Generation Of Rhythmic Movements Research Articles

Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators.

Networks in the spinal cord, which are responsible for the generation of rhythmic movements, commonly known as central pattern generators (CPGs), have remained elusive for decades. Although it is well-known that many spinal neurons are rhythmically active, little attention has been given to the distribution of firing rates across the population. Here, we argue that firing rate distributions can provide an important clue to the organization of the CPGs. The data that can be gleaned from the sparse literature indicate a firing rate distribution, which is skewed toward zero with a long tail, akin to a normal distribution on a log-scale, i.e., a “log-normal” distribution. Importantly, such a shape is difficult to unite with the widespread assumption of modules composed of recurrently connected excitatory neurons. Spinal modules with recurrent excitation has the propensity to quickly escalate their firing rate and reach the maximum, hence equalizing the spiking activity across the population. The population distribution of firing rates hence would consist of a narrow peak near the maximum. This is incompatible with experiments, that show wide distributions and a peak close to zero. A way to resolve this puzzle is to include recurrent inhibition internally in each CPG modules. Hence, we investigate the impact of recurrent inhibition in a model and find that the firing rate distributions are closer to the experimentally observed. We therefore propose that recurrent inhibition is a crucial element in motor circuits, and suggest that future models of motor circuits should include recurrent inhibition as a mandatory element.

Directionality of corticomuscular coupling in essential tremor and cortical myoclonic tremor

ObjectiveA role of the motor cortex in tremor generation in essential tremor (ET) is assumed, yet the directionality of corticomuscular coupling is unknown. Our aim is to clarify the role of the motor cortex. To this end we also study ‘familial cortical myoclonic tremor with epilepsy’ (FCMTE) and slow repetitive voluntary movements with a known cortical drive. MethodsDirectionality of corticomuscular coupling (EEG-EMG) was studied with renormalized partial directed coherence (rPDC) during tremor in 25 ET patients, 25 healthy controls (mimicked) and in seven FCMTE patients; and during a self-paced 2 Hz task in eight ET patients and seven healthy controls. ResultsEfferent coupling around tremor frequency was seen in 33% of ET patients, 45.5% of healthy controls, all FCMTE patients, and, around 2 Hz, in all ET patients and all healthy controls. Ascending coupling, seen in the majority of all participants, was weaker in ET than in healthy controls around 5–6 Hz. ConclusionsPossible explanations are that tremor in ET results from faulty subcortical output bypassing the motor cortex; rate-dependent transmission similar to generation of rhythmic movements; and/or faulty feedforward mechanism resulting from decreased afferent (sensory) coupling. SignificanceA linear cortical drive is lacking in the majority of ET patients.

Decoupling of timescales reveals sparse convergent CPG network in the adult spinal cord

During the generation of rhythmic movements, most spinal neurons receive an oscillatory synaptic drive. The neuronal architecture underlying this drive is unknown, and the corresponding network size and sparseness have not yet been addressed. If the input originates from a small central pattern generator (CPG) with dense divergent connectivity, it will induce correlated input to all receiving neurons, while sparse convergent wiring will induce a weak correlation, if any. Here, we use pairwise recordings of spinal neurons to measure synaptic correlations and thus infer the wiring architecture qualitatively. A strong correlation on a slow timescale implies functional relatedness and a common source, which will also cause correlation on fast timescale due to shared synaptic connections. However, we consistently find marginal coupling between slow and fast correlations regardless of neuronal identity. This suggests either sparse convergent connectivity or a CPG network with recurrent inhibition that actively decorrelates common input.

Influence of Vibration on Motor Responses in the Upper Arm Muscles under Stationary Conditions and during Voluntary and Evoked Arm Movements under Limb Unloading Conditions

Locomotion of mammals, including humans, is based on the rhythmic activity of spinal cord circuitries. The functioning of these circuitries depends on multimodal afferent information and on supraspinal influences from the motor cortex. Using the method of transcranial magnetic stimulation (TMS) of arm muscle areas in the motor cortex, we studied the motor evoked potentials (MEP) in the upper arm muscles in stationary conditions and during voluntary and vibration-evoked arm movements. The study included 13 healthy subjects under arm and leg unloading conditions. In the first series of experiments, with motionless limbs, the effect of vibration of left upper arm muscles on motor responses in these muscles was evaluated. In the second series of experiments, MEP were compared in the same muscles during voluntary and rhythmic movements generated by left arm m. triceps brachii vibration (the right arm was stationary). Motionless left arm vibration led to an increase in MEP values in both vibrated muscle and in most of the non-vibrated muscles. For most target muscles, MEP was greater with voluntary arm movements than with vibration-evoked movements. At the same time, a similar MEP modulation in the cycle of arm movements was observed in the same upper arm muscles during both types of arm movements. TMS of the motor cortex significantly potentiated arm movements generated by vibration, but its effect on voluntary movements was weaker. These results indicate significant differences in the degree of motor cortex involvement in voluntary and evoked arm movements. We suppose that evoked arm movements are largely due to spinal rather than central mechanisms of generation of rhythmic movements.

Modeling and analysis of a new locomotion control neural networks.

Experimental data have shown that inherent bursting of the neuron plays an important role in the generation of rhythmic movements in spinal networks. Based on the mechanism that the spinal neurons of a lamprey generate this inherent bursting, this paper builds a simplified inherent bursting neuron model. A new locomotion control neural network is built that takes advantage of this neuron model and its performance is analyzed mathematically and by numerical simulation. From these analyses, it is found that the new control networks have no restriction on their topological structure for generating the oscillatory outputs. If a network is used to control the motion of bionic robots or build the model of the vertebrate spinal circuitry, its topological structure can be constructed using the unit burst generator model proposed by Grillner. The networks can also be easily switched between oscillatory and non-oscillatory output. Additionally, inactivity and saturation properties of the new networks can also be developed, which will be helpful to increase the motor flexibility and environmental adaptability of bionic robots.

Fast Dynamical Coupling Enhances Frequency Adaptation of Oscillators for Robotic Locomotion Control.

Rhythmic neural signals serve as basis of many brain processes, in particular of locomotion control and generation of rhythmic movements. It has been found that specific neural circuits, named central pattern generators (CPGs), are able to autonomously produce such rhythmic activities. In order to tune, shape and coordinate the produced rhythmic activity, CPGs require sensory feedback, i.e., external signals. Nonlinear oscillators are a standard model of CPGs and are used in various robotic applications. A special class of nonlinear oscillators are adaptive frequency oscillators (AFOs). AFOs are able to adapt their frequency toward the frequency of an external periodic signal and to keep this learned frequency once the external signal vanishes. AFOs have been successfully used, for instance, for resonant tuning of robotic locomotion control. However, the choice of parameters for a standard AFO is characterized by a trade-off between the speed of the adaptation and its precision and, additionally, is strongly dependent on the range of frequencies the AFO is confronted with. As a result, AFOs are typically tuned such that they require a comparably long time for their adaptation. To overcome the problem, here, we improve the standard AFO by introducing a novel adaptation mechanism based on dynamical coupling strengths. The dynamical adaptation mechanism enhances both the speed and precision of the frequency adaptation. In contrast to standard AFOs, in this system, the interplay of dynamics on short and long time scales enables fast as well as precise adaptation of the oscillator for a wide range of frequencies. Amongst others, a very natural implementation of this mechanism is in terms of neural networks. The proposed system enables robotic applications which require fast retuning of locomotion control in order to react to environmental changes or conditions.

Biological clockwork underlying adaptive rhythmic movements

Owing to the complexity of neuronal circuits, precise mathematical descriptions of brain functions remain an elusive ambition. A more modest focus of many neuroscientists, central pattern generators, are more tractable neuronal circuits specialized to generate rhythmic movements, including locomotion. The relative simplicity and well-defined motor functions of these circuits provide an opportunity for uncovering fundamental principles of neuronal information processing. Here we present the culmination of mathematical analysis that captures the adaptive behaviors emerging from interactions between a central pattern generator, the body, and the physical environment during locomotion. The biologically realistic model describes the undulatory motions of swimming leeches with quantitative accuracy and, without further parameter tuning, predicts the sweeping changes in oscillation patterns of leeches undulating in air or swimming in high-viscosity fluid. The study demonstrates that central pattern generators are capable of adapting oscillations to the environment through sensory feedback, but without guidance from the brain.

Storing cycles in Hopfield-type networks with pseudoinverse learning rule: Admissibility and network topology

Cyclic patterns of neuronal activity are ubiquitous in animal nervous systems, and partially responsible for generating and controlling rhythmic movements such as locomotion, respiration, swallowing and so on. Clarifying the role of the network connectivities for generating cyclic patterns is fundamental for understanding the generation of rhythmic movements. In this paper, the storage of binary cycles in Hopfield-type and other neural networks is investigated. We call a cycle defined by a binary matrix Σ admissible if a connectivity matrix satisfying the cycle’s transition conditions exists, and if so construct it using the pseudoinverse learning rule. Our main focus is on the structural features of admissible cycles and the topology of the corresponding networks. We show that Σ is admissible if and only if its discrete Fourier transform contains exactly r=rank(Σ) nonzero columns. Based on the decomposition of the rows of Σ into disjoint subsets corresponding to loops, where a loop is defined by the set of all cyclic permutations of a row, cycles are classified as simple cycles, and separable or inseparable composite cycles. Simple cycles contain rows from one loop only, and the network topology is a feedforward chain with feedback to one neuron if the loop-vectors in Σ are cyclic permutations of each other. For special cases this topology simplifies to a ring with only one feedback. Composite cycles contain rows from at least two disjoint loops, and the neurons corresponding to the loop-vectors in Σ from the same loop are identified with a cluster. Networks constructed from separable composite cycles decompose into completely isolated clusters. For inseparable composite cycles at least two clusters are connected, and the cluster-connectivity is related to the intersections of the spaces spanned by the loop-vectors of the clusters. Simulations showing successfully retrieved cycles in continuous-time Hopfield-type networks and in networks of spiking neurons exhibiting up–down states are presented.

Change in the balance of excitatory and inhibitory midline fiber crossing as an explanation for the hopping phenotype in EphA4 knockout mice

Neuronal networks in the spinal cord termed central pattern generators (CPGs) are responsible for the generation of rhythmic movements, such as walking. The axon guidance molecule EphA4 has been suggested to play a role in the configuration of spinal CPG networks in mammals. In EphA4 knockout (EphA4-KO) mice, the normal alternating walking pattern is replaced by a rabbit-like hopping gait, which can be reproduced when locomotor-like activity is induced in the isolated spinal cord. This hopping phenotype has been explained by an abnormal midline crossing of ipsilateral axons. Here, we investigated the nature of this overcrossing in heterozygous EphA4 (EphA4(lacZ/+) ) mice that showed normal alternating gait and homozygous EphA4 (EphA4(lacZ/lacZ) ) mice with hopping gait. Localized lesions showed that the hopping phenotype is maintained by fibers crossing in the ventral commissure. Using transgenic mouse lines in which glutamatergic, GABAergic and glycinergic neurons are intrinsically labeled, we showed a significant increase in the number of crossing excitatory β-galactosidase-positive neurons and a decrease in the number of inhibitory neurons crossing the midline in EphA4(lacZ/lacZ) mice compared with EphA4(lacZ/+) mice. These results show that the hopping phenotype is the result of a change in the balance between excitatory and inhibitory signals across the midline and that EphA4-positive neurons play an essential role in the mammalian CPG.

Specific role of dopamine D1 receptors in spinal network activation and rhythmic movement induction in vertebrates

Dopamine (DA) is well-recognized for its determinant role in the modulation of various brain functions. DA was also found in in vitro isolated invertebrate preparations to activate per se the central pattern generator for locomotion. However, it is less clear whether such a role as an activator of central neural circuitries exists in vertebrate species. Here, we studied in vivo the effects induced by selective DA receptor agonists and antagonists on hindlimb movement generation in mice completely spinal cord-transected (Tx) at the low-thoracic level (Th9/10). Administration of D1/D5 receptor agonists (0.5-2.5 mg kg(-1), i.p.) was found to acutely elicit rhythmic locomotor-like movements (LMs) and non-locomotor movements (NLMs) in untrained and non-sensory stimulated animals. Comparable effects were found in mice lacking the D5 receptor (D5KO) whereas D1/D5 receptor antagonist-pretreated animals (wild-type or D5KO) failed to display D1/D5 agonist-induced LMs. In contrast, administration of broad spectrum or selective D2, D3 or D4 agonists consistently failed to elicit significant hindlimb movements. Overall, the results clearly show in mice the existence of a role for D1 receptors in spinal network activation and corresponding rhythmic movement generation.

壁打ちジャグリングにおける動的リズムの形成とタイミングスキル

This paper proposes a hierarchical architecture for rhythmic movement generation, which suits to a juggling-like task involving sensory-motor coordination. The approach has a bidirectional weak coupling to the environment and it can adapt a robot to a change in the environment owing to the interaction between the robot and the environment at the ball contact. The proposed architecture consists of an active-control mechanism and two passive-control mechanisms which include open-loop stability of the mechano-physical system and entrainment between mechano-physical and neuro-like pattern generating systems. The synergy of these different mechanisms leads to the emergence of dynamic temporal pattern and enables the robot to perform the stable rhythmic movement of the successful task.

Emergence of a dynamic pattern in wall-bouncing juggling: self-organized timing selection for rhythmic movement

This paper describes a hierarchical architecture for rhythmic movement generation, which suits a juggling-like task involving sensory-motor coordination. Our approach, which is interpreted as a “bidirectional weak coupling” to the environment, does not require a continuous monitoring of the environment, but can adapt a robot to a change in the environment, owing to the interaction between the robot and the environment at the ball contact. The proposed architecture contains two passive-control mechanisms, the “entrainment mechanism” and the “open-loop stable mechanism,” that lead to the emergence of a self-organized temporal order in the whole system. This dynamic temporal pattern enables a robot to perform a stable rhythmic movement. We demonstrate a robot which juggles two balls rebounding off the wall and confirm the effectiveness of our approach.

Principles of rhythmic motor pattern generation.

Rhythmic movements are produced by central pattern-generating networks whose output is shaped by sensory and neuromodulatory inputs to allow the animal to adapt its movements to changing needs. This review discusses cellular, circuit, and computational analyses of the mechanisms underlying the generation of rhythmic movements in both invertebrate and vertebrate nervous systems. Attention is paid to exploring the mechanisms by which synaptic and cellular processes interact to play specific roles in shaping motor patterns and, consequently, movement.

Physiological Insights from Cellular and Network Models of the Stomatogastric Nervous System of Lobsters and Crabs

Synopsis. The stomatogastric nervous system of decapod crustaceans is an ideal system for the study of the processes underlying the generation of rhythmic movements by the nervous system. In this chapter we review recent work that uses mathematical analyses and computer simulations to understand: 1) the role of individual currents in controlling the activity of neurons, and 2) the effects of electrical coupling on the activity of neuronal oscillators. The aim of this review is to highlight, for the physiologist, what these studies have taught us about the organization and function of single cell and multicellular neuronal oscillators.

NEURAL CIRCUITS FOR GENERATING RHYTHMIC MOVEMENTS

Inasmuch as the identified neural circuits discussed in this review pertain only to the nervous systems of two invertebrate species, one may ask whether or not these findings are generally applicable to central nervous oscillators that generate rhythmic movements in animals of other species and phyla, particularly in the vertebrates. This question is not easy to answer at this time, because detailed cellular network analyses thus far have been possible only in a very few neurophysiologically favorable preparations, such as those presented by the cardiac and stomatogastric ganglia of the lobster and the segmental ganglion of the leech. Nevertheless it is significant that the mechanisms according to which these invertebrate circuits are now thought to generate their oscillations--endogenous rhythmic polarization, reciprocal inhibition, and recurrent cyclic inhibition--were all first proposed to account for generation of rhythmic movements in vertebrate animals (7-9, 51, 71, 79). Moreover, the pattern of motor neuron activity in rhythmic movements of vertebrates is not necessarily more complex than the corresponding pattern in analogous movements of invertebrates. Therefore, the very much greater number of neurons in the central nervous system of vertebrates does not necessarily imply a greater complexity of the central oscillators that generate their rhythmic movements; it may only place greater obstacles in the way of identifying the underlying neuronal circuitry. In any case, it is worthy of note that the current list of fundamentally different and theoretically plausible types of neuronal oscillators is not only quite short but also of long standing. Thus, on these grounds, it seems reasonable to expect that the identified circuits discussed here will prove to be of general applicability to the generation of rhythmic movements in the whole animal kingdom.