Sensorimotor Interactions During Locomotion - Semantic Scholar

Autonomous Robots 7, 239–245 (1999) c 1999 Kluwer Academic Publishers. Manufactured in The Netherlands. °

Sensorimotor Interactions During Locomotion: Principles Derived from Biological Systems AVIS H. COHEN AND DAVID L. BOOTHE University of Maryland, Department of Biology, College Park, MD 20742

Abstract. Rhythmic movements in biological systems are produced in part by central circuits called central pattern generators (CPGs). For example, locomotion in vertebrates derives from the spinal CPG with activity initiated by the brain and controlled by sensory feedback. Sensory feedback is traditionally viewed as controlling CPGs cycle by cycle, with the brain commanding movements on a top down basis. We present an alternative view which, in sensory feedback alters the properties of the CPG on a fast as well as a slow time scale. The CPG, in turn, provides feedforward filtering of the sensory feedback. This bidirectional interaction is widespread across animals, suggesting it is a common feature of motor systems, and, therefore, might offer a new way to view sensorimotor interactions in all systems including robotic systems. Bidirectional interactions are also apparent between the cerebral cortex and the CPG. The motor cortex doesn’t simply command muscle contractions, but rather operates with the CPG to produce adaptively structured movements. To facilitate these adaptive interactions, the motor cortex receives feedback from the CPG that creates a temporal activity pattern mirroring the spinal motor output during locomotion. Thus, the activity of the motor cortical cells is shaped by the spinal pattern generator as they drive motor commands. These common features of CPG structure and function are suggested as offering a new perspective for building robotic systems. CPGs offer a potential for adaptive control, especially when combined with the principles of sensorimotor integration described here. Keywords:

1.

locomotion, sensory feedback, central pattern generator

Introduction

Movement through the world of either an organism or robot requires that the environment be integrated and understood. An organism or robot must navigate over or around obstacles, and seek and manipulate desired objects. This requires further that the robot or organism integrates the visual, auditory and other environmental information within its machinery for locomotion. Traditional robotic approaches separate sensory processing systems and the locomotory control system. These two types of systems require a common representation for communication. This representation is often based on an accurate geometric representation of the external world and of the robot itself. Environment and robot models can be difficult and computationally costly to instantiate from instant to instant. As a consequence the behavior of robotic systems is often fragile when presented with complex, realworld stimuli.

Each of us in our daily interactions with the world is familiar with the seamless way in which our nervous system combines sensory and motor information. Thus we know in a simplistic sense that biological sensory and motor systems are integrated. How this integration is performed by a biological system is currently poorly understood. Much work has been done on motor and sensory systems in isolation. Less has been done on how sensory and motor systems interact. In this paper we will offer an interpretation of currently existing empirical evidence from biological systems, and argue from this for some very basic, but perhaps surprising properties of the biological systems performing sensorimotor integration. From these properties, we will propose potential schemes for sensorimotor integration in robotic systems. As opposed to the traditional robotics approach, biological systems have evolved skeletons and muscles

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with mechanics that are highly suited to their tasks. To control these structures, there have co-evolved neural networks that can produce motor output patterns that rhythmically activate the muscles adaptively. These so-called central pattern generators (CPGs) were first proven to exist in vertebrates by Grillner and Zangger (1979). They showed unequivocally that the spinal cord contained complete instructions for the motor pattern required for locomotion. The proof consisted of functionally isolating the cat spinal cord and eliciting locomotion by means of pharmacological excitation of the spinal circuits. This was further demostrated in isolated lamprey spinal cords (Cohen and Wall´en, 1980). Activity in CPGs is typically initiated by structures in the brain or powerful sensory stimuli. Sensory feedback, as we will discuss here, also plays a role in shaping the CPG motor patterns. Much of the experimentation on sensorimotor integration has been focused on how sensory information modulates motor system activity. The flow of information commonly explored is from sensory to motor. Two main types of experiment have been performed to elucidate this relationship, ‘reflex’ type experiments, and studies of sensory inputs and their modulation of CPGs. Peripheral feedback including that from skin, joint, and muscle receptors passes up to the somatosensory cortex, but first the receptors synapse within the spinal cord where there are reflex circuits well known to respond to such sensory inputs. Additionally and more functionally relevant, sensory input is filtered and processed locally where the spinal pattern generator circuitry fits its response into the ongoing locomotion if appropriate. Thus, although the brain receives much of the sensory input, the responses to spinal inputs are first the responsibility of the local spinal circuitry. A discussion of the impact of sensory information on central pattern generators makes up Section 2. The influence of motor systems on sensory activity is less well characterized. Recent evidence has begun to show that motor systems can strongly influence sensory input. We will show that the interplay between sensory feedback and motor output is more complex than had been previously suspected and that behavior is dramatically affected by movement and movement related activity. How motor systems influence sensory systems will form the subject matter of Section 3. Studying motor and sensory systems in isolation one can easily miss the massive interaction between these systems. An understanding of this interaction is crucial

Figure 1. Traditional view treats sensorimotor interactions as unidirectional.

for explaining the simplest behaviors of an organism in its environment. What is most surprising about this interaction is not that it exists, but its pervasiveness. Interaction between motor and sensory systems exists from the first steps of sensory detection to the highest levels of processing. Section 4 will argue that this type of two-way interaction also exists between the cortex and spinal cord. The traditional view is that the spinal cord would perform rhythmic and rapid reflexive type behaviors that required the spinal circuitry and its speed locally whereas more complex activities would be the province of cortex or at least supraspinal centers (Fig. 1). In this view, the descending systems send commands to lower level systems that appropriately respond. For some time now, it has been becoming clear that this distinction between rhythmic movements and complex voluntary movements may not be so marked (e.g. MacCrea, 1995; Jankowska and Lundberg, 1981). Cortical commands now are viewed as acting through CPG circuitry to produce volitional movements. We present evidence that the integration is considerably richer than the old view or even than the emerging new view would suggest. There is no doubt that cortical systems contribute to sensory-motor integration. What is doubtful is that motor cortex sends commands to an obedient spinal cord, and that these commands follow only after spinal circuits have independently and thoroughly processed all incoming information. In the view that we present, spinal cord and cortex are not so separate. Movements result from a more ongoing interactive process than that suggests. The actual form that movements take is largely a product of spinal circuitry and its synergies,

Sensorimotor Interactions During Locomotion

while the cortex is expected to perform other more complex tasks that integrate somatosensory and motor systems with other inputs to which the cortex has unique access such as vision, audition, and olfaction. 2.

Control of CPGs by Sensory Feedback

The traditional view of sensory feedback has been that it provides information regarding the position of the organism in space. Appropriate to this role, feedback can correct a CPG, and the relative position of the limb segments, on a cycle by cycle basis to maintain the organism in a proper relationship to the environment (Rossignol et al., 1988). For example, the hip joint angle of the cat can trigger a new step cycle as the body is propelled over its respective limb on the ground (Forssberg, 1979; Andersson and Grillner, 1983). Activation of muscle stretch receptors has also recently been found to trigger a new step cycle through contractions of several limb muscles (Hiebert et al., 1996). In these ways, the cycle periods will accommodate changes in the velocity of the animal. Similarly, the bending of the tailfin in dogfish or lamprey entrains the swimming so that swim cycles are appropriate lengths for the environmental conditions (Grillner and Wall´en, 1977). Thus, if the body is not able to adequately bend against a strong current, this will be compensated for by a longer cycle. This type of sensory regulation is accomplished at the level of the spinal cord and requires no descending input although descending input is likely to influence the responses if present (Forssberg, 1979). All CPGs must have some stimulus that can trigger or prolong a new cycle as necessary in order to guarantee the CPG’s movements are adaptive. Another well-documented role for sensory feedback is to elicit reflexive responses to environmental perturbations. This is also accomplished at the spinal level where sensory inputs are gated through the CPG during ongoing activity (Rossignol et al., 1988; Sillar, 1991). A sensory stimulus can elicit phase dependent responses that are quite unlike the reflex responses that such stimuli would induce in the absence of CPG activity. For example, an obstacle encountered by the paw dorsum will produce an enhanced flexion during the flexion phase of the step cycle, but it will produce an enhanced extension during the extension phase (Forssberg, 1979). This guarantees that the limb is properly supported at the moment that it is raised to avoid the obstacle. The contralateral limb is also integrated with such responses (Hiebert et al., 1994). That

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is, the limb opposite the stimulus must be positioned to support the responsive limb before it will flex over such an obstacle (Hiebert et al., 1994). Such phase dependent responses to perturbations are very common across CPGs. For each rhythmic movement there are classes of stimuli that elicit such responses (Rossignol et al., 1988). In the case of locomotion the response requires no input from descending systems and is seen in spinal animals as well as intact animals (Forssberg, 1979; Hiebert et al., 1994).

3.

Sensory Feedback and CPGs—A Two-Way Interaction

A. Feedforward Control of Sensory Feedback by CPGS More recently new evidence has accumulated to indicate that the relationship between the CPG and its sensory feedback is not unidirectional (Fig. 2). Dubuc et al. (1988) first found that there is a feedforward signal coming from the CPG that can be recorded as an slow electrical wave at the cut end of the dorsal root nerve. This nerve is composed of the axons from the sensory receptors in the periphery, and the slow electrical wave must be originating in the spinal cord. The slow electrical wave is correlated with the rhythm emanating from the CPG circuits in the spinal cord. The amplitude of the slow wave is sufficient in some cases to trigger action potentials in the sensory fibers even during fictive locomotion, that is locomotor muscle activity in the absence of movement (Gossard et al., 1995). Thus, the signals cannot be coming from any

Figure 2. CPG feedsforward onto sensory receptor inputs and sensory receptors active slow and fast adjustments of CPG pattern.

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source other than the CPG. Libersat et al. (1989) found a similar phenomenon in cockroach flight, as did Wolfe and Burrows (1995) in locust walking, and Vinay et al. (1996) in lamprey swimming. This phenomenon thus seems to be quite general, occurring across a wide range of animals from invertebrates and vertebrates from cats to lampreys. In the cockroach, the role of the phasic modulation seems quite straightforward to interpret. It apparently provides the circal fibers with activity that is phase locked to the flight as a kind of preemptive sensory input during the movement (Libersat et al., 1989). The situation in cat is more problematic, apparently, and the role of the presynaptic modulation remains somewhat uncertain (Nussbaum et al., 1998). B. Long Time Scale Sensory Effects on CPGs Evidence has been accumulating for additional roles that sensory feedback may play. We and others have found that movement and the sensory feedback generated by that movement can change the state of the CPG and significantly alter the pattern of the behavior. In some cases the changes are on a time scale that outlasts a single cycle and have effects which are not predicted by any of the above examples. Two invertebrate and one vertebrate examples will be presented. In the case of locust flight, removal of the hindwing tegulae results in an immediate change in the motor pattern. The wingbeat frequency decreases and the interval between the activity of depressor and elevator muscles increases. Over a period of two weeks, the motor pattern can return to normal even without regeneration of the tegulae (Ramirez and Pearson, 1993). These changes indicate that the intact frequency and phase structure of the movement is normally a function of both the CPG and the input from the wing sensors although other sensors may contribute in their absence. In other examples, the state changes evoked by sensory stimuli can be shown to continue over a time period that outlasts the sensory stimulation by varying numbers of cycles (Dykstra et al., 1995; Katz and HarrisWarrick, 1990). Katz and Harris-Warrick demonstrated that activation of the crustacean gastropyloric receptor cells by gastric mill activity increased the frequency of the pyloric rhythm for a prolonged period of time. It is known that the gastric mill and pyloric rhythms are interrelated (Weimann et al., 1991), and functionally, it is not likely that one would appear without the

other. Thus, activation of the gastropyloric receptors is a natural consequence of stomatogastric ganglion activity, and therefore, likely to alter the frequency of the pyloric rhythm whenever active. In the lamprey, a small amplitude bending movement of the isolated spinal cord has been shown to entrain the rhythm of fictive swimming, but Dykstra et al. (1995) showed that it also leads to a speeding of the bursting that outlasts the bending for one or more cycles. The bending required for this effect can be very low amplitude, and can last for as few as one cycle. Consequently, the slowly decaying excitatory effect of the movement is apt to be seen during intact locomotion. In these three examples the sensory feedback would be a direct consequence of the muscle activation pattern generated by the CPG during its normal activity. This type of slowly decaying excitation seen in lamprey seems to be an example of positive feedback. That is, the CPG generates movement that in turn causes the CPG to go faster. The role for this type of excitation is unclear. A modeling study (Verschure and Cohen, submitted) offers some insights into possible roles this excitation might play. The model consisted of the six element neural network model for the lamprey segmental oscillator coupled to an element descending from the brain (Jung et al., 1996) and receiving steady drive to initiate bursting. This model first proposed by Grillner et al. (1991) had been analyzed extensively by Jung et al. (1996). To it was added slowly decaying positive feedback induced by movement. Remarkably, the bursting remained controlled and stable over a wide range of parameter values. Rather than being destabilizing this type of slowly decaying excitation seems to offer a new potential control mechanism. Interestingly, the system became autonomous with no need for tonic drive and with the frequency of bursting highly sensitive to the gain and the duration of the slowly decaying excitation. This type of response may well depend on the balance of excitation and inhibition in a particular network. There was no attempt to test the effect of input with fewer or weaker inhibitory connections. However, it seems apparent that well studied CPG circuits have no shortage of inhibitory neurons (Grillner et al., 1991; Sharp et al., 1996). While such control mechanisms remain conjectural, the model does suggest that such types of excitation might perhaps be examined in new ways. One fairly definite role that this excitation could play is to change the gain on the system. That is, it seems likely that the excitation could increase the responsiveness of the CPG to other inputs.

Sensorimotor Interactions During Locomotion

Such a mechanism offers interesting additional control strategies for robotics. In the lamprey, the changes induced by the movement have now also been implicated in altering the coupling between the segments, where the units of the CPG are hypothesized to correspond with some small number of segments from one to three. The intersegmental coupling guarantees the phase delays among the segments as well as the frequency of the movement. This change in coupling was found in lamprey spinal cords induced to swim with the muscle present. There was no brain or tail, with the spinal cord kept as it is normally during generation of fictive swimming. The spinal cord was exposed to the bath by removal of the dorsal musculature, but the other muscle was left intact. The swimming movements in such a preparation were consistently faster than the bursting of the respective isolated spinal cord. The phase delays were also consistently shorter. Thus, there is an alteration in the basic parameters of the movement in the presence of the muscle and movement. Using methods developed by Kiemel and Cohen (1998) the characteristics of the intersegmental coupling were measured. In the presence of movement, the total coupling strength was found to be greatly enhanced in all spinal cords tested. There were also changes in the ratio of the ascending to the descending short-range coupling. We now have evidence that in the normal isolated spinal cord of the lamprey that the ascending short-range coupling is stronger than the descending short range coupling (Gormley, Williams, and Kiemel, unpubl.). However, in the presence of the movement and the movement-related feedback, the short coupling is predominantly descending. Thus, movement and movement-related feedback can alter not only the movement parameters but the underlying functional properties of the spinal circuitry (Guan et al., submitted). This complexity is seen in the absence of the brain, and thus depends solely on the spinal cord-body interface. 4.

Brain and CPG Interactions

Modeling studies of spinal cord often treat it as a series of bidirectionally coupled oscillators (Cohen et al., 1992). The coupling between the segmental oscillators in lamprey is very strong (Mellen et al., 1995, Kiemel and Cohen, 1998) and is likely to be strong in other vertebrates as well. As such the removal of one such oscillator or input to any oscillator will alter the behavior

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Figure 3. Interactions between cerebral cortex and CPG are bidirectional.

of other oscillators regardless of their location within the informational stream. One can conclude from this that as sensory input travels up the spinal cord it is shared with local circuits before it reaches the cortex. However, additional evidence will need to be examined to truly make the case that motor and sensory systems are integrated across all levels, and not just across the spinal cord. For additional evidence in this regard, we turn to spinal-cortical interactions which appear to be highly integrated (Beloozerova and Sirota, 1998) (Fig. 3). Indeed, there is evidence that neurons of the primary motor cortex of cats are phasically modulated during locomotion of the animal with phasic modulations also preferentially found in neurons descending from the motor cortex (Armstrong and Drew, 1985; Beloozerova and Sirota, 1993). Thus the strongest descending cortical outputs are most likely to be influenced by preexisting motor activity. This could mean that as the cortical neurons send motor commands to the limbs, the signals are apt to be properly timed to fit within the context of the ongoing locomotion. This is reasonable as descending neurons must act in the proper context of the animal’s movement or the commands will be inappropriate and maladaptive. Neurons in the posterior parietal cortex of awake unrestrained rats have also recently been found to exhibit modulation linked to motor activity (McNaughton et al., 1994). Many of these cells responded strongly to specific types of locomotion. However, often these same cells would not respond to locomotor movements such as left turns or right turns. If the cells were responding to somatosensory or environmental information alone one would expect them to respond in either

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the left turn or right turn case or to some specific position in space. In some animals a specific motion such as turning was compared with an attendant somatosensory state such as passive bending of the animal. Less than twenty percent of the movement related activity seemed to be explained by possible somatosensory or environmental inputs. Thus the vast majority seemed to be predominantly motor in their responses. Recently it has been shown that the presence of the cortical systems can speed up and enhance the responses over that of spinal animals (Hiebert et al., 1994). In some ways, this is counter-intuitive, as the spinal cord should be faster alone. There are a number of possible explanations for this phenomenon. One is that cortical neurons can alter the response properties of the spinal neurons making them more excitable, or perhaps the cortex is more adept at responding to unexpected events. Whatever the explanation, while the spinal cord has the capacity to respond by itself, in intact animals the descending systems will participate to make the responses more adaptive. This more rapid response in intact animals to certain types of stimuli is still consistent with integration taking place locally as spinalized animals do respond to the stimuli, they simply do so much more slowly. What cortex could be providing is some expectation or change in gain such that it picks up that something is novel more quickly than the spinal cord alone. It has been known for some time that there are variations in the vigorousness of responses of decorticated and intact cats to the stumble reflex (Forrsberg, 1979). Taken together with the earlier discussion the above three cases show that sensorimotor interactions across all levels is bidirectional, and CPG generated activity has influences on cortical areas thought only to be influenced by or involved in the processing of sensory activity. 5.

Conclusions

When one considers central pattern generators and their interactions with sensory input, one sees a remarkable range of phenomena. The simple stimulus-response reflex is a minimal component. At the spinal cord level, responses to sensory stimuli are modulated by the CPG in a highly dynamic and non-linear manner to produce adaptively altered responses, and sensory stimuli adjust the length and amplitude of the CPG cycle. However, sensory input is also filtered by the CPG itself, while sensory input alters the state of the spinal

activity on a slower time scale and in more ways than has typically been thought possible. Sensory input performs the traditionally considered corrective and monitoring functions, and in interaction with the CPG it can also change the overall frequency and relative timing of the muscles as well as altering intersegmental coordination. At the cortical level, CPG input modulates the activity of pyramidal cells most likely so that their descending signals are apt to occur at appropriate times in the cycle. The goal is for movement of animals as well as robots to be adaptive and efficient. This can only occur if all levels of the system are aware of and responsive to activity at other levels. We suggest that several general principles for robot design and control come from the empirical studies we presented above. First, we suggest that CPGs modeled as non-linear oscillators offer an efficient design strategy for robots. This is true regardless of the form of the locomotion, especially when the CPG is matched to the mechanics of the robot (Lewis, 1999). An additional principle is that sensory feedback can be used to parametrize the movement such that speed and joint angles accommodate the terrain and ongoing state of the robot. Further, sensory feedback may be filtered by the robot controls to give adaptive phase dependent responses and not simple “reflex” responses. Finally, all levels of control may be well integrated avoiding the use of “driver-slave” relationships in which there is inadequate bidirectional feedback. References Andersson, O. and Grillner, S. 1983. Peripheral control of the cat’s step cycle. II. Entrainment of the central pattern generators for locomotion by sinusoidal hip movements during “fictive locomotion”. Acta Physiol. Scand., 118:229–239. Armstrong, D.M. and Drew, T. 1985. Forelimb electromyographic responses to motor cortex stimulation during locomotion in the cat. J. Physiol. (Lond), 367:327–351. Beloozerova, I.N. and Sirota, M.G. 1998. Cortically controlled gait adjustments in the cat. Ann. N Y Acad. Sci., 860:550–553. Beloozerova, I.N. and Sirota, M.G. 1993. The role of the motor cortex in the control of vigour of locomotor movements in the cat. J. Physiol. (Lond), 461:27–46. Buschges, A., Ramirez, J.-M., and Pearson, K. 1991. Reorganization of sensory regulation of locust flight after partial deafferentation. J. Neurobiol., 23:31–43. Cohen, A.H., Ermentrout, G.B., Kiemel, T., Kopell, N., Sigvardt, K., and Williams, T. 1992. Modelling of intersegmental coordination in the lamprey central pattern or for locomotion. TINS, 15:434– 438. Dubuc, R., Cabelguen, J.-M., and Rossignol, S. 1988. Rhythmic fluctuations of dorsal root potentials and antidromic discharges of

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