Central Pattern Generator Network Research Articles

Analytical CPG model driven by limb velocity input generates accurate temporal locomotor dynamics.

The ability of vertebrates to generate rhythm within their spinal neural networks is essential for walking, running, and other rhythmic behaviors. The central pattern generator (CPG) network responsible for these behaviors is well-characterized with experimental and theoretical studies, and it can be formulated as a nonlinear dynamical system. The underlying mechanism responsible for locomotor behavior can be expressed as the process of leaky integration with resetting states generating appropriate phases for changing body velocity. The low-dimensional input to the CPG model generates the bilateral pattern of swing and stance modulation for each limb and is consistent with the desired limb speed as the input command. To test the minimal configuration of required parameters for this model, we reduced the system of equations representing CPG for a single limb and provided the analytical solution with two complementary methods. The analytical and empirical cycle durations were similar (R2 = 0.99) for the full range of walking speeds. The structure of solution is consistent with the use of limb speed as the input domain for the CPG network. Moreover, the reciprocal interaction between two leaky integration processes representing a CPG for two limbs was sufficient to capture fundamental experimental dynamics associated with the control of heading direction. This analysis provides further support for the embedded velocity or limb speed representation within spinal neural pathways involved in rhythm generation.

Mechatronic Design and Manufacturing of the Intelligent Robotic Fish for Bio-Inspired Swimming Modes

This paper presents mechatronic design and manufacturing of a biomimetic Carangiform-type autonomous robotic fish prototype (i-RoF) with two-link propulsive tail mechanism. For the design procedure, a multi-link biomimetic approach, which uses the physical characteristics of a real carp fish as its size and structure, is adapted. Appropriate body rate is determined according to swimming modes and tail oscillations of the carp. The prototype is composed of three main parts: an anterior rigid body, two-link propulsive tail mechanism, and flexible caudal fin. Prototype parts are produced with 3D-printing technology. In order to mimic fish-like robust swimming gaits, a biomimetic locomotion control structure based on Central Pattern Generator (CPG) is proposed. The designed unidirectional chained CPG network is inspired by the neural spinal cord of Lamprey, and it generates stable rhythmic oscillatory patterns. Also, a Center of Gravity (CoG) control mechanism is designed and located in the anterior rigid body to ensure three-dimensional swimming ability. With the help of this design, the characteristics of the robotic fish are performed with forward, turning, up-down and autonomous swimming motions in the experimental pool. Maximum forward speed of the robotic fish can reach 0.8516 BLs-1 and excellent three-dimensional swimming performance is obtained.

GABA-like immunoreactivity in Biomphalaria: Colocalization with tyrosine hydroxylase-like immunoreactivity in the feeding motor systems of panpulmonate snails.

The simpler nervous systems of certain invertebrates provide opportunities to examine colocalized classical neurotransmitters in the context of identified neurons and well defined neural circuits. This study examined the distribution of γ-aminobutyric acid-like immunoreactivity (GABAli) in the nervous system of the panpulmonates Biomphalaria glabrata and Biomphalaria alexandrina, major intermediate hosts for intestinal schistosomiasis. GABAli neurons were localized in the cerebral, pedal, and buccal ganglia of each species. With the exception of a projection to the base of the tentacle, GABAli fibers were confined to the CNS. As GABAli was previously reported to be colocalized with markers for dopamine (DA) in five neurons in the feeding network of the euopisthobranch gastropod Aplysia californica (Díaz-Ríos, Oyola, & Miller, 2002), double-labeling protocols were used to compare the distribution of GABAli with tyrosine hydroxylase immunoreactivity (THli). As in Aplysia, GABAli-THli colocalization was limited to five neurons, all of which were located in the buccal ganglion. Five GABAli-THli cells were also observed in the buccal ganglia of two other intensively studied panpulmonate species, Lymnaea stagnalis and Helisoma trivolvis. These findings indicate that colocalization of the classical neurotransmitters GABA and DA in feeding central pattern generator (CPG) interneurons preceded the divergence of euopisthobranch and panpulmonate taxa. These observations also support the hypothesis that heterogastropod feeding CPG networks exhibit a common universal design.

Sherlock Holmes and the curious case of the human locomotor central pattern generator.

Evidence first described in reduced animal models over 100 years ago led to deductions about the control of locomotion through spinal locomotor central pattern-generating (CPG) networks. These discoveries in nature were contemporaneous with another form of deductive reasoning found in popular culture, that of Arthur Conan Doyle's detective, Sherlock Holmes. Because the invasive methods used in reduced nonhuman animal preparations are not amenable to study in humans, we are left instead with deducing from other measures and observations. Using the deductive reasoning approach of Sherlock Holmes as a metaphor for framing research into human CPGs, we speculate and weigh the evidence that should be observable in humans based on knowledge from other species. This review summarizes indirect inference to assess "observable evidence" of pattern-generating activity that leads to the logical deduction of CPG contributions to arm and leg activity during locomotion in humans. The question of where a CPG may be housed in the human nervous system remains incompletely resolved at this time. Ongoing understanding, elaboration, and application of functioning locomotor CPGs in humans is important for gait rehabilitation strategies in those with neurological injuries.

Locomotion Control and Gait Planning of a Novel Hexapod Robot Using Biomimetic Neurons

This paper proposes a biomimetic method inspired by central pattern generator (CPG) for locomotion control and gait planning of a novel hexapod robot whose legs can radially free distributed around the robot body. The CPG network which consists of six CPG units based on the Matsuoka’s neural oscillator is used to generate oscillatory signals. The phase signals outputted by CPG network are converted to tip trajectory signals of six legs by adjustment function, and the joint angles are computed by the inverse kinematics of the robot. The relevant parameters which decide the movement of robot are tuned according to the processed feedback information. Then, some experiments are performed to verify the effectiveness of the control method. The experimental results indicate that the proposed method succeeds in enabling a hexapod robot to walk steadily on rugged terrain. The developed hexapod robot has many potential applications for its agility, stable walking, and good environmental adaptability.

Study of real-time biomimetic CPG on FPGA: behavior and evolution

Locomotion is one of the most basic abilities in animals. Neurobiologists have established that locomotion results from the activity of half-center oscillators that provides alternation of bursts. Central Pattern Generators (CPGs) are neural networks capable of producing rhythmic patterned outputs without rhythmic sensory or central input. We propose a network of several biomimetic CPGs using biomimetic neuron model and synaptic plasticity. This network is implemented on a FPGA (Field Programmable Gate Array). The network implementation architecture operates on a single computation core and in real-time. The real-time implementation of this CPGs network is validated by comparing it with biological data of leech heartbeat neural network. From these biomimetic CPGs, we use them for robotic applications and also for biomedical research to restore lost synaptic connections.

Implementation of Caterpillar Inspired Rolling Gait and Nonlinear Control Strategy in a Spherical Robot

This paper presents a novel Central Pattern Generator (CPG) based rolling gait generation in a small-sized spherical robot and its nonlinear control mechanism. A rhythmic rolling pattern mimicking Pleurotya caterpillar is produced for the spherical robot locomotion. A synergetically combined feedforward-feedback control strategy is proposed. The feedforward component is generated from centrally connected network of CPGs in conjunction with nonlinear robot dynamics. Two nonlinear feedback control methods namely integral (first order) Sliding Mode Control (SMC) and High (or second) Order Sliding Mode Control (HOSMC) are proposed to regulate robot stability and gait robustness in the presence of matched parameter uncertainties and bounded external disturbances. Design, implementation and experimental evaluation of both roll gait control strategies for the spherical robot are done on smooth (indoor) and irregular (outdoor) ground surfaces. The performance of robot control is quantified by measuring the roll angle stability, phase plane convergence and wheel velocities. Experimental results show that proposed novel strategy is efficient in producing a stable rolling gait and robust control of a spherical robot on two different types of surfaces. It further shows that proposed high HOSMC strategy is more efficient in robust rolling gait control of a spherical robot compared to an integral first-order SMC on two different ground conditions.

Neuronal oscillations: unavoidable and useful?

Neuronal systems have a high propensity to engage in oscillatory activity because both the properties of individual neurons and canonical circuit motifs favour rhythmic activity. In addition, coupled oscillators can engage in a large variety of dynamical regimes, ranging from synchronization with different phase offsets to chaotic behaviour. Which regime prevails depends on differences between preferred oscillation frequencies, coupling strength and coupling delays. The ability of delay coupled oscillator networks to generate a rich repertoire of temporally structured activation sequences is exploited by central pattern generator networks for the control of movements. However, it is less clear whether temporal patterning of neuronal discharges also plays a role in cognitive processes. Here, it will be argued that the temporal patterning of neuronal discharges emerging from delay coupled oscillator networks plays a pivotal role in all instances in which selective relations have to be established between the responses of distributed assemblies of neurons. Examples are the dynamic formation of functional networks, the selective routing of activity in densely interconnected networks, the attention-dependent selection of sensory signals, the fast and context-dependent binding of responses for further joint processing in pattern recognition and the formation of associations by learning. Special consideration is given to arguments that challenge a functional role of oscillations and synchrony in cognition because of the volatile nature of these phenomena and recent evidence will be reviewed suggesting that this volatility is functionally advantageous.

Spontaneous Symmetry-Breaking in a Network Model for Quadruped Locomotion

Spontaneous symmetry-breaking proves a mechanism for pattern generation in legged locomotion of animals. The basic timing patterns of animal gaits are produced by a network of spinal neurons known as a Central Pattern Generator (CPG). Animal gaits are primarily characterized by phase differences between leg movements in a periodic gait cycle. Many different gaits occur, often having spatial or spatiotemporal symmetries. A natural way to explain gait patterns is to assume that the CPG is symmetric, and to classify the possible symmetry-breaking periodic motions. Pinto and Golubitsky have discussed a four-node model CPG network for biped gaits with [Formula: see text] symmetry, classifying the possible periodic states that can arise. A more specific rate model with this structure has been analyzed in detail by Stewart. Here we extend these methods to quadruped gaits, using an eight-node network with [Formula: see text] symmetry proposed by Golubitsky and coworkers. We formulate a rate model and calculate how the first steady or Hopf bifurcation depends on its parameters, which represent four connection strengths. The calculations involve a distinction between “real” gaits with one or two phase shifts (pronk, bound, pace, trot) and “complex” gaits with four phase shifts (forward and reverse walk, forward and reverse buck). The former correspond to real eigenvalues of the connection matrix, the latter to complex conjugate pairs. The partition of parameter space according to the first bifurcation, ignoring complex gaits, is described explicitly. The complex gaits introduce further complications, not yet fully understood. All eight gaits can occur as the first bifurcation from a fully synchronous equilibrium, for suitable parameters, and numerical simulations indicate that they can be asymptotically stable.

Rhythmic arm cycling training improves walking and neurophysiological integrity in chronic stroke: the arms can give legs a helping hand in rehabilitation.

Training locomotor central pattern-generating networks (CPGs) through arm and leg cycling improves walking in chronic stroke. These outcomes are presumed to result from enhanced interlimb connectivity and CPG function. The extent to which rhythmic arm training activates interlimb CPG networks for locomotion remains unclear and was assessed by studying chronic stroke participants before and after 5 wk of arm cycling training. Strength was assessed bilaterally via maximal voluntary isometric contractions in the legs and hands. Muscle activation during arm cycling and transfer to treadmill walking were assessed in the more affected (MA) and less affected (LA) sides via surface electromyography. Changes to interlimb coupling during rhythmic movement were evaluated using modulation of cutaneous reflexes elicited by electrical stimulation of the superficial radial nerve at the wrist. Bilateral soleus stretch reflexes were elicited at rest and during 1-Hz arm cycling. Clinical function tests assessed walking, balance, and motor function. Results show significant changes in function and neurophysiological integrity. Training increased bilateral grip strength, force during MA plantarflexion, and muscle activation. "Normalization" of cutaneous reflex modulation was found during arm cycling. There was enhanced activity in the dorsiflexor muscles on the MA side during the swing phase of walking. Enhanced interlimb coupling was shown by increased modulation of MA soleus stretch reflex amplitudes during arm cycling after training. Clinical evaluations showed enhanced walking ability and balance. These results are consistent with training-induced changes in CPG function and interlimb connectivity and underscore the need for arm training in the functional rehabilitation of walking after neurotrauma. NEW & NOTEWORTHY It has been suggested but not tested that training the arms may influence rehabilitation of walking due to activation of interneuronal patterning networks after stroke. We show that arm cycling training improves strength, clinical function, coordination of muscle activity during walking, and neurological connectivity between the arms and the legs. The arms can, in fact, give the legs a helping hand in rehabilitation of walking after stroke.

Emergent central pattern generator behavior in chemical coupled two-compartment models with time delay

This paper proposes that modified two-compartment Pinsky–Rinzel (PR) neural model can be used to develop the simple form of central pattern generator (CPG). The CPG is called as ‘half-central oscillator’, which constructed by two inhibitory chemical coupled PR neurons with time delay. Some key properties of PR neural model related to CPG are studied and proved to meet the requirements of CPG. Using the simple CPG network, we first study the relationship between rhythmical output and key factors, including ambient noise, sensory feedback signals, morphological character of single neuron as well as the coupling delay time. We demonstrate that, appropriate intensity noise can enhance synchronization between two coupled neurons. Different output rhythm of CPG network can be entrained by sensory feedback signals. We also show that the morphology of single neuron has strong effect on the output rhythm. The phase synchronization indexes decrease with the increase of morphology parameter’s difference. Through adjusting coupled delay time, we can get absolutely phase synchronization and antiphase state of CPG. Those results of simulation show the feasibility of PR neural model as a valid CPG as well as the emergent behaviors of the particularly CPG.

Gliotransmission and adenosinergic modulation: insights from mammalian spinal motor networks.

Astrocytes are proposed to converse with neurons at tripartite synapses, detecting neurotransmitter release and responding with release of gliotransmitters, which in turn modulate synaptic strength and neuronal excitability. However, a paucity of evidence from behavioral studies calls into question the importance of gliotransmission for the operation of the nervous system in healthy animals. Central pattern generator (CPG) networks in the spinal cord and brain stem coordinate the activation of muscles during stereotyped activities such as locomotion, inspiration, and mastication and may therefore provide tractable models in which to assess the contribution of gliotransmission to behaviorally relevant neural activity. We review evidence for gliotransmission within spinal locomotor networks, including studies indicating that adenosine derived from astrocytes regulates the speed of locomotor activity via metamodulation of dopamine signaling.

Arm motion control model based on central pattern generator

According to the theory of Matsuoka neural oscillators and with the consideration of the fact that the human upper arm mainly consists of six muscles, a new kind of central pattern generator (CPG) neural network consisting of six neurons is proposed to regulate the contraction of the upper arm muscles. To verify effectiveness of the proposed CPG network, an arm motion control model based on the CPG is established. By adjusting the CPG parameters, we obtain the neural responses of the network, the angles of joint and hand of the model with MATLAB. The simulation results agree with the results of crank rotation experiments designed by Ohta et al., showing that the arm motion control model based on a CPG network is reasonable and effective.

Intra- and intersegmental influences among central pattern generating networks in the walking system of the stick insect.

To efficiently move around, animals need to coordinate their limbs. Proper, context-dependent coupling among the neural networks underlying leg movement is necessary for generating intersegmental coordination. In the slow-walking stick insect, local sensory information is very important for shaping coordination. However, central coupling mechanisms among segmental central pattern generators (CPGs) may also contribute to this. Here, we analyzed the interactions between contralateral networks that drive the depressor trochanteris muscle of the legs in both isolated and interconnected deafferented thoracic ganglia of the stick insect on application of pilocarpine, a muscarinic acetylcholine receptor agonist. Our results show that depressor CPG activity is only weakly coupled between all segments. Intrasegmental phase relationships differ between the three isolated ganglia, and they are modified and stabilized when ganglia are interconnected. However, the coordination patterns that emerge do not resemble those observed during walking. Our findings are in line with recent studies and highlight the influence of sensory input on coordination in slowly walking insects. Finally, as a direct interaction between depressor CPG networks and contralateral motoneurons could not be observed, we hypothesize that coupling is based on interactions at the level of CPG interneurons.NEW & NOTEWORTHY Maintaining functional interleg coordination is vitally important as animals locomote through changing environments. The relative importance of central mechanisms vs. sensory feedback in this process is not well understood. We analyzed coordination among the neural networks generating leg movements in stick insect preparations lacking phasic sensory feedback. Under these conditions, the networks governing different legs were only weakly coupled. In stick insect, central connections alone are thus insufficient to produce the leg coordination observed behaviorally.

To swim or not to swim: A population-level model of Xenopus tadpole decision making and locomotor behaviour

We present a detailed computational model of interacting neuronal populations that mimic the hatchling Xenopus tadpole nervous system. The model includes four sensory pathways, integrators of sensory information, and a central pattern generator (CPG) network. Sensory pathways of different modalities receive inputs from an “environment”; these inputs are then processed and integrated to select the most appropriate locomotor action. The CPG populations execute the selected action, generating output in motor neuron populations. Thus, the model describes a detailed and biologically plausible chain of information processing from external signals to sensors, sensory pathways, integration and decision-making, action selection and execution and finally, generation of appropriate motor activity and behaviour. We show how the model produces appropriate behaviours in response to a selected scenario, which consists of a sequence of “environmental” signals. These behaviours might be relatively complex due to noisy sensory pathways and the possibility of spontaneous actions.

Evaluation of walking smoothness using wearable robotic system curara® for spinocerebellar degeneration patients.

This paper aimed to verify the effectiveness of the wearable robotic system "curara" for patients with spinocerebellar degeneration (SCD) by evaluating walking smoothness. The curara system supports the wearer's gait using a synchronization control method that uses a neural oscillator based on a central pattern generator network. The system reflects the motional intention by adjusting the synchronization gains. This modifies the degree of interactive coordinated motion between the curara and the wearer. As a feasibility study, we evaluated the waking smoothness of 10 patients with SCD using three gain condition settings. Harmonic ratio (HR), which has been used extensively to quantify the smoothness during walking, was used to assess their walking. The results show that most HRs in the medio-lateral, anterior-posterior, and vertical directions using the three gain conditions were higher than those for patients not wearing the system. In particular, the increasing rates of the HR in all directions during the gait support were 11.1%, 23.4%, and 23.2% compared with unassisted walking, when the gain condition settings of hip and knee joints are set at 0.4 and 0.5, respectively. Consequently, these results verified the effectiveness of the curara system for patients with SCD.

A generalized framework to achieve coordinated admittance control for multi-joint lower limb robotic exoskeleton.

Traditional joint space admittance controller for N-DOF robotic systems is complexity and easily leads to incongruous movement among all joints. Our study introduces a central pattern generator (CPG) network into one-dimension joint space admittance control for the custom-made lower limb robotic exoskeleton with four DOFs, to guarantee the coordinated movement and security of users. The predefined trajectories for four joints are produced by CPG. Unilateral knee joint torque of subjects is detected based on corresponding muscle EMG signals. The torque is transformed into an additional set of state variables for CPG based on the one-dimension admittance controller. CPG harmonically adjusts the predefined trajectories by the additional state variables. Finally, the robotic exoskeleton completes the predefined trajectories with a classical PID controller.

Morphology and the gradient of a symmetric potential predict gait transitions of dogs

Gaits and gait transitions play a central role in the movement of animals. Symmetry is thought to govern the structure of the nervous system, and constrain the limb motions of quadrupeds. We quantify the symmetry of dog gaits with respect to combinations of bilateral, fore–aft, and spatio-temporal symmetry groups. We tested the ability of symmetries to model motion capture data of dogs walking, trotting and transitioning between those gaits. Fully symmetric models performed comparably to asymmetric with only a 22% increase in the residual sum of squares and only one-quarter of the parameters. This required adding a spatio-temporal shift representing a lag between fore and hind limbs. Without this shift, the symmetric model residual sum of squares was 1700% larger. This shift is related to (linear regression, n=5, p=0.0328) dog morphology. That this symmetry is respected throughout the gaits and transitions indicates that it generalizes outside a single gait. We propose that relative phasing of limb motions can be described by an interaction potential with a symmetric structure. This approach can be extended to the study of interaction of neurodynamic and kinematic variables, providing a system-level model that couples neuronal central pattern generator networks and mechanical models.

Dopamine Pumping Up Spinal Locomotor Network Function.

Central pattern generator networks in the lumbar spinal cord of mammals control a variety of rhythmic movements of the hind limbs. Although these networks can function in isolation, modulatory inputs configure the network to create diversity and stability in network operation. Modulators have

Application of bio-inspired control of AmphiHex-I in detection of Oncomelania hupensis, the amphibious snail intermediate host of Schistosoma japonicum

PurposeThis paper aims to construct a central pattern generator (CPG) network that comprises coupled nonlinear oscillators to implement diversified locomotion gaits of robot AmphiHex-I. With the gaits, AmphiHex-I will have a strong locomotion ability in an amphibious environment, which is motivated by a novel public health application to detect the amphibious snail, Oncomelania hupensis, the snail intermediate host of Schistosoma japonicum, as an amphibious robot-based tool for schistosomiasis surveillance and response in the future.Design/methodology/approachFirst, the basis neural network was built by adopting six Hopf nonlinear oscillators which corresponded to six legs. Then, the correlation between the self-excited harmonic output signals generated from CPGs and various gaits was established. In view of requirements on its field application, the authors added a telecontrol system and an on-board battery to support the real-life remote control and a high-definition camera and a global positioning system module to acquire images and position information. Finally, the authors conducted the testing experiments on several tasks, e.g. detecting the distribution of Oncomelania hupensis snails.FindingsThe results demonstrate that the CPG is effective in controlling the robot’s diversified locomotion gaits. In addition, the robot is capable of fulfilling several testing tasks in the experiments.Originality/valueThe research provides a method based on CPG to control a hexapod robot with multiple motion patterns, which can effectively overcome the difficulty of motion control simply by changing certain mathematical parameters of a nonlinear equation, such as frequency, phase difference and offset angle, so as to realize the gait transitions. Also, using such a robot to probe the distribution of snails offers another way to tackle this laborious job, especially in some odious terrains, which will hence broaden the application of AmphiHex-I to vector surveillance in the fields of public health.