Central Circuits Controlling Locomotion in Young Frog Tadpoles

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Central Circuits Controlling Locomotionin Young Frog Tadpoles

ALAN ROBERTS, S. R. SOFFE, E. S. WOLF,a M. YOSHIDA,b AND

F.-Y. ZHAO

School of Biological Sciences, University of Bristol, Bristol, BS8 1UG,United Kingdom

ABSTRACT: The young Xenopus tadpole is a very simple vertebrate that can swim. Wehave examined its behavior and neuroanatomy, and used immobilized tadpoles tostudy the initiation, production, coordination, and termination of the swimmingmotor pattern. We will outline the sensory pathways that control swimming behaviorand the mainly spinal circuits that produce the underlying motor output. Our recentwork has analyzed the glycinergic, glutamatergic, cholinergic, and electrotonicsynaptic input to spinal neurons during swimming. This has led us to study the non-linear summation of excitatory synaptic inputs to small neurons. We then analyzedthe different components of excitation during swimming to ask which componentscontrol frequency, and to map the longitudinal distribution of the components alongthe spinal cord. The central axonal projection patterns of spinal interneurons andmotoneurons have been defined in order to try to account for the longitudinal distri-bution of synaptic drive during swimming.

This volume illustrates how the central nervous control of locomotion is being exploredin a wide range of animals. Among the vertebrates these range from the highly com-

plex human and adult mammal systems to simpler, more experimentally tractable “modelsystems” such as the neonatal rat and the turtle which walk with limbs, and the zebrafishembyo, frog tadpole, and lamprey which use trunk muscles to swim. Those of us who workon the simpler models hope that consistent features will emerge, particularly in the spinalnetworks, that will throw light on the more complex systems. Our aim has been ratherbroad. We have studied one of the simplest spinal cords to find what neurons it contains,how these develop and connect into circuits, and how they operate to allow locomotorbehavior.

The hatchling Xenopus tadpole was chosen for study because its nervous system andbehavior are extremely simple, but nonetheless it is a free-living animal whose locomotorbehavior must help it survive by selecting suitable resting places and escaping from pre-dation.1 The limited development of the nervous system and the restricted number of cat-egories of neuron in the central nervous system (CNS) are huge advantages. On the otherhand, the small size of the whole animal can be a disadvantage and has made recordingfrom the whole free-moving animal impossible. However, the small size of the CNS hassome benefits: there is no circulatory system to disrupt; there is rapid diffusion of drugs to

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Present addresses:aDepartment of Anatomy, Medical University, Debrecen, Hungary.bApplied Biological Science, Hiroshima University, Higashi-Hiroshima, Japan.

all spinal neurons; and all neurons can be seen from the surface in the wholemount CNS,which avoids the need to cut sections when studying neuron anatomy.

The newly hatched Xenopus tadpole spends 98% of its first day out of the egg hangingfrom a mucus strand secreted by a “cement gland” on the head. Dimming the light, touch,or noxious stimulation to any part of the body leads to swimming where trunk muscle con-tractions alternate at 10 to 25 Hz and spread from head to tail. Pressure to the trunk leadsto “struggling” where slower 2 to 10 Hz lateral contractions alternate and spread from thetail to the head. Both of these locomotor gaits can occur in spinal tadpoles. Swimming canbe stopped by gentle pressure to the head or cement gland as happens in the behaving tad-pole when it swims into objects in the water and attaches with mucus.

This review is presented in two parts. The first will give a brief summary of our pre-sent knowledge of the neuronal circuits controlling the locomotor behavior of younghatchling Xenopus tadpoles. This summary will provide the context for the second part,which gives a more detailed account of our investigations of the nature and longitudinaldistribution of the different synaptic inputs to spinal motoneurons during swimming.

NEURONS AND CIRCUITS CONTROLLING TADPOLE BEHAVIOR

Much of the behavior we have observed in moving tadpoles can occur in immobilizedtadpoles where it is evoked by giving the same sensory stimuli and is monitored by record-ing motor nerve activity. It is most important to note that this “fictive” behavior is seenwithout the use of any of the applied chemical excitants that are required in many verte-brate preparations to evoke locomotor-like motor activity. The principal neuron classes ofthe young Xenopus tadpole are shown diagrammatically in FIGURE 1. It outlines the path-ways which begin to explain the basic responses of the young tadpole. Because our knowl-edge of the spinal circuits is much more detailed than those of the brain circuits, we willconsider them first.

Spinal Circuits

Spinal neurons are assigned to eight classes on the basis of simple anatomical featuressuch as dorsoventral position of the soma, dendrite origin and orientation, and axonal pro-jection.2 All these neurons form longitudinal columns of 100 to 300 neurons on each sideof the cord and extend into the hindbrain. By using intracellular recording (sometimesfrom pairs of neurons), dye filling, transmitter antibodies, and pharmacology, we canassign functions to five or six spinal neuron classes.1 Swimming is initiated by touchingthe skin. Struggling is evoked when pressure leads to repetitive firing in the same skinmechanoreceptors. The repetitive firing causes the behavior to switch to struggling.3

Rohon-Beard (RB) neurons are the mechanoreceptors for trunk skin and excite two typesof dorsolateral sensory pathway interneurons (dl and dlc) in the tadpole equivalent of thedorsal horn. These excite the neurons of the central pattern generator (CPG) on both sidesof the body: commissural interneurons (c) coordinate alternation by glycinergic inhibitionof the opposite side; descending interneurons (d) provide the excitation of neurons on thesame side of the cord that underlies spikes, and they also provide positive feedback exci-tation to sustain swimming; motoneurons (mn) excite the trunk muscles but are also elec-trically coupled and make central nicotinic cholinergic synapses to provide further positivefeedback to the CPG.4 Apart from this cholinergic excitation, all central excitation is “glu-tamate” mediated and activates AMPA and NMDA receptors.

20 ANNALS NEW YORK ACADEMY OF SCIENCES

This leaves three neuron classes whose functions remain poorly defined or unclear: dlinterneurons probably provide the excitatory pathway from sensory RB neurons to ipsilat-eral CPG neurons;5 a interneurons may produce short-latency inhibition following skintouch6 but may also be active during swimming; and KA neurons, which have cilia andmicrovilli in the neural canal (FIG. 1; nc), have GABA activity, are present in all verte-brates, look like olfactory chemoreceptors, but are of unknown function.7

ROBERTS et al.: CENTRAL CIRCUITS AND LOCOMOTION 21

FIGURE 1. Neurons and connections underlying behavior in the hatchling Xenopus tadpole. Sensorypathways which start swimming are on the left and those which stop it are on the right. All neuronsand projections are symmetrical but in many cases neurons are shown on one side only; circles rep-resent populations of neurons; thick black lines are axon projections; small triangles and circles aresynapses; when these contact a box rather than an individual neuron type they synapse with all neu-rons in the box; question marks (?) emphasize some of the many uncertainties; abbreviations areexplained in the text.

Brain Circuits

Three brain pathways for the initiation of swimming have been described using extra-cellular recordings. (1) Dimming the light excites pineal photoreceptors (p), which excitepineal ganglion cells (pg).8 These project to both sides where indirect evidence suggeststhat they excite diencephalic/mesencephalic descending (d/md) interneurons which pro-ject to the hindbrain to excite the CPG.9 (2) Trigeminal touch neurons (Tt) innervate thehead skin and project into the hindbrain. (3) Noxious stimuli to the skin initiate a propa-gated impulse which is relayed to the hindbrain possibly via central trigeminal neurons(Tn).10 Swimming is stopped when pressure to the head skin or cement gland leads to pro-longed firing of trigeminal pressure receptors (Tc). These are thought to exciteGABAergic reticulospinal neurons (ri), which project into the spinal cord to inhibit CPGneurons.11 Many other hindbrain neurons have been seen anatomically, but currently theonly others with a suggested function are raphe-spinal neurons (R), which are thought torelease serotonin to modulate the swimming motor pattern.12

This outline of the differentiated neuron groups and their functions in the newly hatchedtadpole is, of course, a simplification. Nonetheless, the behavior and CNS are extremelysimple, and many major sensory systems such as the lateral eyes and vestibulolateralis sys-tem are not quite functional. It therefore seems feasible that we can obtain a broad pictureof how the whole nervous system operates to control the tadpole’s locomotor behavior.

Operation of the Spinal Swimming Circuits

Simple circuit diagrams like those in FIGURE 1 cannot explain function, which dependson the properties and normal activities of the circuit elements. Fortunately, these can beinvestigated using intracellular microelectrodes or whole-cell patch techniques either in theintact nervous system of tadpoles immobilized with a neuromuscular block or in dissoci-ated spinal neurons.13 This work has led to proposals about the way that the CPG for swim-ming generates the basic alternating motor discharge pattern which is particularly simple atthis early stage of development (FIG. 2). The spinal neurons that are active during swim-ming all fire at the most a single action potential on each cycle, remain depolarized afterfiring, and receive strongly hyperpolarizing inhibition at midcycle. Despite efforts to revealcellular rhythmicity, it has not been found in hatchling tadpole neurons whereas it is pre-sent in spinal neurons of older tadpoles.14 Consequently, rhythmicity is thought to dependprimarily on the properties of the CPG network.15,16 It is proposed that rhythm generationnormally depends importantly on delayed rebound excitation following reciprocal inhibi-tion between the two sides of the nervous system or on recurrent inhibition within a singlehalf of the spinal cord.17 Since no external source of tonic excitation has been found, wehave proposed that swimming activity is sustained from cycle to cycle by positive feedbackexcitation from glutamatergic premotor interneurons and cholinergic motoneurons.4 Theplausibility of these proposals has been tested by showing that physiologically realisticcomputer models of the spinal circuits can generate very stable swimming-type activity18

with or without the voltage dependency of the NMDA receptor-activated conductances.19

Similar activity is also seen when the neurons in the model are given membrane propertiesbased on measurements of channel characteristics in isolated neurons.20,21

SYNAPTIC INPUTS TO MOTONEURONS DURING SWIMMING

One of the most interesting recent discoveries about the CPG for swimming in theyoung Xenopus tadpole is that the motoneurons are a part of the CPG, making central



FIGURE 2. The Xenopus tadpole and activity of spinal neurons and ventral roots during swimming.(A) Scale drawing of the tadpole at hatching showing CNS and trunk muscles with post-otic segmentnumbers indicated. (B) Fictive swimming recorded on the same side from a ventral root at the thirdsegment and a motoneuron at the seventh to show the rostrocaudal delay in activity. The intracellu-lar record shows the single spike riding on an EPSP, the midcycle IPSP, and the tonic depolarizationfrom the resting potential (dotted line). (C) Simulated swimming activity in two motoneurons onopposite sides of the spinal cord evoked by brief “sensory” stimulation at the start of the trace.

cholinergic synapses onto other motoneurons and also onto premotor interneurons22–24

(FIG. 1). In addition to these chemical central synapses, motoneurons also make electrotonicconnections with neighboring motoneurons. Motoneurons therefore receive excitationmediated by glutamate AMPA and NMDA receptors, nicotinic cholinergic receptors, andgap junctions. What are the roles of these different forms of excitation? Are they distributedequally along the length of the spinal cord and, if not, do they serve different roles at dif-ferent positions? Finally, what determines the longitudinal distribution of synaptic input,and can it be predicted from the distribution of presynaptic neurons and their axonal pro-jections? These are the questions that will be addressed in the remainder of this review aswe try to move from a two-dimensional circuit like that in FIGURE 1 to a three-dimensionalcircuit with length whose swimming frequency changes with time.

Control of Swimming Frequency

When fictive swimming is initiated by a sensory stimulus, the frequency starts high(20 to 25 Hz) and then falls, first quickly and then more slowly, to a lower level (10 to 15Hz) before stopping. It has been proposed25 that some of this normal slowdown is due tothe accumulation of adenosine in the spinal cord. However, synaptic mechanisms mustalso be involved because when sensory stimuli like touching the skin or dimming the lightare given during swimming, the frequency can be increased again. Furthermore, whenspinal motoneurons or interneurons are recorded during swimming, the amplitudes ofinhibitory and excitatory synaptic input are greater at higher frequencies. We thereforeasked how the strength of synaptic input could change at different frequencies if all spinalneurons only fire a single impulse on each cycle of swimming.26 The simplest proposal isthat the number of active premotor interneurons decreases as frequency falls. Sensorystimuli given during swimming recruit interneurons that have become silent; thisincreases the synaptic input and frequency rises. The proposal that frequency depends onthe number of active premotor interneurons was then tested. It was shown that premotorinterneurons drop out and stop firing as frequency falls and could be recruited by sensorystimulation. In case this result was influenced by electrode damage to the recorded neu-ron, it was also shown that some IPSPs seen during swimming become unreliable andfinally drop out as frequency falls, indicating that the inhibitory interneurons producingthem had stopped firing.

If the population of excitatory interneurons firing on each cycle of swimming plays animportant role in controlling synaptic excitation to the CPG and in this way determinesfrequency, what is the role of the motoneurons which also provide synaptic excitation tothe CPG? If this excitation is blocked pharmacologically, frequency falls.23 However, inmany hundreds of recordings from motoneurons at different positions along the body, wehave found that they fire very reliably at all frequencies of swimming. This was quiteunexpected. In most adult animals motoneurons are recruited in relation to the strengthof muscular action required.27,28 If nearly all tadpole motoneurons are active on all cyclesof swimming, then the cholinergic synaptic input to the CPG should not change with fre-quency, whereas the glutamatergic input from excitatory premotor interneurons shouldvary. We have tested this prediction by recording from motoneurons during swimmingand using very local microperfusion of antagonists over the recorded neuron to dissect outthe different excitatory synaptic inputs and see if they changed with frequency.29 Whenglutamate-mediated excitation was blocked to leave cholinergic excitation, and when allchemical excitation was blocked by cadmium to leave only the electrotonic componentcoming from other motoneurons, excitation did not change with frequency (FIG. 3).


However, after the application of nicotinic cholinergic antagonists, the remaining gluta-mate component decreased as frequency fell.

These recent results support the proposal that frequency is partly controlled by thenumber of excitatory premotor glutamatergic interneurons active on each cycle of swim-ming. We have concluded that the cholinergic component of excitation to the CPG, whichwe presume comes from the motoneurons, is stable and does not vary with frequencybecause nearly all motoneurons are active at all frequencies. This cholinergic componenttherefore appears to provide a steady baseline excitation which is present whenever thetadpole swims and may help to ensure that swimming is effective.


FIGURE 3. Effects of swimming frequency on components of excitation. The amplitudes of tonicdepolarization (TD) and EPSPs were measured for 1 s near the start of swimming. Each graph showsregression lines from separate experiments and solid lines show a significant change with frequency.(A) When DHβE was microperfused over the recorded motoneuron to block the cholinergic compo-nent, TD and EPSPs still increased with the frequency in most neurons as they did in controls. (B)When kynurenate+NBQX were microperfused to block the glutamate components, TD and EPSPs nolonger increased with frequency. Strychnine was always present to block inhibition.

SOME OBSERVATIONS AND EXPERIMENTS ON LONGITUDINAL COORDINATION

During swimming, motor activity starts at the head and then progresses towards the tail.This means that there is a rostrocaudal delay in motor output which ranges from 1.5 to 5.5ms mm-1.30 This delay is independent of the position along the body and does not usuallyscale significantly with cycle period. We have evaluated the hypothesis that the rostrocau-dal delay during swimming depends on a rostrocaudal gradient in the synaptic input thatspinal neurons receive (see refs. 31 and 32). Intracellular recordings from mns at differentlongitudinal positions showed that during swimming both excitatory and inhibitory synap-tic input to mns decreased from head to tail.33 To test whether these gradients were impor-tant for the rostrocaudal delay we manipulated the gradient pharmacologically.30 Delayswere reduced when the gradient was reduced by application of the glutamate agonistNMDA to the caudal spinal cord. Delays were increased when the gradient was increasedby the caudal application of an NMDA antagonist. The gradient in synaptic input could actby producing a gradient in oscillator frequency in more rostral regions of the nervous sys-tem, but in more caudal regions where intrinsic rhythm generation is not found, the gradi-ent must act through longitudinal synaptic coupling. We concluded that a gradient insynaptic input provides a flexible mechanism to control the longitudinal progression ofmotor activity.

Since this work on the longitudinal gradients in synaptic input was published, we havediscovered cholinergic and electrotonic components to the excitation during swimming.We have already discussed the different roles of this excitation and glutamate-mediatedexcitation during swimming. We now need to ask how cholinergic and electrotonic excita-tion are distributed longitudinally and whether they produce a rostrocaudal gradient. Thismeans that we must compare the strengths of the three excitatory synaptic inputs duringswimming, and this raises a problem of measurement which we must now address.

THE PROBLEM OF EPSP SUMMATION

If EPSPs summed linearly we could use the microperfusion of antagonists to a neuronrecorded during swimming to block each component of excitation in turn, measure thereduction in EPSP amplitude, and calculate the contribution of each component.Unfortunately, this cannot be done as EPSP summation is nonlinear;34 accordingly, wehave investigated this nonlinearity and developed methods that allow us to compare theunderlying synaptic conductances of the different components.35

The first step in understanding the summation of EPSPs was to find a suitable modelof the spinal neurons. Because the neurons are all small (8–12 µm in diameter) and haveshort dendrites (1–50 µm in length), we hoped that neurons could be modeled as a singlecompartment where the dendrites are ignored and all synapses are onto the neuron soma(FIG. 4A). By comparing responses of simulated neurons with and without dendrites, weshowed that this simplification was acceptable. Provided that EPSPs had a similar timecourse, we then found that we could ignore time and use a very simple steady-state model(FIG. 4B). This allowed us to demonstrate that the summation of small EPSPs is nonlinearbecause the opening of synaptic channels causes a significant increase in total neuron con-ductance. As EPSPs become larger the decrease in synaptic driving force becomes anequally important cause of nonlinearity (FIG. 4C). We tested our simple model by measur-ing the summation of AMPA receptor mediated EPSPs directly in tadpole motoneurons.The model allowed us to calculate the EPSP synaptic conductances relative to the resting


neuron conductance and to predict the nonlinearity of EPSP summation provided that thepotentials remained below those where voltage-dependent channels open.


FIGURE 4. The neuron model and nonlinear summation of EPSPs. (A) Synaptic inputs to a motoneu-ron whose membrane potential (Vm) is recorded with a microelectrode come via excitatory chemicalsynapses from interneurons (eIN) and other motoneurons, and via electrotonic connections to othermotoneurons. (B) Motoneuron responses at steady state can be modeled by a one-compartment elec-trical equivalent of the soma-dendrite membrane (Erest, resting membrane potential; Grest, resting neu-ron conductance) with a chemical synapse (Esyn, synaptic reversal potential; Gsyn, synapticconductance) and electrotonic coupling to its neighbors (Eel, membrane potential of the electrotoni-cally connected neurons; Gel, electrotonic junction conductance). The model circuit has a current gen-erator to model external current injections (Iinj). (C) The nonlinearity of EPSP summation due tochanges in neuron conductance and changes in driving force (triangles) is compared to linear sum-mation (circles), and summation caused only by changes in driving force (stars). The horizontal axisis the ratio of synaptic conductance to resting neuron conductance.

This study of EPSP summation in small neurons provided us with a method to estimatethe relative synaptic conductances underlying synaptic potentials recorded during swim-ming. It also suggests that in small electrotonically compact neurons, excitatory synapsesclose to the soma can increase the resting neuron conductance significantly and in this waylimit depolarization. This limit could act as a mechanism for gain control and protect neu-rons from overexcitation.

ESTIMATING THE LONGITUDINAL DISTRIBUTION OF EXCITATORY COMPONENTS

Once our EPSP summation model was established, we were able to use it to estimatethe relative conductances of the different components of synaptic input to motoneuronsduring swimming.36 We made intracellular recordings from motoneurons at a range ofpositions along the spinal cord and microperfused pharmacological antagonists onto therecorded neuron to reveal the different excitatory synaptic inputs. Strychnine was alwayspresent to block glycinergic reciprocal inhibition. DHβE was used to block nicotinicacetylcholine receptors, kynurenate as a general “glutamate” antagonist, and cadmium toblock all chemical transmission and reveal the electrotonic component (cf. ref. 22). Foreach motoneuron we measured the tonic depolarization between spikes (TD) and also thephasic EPSP underlying each spike which was detected as an inflection on the rising phaseof the spike (FIG. 2B). The mixture of DHβE and kynurenate does not block all chemicalexcitation, and a small unidentified component remains. This component was blocked bythe specific AMPA receptor antagonist NBQX, confirming our suspicion that these recep-tors mediate some of the excitation during swimming.

The measurements of synaptic potentials during swimming were then used to calculatethe relative synaptic conductances of the different excitatory components during swim-ming (FIG. 5). These calculations confirmed previous results33 and showed that there wasa rostrocaudal gradient in both the tonic depolarization and the phasic EPSP measuredbefore the application of any excitatory antagonists (FIG. 5A). They showed that the glu-tamatergic components provided most of the excitation and that these componentsdecreased caudally (FIG. 5B). In contrast to this glutamatergic excitation from premotorinterneurons, the two forms of excitation presumed to come from motoneurons remainedrelatively constant along the spinal cord and did not show a significant gradient (FIG. 5Cand D). As a consequence, these cholinergic and electrotonic components provide anincreasing proportion of the excitation to more caudal neurons.

This investigation has shown that the components of excitation that come from premo-tor interneurons and motoneurons during swimming have different distributions along thecord. We therefore proposed that the glutamatergic component is fundamental to rhythmgeneration in the brain stem and rostral spinal cord while the electrotonic and cholinergiccomponents ensure that the CPG activates motoneurons effectively in all parts of the spinalcord, some of which are incapable of independent rhythm generation.

CAN THE DISTRIBUTION OF CENTRAL MOTONEURON ANDINTERNEURON AXONS EXPLAIN THE DISTRIBUTION OF

SYNAPTIC INPUT DURING SWIMMING?

Intracellular recordings have now shown that during swimming there are rostrocaudalgradients in glycinergic inhibition and glutamatergic excitation, but not in the cholinergicand electrotonic excitation to motoneurons. We therefore asked whether these differencesresulted from different longitudinal distributions of the somata and axons of the neurons


mediating the different forms of synaptic input. Unfortunately, we do not yet have goodinformation on the whole population of premotor excitatory descending interneurons andtheir axonal projections. However, we have recently obtained this type of data forinhibitory commissural interneurons and for motoneurons.


FIGURE 5. Longitudinal distributions of the estimated conductances of the excitatory synaptic inputsto motoneurons at the peak of the EPSP (hatched bars) and for tonic depolarization (TD, open bars)during fictive swimming. Rostrocaudal gradients are present in (A) total synaptic input, and (B) glu-tamatergic input with the kynurenate-insensitive NBQX-sensitive component shown in black. Similargradients are not present in (C) cholinergic input (note that the SEM in the rostral cord was 78% sowould not fit on the graph) or in (D) electrotonic input. The strength of the total input and each of itscomponents is compared to the maximum strength of that input at the peak of the EPSP (100%).


FIGURE 6. Longitudinal distribution of motoneurons and their axons. (A) Numbers of motoneuronsbackfilled from the swimming muscles at different longitudinal positions. The line defines the esti-mated total population. (B) Lengths of central motoneuron axons as a function of longitudinal posi-tion. Solid regression line shows trend for axons to be longer caudally (dashed lines are 95%confidence limits). Inset shows diagrammatic dorsal view of a motoneuron on the left side of thespinal cord. (C) Calculated number of motoneuron axons as a function of position (with SDs) showsa broad peak in the midtrunk.

Considering the motoneurons first, we have used horseradish-peroxidase backfillingfrom the swimming muscles to map the distribution of somata and central axons (FIG. 6;A. Roberts and A. Walford, in preparation). The most rostral motoneurons lie near the levelof the vagus in the hindbrain. Their population reaches a rather broad peak in the midtrunkand then declines into the tail (FIG. 6A). Although some rostral motoneurons have a short,ascending central axon, most have a short, descending central axon which then turns toleave the spinal cord and innervate the muscles (FIG. 6B). It is from these central axonsthat motoneurons make synapses with other neurons.37 Using data on the distribution ofsomata and the lengths of central axons, we have calculated the number of motoneuronaxons at each level in the spinal cord (FIG. 6C). Because the axons are short and mainlydescending, their distribution follows that of the somata but is slightly biased towards thetail. Over the main part of the spinal cord (0.7 to 2.5 mm from the midbrain) there is norostrocaudal drop in motoneuron axon numbers, which corresponds well with the absenceof a gradient in cholinergic or electrotonic input during swimming.

The whole population of inhibitory commissural interneurons has been revealed by itsglycine-like immunoreactivity.38 This has allowed us to fit equations which define the pop-ulation as a function of longitudinal position39 (FIG. 7A). We have then used intracellularinjections of biocytin to fill individual commissural interneurons and trace their axonalprojections. The usual pattern is a ventral axon which crosses to the opposite side where itthen branches to ascend and descend; in some interneurons, however, there is no branchand the axon either ascends or descends. The axons of commissural interneurons are muchlonger than the central axons of motoneurons (FIG. 7B), so when the axon numbers werecalculated as a function of longitudinal position they were much higher (FIG. 7C). The dis-tribution of inhibitory axons is also rather different. Axon numbers peak in the rostralspinal cord and then decrease steadily into the tail, a pattern that very closely mirrors therostrocaudal decrease in midcycle reciprocal inhibition found physiologically.33

This anatomical investigation of two spinal neuron classes shows that, in a broad way,the distribution of motoneuron and commissural interneuron axons corresponds to the dis-tribution of their synaptic outputs. This is a first step toward understanding the structuralbasis for longitudinal coupling during swimming. However, it is clear that we also need bet-ter physiological data on how the strengths of synaptic connections change as a function ofdistance. At present we assume that all parts of axons are equally likely to make synapsesand that all synapses are of equal strength. The next step is to test these assumptions.

CONCLUSION

The aim of the second part of this review was to exploit the simplicity of the Xenopustadpole preparation to ask specific quantitative questions about the roles and distributionsof the different components of synaptic input to motoneurons during swimming. Our studyon the summation of excitatory synaptic potentials emphasized an important general fea-ture of small neurons. When synapses are close to the summation site, their conductancecan significantly affect the whole neuron, make summation nonlinear, and limit depolar-ization. We tried to understand the relative roles of the different forms of excitation. It waspossible that the three forms of excitation—glutamatergic, cholinergic and electrotonic—served fundamentally similar functions and were similarly distributed. We have shown thatthis is not the case. The glutamatergic excitation and glycinergic inhibition mediated bypremotor interneurons decreases from head to tail and may control the head to tail pro-gression of motor activity during swimming. The glutamatergic synaptic input alsodecreases as the frequency of swimming decreases and may be partly responsible for theactive control of frequency. In contrast, the central cholinergic and electrotonic excitationthat is thought to come from motoneurons is relatively constant along the body and does



FIGURE 7. Longitudinal distribution of commissural interneurons and their axons. (A) Numbers ofcommissural interneurons as a function of position. (B) The descending and ascending axons lengthsof a sample of 43 commissural interneurons filled with biocytin. Positive lengths are descending andregression lines show trends to shorter axons caudally. Inset shows diagrammatic dorsal view of acommissural interneuron. (C) Calculated numbers of axons as a function of longitudinal positionshowing a peak in total axons at just over 1 mm from the midbrain and then a caudal decline.

not change with frequency. Future research may show whether these features of theXenopus tadpole are only found in developing amphibians or, alternatively, are consistentgeneral features of all vertebrates.

ACKNOWLEDGMENTS

We thank the Wellcome Trust, the Medical Research Council UK, and the JapaneseMinistry of Education for supporting our work.

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Central Circuits Controlling Locomotion in Young Frog Tadpoles

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