Identiﬁed nerve cells and insect behaviorpost.queensu.ca/~locust/Publications/comer and robertson.pdf · Progress in Neurobiology 63 (2001) 409–439 Identiﬁed nerve cells and

Progress in Neurobiology 63 (2001) 409–439

Identified nerve cells and insect behavior

Christopher M. Comer a,*, R. Meldrum Robertson b

a Laboratory of Integrati6e Neuroscience, Department of Biological Sciences, Uni6ersity of Illinois at Chicago, Chicago, IL 60607, USAb Department of Biology, Queens Uni6ersity, Kingston Ont., Canada K7L 3N6

Abstract

Studies of insect identified neurons over the past 25 years have provided some of the very best data on sensorimotor integration;tracing information flow from sensory to motor networks. General principles have emerged that have increased the sophisticationwith which we now understand both sensory processing and motor control. Two overarching themes have emerged from studiesof identified sensory interneurons. First, within a species, there are profound differences in neuronal organization associated withboth the sex and the social experience of the individual. Second, single neurons exhibit some surprisingly rich examples ofcomputational sophistication in terms of (a) temporal dynamics (coding superimposed upon circadian and shorter-term rhythms),and also (b) what Kenneth Roeder called ‘neural parsimony’: that optimal information can be encoded, and complex acts ofsensorimotor coordination can be mediated, by small ensembles of cells. Insect motor systems have proven to be relativelycomplex, and so studies of their organization typically have not yielded completely defined circuits as are known from some otherinvertebrates. However, several important findings have emerged. Analysis of neuronal oscillators for rhythmic behavior havedelineated a profound influence of sensory feedback on interneuronal circuits: they are not only modulated by feedback, but maybe substantially reconfigured. Additionally, insect motor circuits provide potent examples of neuronal restructuring during anorganism’s lifetime, as well as insights on how circuits have been modified across evolutionary time. Several areas where futureadvances seem likely to occur include: molecular genetic analyses, neuroecological syntheses, and neuroinformatics — the use ofdigital resources to organize databases with information on identified nerve cells and behavior. © 2001 Elsevier Science Ltd. Allrights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4101.1. The identified neuron approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410

2. Coding sensory information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4112.1. Historical perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4112.2. Operating principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411

2.2.1. Group/individual differences in central circuitry . . . . . . . . . . . . . . . . . . . 4112.2.2. The importance of temporal dynamics . . . . . . . . . . . . . . . . . . . . . . . . 4132.2.3. The computational sophistication of individual neurons . . . . . . . . . . . . . . 4132.2.4. Coarse coding and computational mapping. . . . . . . . . . . . . . . . . . . . . . 4152.2.5. Neural parsimony and the decoding of central information . . . . . . . . . . . . 418

www.elsevier.com/locate/pneurobio

Abbre6iations: 5HT, 5-hydroxytryptamine (serotonin); AN, ascending neuron; CN cercal nerve; CNS, central nervous system; DCMD,descending contralateral movement detector; DEP, depressor; DMI, descending mechanosensory interneuron; DN, descending neuron; DUM,dorsal unpaired median; EPSP, excitatory post synaptic potential; GABA, gamma amino butyric acid; GF, giant fiber; GI, giant interneuron; dGI,dorsal giant interneuron; vGI, ventral giant interneuron; int, interneuron; STIM, stimulation; TCG, tritocerebral commissural giant; TI, thoracicinterneuron; T3, metathoracic ganglion; URL, universal resource locator; VM, ventromedial; VUM, ventral unpaired median; VCH, ventralcentrifugal horizontal; WBF, wing beat frequency; WWW, world wide web.

* Corresponding author. Tel.: +1-312-9962992; fax: +1-312-4132435.E-mail addresses: [email protected] (C.M. Comer), [email protected] (R.M. Robertson).

0301-0082/01/$ - see front matter © 2001 Elsevier Science Ltd. All rights reserved.

PII: S0301-0082(00)00051-4

C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439410

3. Patterning of motor output. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4213.1. Historical perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4213.2. Operating principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422

3.2.1. Defined circuits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4223.2.2. Organization and reconfiguration . . . . . . . . . . . . . . . . . . . . . . . . . . . 4233.2.3. Neuromodulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4253.2.4. Afferent regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426

3.3. Neuronal restructuring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4263.4. Evolution and homology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428

4. Newer approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4294.1. Molecular/genetic approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4294.2. Neuroecology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4304.3. Neuroinformatics and insect nerve cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431

5. Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432

1. Introduction

The control of insect behavior by uniquely identifiednerve cells is a vast topic, so any review of the areamust be selective. We will concentrate on cases whererelationships between identified nerve cell propertiesand behavior have been convincingly established, orwhere experimental data highlight general principles ofsensory processing or motor control. The organizationof this review thus will reflect the spirit recommendedto neuroethologists by Franz Huber (1988) when hestated, ‘‘Questions of principle should guide ourresearch.’’

Each type of organism (or model system) tends tohave advantages for answering certain types of neurobi-ological questions. For example, the crustacean stom-atogastric system has provided some of the clearestinformation on mechanisms for generating rhythmicbehavior and for the short-term reconfiguration of neu-ral circuitry (e.g. Selverston et al., 1998); while studiesof Mauthner-related startle and escape in fish haveprovided a model for the control of episodic behaviorand have bridged the conceptual space between studieson identified neurons of invertebrates and work onneural pathways in the brains of vertebrates (Eaton etal., this issue). The question then becomes, what sort ofgeneral principles have been uncovered by the past 25years of work on insects? The answer documentedbelow is that analyses of insect identified neurons haveprovided some of the very best data on sensorimotorintegration — tracing information from sensory tomotor networks — collected in such a way that it canbe related to the performance of behavior in intactorganisms.

There is, however, a most important related question:are there fundamental principles of neurobiology yet tobe uncovered for which insect neuroethology could

provide especially valuable input? The answer to thatquestion is, we believe, related primarily to the diversityof insects and the detailed information that has beenobtained about individual cells. First, there is enoughinformation about specific cells in related insect speciesthat meaningful comparative studies, and constructionof evolutionary models of neural circuitry, are possible(see also Murphy, this issue). Second, biochemical andbiophysical details are available for specific cells withknown relationships to organismal behavior; this makesrealistic studies of neural computation possible. We willhighlight material that touches on evolutionary andcomputational neurobiology, and in our conclusion wewill offer a brief guide to recently developed resourcesfor comparing and synthesizing information on iden-tified insect nerve cells; electronic databases and ana-lytic software available on the world wide web(WWW).

1.1. The identified neuron approach

The idea of a truly ‘identified’ nerve cell means onewhich can be uniquely recognized in every member of aspecies (see Kandel, 1976, for an especially lucid discus-sion of the concept). In insect research, cells have beendescribed as identified under a number of differentcircumstances; for example when they were found tooccupy a constant position in a ganglion and so couldbe assigned numbers (Cohen and Jacklet, 1967), orsometimes based purely on invariant physiologicalcharacteristics (see below). However, in insect neurobi-ology high resolution neuroanatomy is a tradition andso, typically, anatomical and physiological informationare available simultaneously. In its most compellingform, an identified nerve cell means a unique cell thathas been anatomically and physiologically character-ized, and whose input and output connections areknown (O’Shea and Rowell, 1977).

C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439 411

In some cases it has been possible only to assign anerve cell to an identifiable class. While it is tempting toinsist that such cells are not truly ‘identified’, the goalof neuroethology is simply to understand how behaviorcan be explained by the characteristics of neurons, sopracticality demands that such cells not be ignored.Indeed, in some sections of the insect brain, e.g. themushroom bodies, there are so many neurons with suchintricate connections that establishing individual identi-ties is problematic, while class identifiability may beenough to allow insights about sensory coding or motorprogramming to be made (e.g. Mizunami et al., 1993;Strausfeld et al., 1998).

It would be easier to discuss identified neurons acrossinsect species if there were a consistent and meaningfulscheme for naming or numbering cells. The lack of sucha system makes discussion of even one restricted neuralsystem cumbersome (for example, see Hennig, 1988,with reference to well-known cricket auditory interneu-rons). A three digit system proposed for locust thoraciccells (Robertson and Pearson, 1983) has gained someacceptance, and has been adapted to other species (e.g.Westin et al., 1988). Wide adoption of some guidelinesfor assigning tags to cells would also facilitate thestorage of cellular data in electronic formats (Rowell,1988) — a process which is underway already (seebelow).

2. Coding sensory information

2.1. Historical perspecti6e

Almost 35 years ago, Kenneth Roeder published thefirst edition of his monograph Nerve Cells and InsectBehavior (Roeder, 1963, second edition, 1967). In thatvolume he summarized general principles underlyingthe neural control of behavior using examples fromseveral insect species. Roeder was quite emphatic thatthe importance of insects to behavioral neurosciencewas due to what he usually termed ‘neural parsimony’:given consistently small body size, this very diversegroup manages well-integrated behavioral responseswith a relatively small number of (sometimes) largenerve cells (see especially, Roeder, 1959). His mono-graph was written just before the widespread use ofmicropipette recordings and single-cell staining tech-niques led to the accumulation of a significant body ofdata on individually identifiable neurons. AlthoughRoeder did not use the term Identified Nerve Cells, itwas embodied in his work and our chapter title repre-sents, quite intentionally, an echo from his monograph.

At the same time, Vincent Dethier published a com-prehensive summary of insect senses (Dethier, 1963).Knowledge of primary sensory cells at that time in-cluded a considerable amount of cytology, but very

little physiology. Since then, studies of individual insectneurons have played a role in elucidating mechanismsof sensory transduction, particularly related to olfac-tion and vision (e.g. Boekhoff et al., 1993; Stengl, 1994;Hardie and Minke, 1993), and in analyzing stimulusencoding and adaptation processes in receptors (e.g.Basarsky and French, 1991; Torkkeli and French,1995). In some cases, progress has been made on under-standing behavior largely with information about thefunction of sensory receptors (in particular, see Dethier,1976), but the greatest insights have come from caseswhere a group of truly identifiable interneurons thatprocess sensory information can be studied along withorganismal behavior. Therefore, we will concentrate onthe interneuron level.

2.2. Operating principles

The descriptions below are organized around severalkey concepts that seem to us the main themes that haveemerged from work on identified neurons over the pastseveral decades. They also are concepts that will beimportant foci for future work.

2.2.1. Group/indi6idual differences in central circuitryIn vertebrates, some differences in neural structure

and function have been described that represent sexualdimorphisms related to behavior. These dimorphismshave been documented in vertebrates only relativelyrecently (Breedlove, 1992). In insects there are pro-found differences in neuronal organization that areassociated with both sex and social experience.

In honeybee brains, the volume of the olfactoryglomeruli, and the volume occupied by the Kenyoncells of the mushroom bodies varies systematically de-pending on whether bees are acting as nurses or for-agers (Withers et al., 1993). While the differencebetween these two groups is normally accompanied byan age difference, the anatomical plasticity is not re-lated in any simple way to age, but rather to thedifferent roles of the individuals in the hive. Thatindividual experience is important to the configurationof this part of the insect brain is consistent with studieson Drosophila indicating that sex and social livingconditions influence mushroom body structure (Heisen-berg et al., 1995). Other reports (on crickets) haveshown that in adult mushroom bodies there is a defin-able cluster of undifferentiated cells that can give rise tonew Kenyon cells under the influence of juvenile hor-mone (Cayre et al., 1994). This provides a basis forunderstanding plasticity in adult insect behavior, espe-cially that related to sex-specific response patterns.

There is ample information about sexual dimorphismin olfactory processing circuits. Insects respond behav-iorally to both plant volatiles and pheromone signalsfrom conspecifics. In fact, it seems that greater progress


has been made in understanding the sexually dimorphicparts of the CNS related to processing pheromonesthan to more generalized parts of the olfactory system.In a number of species, specific glomeruli within theantennal lobe are individually identifiable, and there isa male-specific glomerulus for processing informationabout female attractants (Rospars, 1983; see review inHomberg et al., 1989). (Subsequently, it has been re-ported that individual glomeruli can be uniquely iden-tified in at least one vertebrate (Baier and Korschung,1994), but the sexual dimorphisms found in insectolfaction have yet to be reported in vertebrates.) In-terneurons of insect antennal lobes (and those project-ing into the protocerebrum) are also sexually dimorphic(Matsumoto and Hildebrand, 1981; Burrows et al.,1982; Boeckh and Ernst, 1987).

Another place where sexually dimorphic nerve cellshave been reported is the visual system of flies. In bothblowflies and houseflies, males possess some identifiablevisual interneurons which are not present in females,and some visual interneurons that are found in bothsexes show structural differences between them (Straus-feld, 1980, 1991; see Fig. 1). The particular differencessuggested that the neurons of males selectively processvisual motion information from a region of the retinalarray related to binocular visual space and are impor-tant for precision aerial pursuit of a potential mate (amale-specific behavior). This prediction from anatomi-cal work has been borne-out in subsequent studies withphysiological recording (Gilbert and Strausfeld, 1991),and it has led to a demonstration that the appropriatevisual information is passed to neck and wing muscula-

Fig. 1. Sexual dimorphism at the gross and cellular level of the Calliphorid visual system. Top panel shows a frontal view of the head of male(left) and female (right) Calliphora erythrocephalia. Dotted line encloses area of the male-specific ‘acute’ zone which represents that area of visualspace where males detect and follow females. Bottom panel is a drawing of a golgi-impregnated, male-specific interneuron with dendrite positionedin the lobula so as to receive input from the acute zone. Inset at bottom left shows approximate viewing angle for visual afference to the cell. Forscale, diameter of soma is approximately 25 um. Taken with permission from Strausfeld (1991).


ture by a cluster of individually identifiable premotorinterneurons (Gronenberg and Strausfeld, 1991).

Thus there is abundant documentation that sexualand other group differences are significant both at thelevel of the neuropil and tract organization of the CNS,but also at the level of individual neurons — andespecially in olfactory, visual, and association areas(mushroom bodies). Such differences are profound inthe sense that they underlie fundamental differences insensory capacity between females and males, and theyare an important context within which future informa-tion about identified neurons and circuits must beevaluated.

2.2.2. The importance of temporal dynamicsRhythms are important to neural function on both

the sensory and motor sides of the CNS. Rhythmsrelated to identifiable cells have now been reported inseveral sensory systems and they occur across timescales ranging from milliseconds to hours. For example,far toward the visual periphery, the numbers ofsynapses between photoreceptors and optic interneu-rons, as well as the diameter of interneuron processes,have been reported to undergo daily fluctuations in flies(summary in Meinhertzhagen and Pyza, 1996). Theseare truly circadian changes that persist when animalsare brought to constant lighting conditions. At a muchfiner time scale, the olfactory system processes sensoryinformation within the context of rhythms.

In locusts, olfactory interneurons that project fromthe antennal lobe (the initial CNS relay for olfaction) tothe mushroom bodies encode information on odorantidentity. Each interneuron responds with impulses toseveral specific odors or components of odor mixtures,and for each given odor a slightly different subset of theinterneuron population responds (Laurent and David-owitz, 1994). The activity of all interneurons respond-ing to any odor are spread out in time and aresuperimposed on subthreshold electrical oscillations oc-curing throughout the cell population at 20–30 Hz.Some cells might respond early in the stimulus-inducedoscillations, others late, or others at various odorant-characteristic times. Thus the neuronal signature of anyodorant is a time-varying ensemble code that is charac-terized by oscillatory synchronization of key interneu-rons (Laurent et al., 1996). In recent work withhoneybees it has been shown that picrotoxin, whichdoes not alter the odorant specific response of projec-tion neurons but which disrupts oscillatory synchro-nization, abolishes behavioral discriminations that beesmake between chemically similar compounds withoutabolishing discriminations for dissimilar odorants(Stopfer et al., 1997). This interesting study implicatesoscillatory synchrony in fine sensory discriminations.

The dynamically coded information of the projectionneurons ultimately becomes represented as sequences of

(small numbers of) activated Kenyon cells within thevery large population of Kenyon cells found in themushroom bodies. The Kenyon cell population hasbeen shown in flies to be necessary for odor basedlearning (e.g. deBelle and Heisenberg, 1994). It is cur-rently not known why information about odorantsmust be encoded dynamically in time, but presumably ithas to do with some fundamental aspects of olfactoryrecognition and learning — and this strategy may be ageneral one (see discussion in Laurent, 1996).

Finally, the olfactory system is a place where cellsmay show particularly long-lasting temporal variationsin activity patterns. In moths, pheromone related neu-ronal activity passes through the antennal lobes andmushroom bodies and ultimately activates brain cellswith axons descending toward thoracic motor centers.These interneurons often produce trains of impulseswhich can outlast stimulus application by tens of sec-onds to minutes (Olberg, 1983; Kanzaki et al., 1991). Inaddition, some uniquely identifiable cells show state-de-pendent changes in activity: if they have little activitywhen a stimulus is applied, they markedly increase theirrate of impulse production; but if an identical stimulusis applied when they are already active, they show astimulus related drop in impulse activity (Olberg, 1983;Kanzaki et al., 1994; see Fig. 2, top). This state depen-dency has been called ‘flip-flopping’ — because suchcells have been observed to change firing state sponta-neously. There is some evidence that these changes infiring state correlate with the points at which animalschange direction as they zig-zag up an odor plume(either walking or flying) toward the source ofpheromone (e.g. Olberg, 1983; see Fig. 2, bottom).Consistent with the idea that such descending interneu-rons contain higher-level information to guide flight,they often are multimodal and respond to visual ormechanosensory input in addition to olfactory cues (seeany of the papers cited above).

2.2.3. The computational sophistication of indi6idualneurons

A considerable amount of research has been con-ducted on processing visual input and the relay ofvisual and mechanosensory information toward motorcenters. Such work has highlighted the computationalpower that is present at the level of individual nervecells. Sensory systems typically display ‘subsystems’ forprocessing different stimulus parameters that are bio-logically important. Thus, information derived from theinsect retina is processed by separate interneuronalgroups for extraction of such features as color andmovement. Identified interneurons of flies have recentlyprovided clear examples of how both intrinsic biophysi-cal and synaptic features contribute to motion detectionsubsystems in vision (see Egelhaaf and Borst, 1993, fora digestible review). This analysis is particularly satisfy-


Fig. 2. Temporal dynamics of olfactory interneurons and moth behavior. Top panel displays state dependent response of a single descendinginterneuron of Manduca sexta (recorded from the axon in the cervical connective) to puffs of pheromone blend on the ipsilateral antenna. In trace(a) stimulus caused excitation of the cell from low background activity. Trace (b) demonstrates that the same stimulus caused the cell to decreasefiring if it was already highly active. Scale bar 1s, 40 mV. Bottom panel shows activity profile of descending interneuron from Bombyx mori inrelation to turning behavior. Marker pulses at bottom indicate when puffs of pheromone were applied to one antenna, ipsilateral or contralateralas indicated. Spike frequency diagram above shows that this descending interneuron fired at a high rate for ipsilateral stimulation and a low ratefor contralateral stimulation. The firing state of the interneuron correlates with antennal position as shown in the schematics, and also with turningbehavior (i.e. the insect turns toward the side with the lower antenna). Bombyx males walk toward a source of female pheromone. Scale bar 10s, 20 spikes per s. Taken with permission from Kanzaki et al. (1991) (top panel) and Olberg (1983) (bottom panel).

ing since there is a long history of behavioral analysisof fly vision which led to some of the first formalmodels in neural computation (see Reichardt, 1987),and these are now being given cellular substance.

A set of identified cells, referred to generally astangential cells, collects information from localretinotopic movement detectors and generates signalsthat seem to be used in visual orientation tasks. Opto-motor stabilization is an orientation response that de-pends on cells tuned to wide field motion, but thistuning carries with it an ‘automatic gain control’ sothat the spatial tuning of the cells is sensitive to imagevelocity. This feature of tangential cell physiology cannow be explained quantitatively as part of interactionsbetween excitatory and inhibitory inputs to the den-drites of specific tangential cells that are the basis fordirection selectivity (Borst and Egelhaaf, 1990; Kondohet al., 1995; Single et al., 1997). Target orientation is aseparate response that requires different interneuroncharacteristics — tuning for small moving objects.Tangential cells displaying this feature achieve it bysynaptic interactions with other tangential cells (specifi-cally a type which uses GABA to reduce responses tocoherent large field motion-see Fig. 3) (Warzecha et al.,1993; Egelhaaf et al., 1993). For both of these computa-tions, the fly visual pathway offers the possibility ofdirectly observing where and how they happen. Manyof the interneurons can be observed with intracellular

recording electrodes, and Ca++ sensitive indicators;and they can be removed selectively from the circuit byphotoinactivation (Warzecha et al., 1993; Egelhaaf etal., 1993; Egelhaaf and Borst, 1995; Borst, 1996; andsee Fig. 3). With so much information about onefunctional subset of sensory interneurons, it will bepossible to answer questions about the rationale for thecircuit’s design (e.g. see Bialek et al., 1991; Haag andBorst, 1997).

The visual motion information extracted from theretinal array ultimately is relayed to motor centers —primarily, but not exclusively, at thoracic levels — forguidance of locomotion and other behaviors. Large-cal-iber interneurons performing this function were actuallythe first sensory nerve cells studied as individuals (seeRowell, 1971 for a review), long before afferent net-works in the distal optic neuropils of the brain wereexplored physiologically. Now that a number of majordescending visual interneurons have been identified andcharacterized, a feature that is common among them isapparent: they typically display parallel encoding ofinformation from several receptor types. For example,studies on dragonflies (Olberg, 1981), several dipteranspecies (Bacon and Strausfeld, 1986), and locusts (O’S-hea et al., 1974; O’Shea and Rowell, 1977; and seebelow) have described individual interneurons thatcombine visual afference with mechanosensory signalsfrom various hairs and proprioceptors on the head, or


Fig. 3. Dendritic contributions to the computation of visual motion in the fly, Calliphora erythrocephala. Bottom diagram shows the majorbranches of a VCH-cell in the right half of the brain. VCH is believed to provide inhibitory input to other tangential cells so as to give rise totuning for small object motion. The main arborization on the right is located in the ventral part of the lobula plate, the small arbor on the leftis in the ventrolateral brain. The cell was excited under two different stimulus conditions either by front-to-back motion in the ipsilateral visualfield or back-to-front motion in the contralateral field, after the cell had been injected iontophoretically with the Ca2+ indicator fura-2 [illustratedschematically, top panel]. Color images in middle panels (128×100 pixels; 0.1 s exposure time) indicate the relative change in fluorescence (DF/F,without background subtraction) at 380-nm excitation wavelength induced by the two types of motion stimuli 7.5 s after stimulus onset. Negativechanges in fluorescence indicate an increase in Ca2+ concentration. Ipsilateral motion leads to Ca2+ accumulation only in the lobula plate arbor.In contrast, contralateral motion leads to Ca2+ accumulation in both arbors in the lobula plate and ventrolateral brain. Labelled arbors are likelyto be the postsynaptic sites of the corresponding ipsi and contralateral input elements. Taken with permission from Egelhaaf et al. (1993).

associated with the antennae. It is clear that this paral-lel encoding of visual and mechanosensory informationmakes sense for efficient guidance of flight.

At least two significant themes emerged from thework on descending visual-multimodal interneurons;one illustrates a concept of sensory organization at thecellular level better than almost any other example, andthe other raises a cautionary note. First, a group ofcells in locust, the ‘tritocerebral commissural giant’(TCG) and three descending neurons (DNs), combinewind and visual information in behaviorally coherentways (Bacon and Tyrer, 1978; Griss and Rowell, 1986;Rowell and Reichert, 1986). They clearly detect not justvisual motion, or wind across the head, but rathermeaningful combinations of directional wind and visualafference that would be associated with various types ofcourse deviations during flight (see Reichert, 1989).This phasic feedback information is then interfacedwith the flight motor to correct perturbations. As such,these identified interneurons provide one of the clearestand most elegant examples of single cells as featuredetectors. Second, it is clear that large volumes ofphysiological data do not always translate into a satis-fying understanding of a cell’s role in natural behavior.The ‘descending contralateral movement detector’

(DCMD) of locusts is probably the most-studied of anyidentified neuron. Its response to visual input was ex-tensively described from extracellular and then fromintracellular recordings, and it was described as re-sponding to small novel objects entering a wide area ofthe visual field — presumably to arouse the animal, ortrigger startle behavior such as a jump (e.g. citationsabove, or Pearson and O’Shea, 1984). However, inmore recent work, it has become clear that DCMD isnot necessarily tuned to small moving objects, butrather to objects expanding in the visual field that areon a collision course (Rind and Simmons, 1992; Sim-mons and Rind, 1992; Judge and Rind, 1997). Thissuggests that DCMD may be involved in crash-avoid-ance in flying locusts (e.g. Robertson and Reye, 1992;Gray and Robertson, 1997b) or triggering escape instationary locusts (e.g. Holmqvist and Srinivasan, 1991,for flies), but the humbling truth is that its behavioralrole is still not fully established.

2.2.4. Coarse coding and computational mappingMany insects possess a wind-sensory system that

maps the world at several levels of the CNS. Its generalfunction — localization of predators by way of windcues — has been appreciated for several decades (e.g.


Roeder, 1963), but the details of its sensorimotor trans-actions have been understood only recently. The cerciare paired sensory appendages found on the abdomenof insects from more than 10 different orders (Edwardsand Palka, 1991). The types of cercal receptors vary byspecies, but most possess at least two types ofmechanoreceptors: filiform hairs that detect wind ornear-field acoustic signals (e.g. Edwards and Reddy,1986; Boyan and Ball, 1990), and bristle hairs that aretouch-sensory (e.g. Murphey, 1985). The numerous fili-form hairs (e.g. about 200–2000 per adult cercus, de-pending on species) are each associated with a primarysensory neuron that is individually identifiable. Thiswas initially obvious in cockroach where the hairs lineup along the cercus in ordered columns (Nicklaus,1965; Dagan and Camhi, 1979), but it was recentlyverified to be true in cricket (Landolfa and Jacobs,1995).

The issues that have been raised about sensory cod-ing by cercal systems are seen dramatically in firstinstar cockroach nymphs. They possess only two fili-form hairs per cercus. So the best directional informa-tion that the set of sensory receptors can display in theCNS is defined by the spatial tuning of the four hairafferents (Dagan and Volman, 1982). Basically, each ofthe hairs responds mostly to wind puffs from one of thefour quadrants around the animal (Fig. 4). Althoughthese nymphs possess fewer receptors than adults (bytwo orders of magnitude) they display behavioral re-sponses to wind that are at least, as well directed asthose of adults (Dagan and Volman, 1982). Obviously,this system displays ‘coarse coding’ — the overlap of arelatively few broadly tuned receptive fields — that isnot inconsistent with accurate spatial resolution at thebehavioral level. The usage of this sort of cercal infor-mation in adults involves keeping track of many moreprimary afferents and interneurons.

The axons of the receptor cells associated with eachfiliform hair enter the terminal ganglion, where theyproject to a neuropil, the cercal glomerulus. The partic-ular region of neuropil where a filiform afferent termi-nates in the adult cricket is based largely upon thedirection of wind to which it is sensitive (Bacon andMurphey, 1984). High-resolution analysis of the terri-tory occupied by uniquely identified cercal afferentprojections has shown that the sensory field around theanimal is represented as a map of wind directions alonga spiral shaped contour within the cercal glomerulus(Jacobs and Theunissen, 1996; and see Fig. 5). This isan elegant example of a sensory pathway displayinginformation not by way of topographic projection (po-sition on the cercus does not strictly determine whereafferents project), but rather by functional criteria toproduce a ‘computational map’. The widespread natureof computational maps has been recognized (Knudsenet al., 1987).

Fig. 4. Identifiable filiform hair receptors and the encoding of direc-tional wind-sensory information. Data are from first instar nymphs ofthe cockroach Periplaneta americana. Top (A): scanning electronmicrograph shows a ventro-caudal view of the abdomen; arrowspoint to the two filiform hairs (‘lateral’ and ‘medial’) on the rightcercus. Scale bar is 200 mm. Bottom [B]: polar plots give the averagefrequency of action potentials evoked in the sensory neuron associ-ated with each of the four hair receptors as wind was delivered fromdifferent angles in the horizontal plane. Medial and lateral hairreceptor response fields are indicated by dotted and solid lines asindicated. For scale, maximal response of the medial hair receptors tostandard wind puffs was approximately 300 impulses per sec. Takenwith permission from Dagan and Volman (1982).

Extraction of information from the cercal map ofwind-sensory space is performed by interneurons withcell bodies in the terminal ganglion. These cells havedendritic arborizations within the cercal glomerulus,and large caliber axons ascending the nerve cord. Theaxons of these second order cells have the largest


Fig. 5. Complexity of wind afferent map in adult crickets, and the directional information extracted by ascending ‘giant’ interneurons (GIs). Toppanel (A) displays directional information encoded by two pairs of GIs. Polar plot on right gives response of Left and Right GIs 10–3a (dottedline) and left and right GIs 10–2a (solid line) to winds from different directions in the horizontal plane around the animal. As shown by schematicillustration at left, distance from the origin is equivalent to spike rate such that the maximal spike rate for each cell is scaled as a value of 1.0and is at perimeter (for example, maximum average spike rate for 10–2a was 20 spikes per s). Bottom (panel B): outline of terminal abdominalganglion as seen from a dorsal view. Cubic outline encloses the cercal glomerulus. Clouds of color within the glomerular region represent theprobability density functions for the terminal varicosities of identified primary wind-afferent fibers. The color of each density cloud indicates itsdirectional tuning with respect to the animal’s body axis (key to directions is shown by inset color wheel, yellow indicates stimuli directed towardanimal’s head. Afferents from each cercus form a continuous hemi-map on one side of the ganglion and the hemi-maps are mirror images of eachother across the midline. Bottom (panel C) shows somata and axons of the identified GIs as seen in dorsal view of terminal ganglion. For clarity,only GIs on one side of the nerve cord are shown. 8, 9=nerve roots; CN, cercal nerve; for scale, soma of 9–3a is approximately 50 um indiameter. Panel A adapted from Miller et al. (1991), Panel B taken from Jacobs and Theunissen (1996), Panel C adapted from Jacobs andMurphey (1987); all with permission.

caliber in the CNS; Therefore, they are often called‘giant interneurons’ (GIs), and they rapidly conductimpulses from cercal receptors to motor cells located inthe thoracic ganglia. A reconstruction of the set ofidentified GIs in adult cricket is displayed in Fig. 5(cockroaches and other orthopteroid species have simi-lar sets of GIs but the exact number varies in each andthe naming schemes differ). The connectivity betweenspecific GIs and cercal afferents has been analyzed inseveral species (e.g. cricket — Bacon and Murphey,1984; cockroach — Daley and Camhi, 1988; Hamon etal., 1994; locust — Boyan and Ball, 1989). While

details are beyond the scope of this chapter, the neteffect of cercal afferent input is to elaborate wind-sen-sory receptive fields, with most of the GIs respondingto winds only from certain directions around the ani-mal (cockroach - Westin et al., 1977; cricket — Tobiasand Murphey, 1979). How then is information aboutwind location represented as it is sent to the thoraciclevel (closer to the motor system)?

A recent, careful analysis of information coding bycricket GIs has revealed an interesting regularity totheir receptive field organization (Miller et al., 1991). Asubgroup of cricket GIs that responds to very low


velocity winds consists of two bilateral pairs of in-terneurons. As a group, these four cells have remark-ably similar shaped receptive fields, and their points ofpeak sensitivity are uniformly distributed at 90° inter-vals around the horizontal plane. Thus, they monitorwind from each of the four quadrants around theanimal (Fig. 5), and provide spatial sensory informa-tion by way of coarse coding (Theunissen and Miller,1991) not dissimilar to that which was seen in the firstinstar nymph. There are other GIs (and ‘non-giants’ —Baba et al., 1991) that respond to wind in differentvelocity and/or acceleration ranges, so the adult systemcovers the full wind environment by having severalsubsets of cells that fractionate the spectrum of windvelocity (or acceleration) each coding basic directionalinformation (Shimozawa and Kanou, 1984; Miller etal., 1991). A recent re-analysis of GI wind directionalityin cockroach (Kolton and Camhi, 1995) has shown thateach of two GI subsets (3 pairs of ‘dorsal’ GIs and 3 or4 ‘ventral’ GIs) carries coarsely coded, parallel informa-tion about wind stimulus direction. One reason whythere may be several subsets of GIs coding parallelspatial information is, as suggested above, to fraction-ate the spectrum of wind velocities or accelerations.However, there may be other reasons.

In both cockroach and cricket the GI subgroupscorrelate with anatomy; GIs axons that ascend withinventral tracts of the nerve cord (vGIs) generally arelarger in diameter than those that ascend in dorsaltracts (dGIs). Therefore, considering that reaction timeis crucial for escape from predators, Camhi and Nolen(1981) proposed that an escape response triggered bywind is controlled in a sequential manner; initially byactivity in the vGIs, and only subsequently by the moreslowly conducting dGIs. Another possibility, is thatcontrol by the two GI subgroups differs dependingupon locomotor status. In both cockroach (Daley andDelcomyn, 1980) and cricket (Kohstall-Schnell andGras, 1994) the vGIs are inhibited during walking(especially during fast walking), whereas dGI respon-siveness to wind is enhanced. Therefore, it may be thatvGIs are important for escape in an animal that isstanding or walking very slowly, but that the dGIs area channel for evasive responses in an animal that isalready running. Consistent with this idea, vGIs havebeen shown to be inhibited during flight, but dGIsretain sensitivity to wind puffs during flight, and dis-play appropriate motor outputs to participate in eva-sive responses during flight (Ritzmann et al., 1982;Libersat, 1992; Ganihar et al., 1994). To arrive at abetter understanding of why there may be multipledisplays of sensory information within the GI system,as well as other basic questions, comparative studies ofnymphal and adult interneurons might be especiallyvaluable (both nymphs and adults walk, but only adultsfly).

2.2.5. Neural parsimony and the decoding of centralinformation

In order for sensory information to become overtbehavior, it must be delivered to a motor system. It isclear that in many cases — especially those studied sofar in vertebrates — this requires decoding of informa-tion that is distributed across a large population ofsensory interneurons. The computational mechanismsby which this occurs are not yet entirely clear (e.g.Georgopoulos, 1990; Groh et al., 1997). In insects thesesorts of computations involve especially tractable num-bers of interneurons. Two systems where identifiedinterneurons are known to carry crucial informationthat directs motor activity are found in the auditorysystem of crickets and the wind and somatosensorysystems of cockroaches.

The auditory system may demonstrate the greatestneural parsimony of central elements controlling behav-ior. Acoustic orientation by crickets is a complex be-havior and it is species specific (Huber et al., 1989;Boyan, 1993 are recommended sources of informationon cricket auditory behavior and comparative auditoryorganization). Orientation requires that an animal iden-tify and localize a sound source. The relationship be-tween these two processes is controversial and maydiffer across the various insect species that use acousticcommunication (e.g. crickets — Pollack, 1986; Stabelet al., 1989; Doherty, 1991; grasshoppers von Helversenand von Helversen, 1995). We will restrict our summaryto the processes of sound localization.

When crickets detect a sound, they face the task ofchoosing a direction in which to orient: if an acousticsignal is of low frequency (say 2–15 kHz, and withappropriate temporal structure for the song of a con-specific) a cricket usually will turn toward the signalsource. The decision about where to turn may follow analgorithm as simple as ‘turn to the side of the ear morestrongly stimulated’ (e.g. Horseman and Huber, 1994a).This computation is probably made by cells in thebrain, based on signals received from several key in-terneurons that send information to the brain fromthoracic auditory areas. In particular, paired ascendinginterneurons (usually referred to as AN1) are tuned toappropriately low frequencies, and faithfully copy thetemporal pattern of a calling song (see Schildberger etal., 1989). When sound comes from one side, the AN1on that side is more strongly activated than its con-tralateral homologue and the animal turns toward thesound source (the side of the more active AN1). If anAN1of the cricket Gryllus is hyperpolarized while songis delivered, animals reverse direction and turn awayfrom the sound source (toward the side of the originallyless-active AN1) (Schildberger and Horner, 1988). Inthe cricket Acheta, animals with the equivalent cellunilaterally photoablated have difficulty reaching anattractive sound source (Atkins et al., 1984); and ani-


mals engage in phonotactic circling when the thresholdof this interneuron is lowered unilaterally by applica-tion of hormone (Stout et al., 1991).

These sorts of data suggest that the relative level ofactivity in the left vs. right AN1 determines turningdirection. A second pair of cells, usually referred to asAN2, may also contribute, but seem to be less crucialfor localizing calling song than the AN1 pair (seeHorseman and Huber, 1994a,b). The AN2 cells mayalso be involved in predator evasion (see below).Nonetheless, positive phonotaxis can be modeled effec-tively by assuming a bilateral comparator in the brainthat needs input from only one or two pairs of acousticinterneurons with broadly tuned spatial receptive fields(Huber et al., 1984; Pollack, 1986; Stabel et al., 1989;Horseman and Huber, 1994b). If some temporal filter-ing functions are added, (their exact placement is notagreed upon) then this simple comparator might ex-plain the orienting component of important cricketbehaviors such as mate finding, courtship, and inter-male aggression.

When presented with high frequency sounds (say20–80 Khz) crickets more typically turn away from asound source. This has most often been studied inflying insects and seems to be the mechanism by whichpredators emitting ultrasound (bats) are evaded. Whilein flight mode, tethered crickets respond to a source ofultrasound by producing motor responses that createcontraversive turns (Moiseff et al. 1978). A pair ofidentified interneurons (called int-1 in Teleogryllus, andprobably equivalent to AN2 in Gryllus) have beenlinked with evasive acoustic behavior. Once again, theorigin of turning ‘commands’ is probably the brain,based upon inputs received from the pair of ascendingAN2/int-1 cells. Evasive turning can be initiated bystimulating one member of the interneuron pair electri-cally (the animal turns away from the active int-1), andevasive turning from ultrasound can be blocked if thiscell is inactivated (Nolen and Hoy, 1984). A bilateralcomparator might also explain the orientation of thisnegative phonotactic response (see Hoy et al., 1989, fordiscussion of a general circuit model). Comparativestudies have provided insights here, and may be usefulfor work in the future aimed at understanding thesensorimotor circuitry. For example, mantids contain apair of identified ultrasound sensitive interneurons, butthey receive input from a midline ‘cyclopean’ ear, andare non-directional (Yager and Hoy, 1989). Evasiveresponses of flying mantids do not display directionality(Yager et al., 1990).

The escape system of cockroaches shows some for-mal similarities to the systems for phonotactic orienta-tion in crickets, but displays control by a larger set ofsensory interneurons, and shows multimodal control byat least two sensory systems. When a wind puff isdirected at a stationary cockroach (Camhi and Tom,

Fig. 6. Thoracic circuitry identified in the cockroach for convertingGI activity into leg movements that orient the escape response. Eachneuronal component in this schematic summary represents a popula-tion of bilaterally paired neurons, many of which have been identifiedas individuals. For simplicity, only vGIs and TIs with axons on theright side of the nerve cord are shown. The expanded image of themetathoracic ganglion (T3) shows details of some established connec-tions. TI, thoracic interneuron (those receiving input from ventralGIs-as shown-are referred to as ‘type A’: these TIs have ventromedial(VM) branches, where connections from GIs appear to be made).Taken from Ritzmann and Pollack (1990) with permission.

1978) or a cricket (Gras et al., 1994; Tauber andCamhi, 1995) the animal turns away from the directionof the incident wind, and then runs (crickets also mayjump, and generally display a greater diversity of re-sponses to wind than cockroaches — Baba and Shi-mozawa, 1997). Abundant evidence links turningbehavior with the wind-sensory GIs that ascend withinthe nerve cord (see above) to activate premotor cells inthe thoracic ganglia. In particular, the vGI subset ofcockroach activates identified premotor interneuronswhich can support the leg movements that underlieturning (Ritzmann, 1981; Ritzmann and Pollack 1986;Ritzmann and Pollack, 1990; Figs. 6 and 7 for anexample of wind-evoked escape behavior). Further-more, specific enzymatic deletions of vGIs can cause thedirection of escape turns to be altered, indicating that itis specifically the readout of vGI wind sensory informa-tion that is used to orient escape turns (Comer, 1985;Comer and Dowd, 1993).

The vGI subset consists of four bilaterally pairedinterneurons. It is clear that the laterality of a wind puffis represented by the laterality of GI impulse activity inthe CNS (e.g. Westin et al., 1977; Smith et al., 1991).Wind from one side of the animal activates the vGIs onthe ipsilateral side of the nerve cord more strongly, andalso at a shorter latency, than those on the contralateralside. The algorithm for choosing a turn direction maybe as simple as ‘turn away from the side on which thevGIs display more wind-evoked activity’ (for example,see Dowd and Comer, 1988; Camhi and Levy, 1989).Consistent with this notion, specific unilateral vGI le-sions can cause animals to turn inappropriately towardwinds delivered from the same side as the lesion (see


Comer and Dowd, 1993). More specifically, experimen-tal tests with selective GI stimulation have shown thattiming (relative latency) of GI activity cannot explainturn direction, but relative levels of spiking in left vs.right GIs can explain it (Liebenthal et al., 1994). It isfar less clear how a specific turn angle is chosen,although it may depend upon the relative level ofactivity among the GIs with differing directional sensi-tivities (e.g. Levi and Camhi, 1994).

The thoracic motor circuitry of cockroaches operatesas if it compares not only the relative level of activity inthe bilateral pairs of GIs, but also in other interneuronsystems. When the GIs are completely removed, escapecan be elicited via tactile sensory pathways that descendto thoracic ganglia from the antennae (Comer et al.,1988; Burdohan and Comer, 1990; Stierle et al., 1994).This is not a question of functional substitution; rather,this one particular motor response is normally under

Fig. 7. Multiple GI systems can explain initiation of directed escape movements by cockroaches in response to multiple sensory modalities andtypes of predators. Panels on left show reconstructions of escape responses from videotapes, Panel on right shows two identified ‘giant’interneurons that are involved in the control of these types of responses. (A) Escape triggered by the attack of a wolf spider (red). Spider’s positionprior to movement is labeled 0, front of spider’s body and right foreleg are shown on frames 4 and 8. Cockroach (black) began turning betweenframes 8 and 9 subsequent to antennal contact. (B) Escape triggered by strike of a marine toad (green). Toad’s initial position is labeled 0, frontof toad’s body and position of tongue (dotted outline) are shown on subsequent frames as noted. Cockroach (black) began moving between frames4 and 5 prior to contact and tongue extension. Scale bar=2 cm and it applies to panels A and B. (C) Reconstructions of two cells injected withcobalt hexamine. Descending mechanosensory interneuron (right DMIa-1; red) responds to antennal contact. Its activity, and that of other DMIs,is correlated with direction and angle of escape turns evoked by antennal contact (such as A; see Ye and Comer, 1996). DMIs have the largestcaliber descending axons in the nerve cord, and are similar in size to the classic giants. Ascending giant interneuron (left GI-1; green) respondsto wind. Its activity, and that of other ventral GIs, is necessary for properly oriented escape turns to wind stimuli (such as B, see for exampleLiebenthal et al., 1994; Comer and Dowd, 1993). Scale bar=100 um. Panel A & B taken from Comer et al. (1994), panel C from Comer andDowd (1993).


multisensory control. It is now known that there is alarger somatosensory system involved in eliciting escape(Comer et al., 1988; Ritzmann et al., 1991; Comer etal., 1994; Pollack et al., 1995). Studies with real preda-tors indicate that under normal circumstances the wind-sensory pathway for escape is important to detection oflarge predators that generate wind cues by their move-ments (Camhi et al., 1978). However, the antennaltouch-sensory system is particularly important for de-tecting smaller predators — such as spiders and otherinsects — that are able to approach cockroachesclosely (Comer et al., 1994; Fig. 7). The system fortouch evoked escape displays at least some of thecellular principles that have already been defined forthe wind-sensory system.

Two bilateral pairs of interneurons carrying antennaltouch-sensory information from the head ganglia to thethoracic ganglia have been identified (Burdohan andComer, 1990, 1996) and they have been called Descend-ing Mechanosensory Interneurons (DMIs). Togetherthey possess the largest diameter axons in the cervicalconnectives, making them, in effect, descending ‘giants’(Fig. 7). Touching one antenna activates the DMIs onthe contralateral side of the nerve cord more strongly,and also at a shorter latency, than those on the ipsilat-eral side. So an algorithm that might explain touchevoked escape would be ‘turn toward the side of theDMIs with more, or earlier, touch-evoked neural activ-ity’. Section of a cervical connective to block thissystem unilaterally causes animals to turn typicallytoward, rather than away from, abrupt stimuli touchingthe antenna contralateral to the lesion (Comer et al.,1994) — supporting the idea that the relative level ofDMI activity might determine turn direction. This no-tion has been tested more directly, since the DMIs canbe recorded in behaving animals during the perfor-mance of escape turns (Ye and Comer, 1996). Theserecordings have revealed that the relative number ofimpulses in the DMIs on each side of the CNS, nottheir relative timing, is correlated with the direction,and specific angle, of escape turns evoked by touchingan antenna (Ye and Comer, 1996). This makes a bilat-eral comparator model particularly suitable for explain-ing DMI control of escape.

While it is clear that the thoracic premotor interneu-rons previously shown to receive wind-sensory vGIinput also receive some tactile sensory input (Ritzmannet al., 1991; Ritzmann and Pollack, 1994), the interneu-rons providing that input have not yet been determined.If the DMIs and GIs converge upon the exact samethoracic premotor cells, then there will be some inter-esting issues raised about coordination between tactileand wind-sensory control of behavior; The DMI path-way represents antennal touch information with a con-tralateral bias, and animals turn ipsiversively withrespect to the most active DMIs; however the GI

pathway displays an ipsilateral bias and animals turncontraversively with respect to the side of the moreactive GIs (see Comer et al., 1994). Thus if there is onereadout network for both the ascending (GI) and de-scending (DMI) system, then it must switch its operat-ing logic depending upon which input it is handling.This remains to be determined, but the DMIs and GIsshould provide some interesting tests of movementcontrol by very small populations of sensory interneu-rons and of principles underlying multisensoryintegration.

3. Patterning of motor output

3.1. Historical perspecti6e

The investigation of the role of identified neurons ingenerating the motor patterns that underlie behavior ofinsects has a history at least as long as that of thegeneral field of the neuronal control of behavior. Thishistory has been recounted, at least in part, in severalreviews (e.g. Hoyle, 1983; Robertson, 1987b; see alsoBurrows, 1996) and is not the major focus of thecurrent chapter. Suffice it to say that soon after thedevelopment of intracellular microelectrode techniques,recordings were taken from insect motor neurons (e.g.Hagiwara and Watanabe, 1956) and some of the firstintracellular recordings taken from neurons during ex-pression of behavior in any organism were made fromcricket neurons during singing and the generation offlight-like rhythms (Bentley, 1969a,b).

The original difficulty with this approach was theproblem of identification which was only truly solvedwith the development and sophistication of intracellularstaining techniques (e.g. Pitman et al., 1972; Stewart,1978). Nevertheless, neurons can be identified by theirphysiological characteristics alone (e.g. much of thework on the stomatogastric system of lobsters, Selver-ston et al., 1998). In insects this approach limits theinvestigator, for the most part, to intracellular studiesof motor neurons which can be identified according tothe muscle they innervate; an identification made easierwith invertebrates because of the low number of motorneurons innervating each muscle. Significant in thisregard was the pioneering work of Hoyle and Burrows(1973) on the motor supply of the locust hindleg whichlaid the groundwork for intracellular investigations ofthe control of the locust jump (Heitler and Burrows,1977a,b) and a rich subsequent literature on the controlof jumping, walking and posture in locusts (see Bur-rows, 1996). In a similar fashion, for the control oflocust flight, detailed descriptions of the motor patterns(Wilson and Weis-Fogh, 1962) were followed by intra-cellular recordings from motor neurons identified onphysiological grounds (Kendig, 1968) and subsequent


anatomical descriptions of the motor neurons (Bentley,1970; Burrows, 1973; Tyrer and Altman, 1974). Al-though information on the firing pattern of interneu-rons could be inferred from motor neuron recordings(see Burrows, 1977), in both cases the premotor cir-cuitry remained a mystery until techniques improvedsuch that interneurons could be identified reliably andwith relative ease (e.g. for flight, Robertson and Pear-son, 1982, 1983).

3.2. Operating principles

We have chosen to illustrate the following discussionwith particular but not exclusive reference to the loco-motion of locusts (including walking, jumping andflight) simply because the study of these motor activitiesis well advanced and features prominently in the litera-ture. We note in passing, however, that rivals forattention are increasing in number. As one example,insect stridulation is a behavior that has a respectablepedigree in the field of neuroethology (see Elsner andPopov, 1978; Elsner, 1983; von Helversen and vonHelversen, 1994; for reviews). It also exhibits character-istics that lend themselves to an identified neuron ap-proach in that the behavior is rhythmic, primarilygenerated by central circuits, and resistant to the ma-nipulations necessary to enable intracellular recording.Numerous interneurons in different species of grasshop-per have now been described that are involved instridulatory rhythm generation (Hedwig, 1986a,b,1992a,b, 1994; Gramoll and Elsner, 1987; Lins andLakes-Harlan, 1994; Ocker and Hedwig, 1996). Simi-larly, interneurons involved in cricket stridulation,which is generated by rhythmical movements of thewings rather than the legs, as is the case in grasshop-pers, have been described (Hennig, 1990a,b; Otto andHennig, 1993). A safe prediction would be that moredetailed descriptions of neuronal mechanisms underly-ing this motor activity will not be long in appearing.

3.2.1. Defined circuitsOne of the primary advantages of the identified

neuron approach is the ability to accumulate data fromthe same neurons and synapses in different individualsand thus to reconstruct faithfully a circuit that explainshow information is coded and/or how motor patternsare generated. For motor patterning in a variety ofinvertebrates there has been considerable success in thisventure (see Selverston et al., 1998; Calabrese and DeSchutter, 1992; Marder and Calabrese, 1996) becausethe circuits contain few neurons and one can be confi-dent that most, if not all, of the neuronal participantshave been identified. In vertebrates the notable successhas been necessarily confined, in all but a few cases (seee.g. Eaton, this issue; Buchanan, ibid), to the investiga-tion of classes of neurons (e.g. Grillner et al., 1991;

Grillner, 1996; Marder and Calabrese, 1996). In insectsthe success in defining completely circuits that generatemotor patterns is limited. For a detailed review of theneuronal and circuit properties underlying insect motoractivity there is a recent book that does the topic justice(Burrows, 1996). If there is a single message to be takenfrom this review, with reference to insect motor patterngeneration, it is that a focus on single identified neuronsand attempts to ascribe to them particular functions,has yielded little of lasting value. The original, appar-ently complete, descriptions of the locust jumping cir-cuit (see e.g. Pearson and O’Shea, 1984) were flawed(Gynther and Pearson, 1986, 1989). The description forthe locust flight circuit (Robertson and Pearson,1985a,b; Robertson, 1986) is woefully incomplete, eventhough a computer model of the known circuit canproduce rhythmic activity similar to the deafferentedflight rhythm (Grimm and Sauer, 1995). Moreoverthere have been no significant additions to this circuitsince its first description, and there is little informationon the possible role of local neurons. The latter isdeemed surprising (Burrows, 1996) given the prominentrole of local interneurons in the control of insect legmovements (e.g. for walking, Pearson and Fourtner,1975; Schmitz et al., 1991; Wolf and Laurent, 1994;Buschges et al., 1994; Buschges, 1995; Wolf andBuschges, 1995). There may be good reasons, func-tional or otherwise, for a negligible role for local in-terneurons in the control of wing movements, but untilour knowledge of the circuitry is improved no conclu-sions can be drawn.

The complexity of motor circuits and the allure ofidentified neurons (Robertson, 1989) may to some ex-tent explain the failure to define completely a unitarycircuit for any insect motor act. The problem may alsolie in the fact that the circuits fall in the middle of acontinuum of complexity from the stomatogastric sys-tem to the lamprey swimming system, and probablycloser to the lamprey. For the locust flight circuit,interneurons can be individually identified, encouraginga circuit-breaking approach, yet the numbers of neu-rons involved is several hundred at a very conservativeestimate. There are around 80 motor neurons for thewing muscles. A partial catalog of only flight steeringneurons in the mesothoracic ganglion contains 28 neu-rons (Rowell and Reichert, 1991). Unpublished cata-logs in several laboratories contain descriptions ofmany more interneurons whose activity is modulatedwith the flight rhythm. Finally, circuit-breaking bypaired intracellular recordings from the neuropil seg-ments of flight neurons is time-consuming, difficult andto a large extent arbitrary.

The successes in insect locomotor systems lie, there-fore, not with defined circuits of identified neuronswhich, even if complete, would be idiosyncratic(Robertson, 1989), but with more general concepts (e.g.


for flight, Robertson, 1995) that are developed, or aresupported, using the identified neurons as tools to gainaccess to the systems. The current areas of most intenseresearch interest in motor systems include the organiza-tion and reconfiguration of functional circuitry, the roleand mechanisms of neuromodulation and the afferentregulation of output (Kristan, 1992; Pearson, 1993;Morton and Chiel, 1994; Katz, 1996).

3.2.2. Organization and reconfigurationThe locust flight system has been described as a

single operating circuit with neuronal elements dis-tributed throughout six serially homologous, segmentalneuromeres (Robertson et al., 1982). Outputs from thissingle central source of rhythmicity are thought toactivate the sets of motor neurons for all four of thewings. The evidence for this is based on the locationand postsynaptic targets of interneurons that drivemotor neurons and of those that can be shown toparticipate in rhythm generation using standard tests(Robertson and Pearson, 1983). There is very littleevidence of strict serial homology of patterning ele-ments and experiments to surgically isolate sections ofthe ventral nerve cord have supported the originaldescription (Wolf and Pearson, 1987a). It is importantto note that whereas the central element is conceived ofas a rhythmical unit driving the motor neurons for allfour wings this does not indicate a single mechanismfor rhythm generation in the system. Indeed, there isevidence for multiple oscillatory mechanisms and stim-ulation of different single interneurons recruits differentsubsets of interneurons to evoke flight-like rhythmswith different characteristics (Robertson, 1987b). Thepoint is that there is currently no evidence for wing-spe-cific oscillators in the deafferented system.

This type of organization is unusual for a motorsystem that controls bilateral and serially repeated ap-pendages. Such systems are most often organized ascoupled central oscillators (see Marder and Calabrese,1996, for a discussion of segmental oscillators in lam-prey and leech swimming). In insects, hemisegmentaloscillators (or unit burst generators, Grillner, 1985)have been demonstrated or suggested for locust walking(Ryckebusch and Laurent, 1994), locust grooming(Berkowitz and Laurent, 1996), stick insect walking(Bassler, 1993), grasshopper stridulation (Ronacher,1989), and tymbal sound production by tiger moths(Dawson, 1995).

The most telling pieces of evidence against the ade-quacy of the described flight circuitry and its interpreta-tion have come from lesion experiments designed todemonstrate the existence of hemiganglionic oscillatorsfor wingbeating (Ronacher et al., 1988; Wolf et al.,1988), though these were unable to demonstrate conclu-sively the existence of hemiganglionic oscillators (Wolfet al., 1988). In animals with afferents intact, flight and

rhythmical motor activity are remarkably resistant tolongitudinal hemisections of the segmental ganglia thatwould damage the interneurons in the described flightcircuit. This is true also for deafferented preparationsunder the influence of octopamine. Most interestingly, amesothoracic hemiganglion separated from the majorityof the flight circuitry by hemisection and transection ofthe ipsilateral meso-metathoracic connective could stillproduce robust rhythmical motor neuron activity tomuscle 99 though this was poorly coordinated with therhythm supplying other wing muscles (Ronacher et al.,1988; Fig. 8). The rhythmical drive for this motorneuron could be derived from a putative hemigan-glionic oscillator, however it could also be a result oftiming information from residual flight circuitry beingrelayed through the prothoracic ganglion and down theintact pro-mesothoracic connective. A further possibil-ity is that the intact afferents serve to generate therhythm in a chain-reflex fashion.

Nevertheless, there is very suggestive evidence forhemiganglionic oscillators in flight preparations prior todeafferentation but relatively weak evidence after deaf-ferentation (Figs. 8 and 9). It is an interesting possibil-ity that the capacity for the flight circuit to bereconfigured by proprioceptive input (Wolf and Pear-son, 1989) and the short-term plasticity evident duringtethered flight (Mohl, 1988, 1993) are sufficient toenable a profound reorganization such that wing-spe-cific oscillators are created by the afferent input. Flightrhythms produced by deafferented preparations in theabsence of octopamine rarely last longer than a fewseconds. Thus the central component (the unitary oscil-lator of the deafferented preparation) could act like astarter motor that is then converted to the runningengine (putative coupled oscillators of the intact sys-tem) by proprioceptive feedback.

The relevance of the described flight circuitry(derived from deafferented preparations) for generationof the motor patterns of intact animals is also chal-lenged by information on the reconfiguration of theflight circuit by proprioceptive afferents. A significantadvance in the investigation of the neuronal control offlight was the development of a preparation with whichit is possible to record from neuronal somata of teth-ered flying locusts that have intact afferents (Wolf andPearson, 1987b). This preparation involves tetheringthe locust upside down and has drawn criticism for thisreason (Stevenson and Kutsch, 1987). However, theoriginal findings and interpretations have been confi-rmed by making electromyographic and intracellularrecordings of the motor activity before and after inver-sion of the same preparation (Pearson and Wolf, 1989).With this preparation it became feasible to investigatedirectly the contribution made by afferent input in thegeneration of the rhythm. Several interneurons withweak, and often subthreshold, membrane potential os-


Fig. 8. Rhythmical motor activity recorded from hemisected mesothoracic ganglion in the locust. (A) Electromyographic activity recorded fromright and left forewing depressor muscles (M97r and M99l) and from a left hindwing depressor (M128l) in a preparation with a hemisectedmesothroacic ganglion and a transected left meso-metathoracic connective. Note the generation of a flight-like rhythm although there is poorcoordination between M99l and the other wing muscles. (B) Rhythmical membrane potential oscillations recorded intracellularly from an elevatormotor neuron in a deafferented preparation after the application of octopamine. Note that only two cycles are recorded and the presumed rhythmhas a very low frequency. Taken with permission from Ronacher et al. (1988) and Wolf et al. (1988)

cillations in deafferented preparations showed robustbursting activity in intact animals, and at least oneinterneuron behaved in the opposite fashion with burst-ing activity in deafferented preparations but weak oscil-lations in intact animals (Wolf and Pearson, 1989).These results indicate that the circuit of active interneu-rons is different in deafferented and intact animals.This is to say that afferent input reconfigures the circuitrather than simply regulating the timing of elementswithin a central pattern generator that can be consid-ered as a defined unit (or black box) contributing to theoutput. Similar results have been seen in the ventilatorysystem of locusts in which the number of active neu-ronal elements is dependent upon the vigor of ventila-tion with more elements recruited as ventilationbecomes more vigorous (Ramirez and Pearson, 1989).

Another aspect of reconfiguration concerns the cir-cuitry for the control of appendages (or muscle sets)that are used in different motor acts (Morton andChiel, 1994). Interneurons can be dedicated to functionin the production of a particular behavior or they canparticipate in the production of more than one. Ininsects it has been possible to identify both types oforganization: interneurons for the control of bifunc-tional leg muscles seem to be divided into separate setsfor the production of flight or walking of locusts(Ramirez and Pearson, 1988) and flight or stridulationof crickets (Hennig, 1990a), whereas other neurons can

participate in the control of both ventilation and flightin locusts (Ramirez and Pearson, 1989) ventilation andstridulation in locusts (Otto and Hennig, 1993) and legand wing stridulation in grasshoppers (Elsner, 1974). Amechanism underlying the reconfiguration of circuitscontrolling two behaviors has recently been described inlocusts (Jellema and Heitler, 1997). The different be-haviors (kicking and thrusting of the hindleg) are differ-entially characterized by the flexion angle of the tibiawhen they occur (full-flexed for kicking; around 90%for thrusting). Interestingly, it is feedback from thefemoral chordotonal organ monitoring this joint anglethat mediates the reconfiguration by biasing synapticconnections. During thrusting of the leg this afferentactivity modulates the gain of connections from straindetectors in the leg cuticle and central connectionsbetween leg motor neurons to prevent the co-activationof flexors and extensors that is necessary to produce thestored energy required for a kick.

3.2.3. NeuromodulationIt is now the accepted wisdom that pattern generat-

ing circuitry is under continuous modulatory controlvia neuroactive substances circulating in the blood (orhemolymph) or released locally in the neuropil (forreviews see Harris-Warrick and Marder, 1991; Pearson,1993). The functions of such neuromodulation includeactivating and/or priming the circuitry, and reconfigur-ing the ensemble of participating neurons.


Insect nervous tissue contains the usual complexcocktail of neuroactive substances (e.g. Nassel, 1996)that have been localized in specific neurons using im-munocytochemical techniques. It is to be anticipatedthat these have roles to play in controlling the flexibilityof motor pattern generators, among other things. Inmost cases the studies have progressed little furtherthan identification and detailed description of im-munoreactive neurons (most useful for comparisonacross taxa). With reference to motor patterning, mostinformation about neuromodulation concerns octo-pamine which has multiple roles as a transmitter, neu-romodulator and hormone (see Orchard et al., 1993).

Octopamine is widespread in insect nervous systems(Stevenson and Sporhase-Eichmann, 1995). There hasbeen considerable interest in the role of octopamineever since the suggestion by Hoyle (1975) and confirma-tion by Evans and O’Shea (1978) that dorsal unpairedmedian (DUM) neurons used octopamine as a trans-mitter to modulate a myogenic rhythm of the extensortibiae muscle of the locust leg. More recently it hasbeen demonstrated that the injection of octopamineinto specific regions of flight neuropil in the metatho-racic ganglion can activate flight rhythms (Sombati andHoyle, 1984; Stevenson and Kutsch, 1987). The mecha-nism underlying this is likely to be the induction ofplateau potentials in specific locust flight interneurons(Ramirez and Pearson, 1991a,b) in combination with amore generalized arousal of the system (Fig. 9). Octo-pamine has also been shown to modulate the spikingactivity of a wing-hinge proprioceptor in the locust(Ramirez and Orchard, 1990), a leg proprioceptor instick insects and locusts (Ramirez et al., 1993;

Matheson, 1997), synaptic interactions between in-terneurons in escape circuitry of the cockroach(Casagrand and Ritzmann, 1992), leg motor neurons inlocusts (Parker, 1996) and flight muscle in locust(Whim and Evans, 1991; Stevenson and Meuser, 1997)indicating a modulatory role throughout the behavioralmachine from sensory neurons through to muscle. Ithas been suggested that the local release of octopamineby specific subsets of DUM neurons could tune particu-lar regions of neuropil for the generation of differentbehaviors (the orchestration hypothesis, Sombati andHoyle, 1984; reviewed in Bicker and Menzel, 1989;Burrows, 1996) though there is currently little evidencefor such an overarching coordinating role (but seeBurrows and Pfluger, 1995). Alternatively there aresuggestions that octopamine mediates arousal. This in-terpretation is supported by the observation that octo-pamine dishabituates the response of DCMD when it isreleased from identified octopamine immunoreactiveneurons in the optic lobe of locusts (Bacon et al., 1995).In addition, olfactory reward learning in honeybees isenhanced by octopamine and depolarization of an iden-tified ventral unpaired neuron (VUMmx1, containingoctopamine) can substitute for the reward in olfactoryconditioning (see Hammer, 1997).

The role of acetylcholine as a neuromodulator actingvia an influence on muscarinic receptors is a topic withsome relevance to the control of motor activity (forreview see Trimmer, 1995). The application of pilo-carpine (or other muscarinic agonists) to several differ-ent insect preparations can induce the generation ofrhythmical motor patterns (walking — Ryckebuschand Laurent, 1993; Buschges et al., 1995; stridulation —

Fig. 9. Hyperexcitability of the flight rhythm generated by locust thoracic ganglia after perfusion with octopamine. [Ai] Structure of identifiedflight interneuron 401. [Aii] Brief electrical stimulation (STIM) of interneuron 401 induces a flight- like rhythm recorded intracellularly in 401 anda contralateral depressor motor neuron (DEP). [B] Injection of a long pulse of depolarizing current into interneuron 566 evokes tonic activitybefore (i) and flight like rhythmical activity after (ii) the application of octopamine. Taken with permission from Ramirez and Pearson, 1991a andRamirez and Pearson, 1991b.


Heinrich et al., 1997; see also Trimmer, 1995; crawlingin moth larvae — Johnston and Levine, 1996;mandibular rhythms — Rast and Braunig, 1997).Much of this effect is likely attributable to the increasein excitability of identified neurons when treated withmuscarinic agonists (Trimmer, 1994; David and Pit-man, 1995, 1996), but other effects cannot be ruled out.Indeed, it has been demonstrated that muscarinic an-tagonists can augment the amplitude of postsynapticpotentials from the forewing stretch receptor in locustsand it is proposed that this represents self-regulation atthis cholinergic synapse via presynaptic autoreceptors(Leitch and Pitman, 1995). The studies on the activa-tion of motor patterns require the usual caveats forinvestigations of neuromodulatory agents given that itis difficult to know precisely what the treatment isdoing. The problems of interpretation are only slightlymitigated when the agents are injected into neuropil(e.g. Heinrich et al., 1997) rather than bath-applied (e.g.Ryckebusch and Laurent, 1993). In spite of this, theability to induce walking motor rhythms in reducedpreparations of insects is a significant advance and willallow the control of this behavior to be addressed bythe identified neuron approach.

3.2.4. Afferent regulationMotor patterns in insects are generated by a combi-

nation of central and peripheral mechanisms. The futile(in retrospect) debate over the relative contribution ofeach to pattern generation is largely over and the focushas rightly shifted to a consideration of the precise roleof proprioceptive feedback in structuring the pattern.From research with the locust flight system it is evidentthat proprioceptive input can reconfigure the centralcircuit of active neurons (see above). Notwithstandingthis there is strong evidence that rhythmic propriocep-tive discharges have a prime role in regulating thetiming of phase transitions and that this role is mir-rored in other systems including those of vertebrates(see Pearson, 1993, 1995). In the locust flight systemwing-hinge stretch receptors monitor elevations and thetegulae monitor depressions. Activity of the stretchreceptors promotes an earlier onset of depressor motorneuron bursts by reducing the degree of hyperpolariza-tion between bursts, whereas activity of the tegulaerecruits elevator interneurons and advances the timingof elevator motor neuron bursts (see Pearson andRamirez, 1992). Other proprioceptors exist and arelikely to be involved in patterning the motor output buttheir roles are incompletely understood at present (e.g.Pearson et al., 1989; Stevenson, 1997).

The gain of the connections made by afferents withidentified postsynaptic targets is under presynaptic in-hibitory control (cricket — Levine and Murphey, 1980;locust — Pearson and Goodman, 1981; cockroach —Hue and Callec, 1983). Depolarization of the presynap-

tic terminals of the afferent reduces the efficacy ofaction potentials in the afferent and this can serve avariety of important functions such as to prevent satu-ration in the pathway (Burrows and Matheson, 1994),to reduce hysteresis (Hatsopoulos et al., 1995) and tomitigate the effects of reafference (Hedwig and Bur-rows, 1996). During the generation of motor patternsfor locomotion, rhythmical presynaptic inhibition ofproprioceptive afferents occurs to modulate phase-de-pendent reflexes and, whereas other afferents may beinvolved, it is clear that centrally generated signals mustcontribute to the presynaptic depolarization (Wolf andBurrows, 1995). An interesting idea has been proposedthat a general role for presynaptic inhibition is toreduce the efficacy of feedback from predicted move-ments (predicted by the output of a central patterngenerator) and to allow the feedback from unexpectedperturbations of movement to have greater efficacy(Burrows, 1996). Presynaptic inhibition is a criticalprocess for efficient motor control (in humans themodulation of presynaptic inhibition is affected by thedefects associated with spasticity — Stein, 1995). Thisprocess is eminently accessible to cellular investigationsin insect motor systems.

3.3. Neuronal restructuring

The ability to investigate the properties of identifiedneurons and identified synapses has considerable ad-vantages for investigations of structural plasticity andits consequences in the nervous system. Such changesare associated with long-term reorganization of circuitsduring the processes of maturation, metamorphosis,learning and memory, and recovery from peripheralinjury. Insects have provided several model systemswith which to pursue an understanding of these phe-nomena (e.g. Weeks et al., 1997; Kamper and Mur-phey, 1994) and it is now accepted that results fromthese systems have general relevance across differenttaxa [including vertebrates] (Murphey, 1986).

An interesting feature of the development and matu-ration of insects is that they exhibit age-specific behav-iors associated with new or modified body parts andnew control circuits. For example the hemimetabolouslocust attains wings only at the final molt and, althoughflight rhythms can be generated immediately after molt-ing, it takes two weeks of maturation before an animalis able to generate a flight motor pattern that has anappropriate frequency to sustain lift and thrust (seeKutsch, 1989). The phenomena underlying this increasein wingbeat frequency are incompletely understood forit has proved resistant to a variety of experimentalmanipulations. There is a correlation with growth ofidentified interneurons although several parameters ofidentified synaptic potentials in these interneurons areunaffected by maturation (Gee and Robertson, 1994).


The axonal branching of the stretch receptor afferentshows heteromorphic growth (Gray and Robertson,1996). The conduction velocity and sensitivity of thestretch receptor increases (Gray and Robertson, 1994)and its ability to modify an ongoing flight rhythmincreases (Gray and Robertson, 1997a). These changesseem independent of the levels of circulating ecdysone(Kutsch, 1989) or of juvenile hormone although thismay have a role in the switch from a larval rhythm tothe adult rhythm (Kutsch and Stevenson, 1984). Duringlarval development other identified afferents in thissystem show structural alterations indicating changes inconnectivity and this change is activity-dependent, nothormonally-mediated, and likely to be regulated bycompetition for synaptic sites (Pfluger et al., 1994). Theevidence suggests that the flight circuitry is in a dy-namic state throughout maturation and able to react toexternal influences so that the final form of the circuit isappropriate for an individual’s morphology.

That the profoundly dynamic nature of the locustflight circuit continues into the fully mature adult stageis well demonstrated by the effect of peripheral lesionson the system. The tegulae are proprioceptors thatmonitor wing position during the downstroke and havea significant role in the generation of aerodynamicallyappropriate wing movements (Wolf, 1993). Removal ofthe hindwing tegulae causes a reduction in wingbeatfrequency that recovers during the subsequent 2 weeks(Buschges and Pearson, 1991) although in most animalsthe recovery is not sufficient to enable free flight (Geeand Robertson, 1997). This recovery appears to bemediated by growth of the forewing tegulae afferentsand the formation of new and more reliable connec-tions with flight interneurons (Buschges et al., 1992a,b).The extent and speed of recovery are not affected bythe maturational stage (post imaginal molt) of theanimal (Gee and Robertson, 1996). Interestingly, re-moval of a single hindwing tegula (which projects toonly one side of the thoracic ganglia) has structural andsynaptic consequences for connections on the contralat-eral (uninjured) side indicating a role for retrogradesignals and competitive interactions in the restructuringof these afferent pathways (Wolf and Buschges, 1997;Fig. 10).

These changes are reminiscent of the competitiveinteractions and retrograde signals that have been ele-gantly demonstrated in the cercal system of cricketsduring development (Murphey and Lemere, 1984;Chiba et al., 1988; Davis and Murphey, 1994a,b; Mur-phey and Davis, 1994). It is becoming quite clear fromthese studies that insect circuits are in a dynamic state,continually able to restructure neurites and modifysynaptic connectivity as a consequence of competitiveinteractions. This occurs throughout development andis retained into adult stages. The similarity with mam-malian systems (Kaas, 1991) is striking and perhaps

Fig. 10. Diagrams illustrating the change in connectivity from tegulaeafferents onto a generalized flight interneuron in the metathoracicganglion after recovery from unilateral hindwing tegula removal. (A)shows normal organization and site of lesion (scissors). Lines travel-ing down the meso metathoracic connectives indicate afferents fromthe forewing tegulae. One synapse from an afferent to an interneuronindicates 10% frequency of connection and the size of the terminalsindicate the strength of the connections as measured by the amplitudeof compound EPSPs. Note that the reliability and strength of connec-tions from the forewing tegula on the side ipsilateral to the lesionincrease (B), and that there is also a retrograde effect on the connec-tions from the forewing tegula on the side opposite the lesion. Takenwith permission from Wolf and Buschges (1997).

insects will afford one of the best opportunities to studythe underlying mechanisms of this form of plasticity atthe level of identified neurons.

A more dramatic restructuring of motor circuits oc-curs during the metamorphosis of holometabolous in-sects such as Manduca and Drosophila (Truman, 1990;Weeks and Levine, 1992; Levine et al., 1995). In thesesystems the peripheral structures change dramatically tosupport a completely different life-style: a nervous sys-tem appropriate for larvae requires disassembly prior toconstruction of a nervous system appropriate foradults. The processes that are involved in this transfor-mation include targeted cell death, postembryonic neu-rogenesis, restructuring of neuronal arbors, synapticremodeling, and changes in the expression of voltage-dependent currents of identified neurons. One of themost significant features of this research is that theremodeling is under the control of steroid hormonesallowing a fine-grained, cellular investigation of impor-tant phenomena that may be difficult to address at thislevel in mammalian systems. This is a well-advancedfield that has been amply reviewed in recent years(Weeks and Levine, 1995; Levine et al., 1995; Kent etal., 1995; Weeks and Wood, 1996; Weeks et al., 1997).


3.4. E6olution and homology

It is a truism to say that the current design ofcircuitry is a result of inexorable evolutionary mecha-nisms acting on pre-existing circuits to adapt them suchthat organisms are better suited to their particularniches. The effects of evolution are clearly recognizablein the nervous system (Arbas et al., 1991). The conse-quences of evolution in the context of this article are atleast two-fold. First, current design may owe more tohistory than to functional necessity and this compli-cates the interpretation of the neuroethological utilityof specific features of neuronal structure and operation.This point has been made previously (Dumont andRobertson, 1986) and need not concern us further here.Second, closely related species are likely to be moresimilar in the characteristics of their identified neuronsthan more distantly related species. This being true itshould be possible to recognize neuronal homologsacross species and perhaps determine how evolutionaryprocesses mold circuitry (e.g. Mizunami, 1994; Ed-wards, 1997; Tierney, 1995 for a discussion of theeffects of evolution on neural circuits). Moreover, infor-mation on neuronal homology can be applied by tax-onomists to confirm or refute different evolutionaryscenarios (see Osorio and Bacon, 1994; Osorio et al.,1995). The large number and diversity of insect species,the known sexual dimorphisms of structure and behav-ior (see Hoy, 1990), the fact that insects lend themselvesparticularly well to neuroanatomical study, and the factthat the organization of insect ganglia bestows upontheir neurons distinctive and beautiful structures, allcontribute to making insects arguably the best animalgroup with which to pursue such an endeavor.

Establishing neuronal homology across species isnotoriously difficult especially when the functions of theneurons may be different. Normally it requires thedemonstration of a common lineage from an identifiedneuroblast during development (e.g. Boyan and Ball,1993, for segmental homologs). When neurons are par-ticularly distinctive, structurally and functionally, how-ever, it is possible to make the conclusion withconfidence (e.g. Bacon, 1980). In the orthopteran flightsystem there is evidence in locusts and crickets forsimilar organization of interneurons (Robertson et al.,1982; Robertson, 1987a) and for homologous interneu-rons with roles in pattern generation (Robertson,1987b; Hennig, 1990b). Nevertheless, possibly the bestsystem with which to investigate these sorts of ques-tions is the visual system of the Diptera which canprovide answers even at the level of synaptic ultrastruc-ture (Shaw and Meinertzhagen, 1986). Specific neuronsin the optomotor system are quite distinctive (e.g.Hausen, 1982) and evolution of the visual system hasbeen relatively conservative. Moreover, numerous spe-cies of closely related Diptera occupy a variety of

different visual niches providing an opportunity to dis-sect differences related to adaptation from similaritiesrelated to a common origin. Recent detailed neu-ranatomical studies in a large number of Dipterademonstrate that whereas the retinotopically arrangedelementary motion detectors are conserved across dif-ferent species (Buschbeck and Strausfeld, 1996) thereexist functionally and ecologically relevant differencesin the structure and organization of the output elementsof the lobula plate (the vertical and horizontal motionsensitive neurons) (Buschbeck and Strausfeld, 1997).

The idea that conserved circuitry is tuned to producespecies-relevant behavior is confirmed in the control ofthe leg during predator evasion of stick insects andlocusts (Buschges and Wolf, 1995). Typically stick in-sects become cataleptic on detection of a predator andlocusts generate jumps or kicks. The components andoperation of the motor circuits of the leg in these twoorganisms are relatively well known, though certainlyfar from completely described, and they exhibit muchsimilarity. Putative homologs exist at all levels of thecircuit (motor neurons, non-spiking interneurons, sen-sory neurons) and their interconnections are qualita-tively the same. Interneurons, but not motor neurons,show a difference in the velocity sensitivity of theprocessing of information from the femoral chordo-tonal organ (velocity-dependent in stick insects but notin locusts) and this can be attributed to the functionaldemands imposed by different evasion strategies.

Thus, the characteristics of identified neurons in in-sects can be used to answer questions about evolution-ary relationships among taxa and also can help inparsing out functional vs. phylogenetic features. Cen-tral nervous circuits are relatively conserved, which isnot surprising given the difficulty of constructing thenervous system during development and the phyloge-netic conservation of the mechanisms for neurogenesisand growth cone guidance (Goodman, 1994; Reichertand Boyan, 1997). Fine-tuning of cellular and synapticproperties overlies a common circuitry to fit an organ-ism for an individual life-style, at a given developmen-tal stage (Edwards, 1977), and in an individualecological niche.

4. Newer approaches

4.1. Molecular/genetic approaches

While the 1980s saw a burgeoning of the cellularapproach to understanding neural operating principles,the 1990s have witnessed the flowering of variousmolecular approaches; thanks to the increasing sophis-tication of techniques. It has been demonstrated thatidentified neurons in the lobster stomatogastric systemhave unique genetic identities (Baro et al., 1996) and


the same is without doubt true for insect neurons. Theorganism par excellence with which to perform molecu-lar and genetic manipulations is the fruit fly,Drosophila. This insect has the usual repertoire of com-plex behaviors but its small size, and consequently thesmall size of its neurons (diameter of motor neuronsomata 8 mm, Koenig and Ikeda, 1983), has preventedsustained intracellular investigation of identifiedcircuitry.

One notable success was the investigation of connec-tivity from the descending giant fiber (GF) that medi-ates escape jumping and flight (see Wyman et al., 1984).The effects of mutations that impaired escape could betraced to defects in the GF circuit (see Thomas andWyman, 1983). Much molecular dissection of synapticoperation is now performed using the larval neuromus-cular junction (see Keshishian et al., 1996) and geneticand behavioral experiments with Drosophila combinedwith intracellular investigations in larger flies (e.g.Musca) is advancing the genetic dissection of the neu-ronal basis of visuomotor flight control in flies (seeabove). Now another central synapse, and one that isinvolved in the control of flight, can be added to theGF circuit as one accessible to modern techniques.Intracellular recording from adult motor neurons hasallowed the demonstration that the shaking-B2 muta-tion disrupts synaptic connectivity between haltere af-ferents and flight motor neurons (Trimarchi andMurphey, 1997; Fig. 11). It is to be hoped that contin-

ued efforts in this direction will lead to more completedescriptions of functional circuits of identified neurons.

The particular advantages of Drosophila have meantthat efforts to use molecular and genetic techniques tostudy behavioral circuits in adult animals continue un-abated. One area where these approaches are just be-ginning to yield exciting results concerns the mushroombodies (and see Strausfeld, this volume). This areacontains an enormous population of Kenyon cellswhich appear homogeneous by morphological criteria.However, use of enhancer trap techniques has made itpossible to describe the developmental lineage of mush-room body cells (Ito et al., 1997) and provided therealization that the adult mushroom bodies are com-posed of definable cellular subsets that differ in geneexpression (Yang et al., 1995). Such techniques mayspur studies of mushroom body involvement in sensoryintegration, motor programming, and learning.

4.2. Neuroecology

In some respects to say that an organism’s ecologyconstrains the nature of its neuronal circuits is simplyto restate the concept that evolution has matched thedesign of the circuitry to the functional demands of aparticular ecology. It is this awareness that underpinsthe discipline of neuroethology-control systems canonly properly be investigated in the context of thenatural behavior of the animal and this implies a con-

Fig. 11. The shaking B2 mutation disrupts synaptic connectivity between haltere afferents and flight motor neurons in Drosophila. (A)Comparison in wild-type [Ai] and shaking-B2 [Aii] animals of the latencies of action potentials recorded intracellularly in motor neuron B1 afterstimulation of haltere afferents with increasing stimulus intensities. Note the irregular latencies and the unreliability of the connection in themutant. (B) Effects of electrical stimulation of haltere afferents on electromyographic activity recorded from muscle B1. [Bi] Stimulus at 70V,comparison of five traces in each of a normal and a mutant animal. [Bii] Summary of latencies recorded with a variety of stimulus intensities. Notethe longer and variable latencies in the mutant animals. Taken with permission from Trimarchi and Murphey (1997).


Fig. 12. The visual ecology of voltage-gated K+ conductances ininsect photoreceptors recorded with single electrode voltage clamp. InDiptera [top panel] non-inactivating outward currents are typicallyfound in diurnal, rapidly flying insects (fast) where as in crepuscular,slow flying insects (slow) the outward s currents are found to inacti-vate thus minimizing the energy required to maintain normal K+

concentrations when the ecology dictates that high frequency re-sponses are not necessary. In the locust Schistocerca the inactivationproperties of the outward currents are dependent on the time of day[middle panel] and can be converted from the day state to the nightstate by the application of 5HT [bottom panel]. Taken with permis-sion from Weckstrom and Laughlin (1995).

cially for the migratory locust whose native habitatincludes semiarid regions of equatorial Africa (Uvarov,1966). In a flying locust thoracic temperature can ex-ceed environmental temperature by around 10°C (Weis-Fogh, 1956, 1964) and in hot environments thoracictemperatures of flying insects have been recorded ashigh as 48°C (Coelho, 1991). Increasing thoracic tem-perature has effects on both the output of the locustflight circuit (Foster and Robertson, 1992), on proper-ties of identified neurons (Xu and Robertson, 1994) andon synaptic parameters within the circuit (Robertson,1993). These effects can be interpreted as mechanismsto compensate for changes in temperature and thusmaintain a relatively stable output (Xu and Robertson,1996). Interestingly, prior heat shock (exposure to 45°Cfor 3 h) alters the thermosensitivity of flight behavior(wingbeat frequency during tethered flight) and that ofthe flight circuit (deafferented rhythm frequency) inprofound ways (Robertson et al., 1996; Fig. 13). Thethoracic temperature at which flight rhythm generationfails is 6–7°C higher in heat-shocked animals and thefrequency of flight rhythms (intact or deafferented)becomes insensitive to temperature in the 30–45°Crange (Robertson et al., 1996). Similar reductions ofthermosensitivity can be observed for the conductionvelocity and extracellularly recorded amplitude of thestretch receptor action potential (Gray and Robertson,

Fig. 13. Prior heat shock affects the temperature sensitivity of flightrhythms in the locust Locusta. [A] Wing beat frequency (WBF)recorded electromyographically from intact tethered flying locustsshows minimal thermosensitivity after heatshock (ii) compared withcontrol animals [i], and the temperature at which flight rhythms fail issignificantly higher after heat shock (iii). (B) Flight rhythm frequencyrecorded from deafferented preparations is reduced in thermosensitiv-ity at temperatures above 35°C after heat shock (ii) compared withcontrol preparations [i], and the temperature at which flight rhythmsfail is significantly higher after heatshock (iii). Modified from Robert-son et al. (1996).

sideration, if not a deep understanding, of the animal’secology. Excellent recent demonstrations of the ideathat neural properties are matched to ecology concernthe visual system of flying insects. The distribution ofvoltage-gated ion channels in photoreceptors of differ-ent insects (locust, fly, bee) has been shown to beassociated with the flight speed of the insect and thetime when it is most active (i.e. nocturnal, diurnal)(Weckstrom and Laughlin, 1995; Fig. 12). Indeed,potassium channels in locust photoreceptor membranesare modulated on a daily basis to balance the goals ofincreasing performance while active and decreasingmetabolic demands while inactive (Weckstrom andLaughlin, 1995). Similarly, the tuning of visual motionsensitive neurons in a variety of insects is correlatedwith their flight speeds and hovering ability (O’Carrollet al., 1996).

On the other hand it is increasingly likely that theecology of an organism could have longer term effectsthat condition the circuits in such a way that theiroperating mechanisms are different. For example, diethas been shown to modify the ionic content of thehemolymph and the activity of cockroaches (Pichonand Boistel, 1963). Temperature is arguably the mostpotent environmental variable for a poikilotherm, espe-


1997c) and for parameters of synaptic potentials in theflight circuit (Dawson-Scully and Robertson, unpub-lished). The effects of heat shock on thermotolerancelast for at least one week (Robertson et al., 1996). It isnot yet clear how heat shock causes these effects, but aworking hypothesis is that heat shock proteins (ex-pressed in locusts, Whyard et al., 1986) protect theoperation, and change the thermosensitivity, of mem-brane proteins. The significant feature of all of theabove is not just that environmental conditions altercircuit properties, but also that the history of environ-mental conditions affects how the circuit operates atany future time. This is neuroecology; the study offeatures of neural structure and function that are con-strained by the ecology of an organism.

4.3. Neuroinformatics and insect ner6e cells

In the space of this review it was only possible tocover a small portion of the work on insect modelsystems where identified nerve cells have been related tothe control of behavior. Furthermore, we did not con-sider the importance of using identified insect cells instudies of: neuronal differentiation (e.g. Doe, 1992),learning (e.g. Mauelshagen, 1995), or regeneration (e.g.Spira et al., 1987), and we only touched briefly on thedevelopment of synaptic connections (e.g. Blagburnand Thompson, 1990). Thus it can be appreciated thatthe total amount of published information on identifiednerve cells of insects is enormous.

The data management problem that a neuroscientistfaces when trying to marshal complete informationabout one specific identified cell, to make a comparisonof potentially homologous cells across species, or toobtain design features of different cells for modelingstudies, can be daunting. The same data managementsituation arose in genomics at least a decade ago whenthe number of published sequences was rising rapidly(Smith, 1990). The solution for geneticists and molecu-lar biologists was to rely on electronic databases (Ald-hous, 1993). Searching a sequence database allows oneto quickly compare nucleotide or protein structures andthis can suggest functions for molecules where this waspreviously unknown, or it may point to evolutionaryrelationships.

The importance of storing neurobiological data indigital formats has not escaped the attention of neuro-biologists. The ability to tag molecular, developmentaland physiological information to the structure ofuniquely identified neurons, such as those reviewed inthis chapter, will make it possible for the same broadquestions to be answered for neural circuits, that arenow being asked for the genome of higher animals.This point was persuasively argued in an article byRowell (1988) which seems to have been widely read,but acted upon very little until the last three or fouryears.

Table 1Web sites with information on identified nerve cellsa

Organism-basedganglion.med.cornell.edu/Molluscs1

Arthropods2 web.neurobio.arizona.edu/Evolbrain/index.htmlflybrain.neurobio.arizona.edu/3 Fruitfly (3 mirror

sites)flybrain.uni-freiburg.de/flybrain.nibb.ac.jp/

Cockroach n002bsel.bios.uic.edu/4Bee5 beebrain.neurobio.arizona.edu/

mothbrain.neurobio.arizona.edu/Moth6

Software/toolswww.nervana.montana.edu/projects/NeuroSStructural7ys/soma.npa.uiuc.edu/isnpa/isnpa.htmlPhysiological8

a All URL identification tags should be preceded by ‘‘http://’’.Some sites (listed first) are concentrated primarily on collections ofdata related to a given organism, while others (at bottom) aredevoted primarily to development and dissemination of software forvisualizing neuronal structures or physiological data

Electronic resources initially were implemented fordatabases of images related to clinical and mammalianneurobiology (e.g. Fox et al., 1993; Swanson, 1995).The recent heavy usage of world wide web resources byresearchers has catalyzed the development of neurobio-logical tools, and several electronic databases related toidentified neurons in insects have now appeared. Themost extensive at this point is a collection of sitesdevoted to the Drosophila CNS (Armstrong et al., 1995;Heisenberg and Kaiser, 1995), but others are beginningto emerge (see Table 1) and a federation of insectdatabases has already been envisioned (see links atFlybrain site). We believe that WWW resources willprove to be particularly useful for precisely those areaswhere insect neural studies have their brightest future:comparative, evolutionary, and computationalneuroscience.

5. Concluding remarks

There is no doubt in our minds that research usingidentified neurons in insects is alive, well, and will makesignificant contributions in a wide variety of sub-disci-plines within the general enterprise of understandinghow neurons and their interactions generate appropri-ate behaviors. The growing realization of the computa-tional power of single neurons and dendrites inmammalian circuits (Koch, 1997; Sejnowski, 1997)comes as no surprise to those who follow the literatureon identified neurons in insects (see references above onDipteran visual cells to provide examples from but asingle system) and in the Dipteran examples this com-putational ability can be directly related to the func-tional demands of the neurons.


A theme which has emerged numerous times in thischapter, with respect to both sensory processing andmotor programming, is the value of comparative study.This is quite obvious in the case of evolutionary ques-tions, but even with respect to computational questions,comparisons of identified circuit elements in relatedspecies, and especially between adults and larval formsof the same species offer many opportunities.

Reviews like this chapter are likely to become obso-lete as the mass of information becomes too unwieldyto manage in a single chapter. Possibly the presentreview will be taken as strong evidence by some readersthat we have already passed that point. This, in itself,argues for the importance of readily accessible data-bases of identified neurons to future research thatwould seek to define the interaction between evolution-ary processes and identified circuits.

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Identiﬁed nerve cells and insect behaviorpost.queensu.ca/~locust/Publications/comer and robertson.pdf · Progress in Neurobiology 63 (2001) 409–439 Identiﬁed nerve cells and