Conceptualizing the mammalian locomotor central pattern generator with modelling

Presently, the internal organization of the mammalian locomotor central pattern generator (CPG) is unknown due to the difficulty in identifying and localizing interneurones involved in the network. The CPG was initially thought to be composed of half-centres, which set the basic locomotor rhythm by generating alternating excitation of antagonist motoneurone (MN) pools (e.g. flexors and extensors) via reciprocal inhibition (see Rybak et al. 2006 for a discussion). While this reproduced alternating activity of antagonists it failed to account for various recruitment patterns possible during locomotion. This led to a suggestion that the CPG consisted of multiple, coupled, unit burst generators allowing for flexible recruitment of MNs (Grillner, 1981). However, others stated that half-centres could likewise produce multiple recruitment patterns if additional interneuronal circuits were interposed between networks responsible for rhythm generation and MN activation (Perret, 1983). As a result, which theory better approximates the locomotor CPG has remained contentious. To circumvent the problem of identifying ensembles of neurones involved in locomotor rhythm generation, a method clearly more amenable to conceptualize the functional organization of the mammalian locomotor CPG is to employ ‘in silico’ simulations, using available experimental data, as recently done in an issue of The Journal of Physiology (Rybak et al. 2006). Whether the model by Rybak and colleagues provides an accurate description of the locomotor CPG is unknown but it offers a theoretical framework to refine and test hypotheses regarding the functional organization of the CPG. The purpose of this short review is to briefly describe and discuss some of the model's underlying assumptions, to highlight some key findings, and make a few recommendations. Rybak et al. designed a two-level CPG, which activated antagonist motor pools for a single hindlimb, with one level consisting of half-centre rhythm generators (RG) and a second level comprising pattern formation (PF) networks. Each RG (one for flexors, one for extensors) set the basic locomotor rhythm, defined durations of flexor and extensor phases, and controlled the PF networks that distributed and coordinated the activity of MN pools. Reciprocal inhibition at both levels and between antagonist MNs ensured that a given MN population was active only during a specific phase(s) of the step cycle. The two-layered CPG enabled the locomotor rhythm and pattern of MN activity to be separately controlled. The model was composed of interneurones and MNs and several ionic conductances were implemented using Hodgkin–Huxley equations. Interestingly, the model included persistent (slowly inactivating) sodium currents, which endowed excitatory populations of the RGs with rhythmogenic properties. The model reproduced several aspects of fictive locomotion including alternating rhythmic activity of flexors and extensors evoked by tonic drive to the CPG, appropriate locomotor step cycle periods, increasing speed with increased mesencephalic locomotor region (MLR) drive, slow locomotor-like activity without MLR drive, and synchronized oscillations of flexors and extensors if synaptic inhibition was blocked. As mentioned, the model added persistent sodium currents (INaP) to RG neurones allowing them to generate endogenous oscillations (bursts). The bursting properties of the model were realized through dynamics of INaP within RGs and reciprocal inhibition between antagonistic RGs. During MLR-induced locomotion the membrane potential of rhythm-generating neurones enters a depolarized state highlighted by repetitive action potential discharges. This state, or plateau potential, is sustained by persistent (slowly inactivating) inward currents (PICs). Although the model included a persistent sodium current, at least one other channel, the L-type calcium channel, can produce PICs (Brownstone, 2006). Several investigations report that the L-type calcium channel is largely responsible for PICs in cat, turtle, mouse and rat MNs (Brownstone, 2006). A future step in the model could be to evaluate contributions of L-type calcium channels of RG and PF neurones but also of MNs, since it is believed that MN properties, such as PICs, are standard components of normal motor behaviours, including locomotion. The model incorporated various inhibitory and excitatory connections, with a different weighting value afforded to each. Interestingly, both mutual excitation and reciprocal inhibition were included within and between extensor and flexor RGs. Whereas reciprocal inhibition is a widely accepted property of CPGs, the purpose of mutual excitation between antagonists is less obvious. However, if the weights of all inhibitory connections in the model were set to zero slow spontaneous synchronized bursting of flexor and extensor MNs occurred similar to blockade of inhibitory synaptic transmission during experimental conditions (Rybak et al. 2006). Mutual excitation in the model between RGs ensures that flexors and extensors discharge in-phase and not at an independent rhythm from each other in the absence of synaptic inhibition. Another explanation for synchronized activity of flexors and extensors in the absence of synaptic inhibition could be the presence of only one rhythm generator that equally excites the flexor and extensor PF networks and under normal circumstances the alternating activity of antagonists is mediated by inhibitory circuits at PF and MN levels. The model also provided a clear framework for grouping different types of neurones according to their functional contribution in locomotor pattern generation. One contentious issue is separating MNs from the PF layer. In the model, MNs are simple output nodes omitted from pattern formation. However, MNs are modified directly by sensory feedback, recurrent feedback, neuromodulatory systems, and have intrinsic patterning properties, such as plateau-potentials, that are not included in the current model. As such, the model could test the inclusion of MNs in the PF network thus making some PF neurones redundant for evoking different recruitment patterns during locomotion. The two-layered CPG architecture separating rhythm generation and MN activation was largely based on experimental observations of brief spontaneous failures in the alternating activity of flexors and extensors during fictive locomotion called deletions (Rybak et al. 2006). Deletions are periods of silenced activity in some MN populations accompanied by sustained or rhythmic activity in antagonists. In most cases, the post-deletion rhythm is maintained, meaning that the burst of activity reappears at a time when it would have normally been present if a deletion had not occurred. According to the model, the disruption in excitability occurs within the PF network preventing MNs from discharging while the RG maintains its normal ‘clock function’. Once the disturbance in the PF network is removed, MN activity reappears in line with descending drive from the RG. There are also instances where the post-deletion rhythm is altered (i.e. resetting). For these resetting deletions, the normal rhythm is modified because excitability in the RG is directly altered. Although the two-layered model accounts well for deletions of both types, there are alternative explanations for non-resetting deletions other than separating rhythm generation and MN activation. For instance, if locomotor rhythm is the product of multiple rhythmogenic neural oscillators distributed bilaterally along the spinal cord (Deliagina et al. 1983), it is possible that failure of one of these subunits (e.g. a deletion) could be rectified by compound influences from remaining elements in the network. Indeed, even the ‘non-deleted’ half of the half-centre (RG) could maintain the post-deletion rhythm. For example, in oscillations with a ‘dominant’ phase (e.g. flexor-dominant during MLR locomotion) one may predict that self-inhibitory properties of the dominant oscillator determine the rhythm even when activity of the non-dominant oscillator is suppressed. Thus, although non-resetting deletions result from failure of the non-dominant oscillator, the CPG rhythm remains constant because the dominant oscillator maintained its own endogenous rhythmic bursting. This explanation is supported by observations of non-resetting deletions occurring predominantly in the non-dominant phase. The post-deletion rhythm could also be maintained at the level of interacting CPG networks (e.g. one CPG for each limb). In intact cats, the four limbs normally operate together during locomotion and although each limb can have its own rhythm, as demonstrated when each limb steps at a different speed on a split-belt treadmill, there is no evidence that such rhythmic dissociation occurs during fictive locomotion. Failure at one level could alter the fictive locomotor pattern of all four limbs and not simply represent a local failure. Rybak et al. proposed that forelimb CPGs do not maintain the rhythm since deletions in motor pools of one hindlimb have been reported in a chronic spinal cat. They also indicated that deletions were observed in one hindlimb despite no rhythmic activity in the contralateral hindlimb. However, the model specifies that absence of rhythmic activity in the contralateral hindlimb could simply reflect failure at the PF or MN level. It is possible that RG centres are still active and via their connections with the contralateral side maintain the rhythm during deletions. To eliminate possible influences from other CPGs, deletions would have to be recorded in one hindlimb in complete isolation from the forelimbs (e.g. spinalization) and from the contralateral hindlimb (e.g. spinal hemisection or longitudinal section along the midline of the spinal cord). In conclusion, although some of the CPG properties included in the model could be explored in greater detail, the model faithfully reproduced several features of fictive locomotion and therefore constitutes a remarkable conceptualization of the internal organization of the mammalian locomotor CPG.

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