An optimality principle for locomotor central pattern generators

Two types of neural circuits contribute to legged locomotion: central pattern generators (CPGs) that produce rhythmic motor commands (even in the absence of feedback, termed “fictive locomotion”), and reflex circuits driven by sensory feedback. Each circuit alone serves a clear purpose, and the two together are understood to cooperate during normal locomotion. The difficulty is in explaining their relative balance objectively within a control model, as there are infinite combinations that could produce the same nominal motor pattern. Here we propose that optimization in the presence of uncertainty can explain how the circuits should best be combined for locomotion. The key is to re-interpret the CPG in the context of state estimator-based control: an internal model of the limbs that predicts their state, using sensory feedback to optimally balance competing effects of environmental and sensory uncertainties. We demonstrate use of optimally predicted state to drive a simple model of bipedal, dynamic walking, which thus yields minimal energetic cost of transport and best stability. The internal model may be implemented with neural circuitry compatible with classic CPG models, except with neural parameters determined by optimal estimation principles. Fictive locomotion also emerges, but as a side effect of estimator dynamics rather than an explicit internal rhythm. Uncertainty could be key to shaping CPG behavior and governing optimal use of feedback.

Somatostatin 1.1 contributes to the innate exploration of zebrafish larva

Pharmacological experiments indicate that neuropeptides can effectively tune neuronal activity and modulate locomotor output patterns. However, their functions in shaping innate locomotion often remain elusive. For example, somatostatin has been previously shown to induce locomotion when injected in the brain ventricles but to inhibit fictive locomotion when bath-applied in the spinal cord in vitro. Here, we investigated the role of somatostatin in innate locomotion through a genetic approach by knocking out somatostatin 1.1 (sst1.1) in zebrafish. We automated and carefully analyzed the kinematics of locomotion over a hundred of thousand bouts from hundreds of mutant and control sibling larvae. We found that the deletion of sst1.1 did not impact acousto-vestibular escape responses but led to abnormal exploration. sst1.1 mutant larvae swam over larger distance, at higher speed and performed larger tail bends, indicating that Somatostatin 1.1 inhibits spontaneous locomotion. Altogether our study demonstrates that Somatostatin 1.1 innately contributes to slowing down spontaneous locomotion.

The serotonin reuptake blocker citalopram destabilizes fictive locomotor activity in salamander axial circuits through 5-HT1A receptors

Serotoninergic (5-HT) neurons are powerful modulators of spinal locomotor circuits. Most studies about 5-HT modulation focused on the effect of exogenous 5-HT and these studies provided key information about the cellular mechanisms involved. Less is known about the effects of increased release of endogenous 5-HT with selective serotonin reuptake inhibitors. Such molecules were shown to destabilize the locomotor output of spinal limb networks through 5-HT1A receptors. However, in tetrapods little is known about the effects of increased 5-HT release on the locomotor output of axial networks, which are coordinated with limb circuits during locomotion from basal vertebrates to mammals. Here, we examined the effect of citalopram on fictive locomotion generated in axial segments of isolated spinal cords in salamanders, a tetrapod where raphe 5-HT reticulospinal neurons and intraspinal 5-HT neurons are present as in other vertebrates. Using electrophysiological recordings of ventral roots, we show that fictive locomotion generated by bath-applied glutamatergic agonists is destabilized by citalopram. Citalopram-induced destabilization was prevented by a 5-HT1A receptor antagonist, whereas a 5-HT1A receptor agonist destabilized fictive locomotion. Using immunofluorescence experiments, we found 5-HT-positive fibers and varicosities in proximity with motoneurons and glutamatergic interneurons that are likely involved in rhythmogenesis. Our results show that increasing 5-HT release has a deleterious effect on axial locomotor activity through 5-HT1A receptors. This is consistent with studies in limb networks of turtle and mouse, suggesting that this part of the complex 5-HT modulation of spinal locomotor circuits is common to limb and axial networks in limbed vertebrates.

Synaptic mechanisms underlying modulation of locomotor-related motoneuron output by premotor cholinergic interneurons

Spinal motor networks are formed by diverse populations of interneurons that set the strength and rhythmicity of behaviors such as locomotion. A small cluster of cholinergic interneurons, expressing the transcription factor Pitx2, modulates the intensity of muscle activation via ‘C-bouton’ inputs to motoneurons. However, the synaptic mechanisms underlying this neuromodulation remain unclear. Here, we confirm in mice that Pitx2+ interneurons are active during fictive locomotion and that their chemogenetic inhibition reduces the amplitude of motor output. Furthermore, after genetic ablation of cholinergic Pitx2+ interneurons, M2 receptor-dependent regulation of the intensity of locomotor output is lost. Conversely, chemogenetic stimulation of Pitx2+ interneurons leads to activation of M2 receptors on motoneurons, regulation of Kv2.1 channels and greater motoneuron output due to an increase in the inter-spike afterhyperpolarization and a reduction in spike half-width. Our findings elucidate synaptic mechanisms by which cholinergic spinal interneurons modulate the final common pathway for motor output.

An optimality principle for locomotor central pattern generators

Two types of neural circuits contribute to legged locomotion: central pattern generators (CPGs) that produce rhythmic motor commands (even in the absence of feedback, termed “fictive locomotion”), and reflex circuits driven by sensory feedback. Each circuit alone serves a clear purpose, and the two together are understood to cooperate during normal locomotion. The difficulty is in explaining their relative balance objectively within a control model, as there are infinite combinations that could produce the same nominal motor pattern. Here we propose that optimization in the presence of uncertainty can explain how the circuits should best be combined for locomotion. The key is to re- interpret the CPG in the context of state estimator-based control: an internal model of the limbs that predicts their state, using sensory feedback to optimally balance competing effects of environmental and sensory uncertainties. We demonstrate use of optimally predicted state to drive a simple model of bipedal, dynamic walking, which thus yields minimal energetic cost of transport and best stability. The internal model may be implemented with classic neural half-center circuitry, except with neural parameters determined by optimal estimation principles. Fictive locomotion also emerges, but as a side effect of estimator dynamics rather than an explicit internal rhythm. Uncertainty could be key to shaping CPG behavior and governing optimal use of feedback.New and NoteworthySensory feedback modulates the central pattern generator (CPG) rhythm in locomotion, but there lacks an explanation for how much feedback is appropriate. We propose destabilizing noise as a determinant, where an uncertain environment demands more feedback, but noisy sensors demand less. We reinterpret the CPG as an internal model for predicting body state despite noise. Optimizing its feedback yields robust and economical gait in a walking model, and explains the advantages of feedback-driven CPG control.