Comp.

Biochem.

Physiol.

Vol. 103A,No. I,pp.15-24,1992

0300-9629/92 $5.00+O.lM 0 I992Pergamon Press Ltd

Printed in Great Britain

MINIREVIEW

PREPARATION AND EXECUTION OF MOVEMENT: PARALLELS BETWEEN INSECT AND MAMMALIAN MOTOR SYSTEMS JENNYKIEN and JENNIFERS. ALTMAN Fachbereich fiir Biologie, UniversitBt Regensburg, D-8400 Regensburg, Germany. Tel.: 0941/943 2181; Fax: 0941/943 3304 (Received 24 February 1992)

Abstract-l. The organization of the motor systems underlying locomotion in insects and mammals is surprisingly similar. There are also parallels between the insect motor system and the system underlying reaching and the occulomotor system in primates. 2. The movements generated by all these systems are planned or prepared before their execution and there is a partial separation of circuits for preparation and execution. 3. These circuits consist of multiple descending pathways interconnected to form overlapping loops which work co-operatively to determine the motor output. Thus, both insect and mammalian motor systems can be treated as parallel distributed (PDP) systems. 4. This enables a comparison of functional levels of processing in the different systems and also provides a basis for modelling motor systems with attractor neural networks.

A long-standing concept, deriving from the work of Hughlings Jackson (see Taylor, 1956) and Sherrington (1906), is that motor systems are hierarchical control systems; the “higher” centres in the brain are held to initiate movements, the “lower” centres in the cord execute them. This concept has been reinforced by the anatomical separation, in both insects and mammals, of the rhythm generators for locomotion in the ventral nerve cord/spinal cord, from the integrational centres in the brain (Cohen, 1988; Rossignol et al., 1988; Armstrong, 1988; reviews in Humphrey and Freund, 1991 and Kien et al., 1992). As techniques for recording from more intact and behaving animals have improved, it has become clear that this is misleading; instead, it seems that the local rhythm generators and the higher integration centres work co-operatively to organize the initiation and maintenance of movements as well as their detailed execution. Here we summarize this evidence and conclude that the motor control system in both insects and mammals consists of a number of richly interconnected loops. Thus, it should be considered as a distributed network consisting of different levels of functional complexity, rather than a hierarchy of sequential control levels.

INTRODUCTION It has become increasingly obvious that the nervous systems and the behaviour of invertebrates are by no means simple; the neuronal substrate of even basic movements, like avoidance bending in the leech, cannot be understood without the help of modelling (Kristan et al., 1992). Shedding the belief that the nervous systems of invertebrates are “simple” and acknowledging that they have a level of complexity similar to that of vertebrate nervous systems, puts us in a new position to compare how these very different systems solve similar problems. Here we examine the neuronal organization underlying locomotion in insects and cats, and voluntary hand and eye movements in primates. All of these behaviours can be divided into three phases: preparation, onset and execution. We find direct parallels between insects and mammals in the roles of the descending pathways from the brain and in the way the systems are organized into levels of different functional complexity. The circuits organizing the three phases are only partially separate; the balance of activity shifts from one to the other as the animal progresses from preparation to execution but some neurons are active throughout all the phases. This partial separation may be significant for the structuring of behaviour. We have recently developed a descriptive systems level model for the selection and execution of motor outputs in insects (Altman and Kien, 1987a, 1989; Kien and Altman, 1992); because of the similarities between the insect and mammalian motor systems described here, we suggest that this model can also be applied to mammals. CBPA

10311~-8

NEURONAL

Spontaneous preparation

CORRELATES

movements

OF MOVEMENT

require

a

period

of

Movements may occur in response to an external stimulus or be generated endogenously. Endogenously generated movements are usually preceded by small 15

JENNY KEN and JENNIFERS. ALTMAN

16

changes in muscle tone and small muscle contractions; there are also changes in neuronal firing even before the muscle activity becomes obvious. All these changes occurring before the onset of the movement define the preparation phase. Surprisingly, preparation phases of similar duration are found before spontaneous walking in insects {Kien, 1990a) and voluntary arm movements in primates (Deecke et al., 1976; Alexander et al., 1990). When locusts spontaneously start walking (“starting” Fig. 1) activity can be recorded in neurons descending from the head ganglia (brain and suboesophageal ganglion, SOG) to the thoracic motor centres; many neurons fire hundreds of milliseconds before any muscle activity is visible and there appear to be approximately equal numbers of tonic and bursting units. This firing onset is followed by a period of muscle activity lasting up to 3 sec. before organized stepping movements start; during this preparatory period the early neurons continue to fire and more neurons are recruited, predominantly from

SlP,NDING

I

PREP. PHASE

I

WALKING

WALKING

the SOG. The activity in the descending pathways becomes temporally more complex as the bursting neurons develop their burst patterns (Kien, 1990a). As in starting to walk, the spontaneous transition from walking to standing also takes seconds (“stopping” Fig. 1); some neurons change their activity before any slowing of walking is visible, others during the slowing down, and still others well after the animal has stopped (Kien, 199Oa). In monkeys, visually guided arm movements also require long preparation (see Fig. 2 for the pathways involved). Neurons in the supplementary motor cortex (SMA), premotor cortex and primary motor cortex, as well as in the posterior parietal cortex and the putamen (part of basal ganglia) fire seconds before the arm muscles become active (Okana and Tanji, 1987; Humphrey and Tanji, 1991; Dum and Strick, 1991; Alexander and Crutcher, 1992). Some of these neurons in all these areas continue firing throughout the movement (Alexander and Crutcher, 1992). Similarly, in humans, readiness potentials are visible in the EEG

STANDING

STOPPI N G STARTING Fig. 1. The complexity of activity in the output from the bead ganglia recorded during spontaneous starting of walking and stopping. The response forms during starting and stopping were recorded at sites marked with arrowheads in inset from neurons descending from both the brain and suboesophageal ganglion (SOG). One group of neurons starts to fire before the preparatory phase (standing), one during the preparatory (prep.) phase and a third when walking begins. The changes during stopping are equally complex lasting from several seconds before stopping to several seconds afterwards. Note that this sequential recruitment of neuronal activity during starting is not equivalent to a sequence of activation of different computational levels. The patterns are schematized from the envelopes of inst~~n~us spike Frequencies (f,, cu lOO/Hz) in single neurons recorded from many animals; each pattern represents from one to six neurons. Dashed lines represent variations in a pattern (after Kien, 199Oa). The inset shows the central nervous system of the locust seen in dissection.

Insect and mammalian motor systems

from frontal motor areas up to seconds before finger movements are made (Deecke et cd., 1976; Libet, 1985). Even saccadic eye movements are preceded by preparatory activity. Neurons in the frontal eye fields and superior colliculus discharge before all saccades; neurons in the caudate nucleus discharge only before purposive saccades (M. Goldberg et al., 1991). The pathways in the oculomotor system are summarized in Fig. 3. The slow build up of activity during the preparatory phase could reflect the self-organization of a new motor output. In various invertebrate preparations, a certain level of excitation seems to be required to carve out the appropriate circuits from the range of possibilities available (Getting, 1988, 1989; Dickinson, 1989). This suggestion is supported by observations in insects wben walking is evoked by a sensory stimulus: the preparatory phase is absent and walking starts maximally a few hundred milliseconds after the start of the stimulus; many of the neurons which are recruited sequentially during spontaneous starts now fire simultaneously in response to the stimulus. The sensory input thus provides a rapid increase in

i?

excitation which appears to replace the complex sequence in which these neurons are recruited during spontaneous starting (Kien, 1990a). Primates, unlike insects, appear to have separate preparation systems for evoked and endogenously generated movements. Within the supplementary motor cortex some neurons fire several hundred milliseconds before self-paced movements (Okano and Tanji, i987), but others fire with a short latency before visually cued movements. Furthe~ore, a premotor system including the premotor cortex seems to be more important in the selection of behaviour due to changes in the environment, whereas the SMA is embedded in a system thought to play a more important role in the endogenous selection and generation of behaviour. These two systems could be mutually inhibitory, balancing each other to resolve conSicts between tendencies to mutually exclusive responses to the environment or to endogenously generated signals (G. Goldberg, 1992). Onset of movement Apart from the studies of spontaneous walking in locusts, most studies of the start of locomotion have used either sensory or electrical stimulation to elicit behaviour. In both evoked locust flight and evoked walking in cats, there seems to be an initiation network that is separate from the rhythm generator.

1 parietalltempwallFEF/SEF

I

brain spina

Fig. 2. A schematic and extremely simplified summary of some of the pathways underlying visually guided arm movements in monkeys. Neurons in the basal ganghathalamocortical “motor circuit”-SMA, PMC, MC and putamen-fire in preparation for the movement. These areas provide a major route for the inffuence of the basal ganglia on motor output. Together with parallel loops through the cerebellum they are involved in selecting the general class of movements to be executed via these corticosubcortical-cortical as well as cortico-cortical loops. In many cases each arrow represents a number of parallel pathways which may not interact within the target structure (Dum and Strick, 1991). Excitatory paths are marked with an arrowhead, inhibitory by a bar. SMA, supplementary motor area; PMC, premotor cortex; MC, primary motor cortex; SS, somatosensory cortex; GP, globus pallidus; SNr, substantia nigra pars reticulata; tbal., thalamus; STN, subthalamic nucleus; n., nucleus (after Alexander et al., 1990, Alexander and Cru tcher, 1992; Humphrey and Tanji, 1991).

spinal cord

Fig. 3. A schematic summary of some of the pathways in the primate oculomotor system. arranged as in Fig. 2. Posterior parietal cortex neurons respond when the monkey attends to a target and feed to frontal eye field (FEF) which exerts both direct excitatory control and an indirect release of inhibitory control on the superior colliculus. This push-pull mechanism ensures that saccades will most likely be made when their targets are selected by frontal processes. Frontonigral control can override the parietal signal so that not everything is looked at. FEF, frontal eye field, SEF, supplementary eye field; sup. toll., superior colliculus; inf. coll., inferior coiliculus; retie. form., reticular formation; other abbreviations as in Fig. 2 (derived from Alexander et al,, 1990; M. Goldberg and Wurtz, 1991).

JENNYKIEN and JENNIFERS. ALTMAN

18

These networks may be comparable to the preparatory system in locust walking but they have not been examined under equivalent conditions. The flight rhythm generator in locusts is activated by fast-conducting, wind-sensitive intersegmental interneurons and by a distributed flight-selecting network; little is known about the relationship between the two or their connections to the rhythm generator (see Kien and Altman, 1992 for detailed review). The wind-sensitive neurons (Bicker and Pearson, 1983; Boyan and Ball, 1989) may provide a rapid inflow of excitation to the flight system in the same way that the sensory stimuli rapidly excite the walking system. The flight-selecting network contains neurons originating in brain, SOG and thoracic ganglia that atborize in several of these ganglia (Kien and Altman, 1992). They are mainly tonic (Ramirez, 1988; Kien and Altman, 1992), as distinct from the rhythmically active neurons of the rhythm generating network. Some of the neurons provide an excitatory drive to the flight system but others inhibit neurons that themselves inhibit Aight (Ramirez, 1988). The neurons inhibiting flight could also be involved in activating other behaviours, for waIking may be evoked by microstimulation in the head ganglia at a site inhibit-

ing flight (Kien, 1983). Flight may, thus, be initiated both by the balance of activity in the seiection network and by the baiance between this and other networks (Kien, 1983; Altman and Kien, 1987a; Kien and Altman, 1992). Similarly in cats, descending pathways (Fig. 4). provide a tonic excitatory inflow that both initiates and maintains, or suppresses, locomotory activity in the spinal pattern generators. As in locust flight, there seem to be direct and indirect descending pathwaysa poiysynaptic pathway through the brain stem and a direct reticulospinal pathway to all levels of the spinal cord (Wetzel and Stuart, 1976; Armstrong, 1986, 1988). Evidence for both parallel and co-operative organization within an extensive network also comes from electrical stimulation which can evoke walking at a variety of sites within the cat brain or insect head ganglia (Wetzel and Stuart, 1976; Armstrong, 1986, 1988; Kien, 1983), although lesions at some of these sites do not abolish walking. In cats, such sites are the mesencephalic and subthalamic Iocomotory areas; STEP

CYCLE

r-w

11

spinatcord

Fig. 4. Simplified summary of the pathways underlying walking in cats, arranged as in Fig. 2. For simplicity, mainly the descending paths are shown, although there are several pathways from the spinal cord feeding back to various levels in the brain, This system is a multilayered system with its many individual parts interconnected by numerous parallel loops. Subthalamic and mesencephalic locomotory regions (SLR, MLR) receive inputs from the basal ganglia~alarn~o~i~l circuit and transmit their tonic signals via the pontin~medullary reticular formation and via PMLS to all levels of the spinal cord. Patterned signals are transmitted in parallel via the corticospinal, rubrospinal and lateral vestibulospinal tracts, all under cerebehar supervision. SLR, subthalamic locomotory region. In the brainstem: PPN, nucleus pedunculopontinus; MLR, mesencephalic locomotory region; PMLS, pontomedullary locomotor strip; other abbreviations as in Fig. 2 (after Wetzel and Stuart, 1976; Armstrong, 1986, 1988; Alexander et al., 1990).

Fig. 5. The activity in the output from the head ganglia during ongoing walking. The patterns of activity in the neurons are as complex as those during starting and stopping, although the responses of the neurons descending from the brain (CO corm) are less varied than those descending from the SOG (neck corm). Only some patterns correlate with the step cycle. Plotted as in Fig. 1 (after Kien, 1990b).

Insect and mammalian motor systems in insects, there are many sites in the brain especially in the dorsal deuto- and tritocerebrum. Spontaneous but not evoked walking can be abolished by section below the mammillary body in cat and by removing the brain and reducing the descending output from the SOG in insects. Maintaining locomotion The descending neurons active during the preparation and initiation of walking in locusts continue to fire as long as the animal walks (Fig. 5). Additional neurons are recruited after walking has started; like the neurons that fire during the preparatory phase, some of these transmit tonic, others patterned signals. The tonic firing may maintain walking by providing an excitatory inflow to thoracic pattern generators; a similar function has been suggested for tonically active descending interneurons in locust flight (Ramirez, 1988). The temporally patterned inflow may help maintain walking but it also contributes to shaping the thoracic motor patterns: stimulation of descending interneurons can alter most parameters of the step cycle and also step-by-step switching from one phase to another (Kien, 1983) indicating that these neurons are involved in a detailed ongoing control of stepping (Kien, 1990b). In cats, too, the descending pathways carry both tonic and patterned activity during walking and the functions of the two are not sharply separated (see Wetzel and Stuart, 1976; Armstrong, 1986, 1988 for reviews). For example, walking may be evoked by stimulating the corticospinal tract carrying patterned signals, possibly via collaterals to pons and medullary neurons involved in initiation and maintenance of walking and also via the caudal midbrain (Fig. 4). Some neurons in areas providing tonic signals also show phasic fluctuations in firing rate. The mesencephalit locomotory region-reticulospinal system, which provides tonic excitation to spinal pattern generators, also helps determine the force developed. A parallel can be drawn here with the neurons in the flight selecting network, known as the 404s whose tonic firing levels correlate with wingbeat frequency (Pearson et al., 1985). The patterned signals descend in parallel in the corticospinal, rubrospinal, reticulospinal and lateral vestibulospinal tracts (Fig. 4). During steady forward locomotion, neurons in the rubrospinal tract reinforce the pattern generator activity in flexor motor neurons during the swing phase and neurons in the lateral vestibulospinal tract excite extensor motor neurons during stance. Some reticulospinal neurons phase lock with flexion and others with extension. Many of the brainstem neurons that project to the spinal cord are heavily dependent on patterned input from the cerebellum which may mean that they, like the pattern generators, show a phase-dependence of responsiveness to inputs from other sources. This would ensure that supraspinal adjustments come at the appropriate times and do not disturb the overall equilibrium (Wetzel and Stuart, 1976; Armstrong, 1986, 1988). In insects, only part of the descending patterned activity correlates with step cycle, stepping of individual legs, walking speed or direction. In contrast, in brain-sectioned cats walking on treadmills all phasic

19

activity reported so far correlates with the step cycle. The correlations in both animals are with phases of the step cycle, swing or stance or parts thereof; in locusts, the correfations are clearly not with the actual movement as they are nearly always independent of the direction of walking. During steady walking in the cat, the primary motor cortex can alter the extent to which individual muscles are activated and may help in determining stance duration. Integration between the positioning signal from motor cortex and the rhythm generator signals to the limbs could be achieved by adding excitation or inhibition to the approp~ate combination of motor neuron pools (Armstrong, 1986, 1988; Georgopoulos and Grillner, 1989). The corticospinal tracts appear to be especially important in complex visuomotor co-ordination particularly of forelimbs (Armstrong, 1988). When exact foot placement becomes important, the corticospinal firing increases well above the level seen during steady walking on even surfaces and becomes markedly modulated during the step cycle. There are close analogies between the roles of the vertebrate brainstem and the insect SOG. During walking, both are essentiai for initiation and detailed ongoing control of stepping and carry out these functions through many separate and parallel pathways. They are also both involved in local motor activities, such as driving neck muscles, as well as in overall regulation of motor excitability (respiration, posture, locomotion) and integration with autonomic activity (heart rate, salivation and gut activity). Execution c$ episodic mot’emettts Neuronal activity during the execution of episodic movements, such as reaching in monkeys, can be compared with that during walking in cats (Georgopoulos and Grillner, 1989). The motor cortex contributes both to the independent use of the arm in episodic movements and to specifying the direction of movement and limb positioning; it plays a similar role in regulating forelimb movement when a cat walks on difficult surfaces (Armstrong, 1988). Its output descends to the spinal cord via several parallel tracts including the co~cospina1, rubrospinal, reticuIospina1 and tectospinal tracts (Fig. 2). These have inputs to a separate system of spinal interneurons which he above the C3-4 spinal segments and project to several proximal motor neuron pools. These interneurons which are active during reaching but not during walking on a treadmill are thought to be the route via which the motor cortex exerts these effects. They also feed signals back to the cerebellum and so may be important for the ongoing control of the reaching movement (G~rgopoulis and Grillner, 1989). Nothing is known about episodic movements in insects. FUNCTIONAL

LEVELS IN DESCENDING TO MOTOR CENTRFS

INPUTS

The bursting neurons that correlate with stepping during locust walking show various forms of coincidences with stepping functions, which we call motor fields. Motor fields comprising two or more legs can consist of one set of coincident stepping functions, several such sets or, more rarely, the same

20

JENNYKIEN and JENNIFER S. ALTMAN

step function for several legs, e.g. parts of the stance and swing of the first four legs. The presence of both bursting and tonic neurons and the organisation of the motor fields of the bursting neurons suggest that the descending input to the segmental ganglia contains at least three different levels of functional complexity operating simultaneously and in parallel: I. Tonic activity correlating with the whole behaviour and possibly involved in maintaining it. II. Activity correlating with either the function of the movements or the phases of the step cycle, independent of the direction of walking; these correlations can be with either a single set (predominantly one to two legs) or several sets of functions. III. Activity correlating with details of movements, e.g. femur position or direction of walking (Kien, 1990b). Note that some neurons classified in levels II and III are already active in the preparation phase. There is no sequential level-by-level recruitment of activity, thus levels do not correspond to the times when neurons start firing. A representation of the step cycle is set up predominantly in the head ganglia in terms of movement functions or sets of functions, simultaneously with the coding of the movements themselves, which takes place predominantly in the thoracic motor networks (see Burrows, 1987). This means that the functional levels are distributed although level II is represented predominantly in the head ganglia and level III in the thoracic ganglia. Similar functional levels can be identified in the descending pathways active during walking in cats. The tonic excitatory inflow that initiates and maintains or suppresses the locomotory rhythm generators are equivalent to level I in the locust, and the patterned activity which tunes the rhythm generators and motor neurons is equivalent to levels II and III. Similar levels of representation have been described in the organization of directed hand movements in monkeys: the target in motor co-ordinates; the direction of the planned limb movement, and the pattern of muscle contraptions in the arm. Supplemental motor area and premotor cortex (Fig. 2) seem to be engaged in the first level, higher order planning and programming of voluntary movement which may be equivalent to level I. Premotor cortex is also active at the second functional level; it is important for proper sequencing of motor output, structuring of skilled movement and guidance of limb in extrapersonal space. Cells in motor cortex which are probably corticospinal neurons are coupled more to the muscular details of movement and so would be equivalent to level III (Passingham, 1985; Wise, 1985; Okano and Tanji, 1987; Riehle and Requin, 1989; Humphrey and Tanji, 1991; Dum and Strick, 1991; Alexander and Crutcher, 1992). In the occulomotor system (Fig. 3), two computational levels have been distinguished. A general internal plan of movement (level II) is coded in the frontal eye fields of the frontal cortex and the details of the movement (level III) are represented in the intermediate layers of the superior colliculus. Neurons in the parietal cortex, involved in selecting the target, may be equivalent to level I (M. Goldberg et uf., 1991).

EACH REHAVIOUR

INVOLVES A LARGE NUMBER OF NEURONS

One reason for the popularity of invertebrate preparations and still the strongest argument given for their use is the belief that only small numbers of neurons are involved in each behaviour (e.g. Hoyle, 1976), a belief fuelled by the fact that isolated ganglia can still produce motor rhythms (reviewed in Kennedy and Davis, 1977). However, it is now clear that from hundreds to thousands of neurons are involved in behaviours like locomotion, stridulation, respiration, feeding and egg-laying in arthropods and molluscs (see Kien et al., 1992). Although this is certainly fewer than the millions of neurons active during learned behaviour in mammals (John et al., 1986), such numbers still pose considerable problems for understanding the neuronal interactions underlying motor output and prohibit description in terms of the actions of individual neurons. Invertebrate motor systems must, therefore, like vertebrate systems, be analysed in terms of the overall activity of groups or ensembles of neurons. Population coding has now been demonstrated in several invertebrate and vertebrate preparations. In insect walking, because so many descending fibres fire in different patterns at the same time (Figs 1,5) the only way to describe their activity is in terms of the overall pattern across the ensemble at any instant, i.e. its across-hbre pattern (Kien, 1983, 1990a). During walking in cats, the individual neurons in midbrain and brainstem fire in di~erent patterns and at different times in the step cycle. Their average activity is, however, concentrated at certain parts of the step cycle (Armstrong, 1986) suggesting that it is the population or ensemble rather than the individual neuron that specifies the movement. When a monkey reaches, the direction of arm movement is given by the overall vector of a population of directional neurons (Georgopoulos et al., 1988). In the crayfish, which can walk in any direction, the direction of walking is also given by the vector of the overall activity in an ensemble of dir~tionally selective motor intemeurons (Chrachri and Clarac, 1989). Also in the crayfish, the many intemeurons involved in organizing episodic extensions or Aexions of the abdomen each make only a small contribution to the total motor output, although electrical stimulation of one neuron can recruit activity in synergistic interneurons and inhibit antagonists to drive a movement (Larimer, 1988).

LOOPS AND HOW ENSEMBLES CO~RIBU~ TO THEM

We have previously proposed that the motor centres of the locust head and thoracic ganglia are parts of loops or recurrent pathways (Fig. 6), each concerned with a different combination of functions but all working in parallel (Altman and Kien, 1987a, b, 1989; Kien 1990a, b; Kien and Altman, 1992). The loops between the brain and thoracic ganglia are concerned more with selection and preparation than with detailed organization and execution, whereas the loops between the SOG and thoracic gangfia do both (Kien, 1990a, b). The walking system of vertebrates

Insect and mammalian motor systems

21

CO-OPERATION AND CONSENSUS IN MOTOR SYSTEMS

Wetzel and Stuart (1976) have argued that the single most important feature of supraspinal mechanisms involved in stepping in the vertebrate is co-operative interaction-there is not any single or necessary structure but instead the final output represents the collective actions of a number of pathways. We have ~stulated the equivalent for the system underlying locomotion in insects: that the final output is a consensus represented by the across-fibre pattern in all the loops (Kien, 1983; A&man and Kien, 1987a, b, 1989; Kien and Altman, 1992). In the systems underlying episodic movement in mammals, it is now becoming accepted that the final motor output may be based on the temporal coincidence of various neural representations spatially distributed across parallel pathways (Wise et al., 1991). In all these systems the outputs must interact with the mechanics of the muscles and skeletal systems, so that the actual movements result from a consensus between neuronal and mechanical activity (Altman and Kien, 1987a). Fig. 6. Schematic overview of the pa~~ays of the insect motor system, drawn here without considering bifateratity. The pathways form a number of paraBe and overlapping loops between the brain, suboesophageal ganglion (SOG) and the body ganglia. Each loop carries a different weighting of motor functions; e.g. the brain-body ganglia loop is predominantly involved in preparation of locomotion, the

SOG--body ganglia loop participates both in preparation and detailed ongoing control of movement. Dashed line represents mechanical coupling and reafference, i.e. feedback generated by the animal’s own movements. The output of the whole system, shown here as a single motor pathway, is a consensus of the activity in al1 the loops (Altman and Kien, I987b; Kien, I990a, b).

can also be described as a number of loops working in parallel: the multiple pathways involved in walking form loops between premotor cortex, primary motor cortex, subthalamic and mesencephalic locomotory areas, ponto-medullary reticular formation and cerebellum (Fig. 4). Each loop can be considered as a distributed ensemble of neurons and the final output of the system is determined by the balance of activity among them (Wetzel and Stuart, 1976). The neural system subserving episodic arm movements is also a distributed system (Fig. 2), which may partly overlap with the walking system. During execution of reaching, there is a loop of activity from areas 2 and 5 of parietal cortex to the motor cortex and cerebellum returning to the motor cortex via the thalamus (Georgopoulis and Grillner, 1989). In the occulomotor system (Fig. 3), the network responsible for the selection of targets which evoke saccades consists of loops between neurons in parietal cortex, frontal eye fields, superior colliculus, caudate nucleus and substantia nigra (M. Goldberg et al., 1991). In both arm and eye movements, as in walking, it is the overall balance among excitatory and inhibitory loops which determines whether or not the movement is to be generated (see also Brooks, 1986). Different loops appear to dominate when the movement is endogenously generated than when ii is environmentally cued (G. Goldberg, 1992).

MOTOR OUTPUT IS ACHIEVED BY PARALLEL DISTRIBUTED PROCESSING

The description of the motor system in terms of distributed ensembles arranged in multiple loops leads to a new concept for motor control as the outcome of parallel distributed processing. This view contrasts with the older idea of a sequential process leading from higher to lower motor areas. Clear evidence against such serial or linear models has come from both anatomy and physioiogy. In the mammalian motor system, all cortical motor areas have parallel direct proj~tions to the spinal cord, basal ganglia and cerebellar circuits, which in turn project back either directly or indirectly to motor cortex. Furthermore, there are strong reciprocal connections between the cortical motor areas (Wise et al., 1991). Analyses of neuronal activity during goal-directed arm movements in monkeys demonstrate that there are several functional levels-parallel representations of the target, of limb direction and of pattern of muscle recruitment in the arm regions of the supplementary motor cortex, motor cortex and putamen. However, the neurons operating at these different levels are active at the same time, and not sequentially as would be predicted by a serial model (Alexander and Crutcher, 1992). SimilarIy, in insect motor systems, there are several levefs of increasing functional complexity rather than serial processing (Kien, 1986, 199Ob). The various computational levels required for walking-selection of the overall programme, representation of the step functions in one or more legs and representations of the actual movements-are distributed throughout the nervous system and are active simultaneously and in parallel. Although the higher levels tend to be represented in the head ganglia and the lower levels in the thoracic ganglia, the separation is only partial. Parallel processing does not exclude the hierarchy of functional levels that we have discussed above. Nor does a functional hierarchy necessarily imply a serial process; instead, operations at different functional Ievels can be carried out simultaneously.

22

JENNY KIEN and JENNIFER S. ALTMAN

This requires multiple interconnections between neurons operating at different functional levels but a system with this architecture can achieve rapid changes across the whole system. SHIFTS

IN NEURONAL ACTlVITY WITH PROGRESSION FROM PREPARATION TO EXECUTION

In both insects and mammals, there is a shift in the emphasis of neuronal processing from higher to lower functional levels as behaviour progresses from preparation to execution. Note that this shift is not absolute; some processing at higher levels continues throughout the behaviour. In insects, the brain and suboesophageal ganglion are equally involved in selecting walking (level I). Then, as preparation progresses and with release of walking, more neurons predominantly involved in planning and organizing the details of walking (levels II and III) are recruited from the SOG. As the neurons involved in selecting walking continue to fire throughout walking, this results in an increase in the total number of active neurons of which a greater proportion now operate at lower functional levels. That is, the shift from higher to lower levels of processing results from adding a large proportion of neurons active at lower functional levels to the active population. In mammalian motor systems, a shift of emphasis from processing at high levels to lower levels results from partial separation of the networks involved in preparation from those involved in execution and from an overall change in the population of active neurons (Alexander and Crutcher, 1992). Before a saccade there is activity at the level of selecting the target (level 1) and general planning of the movement (level II) in the parietal cortex, frontal eye fields, substantia nigra, caudate nucleus and superior layers of the superior colliculus. Execution of the movement results from activity directly related to details of the movement (level III) in the intermediate layers of the superior colliculus and the pontine reticular formation. There is, however, some overlap between the circuits as the activity of some neurons in the frontal eye field is also related directly to details of the movement to follow (M. Goldberg et al., 1991). The circuits underlying preparation and execution of reaching movements are also partially separated; preparation and higher level planning of the movement is predominantly subserved by the supplementary motor and premotor cortex, detailed planning and execution by the motor cortex. This separation is only partial as a11 three regions contain neurons active at all levels and some of the neurons active at higher levels continue to fire during the movement (Alexander and Crutcher, 1992). The partial separation of the circuits for planning and execution in mammals allows preparation of the next movements during execution of the previously planned ones. This means that there can be fluent and fast transitions from one set of movements to the next and that the animal need not be helplessly inactive until preparation of a movement is completed. Planning the next set of movements parallel with the execution of the current set could account for the temporal segmentation observed in the natural behaviour

of primates; action units have a very narrow range of durations, 1-4 set in humans and 0.5-3 set in chimpanzees (Kien et al., 1991; Schleidt and Kien, 1992). This temporal segmentation could occur if the preparation circuits have a finite capacity and can only plan the next few seconds ahead. The planned activity would then shift to the execution circuits time segment by time segment. NEURAL NETWORK AND MA~f~ALiAN

MODELS FOR INSECT MOTOR SYSTEMS

The operations of parallel distributed systems containing from thousands to millions of neurons cannot be understood intuitively and so modelling at a systems level is now essential. One such approach which has already led to a new understanding of some electrophysiological results is neuronal network modelling. The receptive fields of the interneurons subserving bending in the leech, for example, did not appear to match the known selectivity of the behaviour. In computer simulations of the bending system, the elements of feed forward neural networks trained by back propagation developed receptive fields similar to those found for the interneurons and the simulations showed how they co-operated to produce appropriate movements (Kristan et al., 1992). The same problem arose in the vertebrate vestibular system where the responses of neurons in the vestibular nucleus are seemingly random. Again, simulations with back propagation trained networks has advanced the understanding of how these responses could drive the final output of the system (Anastasio and Robinson, 1989). The feed forward networks, however, do not capture the looped nature of motor systems nor are they capable of spontaneous change. An alternative approach is an informal model we have developed for the basic motor organization which is based on the insect motor system (Altman and Kien, 1989; Kien and Altman, 1992). The model consists of several stations each containing local networks, e.g. brain, SOG and thoracic ganglia connected together in parallel loops to form a network. The output of each station results from a consensus between the activities of its inputs and its local networks and, as the local networks are unique to that station. the output of each station is different. The stations all receive a version of the same inputs as well as reports from the other stations. As the stations are connected in parallel loops, the output of the whole system is the consensus of the activity in all the loops. Because of the loops, no function can be localized to one station but is carried out in the loops. A single loop may carry a number of functions and one function may be distributed across several loops. For example, selection of walking involves both brain-body and SOG-body loops but the SOG-body loop dominates during the planning and execution of movements. The comparisons between the mammalian and insect motor systems presented here show that the same type of model can be applied to both. That is, our informal consensus model of local stations embedded interactive loops is just as appli~ble to mammalian systems as it is to insects. The use of a model could represent the first step in a systems

23

Insect and mammalian motor systems

treatment for a whole mammalian motor system and would facilitate the fitting of detailed local analyses into an overall systems context. Because our informal model has a feedback architecture it can be treated as an attractor neural network: reaching consensus in the model is equivalent to the relaxation of an attractor network and its output is the attractor-like state that a neural network reaches (Altman and Kien, 1989; Kien, 1992; Kien and Altman, 1992). Attractor networks are best known from their usefulness in simulating less dynamic memory systems (Gelperin, 1986; Amit, 1989). However, it has now been shown that they are easily capable of learning and recreating episodic sequences (Bauer and Krey, 1990) and we argue elsewhere that their dynamics are more than adequate for modelling motor systems (Niitzel ef al., in preparation). Indeed, the attractor neural network is at the moment the only model capable of reproducing spontaneity and the variability so characteristic of biological systems. Aside from their usefulness as models for motor systems, it is further possible that biological motor systems are indeed organized in the same way as attractor networks. Importantly, attractor networks are nonalgorithmic, which means that their activity cannot be calculated or predicted over long periods. The possibility that the neuronal activity underlying movement derives from such nonalgorithmic processes should have far reaching consequences for future approaches to analysis of motor systems. Acknowledgement-Research

was supported by SFB-4 Project H2 of the Deutsche Forschungsgemeinschaft. REFERENCES

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