Maceﬁeld Modeling human postganglionic neuronal ﬁring of the anesthetized rat receive convergent inputs from two or more preganglionic neurons with strong synapses, the majority receiving only one strong input. Similar results have been shown for lumbar postganglionic neurons (Bratton et al., 2010). Given the probabilistic nature in which human postganglionic neurons ﬁre, and that most can intermittently generate spike doublets or triplets with very high instantaneous frequencies (>100 Hz), we have argued that this reﬂects the near-coincident ﬁring of at least two preganglionic neurons possessing strong synaptic coupling with an individual postganglionic neuron (Maceﬁeld et al., 2002). The purpose of the present study was to test the hypothesis that human postganglionic neurons are indeed driven by, on average, the inputs from two preganglionic neurons. To this end model spike trains in one to three preganglionic neurons converging onto a single model postganglionic neuron were combined in order to generate a pattern that matched the actual ﬁring patterns of postganglionic sympathetic neurons. METHODS Artiﬁcial spike trains were generated using the Spike Generator fea- ture in LabChart 7 software (ADInstruments, Sydney, Australia). For each series three “preganglionic neurons” were set up to ﬁre spontaneously with a mean interval distribution of either 1000, 1500, 2000, 2500, or 3000 ms and the data recorded on computer. The distribution was randomly generated and for each set the SD was varied between 0.5×, 1.0×, and 2.0× the mean interval. The discharge frequency of each preganglionic neuron exhibited positive skewness and kurtosis. For each stimulation pattern, i.e., a given mean interval and SD, the “postganglionic neuron” was driven by one, two, or three preganglionic neurons. Accordingly, the total number of ﬁring patterns examined was 45. The ampli- tude of each preganglionic neuron was set at 1 V; for illustration purposes, background Gaussian noise (0.1 V) was added to the resultant postganglionic spike train to emulate the appearance of a real recording. In order to discriminate each unit using the Spike Histogram module in LabChart 7, the spike duration of unit 1 was set at 0.5 ms, unit 2 at 1 ms, and unit 3 at 1.5 ms. Accordingly, the ﬁring of the “postganglionic neuron” could be conﬁgured to rep- resent the spike train input from one, two, or three preganglionic neurons on the basis of spike duration. Spikes trains were recorded for 1020–1085 s. Spikes were sampled over 1 s every second – this represents the average cardiac interval of a subject with a mean heart rate of 60 beats/min. Comparison data were provided by combining data from our previously published unitary recordings from healthy subjects with low and high levels of muscle sympa- thetic nerve activity (Maceﬁeld et al., 1994; Maceﬁeld and Wallin, 1999a). The mean heart rate of the subjects from whom these combined data were obtained was 58 beats/min, similar to that of the artiﬁcially generated data. Numerical analysis was performed using Prism 5 (GraphPad Software Inc., USA). Mean ﬁring rate was computed from the inverse of the mean interval, in order to match the method used in the analysis of unitary recordings from actual sympathetic neurons (Maceﬁeld et al., 1994). RESULTS Using a variety of model spike train patterns, attempts were made to model the discharge of individual muscle vasoconstrictor neurons by combining the stochastic ﬁring patterns of one to three input neurons (preganglionic neurons) converging onto a single output neuron (postganglionic neuron). The lower trace in Figure 1A shows a representative recording of an artiﬁcially gen- erated spike train in a “postganglionic neuron,” together with the spike trains from two of the three “preganglionic neurons,” units 1 and 2, that converged onto this model output neuron. Note the variation in spike amplitude of the modeled postganglionic neu- ron, due to the underlying noise on which each standard spike is riding. Although the overall discharge frequency was low, it can be seen that intermittent high-frequency discharges can be observed: these are generated by the chance occurrence of spikes from the two preganglionic neurons converging onto the postganglionic neuron. Figure 1B shows the ﬁring pattern when all three input neurons were driving the output neuron. Increasing the num- ber of active preganglionic neurons increased the overall ﬁring probability (and mean ﬁring rate) of the postganglionic neuron, the incidence of intermittent high instantaneous frequencies also increasing. Figure 2 shows the distribution of spikes per “cardiac interval” (1 s), calculated when one, two, or three model preganglionic neu- rons – discharging with a mean interspike interval of 1000 ms and a SD of 500 ms – were driving a single model postganglionic neu- ron. Figure 2F shows the distribution of real spikes recorded from 33 muscle vasoconstrictor neurons in healthy subjects. It is read- ily apparent that none of the patterns illustrated in Figures 2A–E matched the ﬁring pattern of the real neurons: when a single pre- ganglionic neuron was active (Figures 2A–C) the overall ﬁring probability was too high (i.e., there were fewer cardiac intervals in which the neuron was silent) and when two or more preganglionic neurons were active the incidence of multiple ﬁring was too high. Moreover, the mean ﬁring rates were higher than those recorded from the real neurons. The same was true when the SD was 1× and 2× the mean interspike interval. At the other extreme, Figures 3A–E shows the spike distribu- tions calculated when one, two, or three preganglionic neurons – each ﬁring with a mean interspike interval of 2500 ms and SD of 5000 ms – were driving the postganglionic neuron on which they converged. For the majority of intervals, the postganglionic neuron was silent: 83% of intervals when only one input neuron was active, 70% when two were active, and 57% when three input neurons were active. As the number of silent cardiac intervals decreased with an increase in the number of active preganglionic neurons, so the number of spikes generated within each cardiac interval increased. It is apparent that the distribution of the num- ber of spikes per cardiac interval was similar between the modeled and actual data shown in Figure 3F: 0 spikes (69.5 vs 66.6%), 1 spike (25.6 vs 21.2%), 2 spikes (4.3 vs 6.4%), 3 spikes (0.5 vs 1.7%), and 4 spikes (0.1 vs 0.7%). The mean discharge interval from this pattern was 2314 ± 1453 (SD) s, corresponding to a mean ﬁring rate of 0.22 Hz, comparable to (but somewhat lower than) that of the population of spontaneously active muscle vasoconstrictor neurons recorded in healthy subjects (0.40 Hz). Table 1 shows the spike distributions for the model postgan- glionic neurons driven by one, two, or three model preganglionic neurons, each ﬁring with a mean interspike interval of 1000, 1500, 2000, or 2500 ms and SDs of 0.5×, 1.0×, or 2.0× that of the mean Frontiers in Neuroscience | Autonomic Neuroscience December 2011 | Volume 5 | Article 132 | 2