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INTRODUCTION |
Synchronized spontaneous activity patterns in neuronal networks are involved in a wide range of CNS-related phenomena such as the development of neuronal networks, regulation of hormone release, epileptic activity, and neuronal plasticity. Two basic types of network organizations have been postulated to generate specific activity patterns in a variety of tissues. Although endogenous pacemaker cells are the origin of phasic activity driving synaptically coupled follower cells in some instances (Johnston and Brown 1984
; Zucker 1988
), spontaneous activity also can result from feedback excitation in synaptically coupled networks (Chamberlin et al. 1990
; Traub and Dingledine 1990
; Wong and Traub 1983
). Apparently both the properties of the individual neurons and the structure of the network of synaptic connections can determine the spontaneous activity generated. However, the distinction between the two fundamental organizations is not always clearly established, and the factors that contribute to the generation of spontaneous activity in the absence of pacemaker cells are not fully understood. In an effort to assess the determinants of network-generated spontaneous activity, we have developed a new approach to probe the functional organization of networks formed by neurons in long-term culture.
The mechanisms of synaptic transmission have been studied in detail in networks of hypothalamic neurons in culture (Misgeld and Swandulla 1989
; Müller and Swandulla 1995
; Swandulla et al. 1993
). Glutamate receptors of the
-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)/kainate type and
-aminobutyric acid-A (GABAA) receptors have been identified as the major excitatory and inhibitory synaptic mechanisms (Swandulla and Misgeld 1990
). The membrane conductances underlying electrical excitability also have been characterized in cultured neurons (Cobbett and Mason 1987
; Mason 1983
; Misgeld and Swandulla 1989
; Müller et al. 1992
; Zeilhofer et al. 1993
) and slices (Inenaga et al. 1993
; Niespodziany and Poulain 1995
; Wuarin and Dudek 1993
). However, the origin of the observed spontaneous activity is not clear. The presence of specialized pacemaker cells has been reported in cultured hypothalamic networks (Gähwiler and Dreyfuss 1979
, 1980
; Swandulla and Misgeld 1990
). Recent evidence, however, suggests that endogenous pacemakers are not essential for the generation of quasirhythmic firing patterns (Müller and Swandulla 1995
).
In the present study, we have probed the organization of the synaptic connections formed by hypothalamic neurons in culture. Using simultaneous patch-clamp recordings from pairs of cells, we have identified some of the patterns that characterize local functional connectivity in these networks. Our results indicate that excitatory neurons outnumber inhibitory neurons; this is in line with histochemical observations (Wahle et al. 1993
). The results also suggest that on average inhibitory neurons form synapses with more postsynaptic cells than excitatory neurons. Furthermore, reciprocalmonosynaptic connections between a glutamatergic and aGABAergic neuron occur significantly more often than predicted by random formation. We postulate that the formation of networks of hypothalamic neurons in culture involves cellular mechanisms that, by promoting this structure, favor the inhibition of network activity. These mechanisms may contribute to the development and function of neuronal networks in vivo.
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METHODS |
Neuronal cell culture
Experiments were carried out on networks of cultured rat hypothalamic neurons. The details of culture preparations have been described previously (Swandulla and Misgeld 1990
). In brief,14-day-old fetal rats were removed from Wistar rats that had been anesthetized and decapitated. Fetal hypothalamic tissue was dissected, mechanically dissociated, and plated on a 2-week-old monolayer of glial cells. Cultures were kept at 37°C and 5% CO2 in Eagle basal medium supplemented with 5-10% heat-inactivated horse serum, L-glutamine (2 mM; all from GIBCO Laboratories), glucose (2 g/l; Merck, Germany), penicillin (25 U/ml), and streptomycin (25 µg/l; both antibiotics from Sigma Chemical).
Electrophysiological recording
Ionic currents were recorded in the whole cell configuration of the patch-clamp technique. Two computer-controlled, patch-clamp amplifiers (EPC-7, List Electronics, Germany) were used for simultaneous recordings from pairs of neurons. During recording, the cultured networks were continuously superfused with extracellular solution containing (in mM) 145 NaCl, 5 KCl, 2 CaCl2, 1 MgCl2,10 N-2-hydroxyethylpiperazine-N
-2-ethanesulfonic acid (HEPES),and 10 D-glucose, pH 7.3. Pipettes with resistances of 3-4 M
were pulled from borosilicate glass (KIMAX 51, Kimble) and filled with a solution containing (in mM) 120 Cs-gluconate, 20 CsCl, 2 MgCl2, 5 ethylene glycol-bis(
-aminoethyl ether)-N,N,N
,N
-tetraacetic acid (EGTA), 10 HEPES, 3 NaATP, and 1 NaGTP, pH 7.3. Both cells recorded from were usually voltage clamped at
80 mV. To elicit synaptic responses, one cell at a time was stimulated with a short depolarizing pulse (0 mV, 10 ms). Liquid junction potentials were not corrected. Extracellular control and drug-containing solutions were applied from a three-barreled application pipette with a tip diameter of ~200 µm (see Carbone and Lux 1987
; Konnerth et al. 1987
). Drugs were from Sigma Chemical (NaATP, NaGTP) and Merck, Germany (EGTA and all inorganic salts).
Probabilistic model
Several numerical models describing the behavior of neuronal networks and the observations from multiunit recording have been proposed (e.g., Ables and Goldstein 1977
; Yang and Shamma 1990
). These models are based on the assumption that single-unit action potentials or spike frequencies are the only observable variables. However, the technique of paired patch-clamp recordings described above is more powerful with respect to probing the network structure in that it can identify the presence and type of individual synaptic connections.
To analyze the results obtained from counting the synaptic connections of different types as they are observed, we have used a simple probabilistic model. With the exception of glutamatergic and GABAergic connections projecting only from neurons of the appropriate type, we assume that the formation of individual synaptic connections essentially satisfies stochastic independence. To avoid ambiguities, we introduce the term oriented cell pair to denote a cell pair where the putative pre- and postsynaptic cell already have been specified. Testing a given cell pair for synaptic connections in both directions then amounts to testing two oriented cell pairs independently. We define the (stochastic) frequency a0 of a glutamatergic connection between a given oriented cell pair as
|
(1)
|
where fa is the frequency of glutamatergic cells and xa the conditional probability that a connection is formed if the presynaptic cell is known to be glutamatergic. The analogous variables b0, fb, and xb are defined for GABAergic connections
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(2)
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For polysynaptic glutamatergic connections with k interneurons, the probability in a given oriented cell pair is approximated by
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(3)
|
where N is the total number of cells in the network and µ the probability that a single excitatory postsynaptic potential in the interneuron triggers an action potential that leads to a synaptic event at the subsequent synapse. For the sake of simplicity, we neglect the possibility that the test stimuli may be propagated by summation via multiple paths converging at an interneuron.
By analogy, the probability for a polysynaptic GABAergic connection in an oriented cell pair is
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(4)
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Note that the term in brackets, which is raised to the kth power, contains the factor a0 and not b0 because for an evoked GABAergic postsynaptic current (PSC) to be observed in the last cell, the first k cells (i.e., the stimulated neuron and the first k
1 interneurons) must be glutamatergic and only the last interneuron must beGABAergic. This term essentially indicates how many neurons will be stimulated above threshold by an excitatory signal. We therefore will use the name propagation factor. It in fact can be determined from pooled data including both inhibitory and excitatory connections because
|
(5)
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The experimental values for ak, bk, and ak + bk (k = 0 ... 3) were calculated from successive peaks of the multimodal distributions fitted to the histogram of synaptic latencies for glutamatergic connections, GABAergic connections and pooled data, respectively. Fitting Eqs. 3 and 4 to these points also yields an improved estimate for a0 and b0. These estimates can then be used to predict the occurrence of other observable configurations of synaptic connections. Reciprocal monosynaptic connections should occur with probabilities of
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(6)
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for pairs of glutamatergic neurons, pairs of GABAergic neurons and mixed pairs, respectively. The probability for a reciprocal glutamatergic connection with at least one polysynaptic connection is given by
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(7)
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For a converging geometric series this leads to
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(8)
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The corresponding expressions for GABAergic and mixed pairs with reciprocal polysynaptic connections are
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(9)
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and
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(10)
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The model description of the network architecture is enhanced substantially by considering connections among three selected cells. The underlying feature of the network architecture is the degree of branching, i.e., the average number of synaptic connections formed by an axon. This is contained indirectly in the model parameters xa and xb, corresponding to excitatory and inhibitory neurons. If, given three cells, cell number one is stimulated, then the probability of observing monosynaptic excitatory responses in both cell two and cell three is
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(11)
|
Note that in comparison with Eq. 1, only the second factor is squared but not the first. This reflects the model assumption that although the events of cell one forming synapses on cells two and three are independent of one another, the type of synapse (excitatory or inhibitory) is determined strictly by the type of cell number one. Polysynaptic connections with, respectively, k and m interneurons in the two branches can be described by a general form of Eq. 11 similar to Eq. 3
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(12)
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A special case of particular interest are those connections schemes that will yield synchronized evoked responses in the two postsynaptic cells because synchronized responses can be identified. The probability of finding such configurations is given by
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(13)
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Because only two cells were recorded simultaneously, this value could not be measured directly. However, synchronized spontaneous events in two cells should occur if there is a common input from any of the N cells in the network, i.e., with a probability of
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(14)
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The analogous probabilities for synchronized inhibitory events are
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(15)
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(16)
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(17)
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and
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(18)
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Although there is no direct estimate for N and it is practically impossible to reliably detect all synchronized spontaneous events, one can determine empirically the relative frequency of the underlying synaptic configurations. Equations 14 and 18 together indicate that cell pairs exhibiting synchronous excitatory or inhibitory events should be observed with a ratio of a2,spont/b2,spont
a2,0/b2,0 if the same criteria are applied to both.
An interesting property of neuronal networks is the ability to sustain spontaneous activity based on mutual stimulation. Spontaneous activity can be sustained only if the fraction of currently excited neurons (
) is able to stimulate at least an equal number of neurons above threshold. For this, the model yields the following estimate when the total fraction of excited neurons is small
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(19)
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Obviously, the condition would be satisfied only for propagation factors Nµa0 >1.
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RESULTS |
Evoked PSCs
To characterize individual synaptic connections, we have recorded simultaneously from pairs of neuronal somata clamped at a hyperpolarized holding potential. As indicated in Fig. 1, the presynaptic cell was stimulated with a 10-ms depolarizing pulse to 0 mV. This elicited a fast-activating and -inactivating inward Na current in the stimulated cell and, after a delay of ~3 ms, a more persistent response in the postsynaptic cell, which, in the example shown in Fig. 1, was directed outward. External application of 1 µM CdCl2 consistently resulted in a complete and reversible block of all spontaneous and evoked postsynaptic responses in hypothalamic neurons (see also Müller et al. 1992
), suggesting that the recorded postsynaptic currents are dependent on presynaptic transmitter release. Hence, a significant contribution of electrical synaptic connections appears unlikely.

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| FIG. 1.
Evoked synaptic potentials. A: membrane current of 2 simultaneously recorded voltage-clamped neurons is shown. In response to a10-ms depolarizing pulse to 0 mV applied to 1 of the neurons, designated presynaptic neuron, this neuron shows a fast transient inward current, whereas an outward postsynaptic current is observed in other neuron. Holding potentials of pre- and postsynaptic neurons were 80 and 0 mV, respectively.
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The sensitivity of excitatory PSPs (EPSCs) and inhibitory PSCs (IPSCs) in hypothalamic neurons to the respective glutamate and GABAA antagonists 6-cyano-7-nitroquinoxaline-2,3-dione, picrotoxin, and bicuculline has been shown previously (Misgeld and Swandulla 1989
). Most of the ionotropic glutamate receptors active in this preparation are of the AMPA/kainate type, whereas only a small population of N-methyl-D-aspartate (NMDA) receptors can be detected by pharmacological means. In the experiments reported here, two types of connections with different time course and voltage dependence of the evoked PSC responses were observed. Recordings from two representative connections are shown in Fig. 2A. The current traces show for each were recorded at different postsynaptic holding potentials in response to identical stimuli. For the sake of simplicity, we refer to evoked EPSCs (left) and IPSCs (right) without regard to the actual direction of the membrane current that was observed at a given postsynaptic holding potential. The slow component of evoked EPSCs observed at positive postsynaptic holding potentials may be in part due to NMDA-receptor channels. Figure 2 also illustrates the key properties used to discriminate the two types of connections. Figure 2B shows the peak I-V relations, which differ with respect to the observed reversal potential. Liquid junction potentials due to the solutions used may have contributed to the slightly positive reversal potential observed for EPSCs. The observed negative reversal potential of IPSCs is expected for GABAA receptor channels in the presence of asymmetrical chloride concentrations. The two example PSCs shown in Fig. 2C were both recorded at
80-mV holding potential. Each is overlaid with a monoexponential function of the indicated time constant. The decay time constant of evoked EPSCs was 4.5 ± 1.4 ms (mean ± SD; n = 12) and did not vary significantly with the postsynaptic holding potential (5.2 ± 1.6 ms; n = 4 at 0 mV). This measurement does not include the additional slow component observed for positive postsynaptic holding potentials. Evoked IPSCs decayed with voltage-dependent time constants ranging from 17 ± 4.4 ms (n = 26) at
80 mV to 39 ± 15 ms (n = 21) at 0 mV. Thus the decay of evoked EPSCs was always faster than that of IPSCs. The voltage dependence and decay time course of the evoked responses were consistent in all experiments and matched the properties of glutamate and GABA receptor channels reported for this preparation. This allowed a highly reliable discrimination of excitatory gutamatergic and inhibitory GABAergic synaptic connections.

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| FIG. 2.
Evoked postsynaptic currents (PSCs) in 2 types of synaptic connections. A: evoked postsynaptic currents are shown in 2 cell pairs representative for 2 types of synaptic connections found [glu and -aminobutyric acid (GABA); see text]. Current traces were recorded at postsynaptic holding potentials of +40 to 80 mV (Glu) and 0 to 80 mV (GABA) in steps of 20 mV from top to bottom. B: I-V relations of peak evoked current responses are shown. Negative reversal potential for GABAergic responses is expected in presence of asymmetrical chloride concentrations. C: decay time course of depicted glutamatergic andGABAergic PSCs is described well by monoexponential functions of indicated time constants ( ). Postsynaptic holding potential was 80 mV in both cases.
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Presently, there is no practical means of mapping all the synaptic connections in a complex neuronal network nor are such maps likely to yield an immediate understanding of the network properties. Here, we have investigated three aspects of the network organization that we wish to term linear path connectivity, branching connectivity, and circular connectivity. They constitute a simple functional characterization of the overall network architecture. Linear path connectivity describes how fast and on average how far an excitatory signal is likely to propagate along any (nonbranching) path between two given neurons. Branching connectivity addresses the question: on average how many other neurons receive input from a given neuron? Circular connectivity describes neuronal paths that lead back to the neuron from which the signal originated, i.e., those paths that are the substrate for positive and negative feedback.

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| FIG. 3.
PSC latencies and linear connectivity. Histogram (A) shows incidence of latencies from presynaptic stimulation to postsynaptic responses for both glutamatergic and GABAergic (dark portions of columns) PSCs. Data are taken from 282 oriented cell pairs (see METHODS). Solid line, fitted distribution consisting of a sum of Gaussian distributions. Contribution of each peak to total distribution also is indicated. First peak corresponds to monosynaptic connections between 2 cells, whereas other peaks represent polysynaptic connections with glutamatergic interneurons. Area under each peak, which is proportional to probability of observing, respectively, a monosynaptic or polysynaptic connection is plotted ( ) vs. putative number of interneurons (i.e., peak number minus one) in B. Last data point reflects fact that probability of observing long latencies is very low. Curve connecting these points is an exponential function as predicted by probabilistic model (see METHODS). On same graph, midpoint of each peak in (A), i.e., average latency, is plotted vs. number of interneurons. Slope of linear function fitted to these values is 4.5 ms per interneuron. Putative recording configuration corresponding to 3 peaks of latency histogram is depicted schematically in C. In each case, leftmost (first presynaptic) and rightmost (last postsynaptic) neurons bear patch pipette. In general, type of last postsynaptic cell is not known, whereas type of next to last cell is identified by type of PSC observed, and all prior neurons must be excitatory, i.e., glutamatergic.
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| FIG. 4.
Spontaneous synchronized PSCs. In a number of paired recordings, spontaneous PSCs appeared synchronized in both cells but independent of any stimulus. These PSCs were either both glutamatergic as in example on left or both GABAergic as in right example but never mixed. Corresponding diagrams indicate most likely configuration underlying such observations.
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PSC latencies: linear path connectivity
The latencies of evoked PSCs were measured from the peak of the presynaptic inward current to the start of the PSC and ranged from 1 to 20 ms. The latency histogram in Fig. 3A shows both the glutamatergic and GABAergic connections so that the total height of each column reflects the pooled data from both types of synaptic connections. The distribution clearly shows multiple peaks that can be fitted with a sum of Gaussian distributions as indicated by the solid line. The observed latencies appear to be grouped around three discrete steps of ~5 ms. From this, it seems likely that PSCs with latencies greater than ~7 ms do not reflect monosynaptic connections, but were evoked through an above-threshold stimulation of an excitatory interneuron. Thus the leftmost peak of the distribution in Fig. 4A contains all the monosynaptic connections, whereas the subsequent peaks reflect connections with one or more excitatory interneurons (see Fig. 3C). This hypothesis is supported by the fact that these peaks are reasonably equidistant as demonstrated by the plot of mean latency versus the number of interneurons in Fig. 3B. The slope of the line connecting the data points (
) reflects the signal propagation time and is 4.5 ms per interneuron. This time presumably includes the synaptic latency, the time required to generate the postsynaptic action potential, and the action potential propagation along the axon. Note that the monosynaptic latency, which corresponds to the position of the first peak, is 3-4 ms. As expected this is less than the propagation time. The relative sizes of the successive peaks are consistent with a simple probabilistic model (see METHODS). The area under each peak yields the number of connections with 0, 1, and 2 interneurons, respectively. When the number of is plotted versus the number of interneurons (Fig. 3B,
), these data points can be fitted with a single exponential function as predicted by Eqs. 3-5 of this model. Note that for the purpose of this plot, the single connection with a 20-ms latency was assumed to have three interneurons. Essentially, the corresponding data point reflects the fact that the probability of observing a polysynaptic connection with three or more interneurons is very low. The agreement of the data points and the fitted curve in Fig. 3B suggests that the model is a good description of linear propagation of neuronal excitation. The empiric values for the model parameters a0 and b0, i.e., the probabilities of detecting a glutamatergic orGABAergic connection when testing a given oriented cell pair (see METHODS), were 0.074 and 0.150, respectively. The propagation factor (see METHODS) was found to be 0.28 for the pooled data set (curve in Fig. 3B). This means that on average only one in three to four excitatory signals is transmitted to another excitatory neuron and results in an above-threshold stimulation.
Synchronized spontaneous PSCs: branching connectivity
The number of glutamatergic and GABAergic connections in the network is determined not only by the number of glutamatergic and GABAergic neurons, but also by their ability to make more or fewer connections to other neurons, i.e., the degree of branching of the neuronal pathways. Although the observed probability of propagation of excitatory signals along given paths of connected neurons is affected by the number of such paths available, the information provided by observations described above does not suffice to determine the average number of branches emanating from glutamatergic or GABAergic neurons. Because both cells were voltage clamped under the same conditions in all paired recordings, spontaneous PSCs could be recorded in both with equal probability. In some cases, as shown in Fig. 4, spontaneous PSCs with a similar time course appeared synchronously in both cells. As indicated in the schematic drawings, the origin of these synchronized PSCs must be a common presynaptic neuron that happened to fire during the recording. Obviously, the probability of detecting such events is influenced by many factors such as the activity level of the network and the duration of the recordings. Therefore, the absolute frequency at which they were detected in our experiments is in itself not very conclusive. However, the relative incidence of cell pairs displaying synchronized EPSCs (sa) and synchronized IPSCs (sb) is related to the ratio of glutamatergic and GABAergic neurons(fa/fb) by Eqs. 1, 2, 14, and 18
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(20)
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We determined sa/sb from a subset of the paired recordings that satisfied several practical criteria. We disregarded all cell pairs that showed (any) spontaneous PSCs >20 events per second, because the presence of more uncorrelated spontaneous activity impedes the reliable identification of synchronized spontaneous PSCs. Furthermore, a common presynaptic neuron was postulated only if in at least three instances PSCs occurred in both cells within 3 ms of one another. Out of the 44 cell pairs selected, synchronized EPSCs were observed in only 3 cases, whereas synchronized IPSCs occurred in 19 cases. EPSCs in one cell synchronized with IPSCs in the other cell were not observed. Solving the left and right parts of Eq. 20 for fa/fb in terms of sa/sb, a0, and b0 and substituting the experimental values yields a 1.5:1 ratio of glutamatergic to GABAergic neurons in the network population. The higher number of synchronized IPSCs is due to a larger number of connections coming from the GABAergic neurons. Solving Eq. 20 for xb/xa in terms of sa/sb and fa/fb indicates that GABAergic neurons form about three times as many synaptic connections as glutamatergic neurons.
Circular connectivity: feedback
Circular connections of two or more neurons are of particular significance because such loops are the substrate of positive and negative feedback, i.e., fundamental control functions. The simplest possible circular connection of a neuron onto itself, i.e., an autapse, was not observed in any of the experiments. Our approach to use the patch-clamp technique for both pre- and postsynaptic cells enabled us to test for synaptic connections in both directions in a given cell pair and, hence, to detect circular connective paths. Of the three possible combinations: two inhibitory connections, one inhibitory and one excitatory connection, or two excitatory connections, the latter was never observed. In 112 cell pairs tested, 5 showed reciprocal inhibitory connections and 9 were connected mutually via one excitatory and one inhibitory path. When the latter are differentiated into monosynaptic and polysynaptic (meaning that either or both of the two connections is polysynaptic) paths, six of those nine were monosynaptic and the remaining three were polysynaptic. Figure 5 compares these results with the predictions of the model described above. In most cases, the observed incidence matches the predicted probability. Only the combination of a monosynaptic excitatory and a monosynaptic inhibitory connection occurred about three times as often as it should if the formation of these connections were entirely independent of one another (significance level
= 0.05).

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| FIG. 5.
Reciprocal synaptic connections. Left diagrams depict 3 possible monosynaptic reciprocal connections between 2 neurons. Bar graph (right) shows predicted and measured probabilities for these configurations (for both monosynaptic and polysynaptic connections) to be observed in paired recordings. Predictions were computed using the formulas for cb,0, cb, poly, ca,0, ca,poly, cab,0, and cab,poly in Eqs. 6-10. Zeros indicate that corresponding configurations were not observed. In most cases, counted observations are in agreement with predicted probablilities. Only mixed reciprocal monosynaptic connections, i.e., 1 excitatory and 1 inhibitory connection occur more often than predicted by the model (significance level = 0.05).
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DISCUSSION |
The ability of neuronal networks to generate spontaneous firing patterns and to synchronize the activity of many cells is central to a wide range of processes in the CNS. Here we have studied the architecture of networks formed by hypothalamic neurons with the aim of understanding network properties that may contribute to the generation of spontaneous activity. In a novel approach to probing the network architecture, we have investigated three functional aspects of neuronal connectivity that we term linear, branching, and circular connectivity. Linear connectivity is characterized by the incidence of excitatory and inhibitory connections and the propagation factor (see METHODS). The latter describes how many excitatory neurons on average are stimulated above threshold by an excitatory signal in the network, somewhat analogous to the length constant of a cable. If it is greater than one, then according to the boundary condition formulated in Eq. 19, excitatory events should on average spread to more and more neurons, i.e., initiate a burst of activity. Bursting activity in the networks studied here indeed is observed when inhibitory connections are suppressed by picrotoxin (Misgeld and Swandulla 1989
). However, Eq. 19 applies only to low levels of activity and may represent a necessary rather than a sufficient condition for the generation of a sustained and discernible burst. The actual spike frequency attained after burst initiation and the duration of a burst may be determined by mechanisms, such as inactivation of Na channels, that can respond to and attenuate changes in activity level (see Müller and Swandulla 1995
). Conversely, factors below one indicate that excitatory events are eventually lost. In this strict sense, the boundary condition applies only to perfectly homogeneous networks. In particular, the variability of individual synapses is not accounted for by the model. Nevertheless one expects propagation factors at least near the critical value for the network to sustain an intermediate level of spontaneous network-generated activity.
The propagation factor is influenced not only by the incidence of excitatory connections and the total number of neurons in the network as indicated in Eqs. 3-5, but also by a variety of other conditions, which are represented collectively in the model parameter µ. These may include, for example, contributions from cellular signaling pathways impacting on membrane conductances and pre- and postsynaptic mechanisms. The empiric value of 0.28 determined for cultured hypothalamic networks indicates that less than one out of three excitatory stimuli is propagated. From this, any prevailing spontaneous activity is not expected to sustain itself via propagation throughout the network. Other sources of excitation therefore must be postulated. Specialized pacemaker cells with an endogenous activity might provide a sustained stimulus driving the rest of the network. However, there is evidence suggesting that spontaneous activity observed in these cultured networks is not generated by pacemaker cells (Müller and Swandulla 1995
). Another possible source for the observed spontaneous activity may be action potential-independent synaptic input, i.e., from miniature EPSPs. Spontaneous activity then would be entertained in part by spontaneous and by evoked transmitter release, whereas the evoked component determines the pattern and synchrony of this activity.
Branching connectivity refers to the number of postsynaptic neurons that are reached by signals from one either excitatory or inhibitory presynaptic cell. For both types, the total number of synaptic connections is determined by this factor and the number of cells. From our observations of synchronized spontaneous PSCs in pairs of cells, we conclude that the network cultures contain ~1.5 times as many glutamatergic neurons as GABAergic neurons. This is consistent with the results of neurochemical analysis of these cultures; the analysis revealed that only one-third of the neuronal population of the network cultures expresses markers characteristic for GABAergic cells (Wahle et al. 1993
). However, when counting functional monosynaptic connections, there appear to be twice as many inhibitory connections as there are excitatory ones. This means that GABAergic neurons form active synaptic connections to about three times as many neurons as glutamatergic ones do. Despite the majority of excitatory neurons, there is a predominance of inhibitory synaptic connections that prevents uncontrolled spreading of excitatory events throughout the entire network.
Circular connection schemes, such as those depicted in Fig. 5, and their polysynaptic counterparts are substrates for positive and negative feedback and therefore might be an important feature of neuronal networks involved in regulatory functions. Most types of circular configurations were detected (or not detected) only as often as in agreement with probabilistic predictions. However, reciprocal monosynaptic connections between mixed pairs of glutamatergic andGABAergic neurons occurred significantly more often than predicted. These results suggests that even in a culture of dissociated neurons, cells are capable of selectively forming or activating specific types of synaptic connections, thereby determining the repertoire of activity patterns of the network formed. The underlying mechanisms also might serve to adapt regulatory control circuitry to changing demands. The hypothalamus is an example of a brain region where different patterns of neuronal activity are related to regulation of hormone release (Cazalis et al. 1985
; Jaffe and Monroe 1980
). Changes in spontaneous network activity patterns (Renaud and Bourque 1991
) and a plastic rearrangement of synaptic connections (Morris and Pow 1993
; Theodosis and Poulain 1992
) are known to occur during parturition and lactation. The approach and the results presented in this study may help to establish a link between spontaneous activity patterns and the underlying network architecture.