Pattern Generation in the Buccal System of Freely Behaving Lymnaea stagnalis

Rene F. Jansen, Anton W. Pieneman, and Andries ter Maat

Faculty of Biology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Jansen, Rene F., Anton W. Pieneman, and Andries ter Maat. Pattern Generation in the Buccal System of Freely Behaving Lymnaea stagnalis. J. Neurophysiol. 82: 3378-3391, 1999. Central pattern generators (CPGs) are neuronal circuits that drive active repeated movements such as walking or swimming. Although CPGs are, by definition, active in isolated central nervous systems, sensory input is thought play an important role in adjusting the output of the CPGs to meet specific behavioral requirements of intact animals. We investigated, in freely behaving snails (Lymnaea stagnalis), how the buccal CPG is used during two different behaviors, feeding and egg laying. Analysis of the relationship between unit activity recorded from buccal nerves and the movements of the buccal mass showed that electrical activity in laterobuccal/ventrobuccal (LB/VB) nerves was as predicted from in vitro data, but electrical activity in the posterior jugalis nerve was not. Autodensity and interval histograms showed that during feeding the CPG produces a much stronger rhythm than during egg laying. The phase relationship between electrical activity and buccal movement changed little between the two behaviors. Fitting the spike trains recorded during the two behaviors with a simple model revealed differences in the patterns of electrical activity produced by the buccal system during the two behaviors investigated. During egg laying the bursts contained less spikes, and the number of spikes per burst was significantly more variable than during feeding. The time between two bursts of in a spike train was longer during egg laying than during feeding. The data show what the qualitative and quantitative differences are between two motor patterns produced by the buccal system of freely behaving Lymnaea stagnalis.


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Central pattern generators (CPGs) are neuronal circuits that drive repeated movements such as walking or swimming. They have been studied intensely both at the network level and the cellular level (e.g., Marder and Calabrese 1996), and we now have (at least in some cases) a fairly detailed knowledge about the cellular composition of pattern generators and their compositional dynamics. One of the questions that remains concerns the relationship between the "fictive motor patterns" observed in vitro and the actual motor patterns that occur in freely behaving animals. Although CPGs (by definition) continue to operate in the isolated nervous system, sensory signals most likely play an important role in adjusting the electrical output of these systems to meet the specific behavioral requirements of the animal (Barnes and Gladden 1985; Clemens et al. 1998; Jansen et al. 1997). One of the steps needed to resolve this issue would be the characterization of the activity of pattern generators in live, freely behaving animals under different behavioral circumstances. In some cases (Heinzel et al. 1993; Kupfermann and Weiss 1982; Morton and Chiel 1993), recordings in restrained intact animals have been made, but in general little is known about how pattern generator circuits operate in freely behaving animals. We therefore recorded the motor output of the buccal CPG of freely behaving snails, Lymnaea stagnalis, during different behaviors.

The buccal system of Lymnaea has been shown to be a useful model for the study of the modulation of CPGs by neurotransmitters and higher order neurons (Benjamin and Rose 1979, Benjamin et al. 1979, 1985; Yeoman et al. 1994). The feeding pattern generator is composed of six types of interneurons that can be driven by a slow oscillator neuron. These interneurons fire in sequence to produce a three-phase rhythm (protraction, rasp, swallow) (Benjamin et al. 1985) that drives the motorneurons. The motorneurons of the buccal system have in the past been described as separate from the pattern generator per se. Recently, however, at least some of the buccal motorneurons have been found to be coupled electrically to interneurons of the pattern generator circuit (Staras et al. 1998). The buccal motorneurons project into the buccal nerves that connect the ganglion with the feeding apparatus, a complex system of 46 muscles that moves a radula over the substrate (Benjamin et al. 1979).

The buccal system of Lymnaea is especially interesting for in vivo recordings because it is used during more than one behavior: rasping movements of the radula are not only used to collect food but also occur during egg laying when the animal cleans the substrate by making frequent rasping movements with the buccal mass at the location where the eggs will be deposited (ter Maat et al. 1989). This behavior aids in the proper attachment of the egg mass to the substrate (Ferguson et al. 1993). Recent behavioral data (Jansen et al. 1997) have shown that the buccal movements seen during egg laying occur at a lower rate than during feeding and have a duration that is different from buccal feeding movements. Correlated with these changes in the buccal movements were changes in the electrical activity of the cerebral giant neurons (Jansen et al. 1997), a pair of identified interneurons that sets the level of excitability of the pattern generating circuit (Yeoman et al. 1994, 1996).

These data suggested that, in vivo, the buccal pattern generator is capable of producing different motor patterns during feeding and egg laying. It is unclear if this is achieved by switching between two dedicated pattern generators or, for example, by some form of circuit modification. To investigate what the qualitative and quantitative differences are between the motor patterns produced by the buccal system during the two behaviors, the electrical activity in nerves that contain axonal projections of buccal motorneurons was recorded in freely behaving animals. Waveform recognition techniques were used to investigate the firing characteristics of unit electrical activity and the correlation of this unit activity with the buccal movements seen during feeding and egg-laying behavior. The firing characteristics of unit electrical activity were used to describe the quantitative differences in pattern generator activity that underlie the behavioral output of the buccal system.


    METHODS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Animals

All experiments were done in vivo using freely behaving pond snails (Lymnaea stagnalis). Adult specimens (age, 4-6 mo; shell length, 25-35 mm) bred under standard laboratory conditions were used in all experiments. The animals were housed in perforated jars placed in a large tank with running fresh water (20°C), and were kept under a 12 h-12 h light-dark cycle and fed a daily ration of lettuce.

Fine wire recording of buccal nerves

Permanently implanted electrodes were used to monitor electrical activity in the buccal nerves. In each animal one electrode was implanted. Stainless-steel wire electrodes (25 µm diam, California Fine Wire) or nickel-chromium wire electrodes (10 µm diam, H. P. Reid, FL) were implanted to record electrical spiking activity. The procedures were as described by Hermann et al. (1994) and Jansen et al. (1997). The nerves recorded from were the posterior jugalis nerve (PJ nerve), the laterobuccal nerve (LB nerve) and the ventrobuccal nerve (VB nerve; see Fig. 2A) (Benjamin et al. 1979). These nerves contain axonal projections of many of the motorneurons of the feeding pattern generator.

In many animals, the latter two nerves emerge from the buccal ganglion as one, only to split up into two nerves at some distance from the ganglion. Therefore and because of the increased durability of these recordings, the LB and VB nerves were in most animals recorded together using a single electrode.

Analysis of behavior

Buccal activity was analyzed from videotape and frame by frame in a manner that ensured that electrical and behavioral activity could be analyzed separately, yet realigned with single-videoframe (40 ms) accuracy as described earlier (Jansen et al. 1997). The buccal movement has been described (Benjamin et al. 1985) as the sequence protraction, rasp, swallow, and inactive. In intact animals, the swallowing movement is not visible and we thus used the terms "open," "rasp," and "closed." In this study, open and rasp presumably correspond to protraction and rasp, respectively, of the radula (see Jansen et al. 1997). The presence of the food in the tank gave rise to a fourth category in the behavior of feeding animals. This category, termed "invisible," was used to indicate the time that the animals' mouth was completely obscured by the food.

Analysis of electrical signals

Electrical activity recorded with a fine wire electrode was fed through a WPI DAM-80 differential amplifier and stored on the hifi-track of the video tape used to store the recording of the animals' behavior. A time-code generator (VITC, Alpermann and Velte, Germany) was used to provide every video frame with a time code. This time code was used to synchronize during analysis the behavior stored on videotape with the digitized electrical activity of the nerves during analysis. The electrical activity was digitized using a Cambridge Electronic Design model 1401 or 1401Plus, 12-bit A/D converter that was running a wavecapture protocol (see Jansen and ter Maat 1992). For each animal, the sampling rate was adjusted such that waveforms were represented by 64 data points. During digitization both the waveform activity as well as the matching VITC signal are read from tape. Obviously, some action potentials that pass through the nerve recorded from are too small in amplitude to record. The trigger of the wavecapture program was set to such a level that all but the smallest spikes were digitized (see Fig. 1).



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Fig. 1. Digitization of electrical activity. Electrical activity was digitized using an AD converter that was running a wavecapture protocol. Trigger of the wavecapture program was set to such a level that all but the smallest spikes were digitized. Figure shows a typical example of the difference between the raw data and the digitized data. This recording shows an example of the electrical activity in the laterobuccal/ventrobuccal (LB/VB) nerves during feeding.

The separation of multiunit electrical activity by means of the shape of the waveform, in general, is based on the idea that the combination of the geometry of the soma (or the axon in the case of nerve recordings), and its distance relative to the recording electrode is different for individual neurons, yielding a unique and identifiable signal. Because this is likely to be true for at least some neurons in a given recording but not necessarily for all of them, the procedure for separating the whole nerve recording into separate units used in this paper was not only based on the shape of the waveforms recorded, but additional criteria such as the biophysical properties that determine the firing characteristics of the neurons were used. The following three-step procedure was used in spike separation. First, a spike sorting program that uses a using a self-organizing clustering method (Jansen and ter Maat 1992) was used to sort the data. The parameters that set the tolerance levels of the algorithm were adjusted such that individual units had interspike-interval histograms that show a clear refractory period. One drawback of template matching techniques such as this one is that they tend to separate anisotropic clusters into multiple units. In a second step we therefore investigated the raw, unsorted, waveform data in three-dimensional (3D) scatterplots (Fig. 2). Each of the three axes of the plot represented the amplitude of the waveforms at 1 of the 64 data points, and each dot in the scatterplot thus represented a single action potential. The entire graph could be rotated in three-dimensional space. In addition, the positions of the three measuring points along the waveforms could be varied to get different projections of the data set. This was used to get an impression of the multidimensional structure of the dataset. In the third step, the units created by the clustering algorithm were compared with the 3D scatterplot of the raw data. If the scatterplot showed different units to be subsets of a larger complex, the interval distributions of this complex were investigated more closely. Units that were continuous or immediately adjacent in the 3D plot were merged if the interspike interval histogram of this newly "merged" unit still showed a refractory period in its interspike-interval histogram similar to that of the original units (Fig. 2C). This procedure can, for example, deal with the anisotropy that occurs in some bursting neurons. This is illustrated in Fig. 2, B-D. A scatterplot of raw data shows two major clusters. One of them is small, the other one is larger and has a elongated, nonspherical shape. This cluster was separated by the spike sorting program into the units 1-3. These three units could be merged without affecting the refractory period seen in the interval histogram (Fig. 2C). It was concluded that units 1, 2, and 3 most likely represent the firings of a single neuron (Fig. 2D) and should be treated as such. The slow variation in spike amplitude is most likely caused by spike broadening that occurs as the firing rate increases.



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Fig. 2. Processing of electrical multiunit activity. A: schematic drawing of the buccal ganglia of Lymnaea and the nerves emanating from it. Electrical activity was recorded from the posterior jugalis nerve (PJN), laterobuccal nerve (LBN), and the ventrobuccal nerve (VBN). CBC, cerbrobuccal connective; DBN, dorsobuccal nerve. B: processing of spike data. After a self-organizing spike-sorting program was used to cluster the data, a scatterplot of the raw, unsorted data was made. Amplitude of all the waveforms measured at 3 differents points were used as x, y, and z coordinates of a 3-dimensional (3D) scatterplot. By moving these points along the waveforms and rotating the plot in 3D space, the structure of the dataset could be investigated. C: interval histogram of the combined units 1-3. Scatterplot suggested that units 1-3 were parts of one single larger cluster. Combined dataset still showed a refractory period (~30 ms in this case). The unit marked 4 is separated from the others and not likely to be a part of this set. Together with the scatterplot, this justified merging units 1-3 into one new unit. D: firing pattern of this combined unit showing the gradual change in amplitude over time.

In the recordings used in this paper between 3 and 10 different units thought to represent the electrical activity of single neurons could be distinguished in each nerve. The number of units recognized depended on the thickness of the nerve with the smaller number of units found in thinner nerves. A similar procedure has been described by Fee et al. (1996).

Data fitting

Firing patterns of neurons were characterized by their interval and autodensity histograms as well as by their joint interval plots. Interval histograms give an estimate for the interval density and the joint interval plots show the interdependency of intervals. With autodensity histograms, we investigate the higher-order density of the spike train generating process by not only looking at the time to the first spike (as in the interval histogram), but also at the times to the second, third, etc. spikes. The values for x axes of the autodensity plots were chosen so that the plots would show more than one full period of the buccal pattern generator.

Because it was not possible to measure objectively parameters that like burst length and spikes per burst that are used to describe burst patterns of activity, experimentally obtained interval histograms and autodensity histograms were quantified by fitting histograms from simulated spike trains produced by a burst generator model.

In this model, bursts of spike events were produced by three processes: first, burst intervals (time between the end of 1 burst and the onset of the next burst) were generated by drawing at random from a normal distribution. Second, the number of spikes within a burst was determined by a second (different) normal distribution. Third, the interspike intervals within a burst were determined by a distribution composed of a refractory period followed by an exponential distribution. The minimum interburst period was 0 s, the minimum number of spikes within a burst was 1 (one). The pseudorandom number generator used was Ranmar (TPMath, J. DeBord). Each simulated spike train contained 5,000 events.

Both experimentally obtained and simulated interval and autodensity histograms were normalized to the total number of spikes events in the train. Thus spike trains of different duration could be compared directly. The six parameters, described in the preceding text, that determine the simulated spike trains (and thus the shape of the simulated interval- and autodensity histograms) were adjusted to minimize the chi 2 between these histograms (Press et al. 1996) using a two-step procedure. First, the values for the refractory period and exponential that are used to fit the interval histograms were estimated scanning the entire two-dimensional parameter space (latency and exponential) for the lowest chi 2 between the experimentally obtained and a simulated interval histograms. A 3D plot (latency vs. exponential vs. chi 2) was used to check that such a minimum actually existed. This is a computationally intensive but simple and robust procedure. Next, the downhill simplex procedure of Nelder and Mead (deBord 1999; Press et al. 1992) was used to minimize the chi 2 calculated between the experimentally obtained and the simulated autodensity plots. Because this optimization routine can get stuck in local minima, the optimization routine was always restarted several times. The best fit (lowest chi 2) over several runs (typically 5) was used. Because the histograms used are all normalized, the goodness of fit obtained could be compared between units (waveforms) within the one animal and between animals.

Permutation tests

The correlation between spiking activity and buccal rasping movements was investigated by means of permutation tests as described before (Jansen et al. 1997). In short, randomization techniques applied to single subjects (permutation tests) are used to calculate the probability that an experimentally found relation was due to chance by calculating all (or a large number of) the theoretical possibilities. If the percentage of permutations that yields a result equal to the null hypothesis is smaller than a preset level (5%), then the experimentally obtained data are not considered to be the result of chance, and the null hypothesis is rejected. In the present case (cross-correlational histograms), experimentally obtained bin counts are compared with a "population" of bin counts obtained from the same recording by data permutation. Because every individual bin of the experimentally obtained histogram is compared with this population, it can be argued that we are thus making multiple comparisons with the number of comparisons being equal to the number of bins in the histogram. Because we accept, for every comparison, a chance P = 0.05 of being wrong, the actual significance level used in each test could be adjusted by dividing P by the number bins in the histogram to reach an overall P = 0.05. This approximation of the Bonferroni adjustment for multiple comparisons was calculated for all experiments (indicated as "protected" in the results) but did not affect the conclusions.


    RESULTS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

We recorded the animals' behavior as well as the electrical activity from buccal nerves in freely behaving L. stagnalis. The PJ, VB, or LB nerves (see Fig. 2A) contain axonal projections of buccal motorneurons that innervate the buccal muscles. The VB and LB nerves were recorded jointly with a single electrode in six animals. Egg laying occurred in four of these animals, feeding occurred in all six animals. The electrical activity in the VB nerve alone was recorded in two animals; one of these animals laid eggs. The posterior jugalis nerve (PJ nerve) is very fragile and many recordings (not included in this paper) did not last more than just a few hours. Feeding occurred in all the four PJ animals included in this paper, but egg laying occurred only in one PJ animal.

Electrical activity in the PJ nerve

The PJ nerve is very thin and probably contains axonal projections of just a few neurons. Using a backfill technique, Goldschmeding et al. (1977) reported two somata filled from each PJ nerve. Benjamin et al. (1979) also reported one to two neurons backfilled from the PJ nerve, but these results were, however, termed "not unambiguous" (Benjamin et al. 1979). The neurons that project into the PJ nerve are thought to include the B6 neurons (terminology after Benjamin), which in vitro are active during the protraction phase of the buccal movement (Benjamin and Rose 1979).

Figure 3, A-C, shows a recording of the electrical activity in the PJ nerve when the animal was neither egg laying, feeding, or showing any other overt buccal activity. The animal was locomoting around the tank, and during this period there were occasional bursts of electrical activity in the PJ nerve but there were no visible buccal movements. A three-dimensional scatterplot of the raw data revealed three distinct clusters (Fig. 3A). These clusters were compact in appearance and the distances between the clusters were relatively large, indicating that there was little variation in the shape of the waveforms within the clusters. The firing characteristics of action potentials contained in these three clusters confirmed that each most likely represents the activity of a single neuron, as the interval histograms of each of these clusters showed a clear refractory period. This is illustrated in Fig. 3B for the largest unit, which had an apparent refractory period of ~70 ms. The autodensity plot of this unit had a peak around t = 0 but was otherwise relatively flat, indicating that in the absence of feeding activity this unit fired bursts of action potentials but with a highly variable interburst period (Fig. 3C). This also visible in the joint interval plot (Fig. 3B).



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Fig. 3. Electrical activity in the PJ nerve in the absence of buccal movements. A: scatterplot of the raw data showing 3 distinct clusters representing 3 different classes of waveforms. B: firing characteristics of the largest unit. In the interval histogram (left), no intervals shorter than ~0.075 s occurred, indicating that this unit represents a single neuron. Autodensity histogram (middle) shows that this unit fires in bursts, but the interburst period is irregular (right), causing the autodensity plot to have a single peak around T = 0. C: firing pattern of this unit.

When animals started feeding, spiking activity in general increased dramatically and became much more periodic. Figure 4A shows the electrical activity in the PJ nerve of an animal that started feeding after coming in contact with a piece of fish food (indicated by the arrow, different animal from that in Fig. 3). The feeding behavior of this animal is shown in Fig. 4A, bottom, as the start and stop times of the CLOSED, OPEN, and RASP movements of the mouth and consisted of sequences CLOSED-OPEN-RASP-CLOSED (see METHODS). Four units of electrical activity could be distinguished in this recording. Before feeding started, the pattern of electrical activity of the largest unit (marked cluster 4) showed the irregular bursting described earlier. The interval histogram showed a clear refractory period of ~60 ms (Fig. 4C), indicating that these were most likely action potentials coming from a single neuron. When the animal came in contact with the food (arrow), it started feeding, the firing rate increased and the firing pattern became rhythmic (Fig. 4, A and D). The joint interval plot (Fig. 4E) showed that most of the interburst intervals were 2-3 s in duration.



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Fig. 4. Electrical activity in the PJ nerve during feeding. A: electrical activity recorded from the PJ nerve (top) and buccal movements of the animal (bottom). Start and stop times of mouth CLOSED, OPEN, and RASP were measured from videotape (see METHODS). Fishfood was presented up-arrow  and left in the tank for the remainder of the recording. B: scatterplot of the raw data (see METHODS). A total of 4 clusters could be distinguished, the projection used here shows cluster 4, the largest unit in this recording. Data in this scatterplot represent the entire (6-h) videotape. C: interval histogram of this unit shows a clear refractory period, indicating that all the spikes indeed come from a single neuron. D: autodensity histogram with a 20-s window showing the bursting firing pattern of this unit. E: joint interval plot showing that long intervals (2-3 s) are followed by short intervals (0-1 s).

To visualize and quantify the timing relationship between electrical activity and behavioral output (buccal movements), unit electrical activity was aligned at the different behavioral transitions. Figure 5B shows all the waveforms that are included in this analysis: there was some variability in the shape of this unit that occurred as the firing rate increased (see also Fig. 4B). In addition, there was some unresolved superposition of small amplitude waveforms. Figure 5A shows a raster diagram of the electrical activity of unit 4 from Fig. 4 around the transition OPEN-RASP. The moment of T = 0 (x axis) is the moment of the transition. For each of the 48 transitions in this record, the graph shows the electrical activity in this unit from 10 s before the start of the RASP movement until 10 s after the start of the RASP movement. Each spike within this 20-s window is marked by a vertical line within the horizontal lane that indicates that particular transition. The electrical activity clearly occurred in phase with the feeding movement. The data shown in Fig. 5A were added and converted to a rate (spikes · min-1 · transition-1) as shown in Fig. 5C. This shows that virtually no spikes occurred from ~200 ms (5 videoframes) before the start of RASP until a few hundred milliseconds after the start of RASP. The median durations of the OPEN (1.40 s) and RASP phases (1.88 s) are indicated by the vertical dotted lines in Fig. 5, A and C, and show that peak in the firing rate of this unit occurred just before the median start of OPEN and after the median end of RASP.



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Fig. 5. Cross-correlation analysis of electrical activity and behavior. A: raster diagram showing the unit electrical activity within a 10-s window around the moment of the transition from OPEN to RASP. T = 0 indicates the moment of this transition. Each row represents 1 transition, each vertical line represents a spike. B: all the spikes used in this recording shown superimposed. Note that the shape of the spikes in this unit is variable, which presumably is caused by the burst firing of this unit. C: histogram of the data shown in A, bin size = 0.2 s. Vertical dotted lines left and right of T = 0 indicate the median duration of the phase preceding T = 0 (OPEN in this case) and the median duration of the phase after T = 0 (RASP in this case). Horizontal dotted lines indicate the upper and lower levels of statistical significance, derived from the data shown in D. Two kinds of dotted lines indicate the normal and protected 2-tailed 5% levels. D: histogram showing how often (% of total count) different bin counts (spikes · min-1 · transition-1) occurrred when cross-correlating this unit with random events rather than behavioral transitions (see METHODS).

Although this cross-correlation histogram of the relation between unit electrical activity and feeding movements shows when this unit is active, it is impossible to tell which features of this histogram are the result of chance and which are statistically significant (rare) events. Because we have no a priori knowledge of the kind of distribution underlying the events in this spike train and also because it is difficult (if not impossible) to find a good control experiment for spontaneous behavior, we next applied data permutation tests to investigate it more closely. If no correlation existed between the unit electrical activity and the feeding movements, then a histogram similar to the one shown in Fig. 5C could have been obtained by aligning the electrical activity of this unit at an equal number of random moments rather than exactly at behavioral transitions such as OPEN-RASP. This can be used to calculate the statistical probability of the current result. We thus produced "random" runs in which spikes were counted in exactly the same way as described in the preceding text (using the same data) but now at randomly chosen moments in the recording rather than at behavioral transitions. The number of samples within a random run was the same as the number behavioral transitions that occurred in the original recording. If we repeat such random alignment N times, we generate N random histograms that can be used to calculate a probability distribution of bin counts that are the result of random alignment. Thus the data permutation runs yield an estimate of the different possible outcomes of random alignment of rasping behavior and unit activity.

Figure 5D shows the probability distribution obtained by 4,000 repetitions of random alignment. For each run, the electrical activity of unit 4 was aligned at 48 randomly chosen moments (see METHODS), and a histogram as in Fig. 5C was constructed. The bin counts of these histograms were accumulated, and this yielded the graph shown in Fig. 5D, which shows that very low spike rates as well as very high spike rates do not occur very often. The horizontal dotted lines in Fig. 5C indicate the 5% (2-tailed) normal and protected probability levels. Figure 5C thus shows that unit 4 fires at statistically significantly low rates from a few hundred milliseconds before the transition (T = 0) until 800 ms after the transition (bin size = 200 ms representing 5 video frames). Note that we do not take into account here the combined probabilities of events occurring in multiple adjacent bins (see DISCUSSION). Also, as with all cross-correlational spike data, results such as the one presented here depend on the spontaneous firing rates encountered in the different units. A statistically significant inhibition is, in general, more difficult to show in slow firing units than in units that fire at higher rates. The pattern of activity described in the preceding text occurred in a very similar fashion in the other units in this recording and in the other animals in which the PJ nerve was recorded during feeding behavior.

These results show that the multiunit extracellular activity recorded from these nerves can be separated activity that most likely represents the activity of single neurons. This activity in the PJ nerve was irregular in the absence of feeding and became strongly rhythmic when feeding started. Surprisingly, the timing of this activity with respect to the feeding movement was different from predicted by in vitro recordings (see DISCUSSION).

LB/VB nerve recordings

Experiments by Benjamin using procion yellow injections (Benjamin et al. 1979) have shown earlier that the LB nerve contains axonal projections of the B4-group motorneurons, as well as a projection of motorneurons B8 and B3. The VB nerve contains projections of B4-group motorneurons. In vitro, the B4-group motorneurons and motorneuron B3 are active during the early rasp phase, whereas the B8 motorneurons are active during the late rasp phase of the buccal movement (Benajmin and Rose 1979). Because nothing is known about the involvement of these neurons in the buccal movements seen during egg laying, we recorded their electrical activity in animals that showed both behaviors and analyzed both the timing of the electrical activity relative to the buccal movement and the rhythm generating process underlying the spike trains.

Electrical activity recorded from the LB/VB nerves was characterized by bursts of large amplitude seen during overt buccal movements both during feeding-related rasping and egg-laying related rasping (Fig. 6). In this raw unsorted data, two broad categories of electrical activity were visible: small-amplitude spikes that occur in the absence of overt buccal movements or between buccal movements and large-amplitude spikes that occurred in bursts during buccal rasping movements. First, we analyzed these bursts of large-amplitude spiking activity. The feeding bursts were generally shorter in duration and occurred at a higher rate than during egg laying, which would correspond well with the durations of the OPEN and RASP phases of buccal movements of egg-laying and feeding animals described earlier (Jansen et al. 1997).



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Fig. 6. Patterns of electrical activity in LB/VB nerves during feeding and egg laying. Top: raw, digitized data for both for feeding and egg-laying behavior; bottom: data for rasping behavior.

The electrical activity recorded from LB/LV nerves was separated into unit activity as described in METHODS. The large-amplitude bursts consisted of several distinct units. A typical example is shown in Figs. 7 and 8 during feeding and egg laying. Aligning the spikes of this unit at the transitions OPEN-RASP (Fig. 7, A and C) showed that there is a significant peak in its electrical activity during the first second of the RASP movement (Fig. 7, C and D; median duration of OPEN =1.08 s, median duration of RASP =1.75 s). The raw waveforms used in this analysis are shown in Fig. 7B.



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Fig. 7. Cross-correlation of electrical unit activity with feeding behavior. Layout is as in Fig. 5. A: rasterplot of the spikes of unit activity aligned at the transition OPEN-RASP. B: spikes in this unit superimposed. C: histogram of the data shown in A. Graphs show the interval histogram (left), the autodensity histogram (middle), and the joint interval histogram (right). D: histogram showing how often (% of total count) different bin counts (spikes · min-1 · transition-1) occurrred when cross-correlating this unit with random events rather than behavioral transitions.



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Fig. 8. Cross-correlation of electrical unit activity with egg-laying behavior. A: rasterplot of the spikes of the unit shown in Fig. 7, aligned at the transition OPEN-RASP. B: spikes in this unit superimposed. C: histogram of the data shown in A. Vertical dotted lines left and right of T = 0 indicate the median duration of the phase preceding T = 0 and the median duration of the phase after T = 0. D: result of the permutation run. Horizontal solid lines indicate the 2-tailed 5% levels, the horizontal solid line indicates the protected 5% level.

Figure 8 shows the firing characteristics of the same unit during egg laying. The raw waveforms used in this analysis are shown in Fig. 8B. Aligning electrical activity at the transition OPEN-RASP showed that this unit was active during first 1.4 s of the RASP (Fig. 8, A and C; median duration of OPEN =1.44 s, median duration of RASP =1.88 s). In addition, the burst of activity during the rasp movement now was preceded and followed by a significant suppression of firing of this unit (Fig. 8, C and D). Although this suppression of firing appeared similar to the pattern seen during feeding, the latter was not significant (see DISCUSSION). This unit thus fired during approximately the same period both during feeding and egg laying. The firing rates of neurons active during these bursts were generally lower during egg laying than during feeding. In the example shown the peak number of spikes/minute per transition dropped from ~600 (Fig. 7) to ~200 (Fig. 8). This example is typical of the other large-amplitude activity recorded in the LB/LV nerves and shows that there are no obvious changes in the phase relationship of this electrical activity relative to the buccal movement between feeding and egg-laying episodes.

Data fitting

Despite this lack of obvious changes in the phase relationship of this electrical activity relative to the buccal movement, it is possible that other differences exist in the electrical activity of these units between feeding and egg-laying episodes. We therefore quantified and compared the spike trains recorded from the LB/LV during feeding and egg laying. Systematic differences in the rhythm generating process underlying unit activity between the two behaviors would indicate differences in pattern generator activity. Eleven units (from 4 animals) in which enough events occurred to do this statistical analysis were used.

Quantifying parameters such as burst length and burst period from spike trains obviously requires that for every individual unit the beginning and the end of a burst of spikes can be recognized unequivocally. Because this is not always the case (for example when there are only 1 or 2 spikes in each period), such a procedure would result in a self-referential kind of "measurement" by using some subjective criterion of what exactly constitutes a "burst." Instead, we used a more objective procedure, in which we fitted both the interval and autodensity histograms with data produced by a burst generator model (see METHODS). The model parameters then can be compared between feeding and egg-laying episodes of individual units.

For interval histograms, the duration of intervals between spikes were measured (binwidth 5 ms), and plotted as the number of events per 100 spikes. This normalization allowed us to compare real and simulated spike trains of different lengths. For the autodensity plots, unit firings that occurred within a ±15-s window of a spike were counted in 0.1-s bins.

Figure 9A shows a unit during feeding activity. The autodensity histogram shows the strong periodicity in the firing of this unit. Fitting the burst model (thick line) yielded an interburst period of 3.12 ± 0.51 s (average ± SD) and a burst duration of 13.89 ± 6.25 spikes using an interval histogram composed of a 34-ms latency followed by an exponential with a time constant of 18 ms. In all animals, this strong periodicity seen during feeding was much reduced during egg laying. The autodensity and interval histograms made during egg laying are shown in Fig. 9B. The autodensity histogram shows that the periodicity is much less pronounced and the burst periods longer in the egg-laying animal than in the feeding animal. Fitting the burst model to the autodensity histogram of this unit yielded the following parameters: interburst period 4.53 ± 0.97 s, burst duration 4.55 ± 2.39 spikes, interval histogram latency 38 ms, and a time constant of 36 ms. Taken together, the interval histograms of the 11 units analyzed here showed that during egg- laying animal units fired at lower rates than in feeding (Table 1 and Fig. 10, Wilcoxon signed rank test, P = 0.006). The difference between the number of spikes per burst was marginal (P = 0.05), but the standard deviations were much smaller in feeding animals (P = 0.002), meaning that the number of spikes per burst was much more variable during egg-laying behavior.



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Fig. 9. A: characterization and simulation of spike trains during feeding. Of each spike train, an autodensity histogram (right) and an interval histogram (left) was made. For the autodensity plot, spikes of this unit that occurred within a 15-s window were binned (bin size = 0.1 s) and plotted (thin line). For the interval histogram, the bin size was 5 ms. Experimental data are plotted as the thin line. Both the autodensity histogram and the interval histogram were normalized to the length of the total number of events in the spike train. Simulated spike trains (see METHODS) were treated as the experimental data and were plotted as the thick lines in both the autodensity histogram and the interval histogram. B: unit used in A but now during egg laying.


                              
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Table 1. Model parameters of fit



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Fig. 10. Summary of the parameters used to fit spike trains of feeding and egg-laying animals. To be able to compare spike trains measured under different behavioral circumstances, experimentally obtained autodensity and interval histograms (see Fig. 9) were fitted with data obtained from a statistical model. Model train was characterized by the following parameters: a normally distributed burst interval and the a normally distributed number of spikes per burst. Interspike interval of the spikes within the burst was determined by an interval distribution that consisted of a refractory period and a single exponential. Median parameter values for the two behaviors are shown.

It is worthwhile to consider for a moment the question of whether we are looking at properties of individual neurons or of whole animals. As mentioned above, the six parameters used in the model were estimated using 11 neurons from four animals. The values of the parameters estimating the interval histograms and the number of spikes per burst are most likely determined to a large extent by membrane properties of the neuron and thus represent properties of individual neurons. In other cases, however, it is more likely that we are dealing with properties of animals: the hypothesis is that buccal movements are driven by a pattern generator, the interburst interval therefore most likely represents a property of the animal rather than a property of individual neurons and should therefore be tested using n = 4. In 10 of 11 neurons tested, the interburst period was longer during egg laying than during feeding, and the median interburst period was longer during egg laying than during feeding in all four animals. This difference is significant (P = 0.0091, paired t-test) and normality is assumed, but given an n = 4, it cannot be tested.

Small-amplitude electrical activity in LB/VB nerve recordings

We next analyzed the small-amplitude activity recorded from LB/LV nerves that occurred between the bursts of large-amplitude activity that are seen during buccal movements (see preceding text). These small-amplitude units were active between the large-amplitude bursts rather than during these bursts. They were also active when there was no overt buccal activity at all. Little is known about a possible function of neurons that are active in the absence of feeding activity and project into the buccal nerves (see DISCUSSION). Because these small-amplitude spikes do not occur during a burst, it is not likely that they contribute to the buccal movement per se. It is conceivable, however, that activity in units like these is related to other aspects of the buccal movement such as the position of the buccal mass within the body (see DISCUSSION).

Figure 11 shows such a unit aligned at the transitions OPEN-RASP during feeding behavior. The waveforms included in this recording are shown in Fig. 11B (shown on the same scale as in Figs. 5B, 7B, and 8B). Because the amplitudes of these units were always very small, some waveforms may have been below the trigger level in some animals. When feeding occurred, these units fired in phase with the buccal movement and show a significant drop in spiking activity during the entire RASP movement (Fig. 11, A and C). Both the raster diagram (Fig. 11A) and the autodensity plots (Fig. 11, E and F) show that these units display two kinds of bursts during feeding. In the rasterplot bursts lasting ~3-10 s are separated by periods of shorter bursts, lasting approximately <= 1 s. These longer bursts occurred when there was a short pause in ongoing feeding activity. This was also visible in the autodensity plot: the dominating rhythm is a slow one, but there clearly is a systematic deviation in the data (thin line) from the fit (thick line) caused by an additional faster rhythm. When we selected from such a recording, a period in which the rate of feeding was close to the maximum the autodensity plot only showed the faster rhythm (Fig. 11F). These units also fire during egg laying, and during this behavior they also fired in phase with the buccal movement (not shown). These data show that in the buccal nerves a second rhythm can be recorded (in addition to the feeding rhythm) that is slow in the absence of feeding and phase-locked with the feeding rhythm during buccal activity.



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Fig. 11. Crosscorrelation of the electrical activity of units that are suppressed during buccal movements. A-D as in Figs. 7 and 8. E: autodensity plot of this unit is dominated by the bursts lasting 3-10 s. Fit (thick line) clearly shows that there is an additional, faster component. F: when we select a period without the short pauses in the feeding that are associated with these longer bursts, the normal rhythm typical of feeding behavior in the larger units becomes visible.


    DISCUSSION
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ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

In the current paper, we have investigated the electrical activity produced by the buccal pattern generator of unrestrained L. stagnalis during different behaviors. Pattern generator activity was characterized by the phase relationship of unit activity with the buccal movements and by its statistical characteristics. We found that there are significant differences in the patterns of electrical activity produced by the buccal system during the two behaviors investigated: the bursts contained less spikes and were significantly more variable in duration than during feeding. Also during egg laying, the time between two bursts of electrical activity was in general longer during egg laying than during feeding. It still is unclear whether there is a dedicated pattern generator for egg laying or just one buccal pattern generator that is modified to produce the motor output seen during egg laying. The current data show, in the intact animal, what exactly the qualitative and quantitative differences are between the two patterns produced by the buccal system.

Because the current recording method does not allow us to identify the recorded neurons, it is difficult to compare the current results with previous intracellular studies. If we assume that there is a correlation between soma diameter and axon diameter of the buccal neurons in general, then neurons with the largest soma diameter will most likely generate the largest membrane current and thus the largest extracellular signal. The axonal projections of the buccal neurons with the largest somata have in the past been investigated (Benjamin and Rose 1980, Staras et al. 1998), and the largest buccal neurons projecting into the LB/VB nerves are the neurons B3, B8, and the B4 group neurons. Furthermore, in vitro experiments have shown (Goldschmeding 1977; Jansen, unpublished data) that during fictive feeding the B4-group motorneurons cause the largest extracellular spikes in LB/VB nerves. It is therefore reasonable to assume that the activity recorded includes activity of the B4 group and B8 motorneurons. In vitro, the B8 motorneurons have been found to be active during the late rasp phase of the buccal movement and the B4-group motorneurons are active during the early rasp phase (Benjamin and Rose 1980; Staras et al. 1998). The present data show that the large amplitude units are active just before and during most of the RASP phase of the buccal movement. These combined data make it likely that the B4 group and B8 motorneurons are indeed active during the RASP phase of the feeding movement in intact animals. The results of the cross-correlation analysis between unit activity suggest that the phase relationship between unit electrical activity and does not change for the RASP movement. In contrast with some of the other CPG-driven systems that have been observed in vivo (such as the stomatogastric system) (Heinzel et al. 1993), the sequence of movements of the buccal system changes little between the two behaviors investigated here. During feeding and egg laying, the frequency, duration, and intensity of the buccal movements are different, but the sequence of movements is identical (CLOSED-OPEN-RASP-CLOSED) for both behaviors. It is, therefore not surprising to find that neurons fire during the same phases during these two behaviors.

In addition to these large-amplitude bursts from B4 group and B8 motorneurons, we also recorded small-amplitude spikes from the LB/VB nerves that were seen between buccal movements and in the absence of feeding. Also in the PJ nerve periodic electrical activity was seen when no rasping occurred. In the absence of overt activity, the interburst period was highly variable (see Fig. 3 for example). Also this activity was not seen constantly and could start or stop spontaneously. It is not clear what the function is of this kind of electrical activity. When overt (rasping) activity started, these units fired in phase with the buccal movement and were strongly inhibited during the RASP phase. During buccal activity, their firing pattern probably resembles most that of N3 tonic interneurons, but these neurons do not have peripheral projections (Elliott and Benjamin 1985). The dorsal buccal nerves that emanate from the buccal ganglia innervate the anterior part of the esophagus, and buccal neurons have been described that produce peptides that can inhibit spontaneous esophagus movements (Li et al. 1996). Sensory neurons of the esophagus also are located in the buccal ganglia (Elliott and Benjamin 1989). Esophagus movements are likely to occur in phase with the rasp movement during feeding for example but may continue for some time after the rasping movements have stopped. It is, therefore, possible that these neurons are involved in the control of other aspects of the feeding system such as the proximal part of the esophagus or the salivary glands.

Conspicuously absent from the current recordings are units that could be involved in the OPEN (protraction) phase. In our definition, the OPEN phase starts when the mouth is opened at the beginning of a buccal rasping movement. This opening of the mouth almost certainly involves muscles of the lip area as much as the buccal muscles, but little is known about the motorneurons that control this area of the mouth. The work of Benjamin and coworkers enabled us to make very specific predictions about the expected phase relationship between electrical activity and buccal movements: unit activity in the PJ nerve that originated from the B6 motorneuron was expected to occur during the OPEN (protraction) phase, whereas motorneurons that project into the LB/LV nerves should, on the other hand, be active during the RASP phase (Benjamin and Rose 1979). The latter indeed was found, the former was not. Electrical activity in the PJ nerve was suppressed during much of the RASP phase and significant activity occurred before OPEN and after RASP. One possible explanation for this apparently anomalous result is that the spikes of the neurons involved in the OPEN phase were all very small in amplitude and therefore not digitized (see METHODS). It is also possible that the nerves that innervate the lip area such as the median lip nerve and the frontal lip nerve are more involved in opening the mouth than the buccal nerves we recorded here.

The results presented here are based on the analysis of unit electrical activity recorded from buccal nerves in freely moving animals, and the interpretation of the results obviously depends on the success or failure of the spike separation method used. We used the shape of the waveforms, the distributions of these waveforms within the dataset as well as their firing properties to identify and separate units of electrical activity. As shown if Fig. 2, this combination of methods is especially useful when the shapes of waveforms are not constant due to burst firing or other changes in the electrical properties of the neurons. Circumstances where the current combination of methods could lead to errors include slow firing units (which do no accumulate enough events to construct a reliable interval histogram) and neurons that show discrete rather than slow changes in the shapes of their waveforms. Units that did not accumulate enough events to construct a reliable interval histogram were not included in the results. Discrete changes in the waveforms of single neurons have been described recently in freely behaving animals Lymnaea (Jansen et al. 1996), but this is likely to be a rare circumstance. Most of the units of activity presented in this paper therefore very likely represent the electrical activities of individual neurons.

In cross-correlational analysis of unit electrical activity and buccal movements, we used a resampling technique to calculate significance levels of unit firing. In this analysis, we did not take into account the combined probabilities of multiple (adjacent) bins. The combined probabilities of multiple bins could be calculated in a simple manner using the random data obtained for single bins, provided that the bins are independent. The autocorrelation histograms, however, clearly show that they are not. We thus would have to recalculate the probability distribution for every combination of bins to be tested. Also we would have to increase dramatically the number of random runs to obtain a large enough sample for every possible combination of bins. Because this clearly would lead to an extremely large amount of computation, we limited ourselves to single bins. In doing so, we probably underestimate the number of significant events.

Although it would be a logical extension of the current work, it is probably difficult to "translate" the observed differences in firing patterns into buccal movements. The reasons for this are multiple. The buccal system is a complex system of 46 muscles (not including the lip muscles), but we recorded only a subset of the neurons involved (lip motorneurons for example, were not recorded). Furthermore the relationship between the spike rate of motorneurons and speed/force of muscle contraction is likely to be a nonlinear one, making predictions difficult. Recent work by Brezina et al. (1997) has shown that changes in the spike patterning may have a large effect on muscle contraction. In addition, the buccal ganglia contain a number of neuromodulatory substances that have been shown to modulate the contraction properties of muscles: the buccal ganglia contain immunoreactivity for myomodulin, small cardioactive peptide and buccalin (Perry et al. 1998, Santama et al. 1994), and direct mass spectrometry showed that neurons in the buccal ganglia contain GAPRFVamide, a neuropeptide that inhibits spontaneous esophagus contractions (Li et al. 1996).

Previous work has shown that egg-laying-related rasping is not just a slower version of normal feeding: the distributions of the duration of the CLOSED and OPEN phases are different during egg laying and feeding, but the distributions of RASP were identical (Jansen et al. 1997). Earlier studies (Goldschmeding et al. 1983) suggested that during egg laying the movement of the radula during RASP is less intense (deep) than during feeding: on a substrate like lettuce egg-laying-related rasping does not result in the ingestion of the substrate, suggesting a less forceful bite than during feeding, when a substrate like lettuce is eaten away completely. The current results show that spiking is slower during egg laying, and this lower rate of spiking most likely contributes to this reduced intensity of the bite. It is not known whether there is a separate pattern generator for egg-laying-related rasping or that the feeding pattern generator is "remodeled" to generate the egg-laying rhythm.

The sensory control of the buccal system during egg laying has earlier been found to be different from that during feeding: egg-laying-related rasping is enabled by neural signals from one of the visceral nerves, the intestinal nerve (Ferguson et al. 1993). This signal is thought to be largely responsible for the fact that the total number of rasps produced during egg laying is a linear function of the size of the egg mass (ter Maat et al. 1989). When this intestinal nerve is cut, egg-laying- related rasping does not occur, but feeding remains unaffected (Ferguson et al. 1993). Similarly, sensory signals have been shown to play a key role in the organization of egg laying behavior in the marine mollusk Aplysia (ter Maat and Ferguson 1996). Consummatory egg-laying behavior and the suppression of feeding that occurs during this phase of egg laying depend on an intact nervous connection between the CNS and the genital pore area.

Egg-laying- related buccal rasping in Lymnaea is caused, either directly or indirectly, by peptides released from the neuroendocrine cells in the CNS that trigger egg laying, the caudodorsal cells (CDCs). Injection of the CDC peptides alpha-CDCP or beta-CDCP into animals immediately triggers buccal rasping movements (Hermann et al. 1997), indicating that neuropeptides encoded on the CDCH gene can activate the buccal pattern generator (Hermann et al. 1997). It is not clear if and how exactly these peptides cause the changes in the buccal system described here. One finding (described by van Minnen et al. 1988) that deserves further attention is the fact that anti-CDCH-positive fibers and cell bodies occur in the buccal ganglia. It is possible that these anti-CDCH-positive neurons play a role in the modulation of the buccal pattern generator during egg laying. Our current hypothesis is that a sensory signal caused by the movement of the eggs through the female tract plays a key role in the organization of consummatory egg-laying behavior. This sensory signal is thought to cause the activation and modification of the feeding pattern generator by means of local release of alpha-CDCP and/or beta CDCP. The present results in Lymnaea provide us with a specific set of predicted actions of these sensory signals on the buccal system that can be tested in future experiments.


    FOOTNOTES

Address for reprint requests: R. F. Jansen, Faculty of Biology, Vrije Universiteit, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands.

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received 9 February 1999; accepted in final form 6 August 1999.


    REFERENCES
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

0022-3077/99 $5.00 Copyright © 1999 The American Physiological Society