) and may be more characteristic of vasomotor C neurons (Dodd and Horn, 1983b
The firing rate of each ganglionic neuron (fout) will be approximated by the sum of firing rates attributable to primary synapses (f1) and secondary synapses (f2):
Because f1 = fpre and f2 =
(5)
An oversimplification in Equation 6 is its failure to place an upper limit on postganglionic firing. Clearly, B neurons do not fire at infinitely high frequencies, and postsynaptic action potentials will transiently nullify the interaction between secondary synapses. One solution to this problem would be to expand the model with equations for the refractory period and inhibitory afterpotential after the action potential. Instead, we limited firing rates in the model using a simpler approach. Postsynaptic firing frequencies calculated from Equation 6 were manually adjusted in accord with two constraints: (1) that secondary nicotinic synapses could never drive B cells to fire faster than 10 Hz and (2) that primary synapses could drive cells at higher frequencies (up to 20 Hz). Both assumptions are physiologically plausible (Ivanoff and Smith, 1995
(6) Dividing the ganglionic output frequency by the preganglionic input frequency gives a measure of cellular synaptic gain (g):
Plotting g as a function of fpre (Fig. 9, right column) reveals the theoretical nonlinear tuning of synaptic gain in sympathetic ganglia. In the absence of secondary synapses or with low preganglionic frequencies, g = 1. Increases in either tsum or n systematically enhance g. Because connections in our model have been assumed to be uniform, the total gain in activity (G) for the entire population of ganglionic B neurons reflects cellular gain and preganglionic divergence (D):
(7)
Because D is invariant over time, the dynamic consequences of secondary synapses for ganglionic function can be understood in terms of their effect on cellular gain.
(8) The model predicts that secondary nicotinic synapses enable sympathetic ganglia to function as activity-dependent synaptic amplifiers. Even with only 1.2 secondary synapses and a narrow window of summation (10 msec), preganglionic firing at 7 Hz is amplified by 10% (i.e., g = 1.1). Increasing tsum to 20 msec almost doubles the amplification (19%), and when tsum = 50 msec, the amplification increases to 41%. These effects grow markedly with higher levels of secondary synaptic convergence, and in addition, g becomes tuned to lower frequencies (Fig. 9). The model therefore predicts that increases in tsum, whether produced by presynaptic facilitation or slow metabotropic EPSPs, will serve to enhance g.
The nonlinear dependence of synaptic gain (g) on the preganglionic firing rate suggests a general mechanism for regulation of the sympathetic outflow (G). Although noisy when observed on a moment-to-moment basis, peripheral sympathetic activity in mammals contains oscillations that can be detected readily by multiunit recording and correlational analysis (McAllen and Malpas, 1997
DISCUSSION
We have presented evidence that multiple rather than single innervation typifies the normal synaptic input to sympathetic B neurons. Although secondary synapses were much weaker than primary synapses, they could initiate action potentials via several mechanisms. These observations form the basis of a simple theory in which sympathetic ganglia function as synaptic amplifiers. Ganglionic gain is postulated to have a fixed component mediated by primary synapses and a variable component mediated by secondary synapses. The theory also links the seemingly disparate phenomena of non-nicotinic synapses in ganglia, oscillatory activity in the brainstem, and developmental mechanisms that specify the strength and convergence of ganglionic synapses. We propose that their common purpose is to regulate synaptic amplification of the sympathetic outflow in amphibians and other vertebrates.
Ipsilateral origin of secondary synapses
Secondary synapses on sympathetic B cells were fortuitously discovered when Ivanoff and Smith (1995)
Classification of nicotinic synapses by strength
Defining primary and secondary nicotinic synapses in terms of their ability to initiate action potentials provides a direct link to function. The safety factor for transmission at primary synapses is very high in B neurons, and it remains so over a broad range of stimulus parameters, even in the face of inhibitory modulation (Shen and Horn, 1995
Primary and secondary synapses probably have mammalian counterparts, which different authors have classified as "dominant" and "strong" synapses and as "accessory" and "weak" synapses (Skok and Ivanov, 1983
Size of the secondary synapse
Quantal content was 0.31-2.33 at secondary synapses, on the basis of direct calculation from evoked and asynchronous EPSPs. The distribution of evoked EPSP amplitudes was consistently humplike and symmetric (Fig. 4C,D). This distribution together with a low m indicates that release at secondary synapses is not a Poisson process (McLachlan, 1978
Organization of the preganglionic neural unit
The assumption of uniform synaptic connections implies each preganglionic B neuron innervates 50 sympathetic B neurons (Fig. 8A). This resembles the preganglionic neural unit in the mouse SCG, which contains 64 neurons (Purves et al., 1986
Uniform connections are not essential for synaptic gain. In the rabbit SCG, there may be independent control of accessory and dominant synapses (Skok and Ivanov, 1983
Brainstem oscillators and ganglionic amplification
Classical studies by Adrian and colleagues [see McAllen and Malpas (1997)
Developmental significance of convergence
The reason convergence of secondary nicotinic synapses (n) varies within sympathetic ganglia and between species might be related to regulation of ganglionic amplification. Perhaps sympathetic neurons controlling different peripheral targets require different levels of amplification. It is, for example, more important to maintain tight temporal control over blood pressure than piloerection. Higher open-loop gain in the circuitry controlling vascular resistance would enable blood pressure to be clamped more rapidly and accurately at its physiological set point. Developmental mechanisms that establish ganglionic convergence may regulate in this manner the amplification of activity by functional subsets of sympathetic neurons. Interspecies variation of convergence would allow for scaling of function in animals of different sizes, as suggested by Purves et al. (1986)
Conclusion
Experiments to analyze a simple example of synaptic convergence have led to a general theory of ganglionic integration, which postulates that sympathetic ganglia are synaptic amplifiers. In the bullfrog B system, we would expect that the effects of ganglionic gain need not be large to be significant. The mucous glands driven by B cells can respond to single preganglionic stimuli and are half-maximally activated by 0.2 Hz stimulation (Jobling and Horn, 1996
FOOTNOTES Received June 7, 1999; revised Nov. 5, 1999; accepted Nov. 11, 1999.
This work was supported by a postdoctoral fellowship to P.K. from the Swedish Foundation for International Cooperation in Research and Higher Education (Stiftelsen för Internationalisering au Högre Utbildning och Forskning) and by National Institutes of Health Grant NS21065 to J.P.H. We thank Eric Frank for advice about quantal analysis, and we are grateful to Elias Aizenman, Jed Hartings, Hermann Schobesberger, and the anonymous reviewers for many helpful comments.
Correspondence should be addressed to Dr. John P. Horn, Department of Neurobiology, University of Pittsburgh, School of Medicine, E1440 Biomedical Science Tower, Pittsburgh, PA 15261. E-mail: jph+{at}pitt.edu.
Dr. Karila's present address: Preclinical Development, B209:2, AstraZeneca R&D Södertälje, SE-151 85 Södertälje, Sweden.
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