Comparative Psychology of Motor Systems Research Paper

To understand locomotor systems requires a multilevel approach ranging from the cellular level (i.e., identifying the neurons involved, their intrinsic properties, the properties of their synaptic connections, and the role of particular transmitters and neuromodulators) to the system level (i.e., determining the functional integration of these networks in complete motor programs). Our current understanding of locomotor networks is the outcome of investigating and comparing various invertebrate and vertebrate locomotor systems in which rhythmic behaviors can be studied on multiple levels, ranging from the interactions between identifiable neurons in identified circuits to the analysis of gait. This review will focus on (a) the principles of cellular and synaptic construction of central pattern-generating networks for locomotion, (b) their location and coordination, (c) the role of sensory signals in generating a functional network output, (d) the main mechanisms underlying their ability to adapt through modifications, and (e) basic features in modulating the network function.

Due to the limited space available for this introduction to this lively and fast-developing field in neurosciences, the authors will restrict citations mostly to recent, in-depth reviews on individual aspects mentioned and will refer to original articles only as specifically needed.

Understanding the Act of Progression: Historical Aspects

The problem of how locomotion is generated has been considered for more than 2,400 years, starting in the time of Aristotle (about 400 B.C.). Between the second and third centuries A.D., the Aristotelian concept of a vital pneuma (transformed from the omnipresent ether by the lungs and transported by the bloodstream to the muscles) as the ultimate cause underlying locomotor ability was first modified by Galen, who discovered that nerves originating in the brain and spinal cord innervate the muscles. In the seventeenth century, Descartes and Borelli integrated Galen’s discoveries in a more mechanically based theory, suggesting that muscles contract by a corpuscular animal spirit released from the nervous system. In the mid-nineteenth century (and based on the work in the eighteenth century by Schwammerdam, Galvani, and others), Matteucci, Helmholtz, and du Bios-Reymond discovered the electrical properties of axons and their implication for neuromuscular transmission (the modern term synapse was adopted much later by Sherrington in 1897). Benefiting from the general progress in detailed anatomical knowledge and with a new basic concept (Cajal’s neuron doctrine), late nineteenth-century physiology initiated a common understanding of the nervous system’s function and its role in the generation and control of behavior (for an in-depth review of the early history, see Bennett, 1999).

The Neural Basis of Locomotor Pattern Generation: A First Concept

At the end of the nineteenth century, the discovery of proprioceptive pathways in the nervous system (e.g., by Bell, Golgi, and Kühne in the mid- nineteenth century; early review: Sherrington, 1900), the description of numerous different reflex responses in the limbs of monkeys, dogs, and cats after skin or nerve stimulation (establishing what are called reflex laws; Pflüger, 1853) and the apparent resemblance of these reflex responses, including scratch reflexes and spinal stepping, to parts of the limb-movement cycle during real locomotion led to the idea that the antagonistic activation of effector organs during locomotion might be triggered by feedback from sense organs in the skin and the moving parts of the body. Coordinated limb movement during locomotion was thought to be the result of a chain arrangement of these reflex arcs. Remarkably, this concept already included the principle of a reciprocal innervation of antagonistic muscles (Sherrington, 1905a, 1905b) and the demonstration of postural reflexes (Sherrington, 1900a).

Toward a Concept of Central Control of Locomotion

In the early twentieth century, the putative role of the spinal cord in the basic generation of locomotion was established by experiments in mammals, mainly in the dog and the cat, that could still produce alternating leg movements after the brain was disconnected (Brown, 1911, 1912; Sherrington, 1905a, 1905b). In cats, Brown (1911) demonstrated that the spinal cord was capable of producing locomotor patterns after complete deafferentation of the moving limb. He concluded that alternating rhythmic movements derive from a central spinal process and proposed a simple half-center model as the basis of the alternating activity of flexors and extensors during walking: Each half-center is responsible for activating either flexors or extensors, and both half-centers are connected by reciprocal inhibition in order to silence one center while the other is active. Muchlater, when reciprocal inhibition was first shown on the interneuron level in the spinal cord (Jankowska, Jukes, Lund, & Lundberg, 1967a, 1967b), the suggested spinal network organization incorporated the basic features of Brown’s half-center model.

The Concept of a Central Pattern Generator (CPG)

The combined evidence from the first half of the twentieth century suggested that the central nervous system does not necessarily require sensory feedback to generate rhythmic movement resembling repetitive behaviors such as locomotion. This conclusion emerged from experiments in a variety of invertebrate and vertebrate species, in which the ability to generate a patterned rhythmic activity was not abolished by (a) paralysis using neuromuscular blockers to prevent proprioceptive input evoked by movements, (b) deafferentation, or (c) the complete physical isolation of the nervous system from all sources of possible feedback (for a review, see Grillner, 1985; see also Delcomyn, 1980). The ensemble of neuronal elements necessary and sufficient for the production of locomotor patterns was defined as a central pattern generator (CPG; Grillner & Zangger, 1975; Wilson, 1961). However, since the motor patterns observed after deafferentation are often imprecise and sometimes even lack important elements of motor output as compared to intact conditions (e.g., Grillner & Zangger, 1979; Pearson, 1985; Sillar, Clarac, & Bush, 1987), the validity of such a concept for completely central locomotor pattern generation was questioned (e.g., Bässler, 1987, 1988; Pearson, 1985). At present it is clear that, in the majority of locomotor systems, sensory feedback and CPG networks interact to generate the functional locomotor program, whereby sense organs form integral elements of the pattern-generating mechanisms (e.g., see the review in Büschges & El Manira, 1998; Pearson, 1995; Prochazka & Mushahwar, 2001) with only few exceptions (e.g., Arshavsky, Orlovsky, Panchin, Roberts, & Soffe, 1993).

Organization of Neural Networks for Locomotion

Most locomotor patterns have in common that they are based on rhythmic movements (i.e., cyclic motor patterns). Each cycle can be generally divided into two phases, a power stroke and a return stroke. During the power stroke, locomotor organs exert force against the surrounding medium and move the organism relative to its environment; during the return stroke, the locomotor organs are moved back to their starting position for the next power stroke. In walking, for example, the power stroke of the locomotor cycle is the stance phase, when the limb is on the ground and generates force to propel the body relative to the ground, either forward or backward. The return stroke of the leg is the swing phase, during which the leg is moved back to its starting position. In general, antagonistic muscles of leg joints exhibit phases of alternating activity during the generation of stepping movements. In vertebrates like fish or agnaths, swimming is generated by a rostral-caudally or caudal-rostrally directed undulating contraction wave, depending on the direction of swimming. This contraction wave wanders along the trunk musculature. In every cycle, the myotomal musculature of both sides of each segment contracts in an alternating fashion.

In the next section we will review the main features of current knowledge of the construction of neural networks and the mechanisms underlying the generation of locomotor patterns in vertebrates and in invertebrates.

When considering the generation of locomotor programs, several different aspects and levels of nervous control are important (Figure 5.1). For a very detailed review of this, see Orlovsky, Deliagina, and Grillner (1999), where the following summary of present knowledge was presented. The highest level of control is represented in the decision to locomote. Activation of this system is mediated by external or internal cues, such as sensory stimuli or motivation. The decision to locomote activates two different systems of descending control. One system has command-like features and controls the starting and stopping of the locomotor program as well as the intensity (e.g., speed) of locomotion. In vertebrates, the reticulospinal pathways, receiving signals from the mesencephalic locomotor region and the subthalamic locomotor region and sending their axons into the spinal cord, are elements of this system (e.g., Armstrong, 1988; Jordan, Brownstone, & Noga, 1992; Mori, Matsuyama, Kohyama, Kobayashi, & Takakusaki, 1992; Orlovsky et al., 1999).

In invertebrates, groups of interneurons or individual descending interneurons have been identified that serve command-like functions in the initiation and maintenance of motor programs (e.g., Bowerman & Larimer, 1974a, 1974b; Brodfuehrer & Friesen, 1986a, 1986b, 1986c; Gamkrelidze, Laurienti, & Blankenship, 1995; Kupfermann & Weiss, 1978). The second system is in charge of generating and controlling the animal’s posture and equilibrium during locomotion, as well as its direction of locomotion. In vertebrates, the cerebellum, brainstem, and spinal cord serve this system; in invertebrates, this system is distributed among various ganglia. The information from these two systems is fed into the neuronal networks of the locomotor system itself, the controllers, located downstream close to the locomotor organs in spinal segments (vertebrates) or ganglia (invertebrates). The construction and action of these controllers will be our main focus. The controllers (Figure 5.1) encompass the neuronal networks, including the CPGs that generate activity of the locomotor organs by driving specific sets of motoneurons. These motoneurons form the neuronal output stage and innervate the muscles moving the locomotor organs. Rhythmic motoneuron activity is generated by alternating excitatory and inhibitory synaptic impulses from the premotor neural networks, the controllers, to the motoneurons. The generation of functional locomotor programs often relies on feedback about the executed action from each level to the next higher level. Information about the activity of the controllers is fed back to the command level. Sensory information reporting the actual movement generated by the locomotor organs is fed back to the controllers. Therefore, in many locomotor systems, sense organs have to be considered important elements enabling the systems to generate functional locomotor programs (see also Orlovsky et al., 1999). Finally, locomotor systems consisting of a multitude of locomotor organs need to generate coordinating mechanisms to adjust and time the sequence of movements among the individual locomotor organs (e.g., Cruse, 1990).

Marked differences appear to exist in the degree of coupling between the actions of individual controllers for locomotor systems, on the one hand, and a multitude of locomotor organs, on the other. For example, evidence suggests that the wing-control system in the locust flight system acts in general as one integrated common pattern generator for driving all four wings (Robertson & Pearson, 1983; Waldron, 1967). However, more recent evidence gathered from various vertebrate and invertebrate organisms suggests that each locomotor organ may indeed have its own controller—that is, each segment of a lamprey for swimming (Grillner et al., 1995), each leg of a vertebrate or invertebrate (Bässler & Büschges, 1998; Orlovsky et al., 1999) and each leg of a human (Gurfinkel, Levik, Kazennikov, & Selionov, 1998) for walking, and each wing of an insect for flying (Ronacher, Wolf, & Reichert, 1988). The construction of such controllers has been well studied for the generation of the swimming motor pattern in mollusks and lower vertebrates (Arshavsky et al., 1993; Grillner et al., 1995) and annelids (Brodfuehrer, Debski, O’Hara, & Friesen, 1995) and for walking pattern generation in crustaceans (Cattaert & LeRay, 2001), insects (Bässler & Büschges, 1998), anurans (Cheng et al., 1998), and mammals (Orlovsky et al., 1999). In humans, less evidence is presently available on the construction principles of the limb controllers themselves, but present data suggest that the main features in the organization of the walking control system of humans has similarities to those of both cats and arthropods (for a recent summary and comparison, see Orlovsky et al., 1999).

The complexity of the controllers’ construction depends on (a) the complexity of the locomotor organs and (b) the requirements of the locomotor behavior to be generated out. Thus, the complexity of the controllers increases with the segmentation of the locomotor organs, from unitary wings to multisegmented legs. For example, in Clione, a mollusk, the swimming motor activity is generated by elevation and depression of wing-like appendages (Arshavsky et al., 1998). In invertebrates and vertebrates, however, walking is generated by the movements of multijointed limbs, which requires the coordination of the activities of several individual leg joints (Bässler, 1983; Grillner, 1979; Orlovsky et al., 1999). Controllers that need minimal sensory feedback, such as the one controlling locomotion in Clione, are constructed more simply than are controllers governing locomotor programs that depend on sensory feedback, such as walking systems. Pattern generation in the latter locomotor systems relies heavily on sensory signals about movements of the joints and the limbs, signals about forces or strain exerted on each segment of the limb, and coordinating signals between adjacent limbs (e.g., Bässler & Büschges, 1998; Pearson, 1995; Prochazka, 1996a). This also applies to the walking system of humans (Dietz, 1992; Gurfinkel et al., 1998; Sinkjaer, Andersen, Ladouceur, Christensen, & Nielsen, 2000).

Construction Principles of Pattern-Generating Networks for Locomotion

Central neuronal networks have been identified that are capable of generating ongoing rhythmic activity in motoneurons that contribute to the cyclic locomotor output generated for swimming, walking, and flying in vertebrates and invertebrates (described previously). These networks can also be activated in very reduced preparations either by the application of drugs, by sensory stimulation, or by stimulation of higher order centers in the central nervous system (for a comparative review, see Orlovsky et al., 1999). Using these approaches, more or less complete patterns of a locomotor program can be generated, allowing their detailed investigation. Neuronal networks have been analyzed on several levels: first, the operational level, focusing within the systems level on mechanisms for the generation of functional motor programs (e.g., Bässler & Büschges, 1998; Grillner, 1979); second, the level of the neuronal networks themselves, analyzed by investigating the topologies of the neural network and the synaptic interactions among its elements (e.g., Arshavsky et al., 1993; Bässler & Büschges, 1998; Grillner et al., 1995; Kiehn, Housgaard, & Sillar, 1997; Roberts, 2000); and third, a subject that has drawn a lot of attention lately: the cellular and subcellular levels and the cellular properties of individual neurons or neuron classes within the networks and their role in generating rhythmic locomotor activity (e.g., Dale, 1997; Grillner, Wallen, Hill, Cangiano, & El Manira, 2001). Finally, in the past two decades, simulation studies using artificial neural networks or computer models have increasingly helped to investigate the necessity and sufficiency of neuronal mechanisms and the construction of the neuronal networks underlying the generation of locomotor patterns as presently understood (e.g., reviews in Cruse et al., 1995; Grillner et al., 1995).

Despite differences among phyla, species, and locomotor tasks, it has become clear by now that there are some specific common outlines of networks generating rhythmic locomotor activity. Two prominent basic neural network topologies have been identified: mutual inhibition and forward excitation–reciprocal inhibition.

Mutual Inhibition

This construction principle found in various locomotor systems is based on mutual inhibition between neurons or groups of neurons within the neuronal networks (Figure5.2, panel A). Each group of neurons is in charge of generating one phase of the locomotor activity. Through this mechanism, only one group of neurons is active at any given time once the activity of the network has been started. Transition between the activity of the two neurons or groups of neurons emerges through mechanisms that either generate fatigue in the activity of the currently active group of neurons or enable the silent, inactive group of neurons to escape inhibition. Such topology is called half-center construction, and, long before experimental verification was possible, Brown (1911) conceived it for the generation of alternating activity during stepping in the cat. Mutual inhibition has been identified as a building block underlying the generation of alternating motor activity in the neuronal networks for swimming in vertebrates (lampreys [Buchanan, 1982; Grillner, 1985] and tadpoles [Roberts, Dale, Evoy, & Soffe, 1985; Soffe, Clarke, & Roberts, 1984]) and for swimming and other locomotor behaviors, such as crawling, in invertebrates (mollusks [Arshavsky, Beloozerova, Orlovsky, Panchin, & Pavlova, 1985a, 1985b, 1985c, 1985d; Getting, 1981; Getting, Lennard, & Hume, 1980; Katz, Getting, & Forst, 1994] and annelids [Friesen & Hocker, 2001; Friesen, Poon, & Stent, 1978; Stent et al., 1978]). For example, in the lamprey, swimming-network alternating activity of both sides of each segment is based on mutual inhibition between groups of crossed inhibitory neurons on each side of the spinal cord (Figure 5.2, panel B; Buchanan, 1982; Grillner, 1985).

Forward Excitation and Reciprocal Inhibition

Another identified network interaction is forward excitation from one neuron to another neuron via a delay and reciprocal inhibition from the second neuron to the first neuron (Figure 5.3, panel A). In the CPG network for locust flight, this element has been found to underlie alternating activation of wing elevator and depressor motoneurons, representing some kind of switch-off mechanism (Figure 5.3, panel B; Robertson & Pearson, 1983, 1985). For example, activity of one neuron (type 301) increases and excites another neuron (type 501) through the action of an excitatory influence with a certain delay. At some point, neuron 501 is pushed past its spike threshold and activated. Its activity then in turn terminates the activity of 301 through the inhibitory synapse (Robertson & Pearson, 1985). Ongoing rhythmic activity in such a circuit relies on some mechanism that enables neuron 301 to have pacemaker or burst-producing properties (described presently).

Finally, it is now known that other locomotor systems also combine different types of building blocks for the generation of rhythmic motor patterns, as with the locomotor network of the nudibranch Tritonia, which includes both elements previously described (Getting, 1981; Getting et al., 1980; Katz et al., 1994).

Although no definite information on network topology is available at this time, the finding that spinalized primates can produce locomotor patterns provides evidence that CPGs for locomotor activity also exist in the spinal cords of higher mammals (Fedirchuck, Nielsen, Petersen, & Hultborn, 1998). Evidence is also growing for the existence of spinal CPGs’ controlling locomotion in humans (Calancie et al., 1994; Dimitrijevic, Gerasimenko, & Pinter, 1998).

In general, locomotor networks are constructed redundantly—that is, they contain multitudes of these small neuronal circuits (e.g., five in case of the locust flight CPG; Grimm & Sauer, 1995). They thereby gain substantial robustness against synaptic noise or functional failure in individual neuronal elements.

The pattern of activity generated by locomotor networks need not be two-phased, as the motor output often suggests. Locomotor networks can be constructed and operate in a way that leads them to generate a rhythm with more than the two phases that are obvious from the locomotor program. For example, the locust flight system generating a two-phase motor output for wing elevation and depression is driven by the output of a three-phase neuronal network (Robertson & Pearson, 1985).

As the previous description makes clear, synaptic interactions within neuronal networks are important prerequisites for generating the rhythmic motor activity underlying locomotion. In addition, intrinsic properties of neurons contribute to and cooperate with the network topology in the generation of rhythmic motor activity. Intrinsic properties of neurons are generated within neurons themselves and have been studied in great detail. Some of the most prominent ones are summarized in Figure 5.4 and will be briefly introduced here:

• Plateau potentials. Besides the generation of action potentials, neurons can be capable of generating plateau potentials, which are spike-like, quasi-stable operating characteristics. A plateau potential is basically a prolonged, rather slow regenerative depolarization (Hille, 1991). It usually results from a voltage-dependent inward current mechanism: Sufficient depolarization initiates an inward current flow, which causes further depolarization, leading to a self-sustained depolarized state of the neuron. The membrane potential remains in this depolarized state for some time. Since the membrane potential of the plateau is usually above spike threshold, the plateau phase is characterized by burst activity of the neuron. Asufficient hyperpolarizing synaptic input or a mechanism for burst termination (discussed next) can terminate the plateau by turning off the voltagedependent inward current. In-depth reviews on the role of this property for pattern generation are found in Kiehn et al. (1997), Marder and Calabrese (1996), and Pearson and Ramirez (1992).
• Burst termination properties. In addition to inhibitory synaptic inputs, intrinsic properties of neurons can contribute to terminating bursts of activity. One example is the calcium-dependent potassium (K_Ca) channel (Hille, 1991). During strong bursts of action potentials, calcium ions enter a neuron through cation channels underlying the depolarization and the burst of activity. Over time, this leads to an accumulation of Ca² ions in the neuron, which in turn activate K_Ca The potassium outward current initiates a hyperpolarization of the neuron below its spike threshold and thereby terminates its depolarization and activity (e.g., Grillner & Wallen, 1985; in-depth review by Grillner et al., 1995).

• Spike frequency adaptation (SFA). There are presently many examples of the activity of neurons adapting over time to a given depolarization in membrane potential. The mechanism behind this phenomenon is often the slow after-hyperpolarization (sAHP) that follows each action potential (Hille, 1991; Schwindt & Crill, 1984). The sAHP is generated by K_Ca With the generation of action potentials, not only Na but also Ca² ions enter a neuron. These Ca² ions activate a K_Ca channel, which initiates an sAHP of the neuron following spike activity. sAHPs accumulate over time and can thereby reduce the excitability of a neuron and thus its activity (in-depth review in Grillner et al., 2000).
• Intrinsic oscillations or endogenous bursting. Neurons can be capable of steadily producing phases of alternating activity consisting of bursts and silence. This property is called endogenous bursting or intrinsic oscillation (Hille, 1991). The underlying ionic mechanisms are diverse here, as well. For example, the active phase of a neuron can display similarities to plateau potentials. There are also automatic ionic mechanisms, that is, conductances in the neuron that terminate activity after some time by opening ion channels (e.g., K channels). These allow an outward current to hyperpolarize the membrane potential below spike threshold. The next cycle of activity is then started either by rebound properties of the neuron or by a tonic background excitation (Grillner & Wallen, 1985; Hochman, Jordan, & Schmidt, 1994; Sigvardt, Grillner, Wallen, & Van, 1985; Sillar & Simmers, 1994b).

Through the action of premotor neuronal networks, a basic rhythmic activity is generated that must be modified for a functional locomotor pattern, depending on the complexity of the locomotor organs and the locomotor task executed. Getting (1989) coined the term building block for identified types of network connections, synaptic properties, and intrinsic neuronal properties in charge of generating rhythmic motor activity.

The controllers of the locomotor organs can contain a multitude of such pattern-generating networks. Where the locomotor organ is segmented (e.g., for walking), the number of pattern-generating networks can be increased as well (Büschges, Schmitz, & Bässler, 1995; Cheng et al., 1998; Edgerton, Grillner, Sjöström, & Zangger, 1976). For example, in the stick insect walking system, each of the three main joints of each leg is driven by an individual neural network capable of generating rhythmic motor activity (Büschges et al.). The activity of the individual pattern generators can be coupled by sensory signals (e.g., Hess & Büschges, 1999; summary in Bässler & Büschges, 1998; and discussed later in this paper). Similar results have recently been presented for the cervical spinal cord controlling the forelimb of a vertebrate, the mudpuppy (Cheng et al.). In this investigation, evidence was presented that the motoneuron pools innervating the elbow joint, that is, the flexor and extensor, are driven by one central pattern-generating network for each of the two antagonistic muscle groups moving the tibia—the flexor and the extensor. These findings verified an old hypothesis, the unit-burst generator concept initially proposed by Edgerton et al., who suggested that there are unitary central patterngenerating networks present in the vertebrate spinal cord for each muscle group of the limb.

The basic rhythmic activity of the pattern-generating networks is shaped for a functional locomotor output by sensory signals from the locomotor organs and synaptically transmitted to the output elements of the locomotor system, the motoneurons. There are only a few examples of locomotor systems in which motoneurons themselves are elements of the pattern-generating networks, for example, in crustaceans (Chrachri & Clarac, 1989), annelids (Poon, Friesen, & Stent, 1978), and a lower vertebrate, the tadpole (Perrins & Roberts, 1995a, 1995b, 1995c).

Location of Pattern-Generating Networks for Locomotion

As stated previously, rhythmic locomotor activity is generated within the controllers of the locomotor organs. These controllers are the neuronal networks located in the central nervous system (CNS), mostly in close apposition to locomotor organs (i.e., in the segments from which the locomotor organs arise and from where they are innervated). Segmental organization of the organism or segmental structure of the locomotor organs has no prejudicative meaning for the localization of the pattern-generating networks in the nervous system. Let us consider the generation of locomotor patterns on the level of rhythmic activity that drives one locomotor organ, for example, the chain of myotomal segments in swimming in the lamprey and the tadpole, each wing in flying (or swimming), or each limb in terrestrial locomotion.

With few exceptions, the controllers for locomotion are distributed across several segments of the CNS. The patterngenerating network for locust flight is a distributed neuronal network encompassing the three thoracic ganglia and some condensed abdominal neuromeres attached to the metathoracic ganglion (Robertson & Pearson, 1985). Distribution is also present in the walking-pattern-generating networks of vertebrates, for example, for the forelimb in the cervical spinal cord (Cheng et al., 1998) and for the hindlimb in the lumbar spinal cord (e.g., Cazalets, Borde, & Clarac, 1995; Kjaerulff & Kiehn, 1996). In the lamprey and the tadpole, the CPG for swimming that drives the chains of myotomal segments is distributed along the segments of the spinal cord (as discussed previously). Similarly, the pattern-generating networks for crawling and swimming in the leech are distributed along the chain of segmental ganglia (comparative summary in Orlovsky et al., 1999). However, these locomotor systems are special in the sense that, for swimming, the motor pattern results from the coordinated action of the subsequent segments of the organism; that is, the locomotor organ is the organism itself. For all controllers generating swimming movements, it is known that the nervous system of each individual segment contains neural networks capable of generating a rhythmic motor output for the segment. This is most obvious in the CPG for swimming in Clione, which is generated by a network of interneurons, most of which are located in the pedal ganglia of the nonsegmented organism (Arshavsky et al., 1985a, 1985b, 1985c, 1985d, 1985e). Only in arthropod walking systems has a clear segmental organization been found, with the controller of each leg restricted mainly to the segmental ganglion of the locomotor organ, which has been studied in great detail for the stick insect (summary in Bässler & Büschges, 1998).

Regarding mammalian locomotion, important new findings were recently presented on the organization and location of the pattern-generating networks for the individual limbs. Individual neuronal networks for the generation of rhythmic motor activity for both elbow flexor and elbow extensor motoneuron pools were identified in the mudpuppy forelimb (Cheng et al., 1998). The data presented support the unitburst generator concept of locomotion described earlier. Similarly, lesion experiments in the neonatal rat lumbar spinal cord have revealed that the CPGs controlling hindlimb movements are distributed throughout the hindlimb enlargement and most likely also in the lower thoracic cord (Cazalets et al., 1995; Kjaerulff & Kiehn, 1996). Together with additional evidence, this suggests that in mammals, too, the CPG for the hindlimb is not a unitary entity, but is again composed of several unit-burst generators controlling single muscles or joints. Between the lumbar segments, the capability to generate rhythmic activity declines from rostral to caudal (summary in Kiehn & Kjaerulff, 1998). In patients with spinal cord injuries, it was reported that the higher the level of the injury was, the more normal the locomotor pattern appeared (Dietz, Nakazawa, Wirz, & Erni, 1999). This indicated that, also in humans, the CPGs for locomotion are not restricted to specific levels of the spinal cord.

Sensory Signals Controlling Locomotor Activity

In the majority of locomotor systems, sensory signals are utilized, first, to generate a functional locomotor pattern, and second, to stabilize the locomotor pattern, by adapting to biomechanical changes and responding to unexpected events. Third, sensory information plays a crucial role in controlling the posture and equilibrium of the locomotor system during the behavioral task (Macpherson, Fung, & Jacobs, 1997; Orlovski et al., 1999). Such information is gathered from multiple sensory systems and integrated in the networks controlling locomotion and related to the current position and condition of the body and the limbs (e.g., the phase of a movement). The dependence of motor control systems on proprioceptive signals has been well characterized in a statement by Prochazka (1996b): “You can only control what you sense.” Proprioceptors located in muscles and joints characterize the positions of the limbs, and together with exteroreceptors they sense contact with the ground or obstacles and the load carried by the limb. Their general role is to establish the temporal order of the locomotor pattern and to reinforce ongoing activity (Bässler & Büschges, 1998; Duysens, Clarac, & Cruse, 2000; Grillner, 1979; Pearson, 1995; Pearson & Ramirez, 1997; Prochazka).

Other sensory information involved in the generation and control of locomotor behavior itself is provided by visual cues. Visual cues play a decisive role in controlling goal direction in locomotion, allowing the preadjustment of the locomotor activity and the interpretation of visual flow that yields information on walking speed and direction (review in Rossignol, 1996b). Furthermore, together with the vestibular apparatus or comparable gravity-sensor systems in invertebrates, visual input is involved in controlling the body’s orientation in space. This is especially important for animals locomoting in a three-dimensional environment (i.e., flying or swimming; Orlovsky et al., 1992; Reichardt, 1969; Ullen, Deliagina, Orlovsky, & Grillner, 1995).

The following sections briefly review the major sensory systems and their roles in the control of locomotion.

Visual Regulation of Locomotion

Visual control of locomotion is very powerful. Apparently, visual input is used to direct locomotion, to avoid obstacles on the way to reach a target, and for orientation. Due to its complex nature, very little was known until recently about the visual control of locomotion; however, advances in computer technology allowing artificial simulation of the optical system (as with virtual reality; Warren, Kay, Zosh, Duchon, & Sahuc, 2001) provided deeper insight into the mechanisms of visuomotor control.

As introduced by Gibson (1958), movements of the body in space generate a continuously changing pattern of motion on the retina, a pattern referred to as optic flow. This selfinduced optic flow must be distinguished in speed and direction from the optic flow induced by moving objects. Confusion about this distinction is typically experienced by a person who is sitting in one train and observing another train, and is unable to identify which train is moving. Generally, optical flow is used to assess the velocity of the locomotion and the direction of self-movement. Consequently, information gained from visual flow is used to control multiple aspects of locomotion, including goal-directed spatial behavior, locomotor speed, and gross adaptation to environmental changes. The association of changes in optic flow with changes in movement is so strong that artificially induced or perturbed optic flow can modulate the velocity and direction of locomotion or even initiate locomotor behavior. This is an observation commonly found throughout the animal kingdom, for example in lobsters and crayfish. A front-to-rear optokinetic stimulation provided by horizontal stripes on an underlying treadmill can trigger forward locomotion with the velocity depending on the velocity of the stripes (Davis & Ayers, 1972). Insects flying tethered inside a striped drum will tend to turn in the direction in which the drum is rotated (Reichardt, 1969), and expanding the size of a target during the time a gerbil is walking (giving the impression that the target is getting closer) causes the animal to decrease its velocity (Sun, Carey, & Goodale, 1992).

Powerful effects of changes in visual flow have also been reported in humans. For example, during forward walking in a room in which the walls can be displaced, moving them forward (instead of backward as it would appear during forward locomotion) will create the impression of walking backward, despite contradictory proprioceptive signals from the limbs (Lee & Thompson, 1982). Toddlers who have just learned to walk will tip over if the walls are set in motion (Stoffregen, Schmuckler, & Gibson, 1987). Comparable experimental approaches showed that, as in animals, walking velocity in humans is adjusted to visually perceived walking speed (Konczak, 1994; Prokop, Schubert, & Berger, 1997). Thus, visual input during locomotion in vertebrates and invertebrates is used not only to avoid obstacles, but also to guide locomotor direction and velocity.

In the field of visual locomotor control, there is an ongoing discussion whether visual flow is the dominant optical influence on target-directed locomotion or whether the walking direction is simply determined by the current body orientation and the perceived direction of the target. In light of this dispute, it has recently been demonstrated that humans do not guide locomotion by relying either on visual flow alone or on egocentric direction alone. Both components are probably used in a complementary way—for example, when the optical flow is reduced or distorted: On a grass lawn or at night, behavior tends to be governed by egocentric direction (Harris & Carre, 2001; Rushton, Harris, Lloyd, & Wann, 1998; Warren et al., 2001; Wood, Harvey, Young, Beedie, & Wilson, 2000).

Visual information is also essential to perform anticipatory foot placement in order to avoid obstacles. To avoid bumping into an object, it is crucial to calculate how much time is left for corrective action. The distance to the obstacle is relevant only in relation to the speed of self-motion. Thus, visual information is used in a feed-forward rather than an on-line control mode to regulate locomotion. Humans do not fixate on obstacles as they step over them, but perform a planning in the steps before (Hollands & Marple Horvat, 1996, 2001; Patla, Adkin, Martin, Holden, & Prentice, 1996; Patla & Vicker, 1996). This feed-forward information is very important in the control of walking, and it is not possible to walk more than 8 s or 5 m without visual feedback (Lee & Thompson, 1982).

Compared to our knowledge of invertebrate systems, knowledge about the mechanisms of visual locomotor control in vertebrates is rather incomplete. The approach of recording cortical cells during visually guided locomotion in cats demonstrated increased firing in pyramidal cells when modification of the step cycle was required to clear an obstacle (Drew, Jiang, Kably, & Lavoie, 1996). It has been suggested that the increased discharge is used to modify the step cycle, since it has been shown that inactivation of these cortical areas results in the inability to clear visible obstacles.Acomparable feed-forward mechanism to anticipate collision was recently described in locusts (Hatsopoulos, Gabbiani, & Laurent, 1995). The lobula giant motion detectors (LGMD) in the locust optic lobe (Strausfeld & Nässel, 1981) are neurons that receive inputs from afferents that are sensitive to local motion over the entire visual hemifield (Rowell, O’Shea, &Williams, 1977) and that respond most strongly to objects approaching the eye (Rind & Simmons, 1992). LGMD synapse directly to a large neuron (descending contralateral motion detector, or DCMD), which is involved in the generation and control of flight and jump maneuvers (Pearson, Heitler, & Steeves, 1980; Robertson & Pearson, 1983). In the visual system of the fly, a number of processing stages have been identified and several interneurons with sensitivity to various directions of motion have been found (Borst & Egelhaaf, 1989), making the visual system of flies the best-described model in visual processing (reviewed in Krapp, 2000).

Proprioceptive Regulation of Locomotion

The question of how proprioceptive signals regulate locomotor activity has been an intensive field of research, including studies in humans, cats, and various arthropods (reviewed in Büschges & El Manira, 1998; Duysens et al., 2000; Pearson, 1995; Pearson & Ramirez, 1997; Prochazka, 1996b). These studies made it clear that there are common principles in the proprioceptive control of locomotion throughout the entire animal kingdom, indicating their general importance in the generation of functionally relevant locomotor movements (Pearson, 1993). A prominent example providing evidence that local proprioceptive reflex pathways are strongly involved in the control of stepping is the finding that spinalized cats walking with their hindlimbs on a treadmill are able to adapt the speed of stepping to the speed of the treadmill belt (Brown, 1911). The only explanation for this ability is that sensory feedback from proprioceptors in the limbs is involved in controlling the step cycle. Generally, proprioceptive feedback serves two separate functions in the control of locomotion: (a) the control of phase transition and (b) the regulation of the magnitude of muscle activity.

A common principle in sensory control of locomotion is the task- or phase-dependency of sensory feedback. Transmission in proprioceptive reflex pathways, for example, strongly depends on the motor task and the phase of the movement (reviewed in Büschges & El Manira, 1998; Pearson, 1995). For example, reflex pathways can generate opposite motor outputs of varying strength or gain, depending on the actual behavior or the phase of the movement (e.g., standing compared with walking; Figure 5.5). This phenomenon has been termed reflex reversal and is found in vertebrates (Pearson, 1993, 1995; Prochazka, 1996b) as well as in invertebrates (Bässler & Büschges, 1998; Cattaert & LeRay, 2001; Clarac, Cattaert, & LeRay, 2000). This flexibility of reflex pathways ensures that the motor output is adjusted properly to the actual behavioral task, depending on the behavioral and the biomechanical states of the locomotor apparatus.

Proprioceptive Control of Phase Transition

Sherrington (1910) already introduced the concept that somatosensory afferents from the limbs are involved in the regulation of the step cycle during walking in vertebrates. One mechanism regulating the duration of the stance phase in vertebrates is hip extension, since preventing hip extension in a limb prevents the onset of the swing phase and thus of stepping in cats and rats (e.g., Fouad & Pearson, 1997a; Grillner & Rossignol, 1978). The afferents responsible for signaling the hip angle and subsequently for the initiation of the swing phase are probably muscle spindles in hip flexor muscles (Hiebert, Whelan, Prochazka, & Pearson, 1996). Another signal in the control of the step cycle in vertebrates arises from Golgi tendon organs and muscle spindles from extensor muscles (Conway, Hultborn, & Kiehn, 1987; Whelan, Hiebert, & Pearson, 1995b). Both sensors are active during stance, with the Golgi tendon organs providing a gauge of the load carried by the leg (reviewed in Dietz & Duysens, 2000). The excitatory activity during walking is opposite to its inhibiting action during standing (reflex reversal). The functional consequence is that the swing phase is not initiated until the load is taken off the limb (otherwise, balance would be lost), as occurs at the end of the stance phase when the weight of the animal is borne by the other limbs.

The fact that, at the end of the stance phase, signals both about joint and limb displacement and about load on the limb are involved in the initiation of the swing phase is a general rule in vertebrate and invertebrate walking systems. It has been commonly found in stick insects, cockroaches, lobsters, cats, and humans (Figure 5.6; Anderson & Grillner, 1983; Bässler & Büschges, 1998; Clarac, 1982; Pang &Yang, 2000; Pearson, 1993; Wendler, 1974). In the stick insect, for example, two types of proprioceptors have been found to influence the timing of the onset of the swing phase. These sensors are (a) the campaniform sensillae, which measure load on a limb orstrainonthecuticle,inamanneranalogoustotheGolgitendon organs in vertebrates, and (b) the femoral chordotonal organ, which, by being stretch-sensitive in a manner analogous to muscle spindles in vertebrates, signals the movement and position of the femur-tibia joint (Bässler & Büschges).

Prochazka (1996b) has formulated a general rule for the transition from the stance phase to the swing phase during stepping in vertebrates:

IF extensor force low

AND hip extended

THEN initiate swing.

However, phase transitions controlled by proprioceptive signals are not only found in walking systems (reviewed in Pearson,1993;Pearson&Ramirez,1997).Intheflightsystem of insects, especially well studied in locusts, sensory information about movements of the wings is also utilized for phase transition in motor activity. Two wing-sensory systems—that is, the wing tegulae—a hinge mechanoreceptor, and the stretch receptors control the initiation and duration of elevator activity during flight motor activity (Wolf & Pearson, 1988). Similarly, in vertebrate swimming (e.g., in the lamprey spinal locomotor network), sensory signals that report bending of the spinal cord contribute to the alternation of motor activity between the myotomal motoneuron pools of both sides of the spinal cord (Grillner et al., 1995).

The functional significance of regulating phase transition by means of afferent pathways might be to limit a movement, such as the amount of leg extension (Whelan et al., 1995b) or the amplitude of wing depression during flight (Wolf & Pearson, 1988), to a range compatible with effective function. A second advantage of afferent phase control is to ensure that a certain phase of the movement is not initiated until a defined biomechanical state has been reached. This allows the transition without destabilization of the system.

Regulation of the Magnitude of Muscle Activity

The second principle of locomotor control found in vertebrates and invertebrates is the control of motor activity via afferent feedback (Duysens et al., 2000; Pearson, 1993). A generalization emerging from studies in various walking systems is that afferent feedback from leg proprioceptors contributes to the generation of stance-phase activity (see Figure 5.6). For example, in invertebrates such as the stick insect, the chordotonal organ in the femur of the front leg signals flexion of the femur-tibia joint during the stance phase.

In walking animals, these sensory signals reinforce the activity in flexor motoneurons (Bässler, 1986) as a result of the action of a parallel and distributed neuronal network driving the leg motoneurons (Büschges, Sauer, & Bässler, 2000). Also, in the walking system of the crayfish, sensory signals are utilized to reinforce motor activity during stance (El Manira, DiCaprio, Cattaert, & Clarac, 1991; Sillar, Skorupski, Elson, & Bush, 1986). In vertebrates, sensory signals underlying the reinforcement of motor activity arise both from Golgi tendon organs and primary muscle spindle afferents (Guertin, Angel, Perreault, & McCrea, 1995; McCrea, Shefchyk, Stephens, & Pearson, 1995; Whelan et al., 1995b). At least three excitatory reflex pathways transmit proprioceptive information from extensor muscles to motoneurons or the CPG (Figure 5.7): (a) the well-known monosynaptic pathway from muscle spindles to motoneurons; (b) a disynaptic pathway from spindles and Golgi tendon organs that is opened during the stance phase; and (c) a polysynaptic pathway. The latter pathway includes the extensor half-center in a way that also controls the timing of the stepping pattern (Pearson & Ramirez, 1997). The neural mechanisms that contribute to the modulation of sensory pathways in the control of locomotion have been investigated in great detail in the past decade. Two key factors are currently known: (a) In many locomotor systems the actual motor output is the result of the action of a distributed neural network that can modulate the magnitude of motor activity generated by differentially weighting (or opening and closing) individual parallel (sometimes opposing) interneuronal pathways between sense organs and motoneurons (Bässler & Büschges, 1998; Büschges et al., 2000; Cattaert & LeRay, 2001; Pearson, 1995; also refer to Figure 5.5). Phase dependency of this mechanism in the locomotor cycle is generated by the action of the central pattern generators. (b) Phasic modulation of efficacy of the individual pathways from sense organs onto motoneurons are the target of pre- and postsynaptic modulatory mechanisms at the intercalated synapses, for example, presynaptic inhibition (Clarac, El Manira, & Cattaert, 1992; Nusbaum, El Manira, Gossard, & Rossignol, 1997; Rossignol, 1996a; and Figure 5.5). In humans, as well, the load carried by the extensor muscles increases the magnitude of their activity (Dietz & Duysens, 2000; Stephens & Yang, 1999). In cats, removing feedback from these afferents reduces extensor activity by more than 50% and, in humans, the contribution to extensor muscle activity has been estimated to be about 30% (Yang, Stein, & James, 1991).

The general integration of proprioceptive feedback in locomotor systems throughout the animal kingdom strongly indicates the functional necessity of such a feature. The benefits are the appropriate and effective control of motor rhythm, integrating biomechanical changes and external perturbations in the system.

Role of Exteroceptive Input

Compared to proprioceptive control, much less knowledge has been gathered on the role of exteroceptive inputs (e.g., cutaneous afferents in vertebrates) in the control of locomotion. Exteroceptors in the skin have a strong influence on the central pattern generator for walking (Forssberg, 1979) and on brainstem areas controlling locomotion (Drew, Cabana, & Rossignol, 1996). One important function is to respond to unpredicted perturbations from obstacles on the ground. To be functionally meaningful, these reflexes are strongly modulated during the gait cycle, as has been demonstrated in cats (Abraham, Marks, & Loeb, 1985; Andersson, Forssberg, Grillner, & Lindquist, 1978; Duysens, Loeb, & Weston, 1980; Forssberg, 1979) and humans (Duysens, Trippel, Horstmann, & Dietz, 1990; Yang & Stein, 1990). A mechanical stimulus applied to the dorsal part of a paw in cats or to a cutaneous nerve in humans during the onset and middle of the swing phase produces a strong, short latency excitement of flexor motoneurons and inhibition of extensor motoneurons to increase elevation of the limb and clear the obstacle. Forssberg introduced this reflex pattern as the stumbling corrective response. In contrast, the same stimulus applied during the end of the swing phase and during the stance phase produces the opposite response; the limb cannot be lifted at this phase in the step cycle because otherwise the animal or person would fall. However, the stimulus evokes increased flexor activity in the subsequent step. This state-dependent modulation has been found to be mediated by the convergence of primary afferents and the output from the CPG to premotor interneurons (Degtyarenko, Simon, & Burke, 1996).

Exteroceptive signals, such as cutaneous stimuli, are also known to trigger swimming or turning in animals. A welldefined system has been described for swimming behavior in tadpoles: A brief stimulus to the skin, (e.g., of the head) can trigger sustained swimming or struggling sequences, depending on stimulus intensity and duration (Soffe, 1991). This response is mediated via a single skin-sensory pathway directly accessing the CPGs in the spinal cord: the Rohon-Beard sensory neurons.

In conclusion, in many locomotor systems, sensory input plays a prominent role in shaping the motor output of the controllers toward functional locomotor behavior. The major tasks of sensory signals are (a) to control the direction of locomotion, (b) to control posture and equilibrium, (c) to control phase transitions in the step cycle, (d) to control the magnitude of muscle activity, (e) to avoid obstacles, and finally, (f) to respond to perturbations.

Plasticity in Motor Systems

For many years, the central nervous system in adult mammals has been seen as a hard wired and rigid structure.The same was believed about the nervous systems of invertebrates, whose relatively short life spans were thought to make adaptive processes in the nervous system unnecessary. This view has changed, and today it is accepted that the CNS in vertebrates and invertebrates is capable of major reorganizations in response to injury or loss of parts of the nervous system under experimental or pathological conditions (Meinertzhagen, 2001; Raineteau & Schwab, 2001). Theoretically, reorganization can occur on multiple levels in preexisting neural circuits: by changing synaptic strength (referred to as synaptic plasticity), by anatomical reorganization through the sprouting of uninjured axonal branches and dendrites (referred to as anatomical plasticity), or by changes in neuronal properties. Because axonal plasticity after injuries to the CNS is frequently associatedwithfunctionalrecovery,ongoingresearch in adult vertebrates focuses on understanding the mechanism of plasticity, which could lead to new treatments for patients suffering from stroke or traumatic injuries of the brain or spinal cord.

This research paper will introduce examples of plastic rearrangements in locomotor systems of the nervous system and discuss possible mechanisms of spontaneous recovery after injuries to the nervous system. Lately, this topic has been extensively reviewed by Bizzi, Tresch, Saltiel, and d’Avella (2000), Pearson (2000), and Raineteau and Schwab.

Injury-Induced Adaptations of Central Pattern-Generating Networks

Aprominentexampleofplasticityinalocomotorsystemisthe finding that cats with complete thoracic spinal cord transection can be trained to walk on a treadmill with their hind limbs (Barbeau & Rossignol, 1994; De Leon, Hodgson, Roy, & Edgerton, 1998; Lovely, Gregor, Roy, & Edgerton, 1990; and reviewed in Rossignol, 2000). Directly after spinalization, cats that had their forelimbs placed on a platform could not self-support their hindquarters during stepping movements on a treadmill. Within 2 weeks, these cats gained partial weight control and were sometimes even able to place the plantar surface of the paws on the treadmill belt.After about four weeks, themovementofthetreadmillbeltwasabletotriggerlocomotion, and the cats could transiently support their own weight (Barbeau & Rossignol; Lovely et al.; De Leon et al.). Consistent with the idea of the activity-dependent acquisition of a motor task, the training effects persist. With longer delays in training, however, the effects of the treadmill training cease (De Leon et al., 1999). In humans, too, treadmill training has proven its validity in the rehabilitation phase of patients with spinal cord injuries (Behrman & Harkema, 2000; Dietz, Colombo, & Jensen, 1994; Wernig & Muller, 1992). Regular training with partial weight support by suspending patients in a parachute belt over a treadmill increased the return of rhythmic muscle activation, improved weight support capability, and decreased spasticity in patients with anatomically incomplete spinal cord injury. A mechanism probably involved in training-induced functional recovery is the enhanced excitability of spinal pattern-generating networks after spinal cord injury (De Leon et al., 1999; Tillakaratne et al., 2000).

Plasticity in Afferent Pathways Controlling Locomotion

The finding that treadmill training in spinalized cats enhances locomotor recovery indicated that adaptive changes in the pattern-generating networks are driven by sensory signals from the stepping limbs. A good example that afferent pathways are modifiable is the conditioning of the wellknown H-reflex in a learning task in rats, monkeys, and humans (Segal & Wolf, 1994; Wolpaw, 1997). It is also known that, after injuries to the CNS (especially the spinal cord), reflexes are exaggerated (Burke, Gillies, & Lance, 1970; Hochman & McCrea, 1994; Nelson & Mendell, 1979). One factor in the increased reflex gain is the sprouting of sensory afferents and a simultaneous increase in their effectiveness.

Another example of injury-induced reflex plasticity is the finding that afferent pathways that are involved in the initiation of the swing phase and in reinforcing extensor activity are enhanced by partial denervation of extensor muscles (Gritsenko, Mushahwar, & Prochazka, 2001; Pearson, Fouad, & Misiaszek, 1999; Whelan et al., 1995a). Increases in proprioceptive reflex strength occur within a week and are paralleled by the recovery of stepping. The location of the adaptive changes is probably in the lumbar spinal cord, since the amplitudes of group I (rising from Golgi tendon organs and muscle spindles) field potentials from a spared synergistic muscle are increased in the intermediate nucleus of lumbar segments (Fouad & Pearson, 1997b). The finding that a comparable increase in the effectiveness of group I input from this muscle can also be found in chronically spinalized cats also indicates that reflex adaptations are occurring at the level of the spinal cord (Bouyer, Whelan, Pearson, & Rossignol, 2001). In the light of the adaptive capabilities of the spinal cord, Pearson (2001) recently reviewed the role of plasticity in reflex function and in the recovery of locomotion after injuries to the CNS.

The fact that plasticity can occur on several levels (e.g., that spinal reflex pathways are able to learn) has been also demonstrated by Lou and Bloedel (1988), who showed that decerebrated walking ferrets were able to change the trajectory of the swing phase when an obstacle was interjected into the step cycle during the swing phase.

In insects as well, the removal of sensory organs involved in the regulation of locomotion or even amputation of a limb can be compensated. For example, in the locust flight system, complete or partial removal of the tegulae (mechanoreceptors at the wing base) leads to compensatory anatomical and synaptic rearrangements resulting in functional recovery of flight motor behavior within 2 weeks following the lesion (Büschges, Ramirez, Driesang, & Pearson, 1992a, 1992b; Fischer & Ebert, 1999; Gee & Robertson, 1996; Wolf & Büschges, 1997). Interestingly, it is reported that this recovery occurs spontaneously and does not depend on training or activity. The plasticity of locomotor systems can also be expressed on an immediate short-term scale. An example of such injury-induced plasticity is the recovery of walking after leg amputation in cockroaches. The walking pattern adapts to the loss of the limb by switching interleg locomotor coordination from hexapods to that of tetrapods (Hughes, 1957; Wilson, 1966).

Injury-Induced Plasticity in the Corticospinal Tract

Due to limited self-repair after traumatic injuries to the CNS in higher vertebrates, it is believed that functional recovery, too, is rather limited. However, significant functional recovery occurs after stroke (Ferrucci et al., 1993; Speach & Dombovy, 1995) traumatic head injury (Sbordone et al., 1995), or spinal cord injury (Bracken et al., 2000). The underlying mechanisms of this spontaneous recovery are rather unclear. A phenomenon often observed in patients and animal models after such injuries and intensive rehabilitative training conducted parallel to functional recovery or in training paradigms is found at the cortical level in rearrangements of the sensory and motor representation maps (Bruehlmeier et al., 1998; Levy, Amassian, Traad, & Caldwell, 1990; Nudo, Milliken, Jenkins, & Merzenich, 1996;Wu & Kaas, 1999; and see Klintsova & Greenough, 1999, for a review). The underlying mechanisms of these rearrangements are probably multiple and rather unclear. It has been suggested that unmasking horizontal connections between the cortical subregions may play a crucial role (Chen, Corwell, Yaseen, Hallett, & Cohen, 1998; Huntley, 1997; Jacobs & Donoghue, 1991). A possible contribution of structural plasticity in descending corticospinal tract (CST) axons has been discussed since the recent finding that the CST in adult rats has a greater potential for anatomical rearrangements than was previously believed. Injury-induced sprouting and increased collateralization was found in severed CST fibers rostral to the lesion, in parallel with shifts in hindlimb motor cortex representations (Fouad, Pedersen, Schwab, & Brösamle, 2001). Structural adaptations have also been reported in spared CST fibers. Significant anatomical plasticity was correlated with spontaneous functional recovery in a grasping task in adult rats (Weidner, Ner, Salimi, & Tuszynski, 2001). It is thus currently speculated that such spontaneous growth of lesioned and unlesioned axons contributes to changes in cortical motor representations and to functional recovery after incomplete spinal cord lesions.

Functional and anatomical studies clearly show that the potential for adaptive reorganization in the adult nervous system in vertebrates and invertebrates has been underestimated. Adaptive changes can occur on several levels of the nervous system and are often activity dependent. Current approaches to experimentally increase the plastic capabilities of the nervous system, together with the progress in understanding rehabilitative strategies, might open new avenues in the treatment of injuries to the nervous system.

Modulation of Locomotor Activity

In order to initiate, maintain, adapt, or terminate locomotor activity to meet the requirements of the current environmental conditions, neuronal networks for motor control must be flexible.Whereasthefast,task-dependent,cycle-by-cycleadaptation of locomotor activity is achieved by sensory feedback, which, in a broad sense, can be viewed as being an integral part of the pattern-generating networks, neuromodulatory inputs can reshape the motor output by affecting intrinsic network properties of motor circuits and thus provide the general flexibility observed in motor behavior. The term neuromodulation has been in common usage for more than two decades and was originally defined as the alteration of the cellular or synaptic properties of a neuron mediated by a substance released from another neuron (Kaczmarek & Levitan, 1987; Kupfermann, 1979). However, this definition no longer strictly conforms to many other definitions of neuromodulation used in scientific literature, primarily because in many systems neurotransmission and neuromodulation apparently involve common mechanisms and address common targets (for an overview of the large variety of phenomena nowadays referred to as neuromodulation, see Katz, 1999). This section introduces the main mechanism of generating locomotor flexibility, the modulation of locomotor network function by neural active substances (neuromodulators).

Sources of Neuromodulators

In vertebrates and invertebrates, neuromodulators are mostly released from cell groups consisting of relatively few neurons located in the CNS, but outside the specific locomotor circuits and not participating in the basic locomotor rhythm generation (referred to as extrinsic neuromodulation; Katz, 1999). Most of the modulators of vertebrate locomotor networks are synthesized in distinct cell clusters in the brainstem that produce, among other things, the biogenic amines serotonin (5-HT, nucleus raphe), noradrenaline (norepinephrine, locus coeruleus), or dopamine. In many vertebrate systems, the axonal projections of these relatively few cells however, can supply large areas of the brain and spinal cord (Kuypers & Huisman, 1982; van Mier, Joosten, van Rheden, & ten Donkelaar, 1986). Invertebrate neuromodulators, such as the neuropeptide proctolin, are normally released from neurosecretory cells either clustered in cerebral ganglia regions or from single, paired, or small groups of cells spread over the whole ventral nerve cord (Beltz & Kravitz, 1987; Keshishian & O’Shea, 1985; Stevenson & Sporhase-Eichmann, 1995). This not only enables the coordinated release of neuromodulators to defined targets within a particular CPG, but also underlies the simultaneous control of locomotor networks consisting of multiple CPGs (e.g., networks driving leg joints or spinal networks for swimming). For vertebrates and invertebrates, classic neuromodulators associated with locomotor systems and their controllers—that is, the spinal cord in vertebrates and the segmental ganglia in invertebrates—are summarized in Table 5.1.

Effects of Neuromodulators on the Output of Locomotor Networks

In principle, neuromodulators alter the expression of motor patterns by affecting the controllers of a locomotor system that drives the effector organs, such as the muscles in the body wall or within limbs or wings. The best-recognized function of neuromodulators in vertebrate and invertebrate motor control is the alteration of ongoing motor activity. Such alterations in the intensity of locomotor activity are important, for instance, in adjusting the instantaneous speed of locomotion, which involves acceleration as well as deceleration and termination, or in changing locomotor intensity. Furthermore, in a variety of vertebrate and invertebrate species, neuromodulators are able to initiate locomotor activity (Table 5.1).

Initiation of Locomotor Activity

In the simplest examples, initiation of long-lasting periods of locomotor activity often requires no more than a short external stimulus that triggers long-lasting, self-sustained network activity (e.g., escape swimming in tadpoles; Roberts, 1990). However, locomotor activity can also be initiated by administration of neuromodulators. Intravenous injection of dopamine (L-DOPA) elicits locomotor activity in spinalized cats on a treadmill (Forssberg & Grillner, 1973; Jankovska et al., 1967a, 1967b). As well as in rabbits (Viala & Buser, 1969) and decerebrated adult rats (Iles & NicolopoulosStournaras, 1996). Intrathecal application of noradrenaline (Kiehn, Hultborn, & Conway, 1992) or intravenous injection of adrenoreceptor agonists to acutely (Forssberg & Grillner) or chronically spinalized cats (e.g., Barbeau & Rossignol, 1991) also evokes locomotor activity (reviewed in Rossignol et al., 1998). Sublesional transplantation of noradrenergic embryonic neurons from the locus coeruleus into the spinal cord (i.e., close to the locomotor networks) is also able to trigger automatic locomotion in spinalized cats (Yakovleff et al., 1989). In addition, noradrenaline or dopamine, when applied to in vitro spinal cords of vertebrates, activate the locomotor networks (Kiehn & Kjaerulff, 1996; Kiehn, Sillar, Kjaerulff, & McDearmid, 1999; Squalli-Houssaini & Cazalets, 2000).

Serotonin (5-HT) is a neuromodulator that can initiate locomotion in some vertebrates, but not in others. In the rabbit (Viala & Buser, 1969) and the neonatal rat (Cazalets, SqualliHoussaini, & Clarac, 1992; Kiehn & Kjaerulff, 1996), 5-HT induces alternating rhythmic activity in muscle antagonists. Furthermore, sublesional transplantation of serotonergic brain stem cells into the spinal cord of chronically spinalized rats was shown to activate spinal locomotor networks and to improve locomotion (Feraboli-Lohnherr, Orsal, Yakovleff, Gimenez y Ribotta, & Privat, 1997). However, 5-HT cannot initiate locomotion in the cat (Barbeau & Rossignol, 1991), the lamprey (Harris-Warrick & Cohen, 1985), or the tadpole (Sillar, Wedderburn, & Simmers, 1992). Finally, in some vertebrates, acetylcholine can cause a strong activation of spinal pattern generators (Cowley & Schmidt, 1994; Panchin, Perrins, & Roberts, 1991). In invertebrates, injection or bath application of neuromodulators to isolated nervous systems elicits locomotion or locomotor-like pattern (summary in Table 5.1). However, we do not at present fully understand what mechanisms underlie such an initiation or whether neuromodulators function to activate locomotor networks in vivo (e.g., Pearson, 1993). Evidence from invertebrate systems suggests that neuromodulators might either directly alter cellular properties of specific network neurons (Kleinhaus & Angstadt, 1995; Ramirez & Pearson, 1991a, 1991b), resulting in locomotor activity onset, or activate modulatory pathways external to the locomotor network, successively initiating pattern generation (Katz & Frost, 1995).

Modulation of Ongoing Locomotor Activity

In general, a neuromodulator can alter ongoing locomotor activity by affecting three major parameters of the locomotor pattern: (a) It can change the cycle period of the locomotor pattern; (b) it can change muscle force within each activity cycle by altering the duration and intensity of motoneuronal activity bursts; and (c) it can change the coordination of the activity cycles, not only between different neuron pools driving particular muscles (e.g., within one limb during walking) but also the longitudinal coordination of body segments (e.g., during swimming in aquatic animals).

In vertebrates, noradrenergic pathways particularly affect the duration of the movement cycle. For example, administration of noradrenergic agonists increases spinalized cats’ step-cycle length during walking (reviewed in Ribotta et al., 1998). Noradrenaline and its agonists consistently lengthen cycle periods during swimming in amphibian tadpoles (Fischer, Merrywest, & Sillar, 2001; McDearmid, Scrymgeour-Wedderburn, & Sillar, 1997). However, an increased duration of the movement cycles is also mediated by 5-HT and dopamine (Grillner et al., 1995; Kiehn & Kjaerulff, 1996). In a wide range of vertebrates, 5-HT increases the duration and intensity of the activity bursts within each movement cycle—not only in swimming animals, such as the lamprey (Harris-Warrick & Cohen, 1985) and amphibian tadpoles (Sillar et al., 1992), but also in the locomotor systems of walking animals, such as rabbits (Viala & Buser, 1969), rats (Kiehn & Kjaerulff, 1996; Squalli-Houssaini et al., 1993), and cats (Barbeau & Rossignol, 1991). Finally, modulators such as noradrenaline, 5-HT, and dopamine not only influence the longitudinal coordination of the locomotor pattern between successive body segments in swimming animals (Fischer et al., 2001; Grillner et al., 1995), but also shift the activation of particular muscles within a step cycle and thus may alter the complete movement pattern of the limb (Kiehn & Kjaerulff).

In many systems, the effects of neuromodulators are mediated via numerous pharmacologically distinct receptor subclasses (for 5-HT see, e.g., Wedderburn & Sillar, 1994; Wikstrom, Hill, Hellgren, & Grillner, 1995), enabling a multimodal control of the motor pattern. In vertebrates, direct pharmacological activation of, for example, the adrenoreceptors, which are defined as putative target receptors for catecholamines such as noradrenaline (e.g., Hirst & Nield, 1980), can modulate motor output (Barbeau & Rossignol, 1991; Forssberg & Grillner, 1973; Kiehn et al., 1992), with each subclass affecting particular facets of the motor pattern (Fischer et al.; Squalli-Houssaini & Cazalets, 2000).

Neuromodulators Affect Cellular Properties and the Synaptic Efficacy of Network Neurons

Most of the classic neuromodulators exert their effects by changing intrinsic properties of one or a few network neurons or of one or more particular synaptic connections, which affects the overall network output, resulting in a more or less extensive alteration of the motor pattern (discussed previously). For many motor systems, more than one neuromodulatory substance is known (e.g., Grillner, Parker, & El Manira, 1998; Schotland et al., 1995), each of which has distinct effects on the network output (Kiehn & Kjaerulff, 1996; Sillar, Keith, & McDearmid, 1998) and must be coordinated for proper pattern modulation. However, in the majority of motor systems, we are just beginning to understand how such multiple and sometimes even contradictory modulatory inputs are processed and integrated and thus enable a functional pattern modulation. The following sections summarize the most common elementary effects of neuromodulators acting extrinsically on particular cellular and synaptic properties of neurons within locomotor networks (for an in-depth review, see Kiehn & Katz, 1999).

Alteration of Intrinsic Cellular Properties of Network Neurons

Features of neuronal activity such as action potential formation, spike rate, and activity threshold of neurons may vary widely among the different classes of neurons within a network. The shape of a particular type of neuron’s characteristic activity pattern in response to synaptic drive depends on intrinsic biophysical membrane parameters, that is, on its set of steady and transient voltage-dependent ionic conductances within the membrane. These intrinsic cellular properties determine (a) membrane resting potential (i.e., the state of excitability of a neuron); (b) burst activity, including SFA (i.e., codetermining the activity period of a neuron); (c) mechanisms underlying membrane bistability (i.e., the ability of plateau potential generation to maintain a prolonged period of activity); and (d) the mechanisms enabling a postinhibitory rebound (PIR) of a neuron (helping to escape a phase of strong inhibition)—all of which may contribute to the basic shaping of the network output. Most of the classic neuromodulators alter such intrinsic cellular properties by affecting one or more transient ionic conductances. An overview is given in Table 5.2.

Alteration of Synaptic Transmission Between Network Neurons

Besides their effects on intrinsic cellular properties, neuromodulators can affect synaptic transmission either by targeting presynaptic neurons (i.e., resulting in an altered amount of transmitter release; e.g., Shupliakov, Pieribone, Gad, & Brodin, 1995) or by changing the responses of the postsynaptic cell to a transmitter (e.g., by alterating particular membrane properties; Parker, 1995). In spinal locomotor networks, biogenic amines such as 5-HT and noradrenaline can control locomotor intensity by increasing or decreasing the amount of the inhibitory transmitter released from neurons responsible for the reciprocal coupling between antagonistic motoneuron pools. Strengthening (or weakening) an inhibitory phase between two consecutive movement cycles causes a delayed (or an earlier) onset of activity in the succeeding cycle and thus modulates the cycle duration during ongoing locomotor activity (e.g., Sillar et al., 1998). During locomotor activity, the properties of the synaptic transmission between neurons in a locomotor network can also depend on the connection’s activityhistory(i.e.,onthepreviouscyclesofmovementinthe same episode of locomotion, so-called activity-dependent synaptic plasticity; e.g., Parker & Grillner, 2000). Neuromodulators can affect these activity-dependent properties of a synaptic connection, enabling synaptic metaplasticity (reviewed in, e.g., Parker, 2001), which adds a further degree of functional flexibility to the network output. In some cases, neuromodulators can even reverse the sign of a particularsynaptic connection (Johnston, Peck, & Harris-Warrick, 1993).

Conclusions

Research in the field of locomotor control has greatly benefited from studies in lower vertebrates and invertebrates, such as the lamprey and tadpole for swimming pattern generation and the crayfish and stick insect for walking pattern generation. These animal models, in being experimentally well accessible, helped to unravel basic principles for neuronal networks controlling locomotion. However, a major outcome of the past research in various lower and higher animal species is that many of the mechanisms described also contribute to locomotor pattern generation in higher vertebrates (including humans) that are often less accessible experimentally, indicating that common principles appear to underlie the design of locomotor networks in the entire animal kingdom. However, it must be acknowledged that we are still far from completely understanding the mechanisms underlying locomotor network function, even in the few networks that have been characterized in detail on the cellular level. Important issues that must be addressed in the future are (a) the mechanisms underlying descending control and action selection (e.g., changes in gait or locomotor speed and selection of particular locomotor responses); (b) the structure and functioning of pattern-generating networks for terrestrial locomotion, such as walking, including the level of cell-to-cell interaction and mechanisms of coupling limbs, joints, and segments; and (c) interactions among neuromodulatory influences on locomotor patterns and neuronal activity.

Another major outcome of recent locomotor research is that networks are by no means hardwired but are flexible regarding their topology and the properties of the connections between their components. This not only enables a functional flexibility during ongoing behavior but also underlies the ability of these networks to establish locomotor ability during the development of an individual, to maintain locomotion throughout its life span as well as to enable a certain degree of functional recovery (following, e.g., the damage of peripheral nerves and muscles or the CNS). Discovering the mechanisms underlying such adaptive rearrangements in the adult mammalian nervous system is of immediate clinical interest because they are frequently related to functional recovery, and might offer a key to the treatment of patients with injuries of the CNS.

New techniques that were recently established offer experimental access to locomotor systems on levels required to address all these questions. For example, the development of in vitro preparations of the spinal cord for mammals opened new perspectives in exploring their complex neuronal networks. Recentadvancesinneurogenetics,inparticulartheavailability of so-called knockout mutants, offer new approaches to studying a wide range of functional levels within motor systems ranging from the role of single molecules (e.g., receptors or transmitters)tomuchhigherlevels(e.g.,supraspinalprocesses involved in the generation and control of locomotion).

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