Biological pattern generation: the cellular and computational logic of networks in motion

Feasibility and safety of automated multi-channel FES-assisted gait training in incomplete spinal cord injury

OBJECTIVE

The feasibility, safety, and efficacy of automated multi-channel functional electrical stimulation-assisted gait training was assessed in individuals with chronic incomplete spinal cord injury, using an electrical stimulation suit with built-in surface electrodes and motion capture sensors (Teslasuit).

DESIGN

10-week functional electrical stimulation-assisted gait training, twice weekly for 30 min.

SUBJECTS/PATIENTS

Five individuals with chronic incomplete spinal cord injury (≥ 12 months post-injury, ASIA C/D, minimal Walking Index Spinal Cord Injury II ≥ 9).

METHODS

The quadriceps, gluteii, hamstrings, tibialis anterior, and gastrocnemius muscles were stimulated bilaterally during gait. Feasibility and safety were evaluated via questionnaires, session adherence, and adverse events. Gait function was assessed using a 10 m walk test, Walking Index Spinal Cord Injury II, and Hoffer classification at baseline, post-intervention, and follow-up. Surface electromyography and spatiotemporal parameters (walking speed, step length and width, cadence) were recorded during the 10 m walk test.

RESULTS

All participants completed the training (91% adherence) with no serious adverse events. Temporary skin redness, muscle soreness, and fatigue were reported by participants. Post-intervention, 4 participants increased their walking speed, step length, and cadence, with 2 maintaining and 2 further improving at follow-up. No consistent changes were found in muscle activity post training.

CONCLUSION

Automated multi-channel functional electrical stimulation-assisted gait training was feasible, safe, and well received. Preliminary findings suggest that gait improved in most participants, though individual responses varied. The results highlight the potential of multi-channel functional electrical stimulation-assisted gait training as a valuable tool for enhancing gait recovery.

The VAPB Axis Precisely Coordinates the Timing of Motoneuron Dendritogenesis in Neural Map Development

In Drosophila motoneurons, spatiotemporal dendritic patterns are established in the ventral nerve cord. While many guidance cues have been identified, the mechanisms of temporal regulation remain unknown. Previously, we identified the actin modulator Cdc42 GTPase as a key factor in this process. In this report, we further identify the upstream factors that activate Cdc42. Using single-cell genetics, FRET-based imaging, and biochemical techniques, we demonstrate that the guanine nucleotide exchange factor Vav is anchored to the plasma membrane via the Eph receptor tyrosine kinase, enabling Cdc42 activation. VAMP-associated protein 33 (Vap33), an Eph ligand supplied non-cell-autonomously, may induce Eph autophosphorylation, initiating downstream signaling. Traditionally known as an ER-resident protein, Vap33 is secreted extracellularly at the onset of Cdc42 activation, acting as a temporal cue. In humans, VAPB-the ortholog of Vap33-is similarly secreted in the spinal cord, and its dysregulation leads to amyotrophic lateral sclerosis type 8 (ALS8) and spinal muscular atrophy (SMA). Our findings provide a framework linking VAPB signaling to motor circuitry formation in both health and disease.

Pharyngeal mechanosensory neurons control food swallow in Drosophila melanogaster

As the early step of food ingestion, the swallow is under rigorous sensorimotor control. Nevertheless, the mechanisms underlying swallow control at a molecular and circuitry level remain largely unknown. Here, we find that mutation of the mechanotransduction channel genes nompC, Tmc, or piezo impairs the regular pumping rhythm of the cibarium during feeding of the fruit fly Drosophila melanogaster. A group of multi-dendritic mechanosensory neurons, which co-express the three channels, wrap the cibarium and are crucial for coordinating the filling and emptying of the cibarium. Inhibition of them causes difficulty in food emptying in the cibarium, while their activation leads to difficulty in cibarium filling. Synaptic and functional connections are detected between the pharyngeal mechanosensory neurons and the motor circuit that controls swallow. This study elucidates the role of mechanosensation in swallow, and provides insights for a better understanding of the neural basis of food swallow.

Central pattern generator control of a vertebrate ultradian sleep rhythm

The mechanisms underlying the mammalian ultradian sleep rhythm-the alternation of rapid-eye-movement (REM) and slow-wave (SW) states-are not well understood but probably depend, at least in part, on circuits in the brainstem1-6. Here, we use perturbation experiments to probe this ultradian rhythm in sleeping lizards (Pogona vitticeps)7-9 and test the hypothesis that it originates in a central pattern generator10,11-circuits that are typically susceptible to phase-dependent reset and entrainment by external stimuli12. Using light pulses, we find that Pogona's ultradian rhythm8 can be reset in a phase-dependent manner, with a critical transition from phase delay to phase advance in the middle of SW. The ultradian rhythm frequency can be decreased or increased, within limits, by entrainment with light pulses. During entrainment, Pogona REM (REMP) can be shortened but not lengthened, whereas SW can be dilated more flexibly. In awake animals, a few alternating light/dark epochs matching natural REMP and SW durations entrain a sleep-like brain rhythm, suggesting the transient activation of an ultradian rhythm generator. In sleeping animals, a light pulse delivered to a single eye causes an immediate ultradian rhythm reset, but only of the contralateral hemisphere; both sides resynchronize spontaneously, indicating that sleep is controlled by paired rhythm-generating circuits linked by functional excitation. Our results indicate that central pattern generators of a type usually known to control motor rhythms may also organize the ultradian sleep rhythm in a vertebrate.

Operation regimes of spinal circuits controlling locomotion and the role of supraspinal drives and sensory feedback

Locomotion in mammals is directly controlled by the spinal neuronal network, operating under the control of supraspinal signals and somatosensory feedback that interact with each other. However, the functional architecture of the spinal locomotor network, its operation regimes, and the role of supraspinal and sensory feedback in different locomotor behaviors, including at different speeds, remain unclear. We developed a computational model of spinal locomotor circuits receiving supraspinal drives and limb sensory feedback that could reproduce multiple experimental data obtained in intact and spinal-transected cats during tied-belt and split-belt treadmill locomotion. We provide evidence that the spinal locomotor network operates in different regimes depending on locomotor speed. In an intact system, at slow speeds (<0.4 m/s), the spinal network operates in a non-oscillating state-machine regime and requires sensory feedback or external inputs for phase transitions. Removing sensory feedback related to limb extension prevents locomotor oscillations at slow speeds. With increasing speed and supraspinal drives, the spinal network switches to a flexor-driven oscillatory regime and then to a classical half-center regime. Following spinal transection, the model predicts that the spinal network can only operate in the state-machine regime. Our results suggest that the spinal network operates in different regimes for slow exploratory and fast escape locomotor behaviors, making use of different control mechanisms.

Molecular and Cellular Mechanisms of Motor Circuit Development

Motor circuits represent the main output of the central nervous system and produce dynamic behaviors ranging from relatively simple rhythmic activities like swimming in fish and breathing in mammals to highly sophisticated dexterous movements in humans. Despite decades of research, the development and function of motor circuits remain poorly understood. Breakthroughs in the field recently provided new tools and tractable model systems that set the stage to discover the molecular mechanisms and circuit logic underlying motor control. Here, we describe recent advances from both vertebrate (mouse, frog) and invertebrate (nematode, fruit fly) systems on cellular and molecular mechanisms that enable motor circuits to develop and function and highlight conserved and divergent mechanisms necessary for motor circuit development.

Evolution of staining methods in neuroanatomy: Impetus for emanation of neuron doctrine during the turn of 20th century

The nervous system is distinctive as compared to other tissue systems in human body owing to intricate structural organization. Histological studies played a key role in unveiling complex details of nervous tissue. However, the process of developing suitable staining method for nerve cells was arduous and spanned across almost half a century. The present study explored details of the journey involving quest for propitious staining method in neuroanatomy culminating in promulgation of neuron doctrine at the onset of 20th century. Initial efforts involving hematoxylin (including its diverse modifications) and subsequent adoption of analogous dye-based stains (like Nissl's method) had limited success in visualization of different parts of a nerve cell and structural details of nervous tissue. This was due to inability of dye-based stains to penetrate the connective tissue sheath of nervous tissue. Eventually, advent of metallic stains in form of silver impregnation method (Golgi stain), reduced silver impregnation method with gold stain (Cajal's stain) and silver carbonate staining method of Río-Hortega unraveled the structure of nervous tissue. The evolution of staining methods catalyzed the refinement of theories pertinent to constitution of nervous tissue. Golgi's staining led to emergence of reticular theory (neurons exist as a network) and Nissl's staining was the basis of the concept of Nervösen Grau (nerve cells and glial cells are embedded in mass of gray matter). Finally, Cajal's staining method successfully elucidated the complex anatomy of nerve terminals and resulted in emanation of neuron doctrine (neurons exists as individual units with adjacent connections).

The functional role of conscious sensation of movement

This paper proposes a new framework for investigating neural signals sufficient for a conscious sensation of movement and their role in motor control. We focus on signals sufficient for proprioceptive awareness, particularly from muscle spindle activation and from primary motor cortex (M1). Our review of muscle vibration studies reveals that afferent signals alone can induce conscious sensations of movement. Similarly, studies employing peripheral nerve blocks suggest that efferent signals from M1 are sufficient for sensations of movement. On this basis, we show that competing theories of motor control assign different roles to sensation of movement. According to motor command theories, sensation of movement corresponds to an estimation of the current state based on afferent signals, efferent signals, and predictions. In contrast, within active inference architectures, sensations correspond to proprioceptive predictions driven by efferent signals from M1. The focus on sensation of movement provides a way to critically compare and evaluate the two theories. Our analysis offers new insights into the functional roles of movement sensations in motor control and consciousness.

Bifurcation analysis of a two-neuron central pattern generator model for both oscillatory and convergent neuronal activities

The neural oscillator model proposed by Matsuoka is a piecewise affine system that exhibits distinctive periodic solutions. Although such typical oscillation patterns have been widely studied, little is understood about the dynamics of convergence to certain fixed points and bifurcations between the periodic orbits and fixed points in this model. We performed fixed point analysis on a two-neuron version of the Matsuoka oscillator model, the result of which explains the mechanism of oscillation and the discontinuity-induced bifurcations such as subcritical/supercritical Hopf-like, homoclinic-like and grazing bifurcations. Furthermore, it provided theoretical predictions concerning a logarithmic oscillation-period scaling law and noise-induced oscillations observed around those bifurcations. These results are expected to underpin further investigations into oscillatory and transient neuronal activities concerning central pattern generators.

A two-layer neural circuit controls fast forward locomotion in Drosophila

Fast forward locomotion is critical for animal hunting and escaping behaviors. However, how the underlying neural circuit is wired at synaptic resolution to decide locomotion direction and speed remains poorly understood. Here, we identified in the ventral nerve cord (VNC) a set of ascending cholinergic neurons (AcNs) to be command neurons capable of initiating fast forward peristaltic locomotion in Drosophila larvae. Targeted manipulations revealed that AcNs are necessary and sufficient for fast forward locomotion. AcNs can activate their postsynaptic partners, A01j and A02j; both are interneurons with locomotory rhythmicity. Activated A01j neurons form a posterior-anteriorly descendent gradient in output activity along the VNC to launch forward locomotion from the tail. Activated A02j neurons exhibit quicker intersegmental transmission in activity that enables fast propagation of motor waves. Our work revealed a global neural mechanism that coordinately controls the launch direction and propagation speed of Drosophila locomotion, furthering the understanding of the strategy for locomotion control.

Sensory feedback and central neuronal interactions in mouse locomotion

Locomotion is a complex process involving specific interactions between the central neural controller and the mechanical components of the system. The basic rhythmic activity generated by locomotor circuits in the spinal cord defines rhythmic limb movements and their central coordination. The operation of these circuits is modulated by sensory feedback from the limbs providing information about the state of the limbs and the body. However, the specific role and contribution of central interactions and sensory feedback in the control of locomotor gait and posture remain poorly understood. We use biomechanical data on quadrupedal locomotion in mice and recent findings on the organization of neural interactions within the spinal locomotor circuitry to create and analyse a tractable mathematical model of mouse locomotion. The model includes a simplified mechanical model of the mouse body with four limbs and a central controller composed of four rhythm generators, each operating as a state machine controlling the state of one limb. Feedback signals characterize the load and extension of each limb as well as postural stability (balance). We systematically investigate and compare several model versions and compare their behaviour to existing experimental data on mouse locomotion. Our results highlight the specific roles of sensory feedback and some central propriospinal interactions between circuits controlling fore and hind limbs for speed-dependent gait expression. Our models suggest that postural imbalance feedback may be critically involved in the control of swing-to-stance transitions in each limb and the stabilization of walking direction.

Operation regimes of spinal circuits controlling locomotion and role of supraspinal drives and sensory feedback

Locomotion in mammals is directly controlled by the spinal neuronal network, operating under the control of supraspinal signals and somatosensory feedback that interact with each other. However, the functional architecture of the spinal locomotor network, its operation regimes, and the role of supraspinal and sensory feedback in different locomotor behaviors, including at different speeds, remain unclear. We developed a computational model of spinal locomotor circuits receiving supraspinal drives and limb sensory feedback that could reproduce multiple experimental data obtained in intact and spinal-transected cats during tied-belt and split-belt treadmill locomotion. We provide evidence that the spinal locomotor network operates in different regimes depending on locomotor speed. In an intact system, at slow speeds (< 0.4 m/s), the spinal network operates in a non-oscillating state-machine regime and requires sensory feedback or external inputs for phase transitions. Removing sensory feedback related to limb extension prevents locomotor oscillations at slow speeds. With increasing speed and supraspinal drives, the spinal network switches to a flexor-driven oscillatory regime and then to a classical half-center regime. Following spinal transection, the model predicts that the spinal network can only operate in the state-machine regime. Our results suggest that the spinal network operates in different regimes for slow exploratory and fast escape locomotor behaviors, making use of different control mechanisms.

Continuous neural control of a bionic limb restores biomimetic gait after amputation

For centuries scientists and technologists have sought artificial leg replacements that fully capture the versatility of their intact biological counterparts. However, biological gait requires coordinated volitional and reflexive motor control by complex afferent and efferent neural interplay, making its neuroprosthetic emulation challenging after limb amputation. Here we hypothesize that continuous neural control of a bionic limb can restore biomimetic gait after below-knee amputation when residual muscle afferents are augmented. To test this hypothesis, we present a neuroprosthetic interface consisting of surgically connected, agonist-antagonist muscles including muscle-sensing electrodes. In a cohort of seven leg amputees, the interface is shown to augment residual muscle afferents by 18% of biologically intact values. Compared with a matched amputee cohort without the afferent augmentation, the maximum neuroprosthetic walking speed is increased by 41%, enabling equivalent peak speeds to persons without leg amputation. Further, this level of afferent augmentation enables biomimetic adaptation to various walking speeds and real-world environments, including slopes, stairs and obstructed pathways. Our results suggest that even a small augmentation of residual muscle afferents restores biomimetic gait under continuous neuromodulation in individuals with leg amputation.

Workspace trajectory generation with smooth gait transition using CPG-based locomotion control for hexapod robot

-This paper presents a new control methodology for achieving smooth gait transitions for a hexapod robot using Central Pattern Generators (CPGs). The approach involves modifying the Phase Oscillator within the CPG network to enable smooth transitions between different gaits in order to improve the adaptability to changing environmental conditions. The foot trajectory generator is designed based on the CPG output, allowing the possibility of online adjustment of foot trajectory parameters, such as step height and size, as well as the robot's speed and direction. Our simulation demonstrates the effectiveness of the modified oscillator in achieving smoother gait transitions with a transition time falls close to the output period of the CPG oscillators, and experiments on a real hexapod robot validate the feasibility and efficiency of our approach in considering online adjustability of trajectory parameters, confirming the potential of this methodology to enhance the locomotion capabilities of legged robots for navigating complex terrains.

Pathological cell assembly dynamics in a striatal MSN network model

Under normal conditions the principal cells of the striatum, medium spiny neurons (MSNs), show structured cell assembly activity patterns which alternate sequentially over exceedingly long timescales of many minutes. It is important to understand this activity since it is characteristically disrupted in multiple pathologies, such as Parkinson's disease and dyskinesia, and thought to be caused by alterations in the MSN to MSN lateral inhibitory connections and in the strength and distribution of cortical excitation to MSNs. To understand how these long timescales arise we extended a previous network model of MSN cells to include synapses with short-term plasticity, with parameters taken from a recent detailed striatal connectome study. We first confirmed the presence of sequentially switching cell clusters using the non-linear dimensionality reduction technique, Uniform Manifold Approximation and Projection (UMAP). We found that the network could generate non-stationary activity patterns varying extremely slowly on the order of minutes under biologically realistic conditions. Next we used Simulation Based Inference (SBI) to train a deep net to map features of the MSN network generated cell assembly activity to MSN network parameters. We used the trained SBI model to estimate MSN network parameters from ex-vivo brain slice calcium imaging data. We found that best fit network parameters were very close to their physiologically observed values. On the other hand network parameters estimated from Parkinsonian, decorticated and dyskinetic ex-vivo slice preparations were different. Our work may provide a pipeline for diagnosis of basal ganglia pathology from spiking data as well as for the design pharmacological treatments.

Refinement of efficient encodings of movement in the dorsolateral striatum throughout learning

The striatum is required for normal action selection, movement, and sensorimotor learning. Although action-specific striatal ensembles have been well documented, it is not well understood how these ensembles are formed and how their dynamics may evolve throughout motor learning. Here we used longitudinal 2-photon Ca2+ imaging of dorsal striatal neurons in head-fixed mice as they learned to self-generate locomotion. We observed a significant activation of both direct- and indirect-pathway spiny projection neurons (dSPNs and iSPNs, respectively) during early locomotion bouts and sessions that gradually decreased over time. For dSPNs, onset- and offset-ensembles were gradually refined from active motion-nonspecific cells. iSPN ensembles emerged from neurons initially active during opponent actions before becoming onset- or offset-specific. Our results show that as striatal ensembles are progressively refined, the number of active nonspecific striatal neurons decrease and the overall efficiency of the striatum information encoding for learned actions increases.

Basal ganglia-spinal cord pathway that commands locomotor gait asymmetries in mice

The basal ganglia are essential for executing motor actions. How the basal ganglia engage spinal motor networks has remained elusive. Medullary Chx10 gigantocellular (Gi) neurons are required for turning gait programs, suggesting that turning gaits organized by the basal ganglia are executed via this descending pathway. Performing deep brainstem recordings of Chx10 Gi Ca2+ activity in adult mice, we show that striatal projection neurons initiate turning gaits via a dominant crossed pathway to Chx10 Gi neurons on the contralateral side. Using intersectional viral tracing and cell-type-specific modulation, we uncover the principal basal ganglia-spinal cord pathway for locomotor asymmetries in mice: basal ganglia → pontine reticular nucleus, oral part (PnO) → Chx10 Gi → spinal cord. Modulating the restricted PnO → Chx10 Gi pathway restores turning competence upon striatal damage, suggesting that dysfunction of this pathway may contribute to debilitating turning deficits observed in Parkinson's disease. Our results reveal the stratified circuit architecture underlying a critical motor program.

Integrated Modular Neural Control for Versatile Locomotion and Object Transportation of a Dung Beetle-Like Robot

Dung beetles can effectively transport dung pallets of various sizes in any direction across uneven terrain. While this impressive ability can inspire new locomotion and object transportation solutions in multilegged (insect-like) robots, to date, most existing robots use their legs primarily to perform locomotion. Only a few robots can use their legs to achieve both locomotion and object transportation, although they are limited to specific object types/sizes (10%-65% of leg length) on flat terrain. Accordingly, we proposed a novel integrated neural control approach that, like dung beetles, pushes state-of-the-art insect-like robots beyond their current limits toward versatile locomotion and object transportation with different object types/sizes and terrains (flat and uneven). The control method is synthesized based on modular neural mechanisms, integrating central pattern generator (CPG)-based control, adaptive local leg control, descending modulation control, and object manipulation control. We also introduced an object transportation strategy combining walking and periodic hind leg lifting for soft object transportation. We validated our method on a dung beetle-like robot. Our results show that the robot can perform versatile locomotion and use its legs to transport hard and soft objects of various sizes (60%-70% of leg length) and weights (approximately 3%-115% of robot weight) on flat and uneven terrains. The study also suggests possible neural control mechanisms underlying the dung beetle Scarabaeus galenus' versatile locomotion and small dung pallet transportation.

Characterizing the supraspinal sensorimotor control of walking using MRI-compatible system: a systematic review

BACKGROUND

The regulation of gait is critical to many activities of everyday life. When walking, somatosensory information obtained from mechanoreceptors throughout body is delivered to numerous supraspinal networks and used to execute the appropriate motion to meet ever-changing environmental and task demands. Aging and age-related conditions oftentimes alter the supraspinal sensorimotor control of walking, including the responsiveness of the cortical brain regions to the sensorimotor inputs obtained from the peripheral nervous system, resulting in diminished mobility in the older adult population. It is thus important to explicitly characterize such supraspinal sensorimotor elements of walking, providing knowledge informing novel rehabilitative targets. The past efforts majorly relied upon mental imagery or virtual reality to study the supraspinal control of walking. Recent efforts have been made to develop magnetic resonance imaging (MRI)-compatible devices simulating specific somatosensory and/or motor aspects of walking. However, there exists large variance in the design and functionality of these devices, and as such inconsistent functional MRI (fMRI) observations.

METHODS

We have therefore completed a systematic review to summarize current achievements in the development of these MRI-compatible devices and synthesize available imaging results emanating from studies that have utilized these devices.

RESULTS

The device design, study protocol and neuroimaging observations of 26 studies using 13 types of devices were extracted. Three of these devices can provide somatosensory stimuli, eight motor stimuli, and two both types of stimuli. Our review demonstrated that using these devices, fMRI data of brain activation can be successfully obtained when participants remain motionless and experience sensorimotor stimulation during fMRI acquisition. The activation in multiple cortical (e.g., primary sensorimotor cortex) and subcortical (e.g., cerebellum) regions has been each linked to these types of walking-related sensorimotor stimuli.

CONCLUSION

The observations of these publications suggest the promise of implementing these devices to characterize the supraspinal sensorimotor control of walking. Still, the evidence level of these neuroimaging observations was still low due to small sample size and varied study protocols, which thus needs to be confirmed via studies with more rigorous design.

Inspiratory and sigh breathing rhythms depend on distinct cellular signalling mechanisms in the preBötzinger complex

Breathing behaviour involves the generation of normal breaths (eupnoea) on a timescale of seconds and sigh breaths on the order of minutes. Both rhythms emerge in tandem from a single brainstem site, but whether and how a single cell population can generate two disparate rhythms remains unclear. We posit that recurrent synaptic excitation in concert with synaptic depression and cellular refractoriness gives rise to the eupnoea rhythm, whereas an intracellular calcium oscillation that is slower by orders of magnitude gives rise to the sigh rhythm. A mathematical model capturing these dynamics simultaneously generates eupnoea and sigh rhythms with disparate frequencies, which can be separately regulated by physiological parameters. We experimentally validated key model predictions regarding intracellular calcium signalling. All vertebrate brains feature a network oscillator that drives the breathing pump for regular respiration. However, in air-breathing mammals with compliant lungs susceptible to collapse, the breathing rhythmogenic network may have refashioned ubiquitous intracellular signalling systems to produce a second slower rhythm (for sighs) that prevents atelectasis without impeding eupnoea. KEY POINTS: A simplified activity-based model of the preBötC generates inspiratory and sigh rhythms from a single neuron population. Inspiration is attributable to a canonical excitatory network oscillator mechanism. Sigh emerges from intracellular calcium signalling. The model predicts that perturbations of calcium uptake and release across the endoplasmic reticulum counterintuitively accelerate and decelerate sigh rhythmicity, respectively, which was experimentally validated. Vertebrate evolution may have adapted existing intracellular signalling mechanisms to produce slow oscillations needed to optimize pulmonary function in mammals.

Quantifying and Maximizing the Information Flux in Recurrent Neural Networks

Free-running recurrent neural networks (RNNs), especially probabilistic models, generate an ongoing information flux that can be quantified with the mutual information I[x→(t),x→(t+1)] between subsequent system states x→. Although previous studies have shown that I depends on the statistics of the network's connection weights, it is unclear how to maximize I systematically and how to quantify the flux in large systems where computing the mutual information becomes intractable. Here, we address these questions using Boltzmann machines as model systems. We find that in networks with moderately strong connections, the mutual information I is approximately a monotonic transformation of the root-mean-square averaged Pearson correlations between neuron pairs, a quantity that can be efficiently computed even in large systems. Furthermore, evolutionary maximization of I[x→(t),x→(t+1)] reveals a general design principle for the weight matrices enabling the systematic construction of systems with a high spontaneous information flux. Finally, we simultaneously maximize information flux and the mean period length of cyclic attractors in the state-space of these dynamical networks. Our results are potentially useful for the construction of RNNs that serve as short-time memories or pattern generators.

Human arm redundancy: a new approach for the inverse kinematics problem

The inverse kinematics (IK) problem addresses how both humans and robotic systems coordinate movement to resolve redundancy, as in the case of arm reaching where more degrees of freedom are available at the joint versus hand level. This work focuses on which coordinate frames best represent human movements, enabling the motor system to solve the IK problem in the presence of kinematic redundancies. We used a multi-dimensional sparse source separation method to derive sets of basis (or source) functions for both the task and joint spaces, with joint space represented by either absolute or anatomical joint angles. We assessed the similarities between joint and task sources in each of these joint representations, finding that the time-dependent profiles of the absolute reference frame's sources show greater similarity to corresponding sources in the task space. This result was found to be statistically significant. Our analysis suggests that the nervous system represents multi-joint arm movements using a limited number of basis functions, allowing for simple transformations between task and joint spaces. Additionally, joint space seems to be represented in an absolute reference frame to simplify the IK transformations, given redundancies. Further studies will assess this finding's generalizability and implications for neural control of movement.

Role of the basal ganglia in innate and learned behavioural sequences

Integrating individual actions into coherent, organised behavioural units, a process called chunking, is a fundamental, evolutionarily conserved process that renders actions automatic. In vertebrates, evidence points to the basal ganglia - a complex network believed to be involved in action selection - as a key component of action sequence encoding, although the underlying mechanisms are only just beginning to be understood. Central pattern generators control many innate automatic behavioural sequences that form some of the most basic behaviours in an animal's repertoire, and in vertebrates, brainstem and spinal pattern generators are under the control of higher order structures such as the basal ganglia. Evidence suggests that the basal ganglia play a crucial role in the concatenation of simpler behaviours into more complex chunks, in the context of innate behavioural sequences such as chain grooming in rats, as well as sequences in which innate capabilities and learning interact such as birdsong, and sequences that are learned from scratch, such as lever press sequences in operant behaviour. It has been proposed that the role of the striatum, the largest input structure of the basal ganglia, might lie in selecting and allowing the relevant central pattern generators to gain access to the motor system in the correct order, while inhibiting other behaviours. As behaviours become more complex and flexible, the pattern generators seem to become more dependent on descending signals. Indeed, during learning, the striatum itself may adopt the functional characteristics of a higher order pattern generator, facilitated at the microcircuit level by striatal neuropeptides.

Supraspinal control of motoneurons after paralysis enabled by spinal cord stimulation

Spinal cord stimulation (SCS) restores motor control after spinal cord injury (SCI) and stroke. This evidence led to the hypothesis that SCS facilitates residual supraspinal inputs to spinal motoneurons. Instead, here we show that SCS does not facilitate residual supraspinal inputs but directly triggers motoneurons action potentials. However, supraspinal inputs can shape SCS-mediated activity, mimicking volitional control of motoneuron firing. Specifically, by combining simulations, intraspinal electrophysiology in monkeys and single motor unit recordings in humans with motor paralysis, we found that residual supraspinal inputs transform subthreshold SCS-induced excitatory postsynaptic potentials into suprathreshold events. We then demonstrated that only a restricted set of stimulation parameters enables volitional control of motoneuron firing and that lesion severity further restricts the set of effective parameters. Our results explain the facilitation of voluntary motor control during SCS while predicting the limitations of this neurotechnology in cases of severe loss of supraspinal axons.

Modulation by Neuropeptides with Overlapping Targets Results in Functional Overlap in Oscillatory Circuit Activation

Neuromodulation lends flexibility to neural circuit operation but the general notion that different neuromodulators sculpt neural circuit activity into distinct and characteristic patterns is complicated by interindividual variability. In addition, some neuromodulators converge onto the same signaling pathways, with similar effects on neurons and synapses. We compared the effects of three neuropeptides on the rhythmic pyloric circuit in the stomatogastric ganglion of male crabs, Cancer borealis Proctolin (PROC), crustacean cardioactive peptide (CCAP), and red pigment concentrating hormone (RPCH) activate the same modulatory inward current, I MI, and have convergent actions on synapses. However, while PROC targets all four neuron types in the core pyloric circuit, CCAP and RPCH target the same subset of only two neurons. After removal of spontaneous neuromodulator release, none of the neuropeptides restored the control cycle frequency, but all restored the relative timing between neuron types. Consequently, differences between neuropeptide effects were mainly found in the spiking activity of different neuron types. We performed statistical comparisons using the Euclidean distance in the multidimensional space of normalized output attributes to obtain a single measure of difference between modulatory states. Across preparations, the circuit output in PROC was distinguishable from CCAP and RPCH, but CCAP and RPCH were not distinguishable from each other. However, we argue that even between PROC and the other two neuropeptides, population data overlapped enough to prevent reliable identification of individual output patterns as characteristic for a specific neuropeptide. We confirmed this notion by showing that blind classifications by machine learning algorithms were only moderately successful.Significance Statement It is commonly assumed that distinct behaviors or circuit activities can be elicited by different neuromodulators. Yet it is unknown to what extent these characteristic actions remain distinct across individuals. We use a well-studied circuit model of neuromodulation to examine the effects of three neuropeptides, each known to produce a distinct activity pattern in controlled studies. We find that, when compared across individuals, the three peptides elicit activity patterns that are either statistically indistinguishable or show too much overlap to be labeled characteristic. We ascribe this to interindividual variability and overlapping subcellular actions of the modulators. Because both factors are common in all neural circuits, these findings have broad significance for understanding chemical neuromodulatory actions while considering interindividual variability.

Optimization strategies to obtain smooth gait transitions through biologically plausible central pattern generators

Central pattern generators are small networks that contribute to generating animal locomotion. The models used to study gait generation and gait transition mechanisms often require biologically accurate neuron and synapse models, with high dimensionality and complex dynamics. Tuning the parameters of these models to elicit network dynamics compatible with gait features is not a trivial task, due to the impossibility of inferring a priori the effects of each parameter on the nonlinear system's emergent dynamics. In this paper we explore the use of global optimization strategies for parameter optimization in multigait central pattern generator (CPG) models with complex cell dynamics and minimal topology. We first consider an existing quadruped CPG model as a test bed for the objective function formulation, then proceed to optimize the parameters of a newly proposed multigait, interlimb hexapod CPG model. We successfully obtain hexapod gaits and prompt gait transitions by varying only control currents, while all CPG parameters, once optimized, are kept fixed. This mechanism of gait transitions is compatible with short-term synaptic plasticity.

Vibration perception among children and adolescents with Charcot-Marie-tooth disease and implications for foot posture

BACKGROUND

Alterations in vibration perception among children and adolescents with Charcot-Marie-Tooth disease might explain observed changes in foot posture. Therefore, this cross-sectional study compared the vibration perception of the lower limbs in youths with and without Charcot-Marie-Tooth disease and verified the cut-off value of the distal vibration perception for the Charcot-Marie-Tooth group. In addition, associations between dynamic plantar pressure, vibration perception and isometric muscle strength were investigated.

METHODS

Participants aged 9-18 (Charcot-Marie-Tooth group n = 32; Typical group n = 32) had vibration perception measured by a 128-Hz graduated tuning fork. The static and dynamic foot posture were evaluated by the Foot Posture Index and pressure distribution measuring system, respectively. For the Charcot-Marie-Tooth group, a hand-held dynamometer evaluated the isometric muscle strength of the lower limbs.

FINDINGS

Children with Charcot-Marie-Tooth disease presented impaired vibration perception at the distal phalanx of the hallux and head of the first metatarsal compared to their typically developing peers, while adolescents with Charcot-Marie-Tooth disease showed impairment in all the tested regions compared to their typically developing peers. The cut-off value for vibration perception for participants with Charcot-Marie-Tooth disease was 5.7, considering the original grade of the tuning-fork 128 Hz. Among the associations established for the Charcot-Marie-Tooth group, a greater vibration perception at the distal phalanx of the hallux was associated with a longer rearfoot contact time (β = 31.02, p = 0.04).

INTERPRETATION

These new findings may guide the clinical evaluation and rehabilitation treatment for children and adolescents with Charcot-Marie-Tooth disease.

Control of movement of underwater swimmers: Animals, simulated animates and swimming robots

The control of movement in living organisms represents a fundamental task that the brain has evolved to solve. One crucial aspect is how the nervous system organizes the transformation of sensory information into motor commands. These commands lead to muscle activation and subsequent animal movement, which can exhibit complex patterns. One example of such movement is locomotion, which involves the translation of the entire body through space. Central Pattern Generators (CPGs) are neuronal circuits that provide control signals for these movements. Compared to the intricate circuits found in the brain, CPGs can be simplified into networks of neurons that generate rhythmic activation, coordinating muscle movements. Since the 1990s, researchers have developed numerous models of locomotive circuits to simulate different types of animal movement, including walking, flying, and swimming. Initially, the primary goal of these studies was to construct biomimetic robots. However, it became apparent that simplified CPGs alone were not sufficient to replicate the diverse range of adaptive locomotive movements observed in living organisms. Factors such as sensory modulation, higher-level control, and cognitive components related to learning and memory needed to be considered. This necessitated the use of more complex, high-dimensional circuits, as well as novel materials and hardware, in both modeling and robotics. With advancements in high-power computing, artificial intelligence, big data processing, smart materials, and electronics, the possibility of designing a new generation of true bio-mimetic robots has emerged. These robots have the capability to imitate not only simple locomotion but also exhibit adaptive motor behavior and decision-making. This motivation serves as the foundation for the current review, which aims to analyze existing concepts and models of movement control systems. As an illustrative example, we focus on underwater movement and explore the fundamental biological concepts, as well as the mathematical and physical models that underlie locomotion and its various modulations.

SUPRASPINAL CONTROL OF MOTONEURONS AFTER PARALYSIS ENABLED BY SPINAL CORD STIMULATION

Spinal cord stimulation (SCS) restores motor control after spinal cord injury (SCI) and stroke. This evidence led to the hypothesis that SCS facilitates residual supraspinal inputs to spinal motoneurons. Instead, here we show that SCS does not facilitate residual supraspinal inputs but directly triggers motoneurons action potentials. However, supraspinal inputs can shape SCS-mediated activity, mimicking volitional control of motoneuron firing. Specifically, by combining simulations, intraspinal electrophysiology in monkeys and single motor unit recordings in humans with motor paralysis, we found that residual supraspinal inputs transform subthreshold SCS-induced excitatory postsynaptic potentials into suprathreshold events. We then demonstrated that only a restricted set of stimulation parameters enables volitional control of motoneuron firing and that lesion severity further restricts the set of effective parameters. Our results explain the facilitation of voluntary motor control during SCS while predicting the limitations of this neurotechnology in cases of severe loss of supraspinal axons.

Sensory Feedback and Central Neuronal Interactions in Mouse Locomotion

Locomotion is a complex process involving specific interactions between the central neural controller and the mechanical components of the system. The basic rhythmic activity generated by locomotor circuits in the spinal cord defines rhythmic limb movements and their central coordination. The operation of these circuits is modulated by sensory feedback from the limbs providing information about the state of the limbs and the body. However, the specific role and contribution of central interactions and sensory feedback in the control of locomotor gait and posture remain poorly understood. We use biomechanical data on quadrupedal locomotion in mice and recent findings on the organization of neural interactions within the spinal locomotor circuitry to create and analyze a tractable mathematical model of mouse locomotion. The model includes a simplified mechanical model of the mouse body with four limbs and a central controller composed of four rhythm generators, each operating as a state machine controlling the state of one limb. Feedback signals characterize the load and extension of each limb as well as postural stability (balance). We systematically investigate and compare several model versions and compare their behavior to existing experimental data on mouse locomotion. Our results highlight the specific roles of sensory feedback and some central propriospinal interactions between circuits controlling fore and hind limbs for speed-dependent gait expression. Our models suggest that postural imbalance feedback may be critically involved in the control of swing-to-stance transitions in each limb and the stabilization of walking direction.

Bolstering Cognitive and Locomotor Function in Post-Stroke Dementia Using Human-Robotic Interactive Gait Training

Studies have reported inconclusive results regarding the effectiveness and clinical indications of the exclusive use of human-robotic interactive gait training (HIT) in patients with post-stroke dementia (PSD). This study aimed to compare the effects of human-robotic interactive gait training (HIT) and conventional physiotherapy (CPT) on cognitive and sensorimotor functions, trunk balance and coordination, dynamic and static balance, and activities related to daily living performance in patients with PSD. Forty-eight patients with PSD who received 60-minute therapy sessions three times per week for 6 weeks were assigned to either the CPT (n = 25) or HIT (n = 23) group. The clinical outcomes included the scores of the mini-mental state examination (MMSE), Fugl-Meyer assessment (FMA), trunk impairment scale (TIS), Berg balance scale (BBS), and modified Barthel index (MBI). Friedman tests were conducted at p < 0.05. The Friedman tests showed that HIT had superior effects to CPT in relation to MMSE, FMA, and TIS (p < 0.05), but not in relation to BBS and MBI (p > 0.05). Our results provide promising clinical evidence that HIT significantly improves cognitive and sensorimotor recovery functions, as well as trunk balance and coordination, in patients with PSD who cannot concurrently perform dual cognitive-locomotor tasks.

Linking neural circuits to the mechanics of animal behavior in Drosophila larval locomotion

The motions that make up animal behavior arise from the interplay between neural circuits and the mechanical parts of the body. Therefore, in order to comprehend the operational mechanisms governing behavior, it is essential to examine not only the underlying neural network but also the mechanical characteristics of the animal's body. The locomotor system of fly larvae serves as an ideal model for pursuing this integrative approach. By virtue of diverse investigation methods encompassing connectomics analysis and quantification of locomotion kinematics, research on larval locomotion has shed light on the underlying mechanisms of animal behavior. These studies have elucidated the roles of interneurons in coordinating muscle activities within and between segments, as well as the neural circuits responsible for exploration. This review aims to provide an overview of recent research on the neuromechanics of animal locomotion in fly larvae. We also briefly review interspecific diversity in fly larval locomotion and explore the latest advancements in soft robots inspired by larval locomotion. The integrative analysis of animal behavior using fly larvae could establish a practical framework for scrutinizing the behavior of other animal species.

Variation in the rate of recovery in motor function between the upper and lower limbs in patients with stroke: some proposed hypotheses and their implications for research and practice

Background

Stroke results in impairment of motor function of both the upper and lower limbs. However, although it is debatable, motor function of the lower limb is believed to recover faster than that of the upper limb. The aim of this paper is to propose some hypotheses to explain the reasons for that, and discuss their implications for research and practice.

Method

We searched PubMED, Web of Science, Scopus, Embase and CENTRAL using the key words, stroke, cerebrovascular accident, upper extremity, lower extremity, and motor recovery for relevant literature.

Result

The search generated a total of 2,551 hits. However, out of this number, 51 duplicates were removed. Following review of the relevant literature, we proposed four hypotheses: natural instinct for walking hypothesis, bipedal locomotion hypothesis, central pattern generators (CPGs) hypothesis and role of spasticity hypothesis on the subject matter.

Conclusion

We opine that, what may eventually account for the difference, is the frequency of use of the affected limb or intensity of the rehabilitation intervention. This is because, from the above hypotheses, the lower limb seems to be used more frequently. When limbs are used frequently, this will result in use-dependent plasticity and eventual recovery. Thus, rehabilitation techniques that involve high repetitive tasks practice such as robotic rehabilitation, Wii gaming and constraint induced movement therapy should be used during upper limb rehabilitation.

Locomotor pattern generation and descending control: a historical perspective

The ability to generate and control locomotor movements depends on complex interactions between many areas of the nervous system, the musculoskeletal system, and the environment. How the nervous system manages to accomplish this task has been the subject of investigation for more than a century. In vertebrates, locomotion is generated by neural networks located in the spinal cord referred to as central pattern generators. Descending inputs from the brain stem initiate, maintain, and stop locomotion as well as control speed and direction. Sensory inputs adapt locomotor programs to the environmental conditions. This review presents a comparative and historical overview of some of the neural mechanisms underlying the control of locomotion in vertebrates. We have put an emphasis on spinal mechanisms and descending control.

Modulation by neuropeptides with overlapping targets results in functional overlap in oscillatory circuit activation

Neuromodulation lends flexibility to neural circuit operation but the general notion that different neuromodulators sculpt neural circuit activity into distinct and characteristic patterns is complicated by interindividual variability. In addition, some neuromodulators converge onto the same signaling pathways, with similar effects on neurons and synapses. We compared the effects of three neuropeptides on the rhythmic pyloric circuit in the crab Cancer borealis stomatogastric nervous system. Proctolin (PROC), crustacean cardioactive peptide (CCAP), and red pigment concentrating hormone (RPCH) all activate the same modulatory inward current, IMI, and have convergent actions on synapses. However, while PROC targets all four neuron types in the core pyloric circuit, CCAP and RPCH target the same subset of only two neurons. After removal of spontaneous neuromodulator release, none of the neuropeptides restored the control cycle frequency, but all restored the relative timing between neuron types. Consequently, differences between neuropeptide effects were mainly found in the spiking activity of different neuron types. We performed statistical comparisons using the Euclidean distance in the multidimensional space of normalized output attributes to obtain a single measure of difference between modulatory states. Across preparations, circuit output in PROC was distinguishable from CCAP and RPCH, but CCAP and RPCH were not distinguishable from each other. However, we argue that even between PROC and the other two neuropeptides, population data overlapped enough to prevent reliable identification of individual output patterns as characteristic for a specific neuropeptide. We confirmed this notion by showing that blind classifications by machine learning algorithms were only moderately successful.

Flexible neural control of transition points within the egg-laying behavioral sequence in Drosophila

Innate behaviors are frequently comprised of ordered sequences of component actions that progress to satisfy essential drives. Progression is governed by specialized sensory cues that induce transitions between components within the appropriate context. Here we have characterized the structure of the egg-laying behavioral sequence in Drosophila and found significant variability in the transitions between component actions that affords the organism an adaptive flexibility. We identified distinct classes of interoceptive and exteroceptive sensory neurons that control the timing and direction of transitions between the terminal components of the sequence. We also identified a pair of motor neurons that enact the final transition to egg expulsion. These results provide a logic for the organization of innate behavior in which sensory information processed at critical junctures allows for flexible adjustments in component actions to satisfy drives across varied internal and external environments.

Lampreys and spinal cord regeneration: "a very special claim on the interest of zoologists," 1830s-present

Employing history of science methods, including analyses of the scientific literature, archival documents, and interviews with scientists, this paper presents a history of lampreys in neurobiology from the 1830s to the present. We emphasize the lamprey's roles in helping to elucidate spinal cord regeneration mechanisms. Two attributes have long perpetuated studies of lampreys in neurobiology. First, they possess large neurons, including multiple classes of stereotypically located, 'identified' giant neurons in the brain, which project their large axons into the spinal cord. These giant neurons and their axonal fibers have facilitated electrophysiological recordings and imaging across biological scales, ranging from molecular to circuit-level analyses of nervous system structures and functions and including their roles in behavioral output. Second, lampreys have long been considered amongst the most basal extant vertebrates on the planet, so they have facilitated comparative studies pointing to conserved and derived characteristics of vertebrate nervous systems. These features attracted neurologists and zoologists to studies of lampreys between the 1830s and 1930s. But, the same two attributes also facilitated the rise of the lamprey in neural regeneration research after 1959, when biologists first wrote about the spontaneous, robust regeneration of some identified CNS axons in larvae after spinal cord injuries, coupled with recovery of normal swimming. Not only did large neurons promote fresh insights in the field, enabling studies incorporating multiple scales with existing and new technologies. But investigators also were able to attach a broad scope of relevance to their studies, interpreting them as suggesting conserved features of successful, and sometimes even unsuccessful, CNS regeneration. Lamprey research demonstrated that functional recovery takes place without the reformation of the original neuronal connections, for instance, by way of imperfect axonal regrowth and compensatory plasticity. Moreover, research performed in the lamprey model revealed that factors intrinsic to neurons are integral in promoting or hindering regeneration. As this work has helped illuminate why basal vertebrates accomplish CNS regeneration so well, whereas mammals do it so poorly, this history presents a case study in how biological and medical value have been, and could continue to be, gleaned from a non-traditional model organism for which molecular tools have been developed only relatively recently.

A CPG-Based Versatile Control Framework for Metameric Earthworm-Like Robotic Locomotion

Annelids such as earthworms are considered to have central pattern generators (CPGs) that generate rhythms in neural circuits to coordinate the deformation of body segments for effective locomotion. At present, the states of earthworm-like robot segments are often assigned holistically and artificially by mimicking the earthworms' retrograde peristalsis wave, which is unable to adapt their gaits for variable environments and tasks. This motivates the authors to extend the bioinspired research from morphology to neurobiology by mimicking the CPG to build a versatile framework for spontaneous motion control. Here, the spatiotemporal dynamics is exploited from the coupled Hopf oscillators to not only unify the two existing gait generators for restoring temporal-symmetric phase-coordinated gaits and discrete gaits but also generate novel temporal-asymmetric phase-coordinated gaits. Theoretical and experimental tests consistently confirm that the introduction of temporal asymmetry improves the robot's locomotion performance. The CPG-based controller also enables seamless online switching of locomotion gaits to avoid abrupt changes, sharp stops, and starts, thus improving the robot's adaptability in variable working scenarios.

Contrasting walking styles map to discrete neural substrates in the mouse brainstem

Walking is a slow gait which is particularly adaptable to meet internal or external needs and is prone to maladaptive alterations that lead to gait disorders. Alterations can affect speed, but also style (the way one walks). While slowed speed may signify the presence of a problem, style represents the hallmark essential for clinical classification of gait disorders. However, it has been challenging to objectively capture key stylistic features while uncovering neural substrates driving these features. Here we revealed brainstem hotspots that drive strikingly different walking styles by employing an unbiased mapping assay that combines quantitative walking signatures with focal, cell type specific activation. We found that activation of inhibitory neurons that mapped to the ventromedial caudal pons induced slow motion-like style. Activation of excitatory neurons that mapped to the ventromedial upper medulla induced shuffle-like style. Contrasting shifts in walking signatures distinguished these styles. Activation of inhibitory and excitatory neurons outside these territories or of serotonergic neurons modulated walking speed, but without walking signature shifts. Consistent with their contrasting modulatory actions, hotspots for slow-motion and shuffle-like gaits preferentially innervated different substrates. These findings lay the basis for new avenues to study mechanisms underlying (mal)adaptive walking styles and gait disorders.

Graphical abstract

The enteric nervous system

Of all the organ systems in the body, the gastrointestinal tract is the most complicated in terms of the numbers of structures involved, each with different functions, and the numbers and types of signaling molecules utilized. The digestion of food and absorption of nutrients, electrolytes, and water occurs in a hostile luminal environment that contains a large and diverse microbiota. At the core of regulatory control of the digestive and defensive functions of the gastrointestinal tract is the enteric nervous system (ENS), a complex system of neurons and glia in the gut wall. In this review, we discuss 1) the intrinsic neural control of gut functions involved in digestion and 2) how the ENS interacts with the immune system, gut microbiota, and epithelium to maintain mucosal defense and barrier function. We highlight developments that have revolutionized our understanding of the physiology and pathophysiology of enteric neural control. These include a new understanding of the molecular architecture of the ENS, the organization and function of enteric motor circuits, and the roles of enteric glia. We explore the transduction of luminal stimuli by enteroendocrine cells, the regulation of intestinal barrier function by enteric neurons and glia, local immune control by the ENS, and the role of the gut microbiota in regulating the structure and function of the ENS. Multifunctional enteric neurons work together with enteric glial cells, macrophages, interstitial cells, and enteroendocrine cells integrating an array of signals to initiate outputs that are precisely regulated in space and time to control digestion and intestinal homeostasis.

Adapting the pantograph limb: Differential robustness of fore- and hindlimb kinematics against genetically induced perturbation in the neural control networks and its evolutionary implications

The evolutionary transformation of limb morphology to the four-segmented pantograph of therians is among the milestones of mammalian evolution. But, it is still unknown if changes of the mechanical limb function were accompanied by corresponding changes in development and sensorimotor control. The impressive locomotor performance of mammals leaves no doubt about the high integration of pattern formation, neural control and mechanics. But, deviations from normal intra- and interlimb coordination (spatial and temporal) become evident in the presence of perturbations. We induced a perturbation in the development of the neural circuits of the spinal cord of mice (Mus musculus) using a deletion of the Wilms tumor suppressor gene Wt1 in a subpopulation of dI6 interneurons. These interneurons are assumed to participate in the intermuscular coordination within the limb and in left-right-coordination between the limbs. We describe the locomotor kinematics in mice with conditional Wt1 knockout and compare them to mice without Wt1 deletion. Unlike knockout neonates, knockout adult mice do not display severe deviations from normal (=control group) interlimb coordination, but the coordinated protraction and retraction of the limbs is altered. The forelimbs are more affected by deviations from the control than the hindlimbs. This observation appears to reflect a different degree of integration and resistance against the induced perturbation between the limbs. Interestingly, the observed effects are similar to locomotor deficits reported to arise when sensory feedback from proprioceptors or cutaneous receptors is impaired. A putative participation of Wt1 positive dI6 interneurons in sensorimotor integration is therefore considered.

A Single Central Pattern Generator for the Control of a Locomotor Rolling Wave in Mollusc Aplysia

Locomotion in mollusc Aplysia is implemented by a pedal rolling wave, a type of axial locomotion. Well-studied examples of axial locomotion (pedal waves in Drosophila larvae and body waves in leech, lamprey, and fish) are generated in a segmented nervous system via activation of multiple coupled central pattern generators (CPGs). Pedal waves in molluscs, however, are generated by a single pedal ganglion, and it is unknown whether there are single or multiple CPGs that generate rhythmic activity and phase shifts between different body parts. During locomotion in intact Aplysia, bursting activity in the parapedal commissural nerve (PPCN) was found to occur during tail contraction. A cluster of 20 to 30 P1 root neurons (P1Ns) on the ventral surface of the pedal ganglion, active during the pedal wave, were identified. Computational cluster analysis revealed that there are 2 phases to the motor program: phase I (centered around 168°) and phase II (centered around 357°). PPCN activity occurs during phase II. The majority of P1Ns are motoneurons. Coactive P1Ns tend to be electrically coupled. Two classes of pedal interneurons (PIs) were characterized. Class 1 (PI1 and PI2) is active during phase I. Their axons make a loop within the pedal ganglion and contribute to locomotor pattern generation. They are electrically coupled to P1Ns that fire during phase I. Class 2 (PI3) is active during phase II and innervates the contralateral pedal ganglion. PI3 may contribute to bilateral coordination. Overall, our findings support the idea that Aplysia pedal waves are generated by a single CPG.

Computational modeling predicts regulation of central pattern generator oscillations by size and density of the underlying heterogenous network

Central pattern generators are characterized by a heterogeneous cellular composition, with different cell types playing distinct roles in the production and transmission of rhythmic signals. However, little is known about the functional implications of individual variation in the relative distributions of cells and their connectivity patterns. Here, we addressed this question through a combination of morphological data analysis and computational modeling, using the pacemaker nucleus of the weakly electric fish Apteronotus leptorhynchus as case study. A neural network comprised of 60-110 interconnected pacemaker cells and 15-30 relay cells conveying its output to electromotoneurons in the spinal cord, this nucleus continuously generates neural signals at frequencies of up to 1 kHz with high temporal precision. We systematically explored the impact of network size and density on oscillation frequencies and their variation within and across cells. To accurately determine effect sizes, we minimized the likelihood of complex dynamics using a simplified setup precluding differential delays. To identify natural constraints, parameter ranges were extended beyond experimentally recorded numbers of cells and connections. Simulations revealed that pacemaker cells have higher frequencies and lower within-population variability than relay cells. Within-cell precision and between-cells frequency synchronization increased with the number of pacemaker cells and of connections of either type, and decreased with relay cell count in both populations. Network-level frequency-synchronized oscillations occurred in roughly half of simulations, with maximized likelihood and firing precision within biologically observed parameter ranges. These findings suggest the structure of the biological pacemaker nucleus is optimized for generating synchronized sustained oscillations.

Postinspiratory complex acts as a gating mechanism regulating swallow-breathing coordination and other laryngeal behaviors

Breathing needs to be tightly coordinated with upper airway behaviors, such as swallowing. Discoordination leads to aspiration pneumonia, the leading cause of death in neurodegenerative diseases. Here we study the role of the postinspiratory complex, (PiCo) in coordinating breathing and swallowing. Using optogenetic approaches in freely breathing-anesthetized ChATcre, Vglut2cre and co-transmission of ChATcre/Vglut2FlpO mice reveals this small brainstem microcircuit acts as a central gating mechanism for airway protective behaviors. Activation of PiCo during inspiration or the beginning of postinspiration triggers swallow behavior, while there is a higher probability for stimulating laryngeal activation when activated further into expiration, suggesting PiCo's role in swallow-breathing coordination. PiCo triggers consistent swallow behavior and preserves physiologic swallow motor sequence, while stimulates laryngeal activation variable to stimulation duration. Sufficient bilateral PiCo activation is necessary for gating function since activation of only a few PiCo neurons or unilateral activation leads to blurred behavioral response. Viral tracing experiments reveal projections from the caudal nucleus of the solitary tract (cNTS), the presumed swallow pattern generator (SPG), to PiCo and vice versa. However, PiCo does not directly connect to laryngeal muscles. Investigating PiCo's role in swallow and laryngeal coordination will aid in understanding discoordination in breathing and neurological diseases.

Piecewise quadratic neuron model: A tool for close-to-biology spiking neuronal network simulation on dedicated hardware

Spiking neuron models simulate neuronal activities and allow us to analyze and reproduce the information processing of the nervous system. However, ionic-conductance models, which can faithfully reproduce neuronal activities, require a huge computational cost, while integral-firing models, which are computationally inexpensive, have some difficulties in reproducing neuronal activities. Here we propose a Piecewise Quadratic Neuron (PQN) model based on a qualitative modeling approach that aims to reproduce only the key dynamics behind neuronal activities. We demonstrate that PQN models can accurately reproduce the responses of ionic-conductance models of major neuronal classes to stimulus inputs of various magnitudes. In addition, the PQN model is designed to support the efficient implementation on digital arithmetic circuits for use as silicon neurons, and we confirm that the PQN model consumes much fewer circuit resources than the ionic-conductance models. This model intends to serve as a tool for building a large-scale closer-to-biology spiking neural network.

Stimulation of the cuneiform nucleus enables training and boosts recovery after spinal cord injury

Severe spinal cord injuries result in permanent paraparesis in spite of the frequent sparing of small portions of white matter. Spared fibre tracts are often incapable of maintaining and modulating the activity of lower spinal motor centres. Effects of rehabilitative training thus remain limited. Here, we activated spared descending brainstem fibres by electrical deep brain stimulation of the cuneiform nucleus of the mesencephalic locomotor region, the main control centre for locomotion in the brainstem, in adult female Lewis rats. We show that deep brain stimulation of the cuneiform nucleus enhances the weak remaining motor drive in highly paraparetic rats with severe, incomplete spinal cord injuries and enables high-intensity locomotor training. Stimulation of the cuneiform nucleus during rehabilitative aquatraining after subchronic (n = 8 stimulated versus n = 7 unstimulated versus n = 7 untrained rats) and chronic (n = 14 stimulated versus n = 9 unstimulated versus n = 9 untrained rats) spinal cord injury re-established substantial locomotion and improved long-term recovery of motor function. We additionally identified a safety window of stimulation parameters ensuring context-specific locomotor control in intact rats (n = 18) and illustrate the importance of timing of treatment initiation after spinal cord injury (n = 14). This study highlights stimulation of the cuneiform nucleus as a highly promising therapeutic strategy to enhance motor recovery after subchronic and chronic incomplete spinal cord injury with direct clinical applicability.

Precise motor rhythmicity relies on motor network responsivity

Rhythmic movements are the building blocks of human behavior. However, given that rhythmic movements are achieved through complex interactions between neural modules, it remains difficult to clarify how the central nervous system controls motor rhythmicity. Here, using a novel tempo-precision trade-off paradigm, we first modeled interindividual behavioral differences in tempo-dependent rhythmicity for various external tempi. We identified 2 behavioral extremes: conventional and paradoxical tempo-precision trade-off types. We then explored the neural substrates of these behavioral differences using task and resting-state functional magnetic resonance imaging. We found that the responsibility of interhemispheric motor network connectivity to tempi was a key to the behavioral repertoire. In the paradoxical trade-off type, interhemispheric connectivity was low at baseline but increased in response to increasing tempo; in the conventional trade-off type, strong baseline connectivity was coupled with low responsivity. These findings suggest that tunable interhemispheric connectivity underlies tempo-dependent rhythmicity control.

Cholinergic Modulation of Locomotor Circuits in Vertebrates

Locomotion is a basic motor act essential for survival. Amongst other things, it allows animals to move in their environment to seek food, escape predators, or seek mates for reproduction. The neural mechanisms involved in the control of locomotion have been examined in many vertebrate species and a clearer picture is progressively emerging. The basic muscle synergies responsible for propulsion are generated by neural networks located in the spinal cord. In turn, descending supraspinal inputs are responsible for starting, maintaining, and stopping locomotion as well as for steering and controlling speed. Several neurotransmitter systems play a crucial role in modulating the neural activity during locomotion. For instance, cholinergic inputs act both at the spinal and supraspinal levels and the underlying mechanisms are the focus of the present review. Much information gained on supraspinal cholinergic modulation of locomotion was obtained from the lamprey model. Nicotinic cholinergic inputs increase the level of excitation of brainstem descending command neurons, the reticulospinal neurons (RSNs), whereas muscarinic inputs activate a select group of hindbrain neurons that project to the RSNs to boost their level of excitation. Muscarinic inputs also reduce the transmission of sensory inputs in the brainstem, a phenomenon that could help in sustaining goal directed locomotion. In the spinal cord, intrinsic cholinergic inputs strongly modulate the activity of interneurons and motoneurons to control the locomotor output. Altogether, the present review underlines the importance of the cholinergic inputs in the modulation of locomotor activity in vertebrates.