Central Pattern Generator Network Research Articles

Wt1 Positive dB4 Neurons in the Hindbrain Are Crucial for Respiration.

Central pattern generator (CPG) networks coordinate the generation of rhythmic activity such as locomotion and respiration. Their development is driven by various transcription factors, one of which is the Wilms tumor protein (Wt1). It is present in dI6 neurons of the mouse spinal cord, and involved in the coordination of locomotion. Here we report about the presence of Wt1 in neurons of the caudoventral medulla oblongata and their impact on respiration. By employing immunohistofluorescence staining, we were able to characterize these Wt1 positive (+) cells as dB4 neurons. The temporal occurrence of Wt1 suggests a role for this transcription factor in the differentiation of dB4 neurons during embryonic and postnatal development. Conditional knockout of Wt1 in these cells caused an altered population size of V0 neurons already in the developing hindbrain, leading to a decline in the respiration rate in the adults. Thereby, we confirmed and extended the previously proposed similarity between dB4 neurons in the hindbrain and dI6 neurons of the spinal cord, in terms of development and function. Ablation of Wt1+ dB4 neurons resulted in the death of neonates due to the inability to initiate respiration, suggesting a vital role for Wt1+ dB4 neurons in breathing. These results expand the role of Wt1 in the CNS and show that, in addition to its function in differentiation of dI6 neurons, it also contributes to the development of dB4 neurons in the hindbrain that are critically involved in the regulation of respiration.

A CORDIC based real-time implementation and analysis of a respiratory central pattern generator

Central pattern generators (CPGs) are dedicated neural circuits which can generate rhythmic motor patterns even in absence of sensory input with extraordinary robustness and flexibility. In this paper, a biologically realistic model of a respiratory CPG with four neurons is implemented on a reconfigurable Field-Programmable Gate Array (FPGA) system. Considering the limitations of hardware resources, we first propose a modified respiratory CPG model with Coordinate Rotation Digital Computer (CORDIC) algorithm to save limited resources and reduce complexity. And then, all the multipliers are replaced with a method which is appropriate and effective for hardware implementation to avoid the use of the area-intensive multipliers. The implementation results show that rhythmic oscillations are successfully generated by the respiratory CPG network and the resource utilization is greatly reduced, which shows the potential for building large-scale spiking neural networks. The proposed high-performance and real-time implementation of the respiratory CPG network on the FPGA system can speed up the process to gain new insights into the respiratory network and can also be developed into applications for respiratory rhythm generation and modulation.

On the Organization of the Locomotor CPG: Insights From Split-Belt Locomotion and Mathematical Modeling.

Rhythmic limb movements during locomotion are controlled by central pattern generator (CPG) circuits located in the spinal cord. It is considered that these circuits are composed of individual rhythm generators (RGs) for each limb interacting with each other through multiple commissural and long propriospinal circuits. The organization and operation of each RG are not fully understood, and different competing theories exist about interactions between its flexor and extensor components, as well as about left–right commissural interactions between the RGs. The central idea of circuit organization proposed in this study is that with an increase of excitatory input to each RG (or an increase in locomotor speed) the rhythmogenic mechanism of the RGs changes from “flexor-driven” rhythmicity to a “classical half-center” mechanism. We test this hypothesis using our experimental data on changes in duration of stance and swing phases in the intact and spinal cats walking on the ground or tied-belt treadmills (symmetric conditions) or split-belt treadmills with different left and right belt speeds (asymmetric conditions). We compare these experimental data with the results of mathematical modeling, in which simulated CPG circuits operate in similar symmetric and asymmetric conditions with matching or differing control drives to the left and right RGs. The obtained results support the proposed concept of state-dependent changes in RG operation and specific commissural interactions between the RGs. The performed simulations and mathematical analysis of model operation under different conditions provide new insights into CPG network organization and limb coordination during locomotion.

Neural coupled central pattern generator based smooth gait transition of a biomimetic hexapod robot

In this paper, a novel Central Pattern Generator (CPG) network topology based locomotion control strategy for a smooth gait transition of a biomimetic hexapod robot is proposed. Some preliminaries and correlations have been discussed to provide more suitable CPG network topology for both gait patterns that adapt to different environments, both in terms of transient state time and amplitude overshoot. The design network structure is developed with bidirectional diffusive coupling topologies to obtain robustness and efficient gait transitions. The stability of the proposed network is proved using coupling analyses. In contrast to conventional methods in the CPG network, the proposed method provides remarkable results that could generate four typical hexapod gaits transitions under rapid transient-state and steady-state conditions depending on the frequency, amplitude, and phase relationships among neurons. In order to govern the swing and stance phases according to the proposed network, the leg trajectory generator is designed and an inverse kinematics module is added to compute the link angles of the legs. By applying the proposed locomotion control strategy, the hexapod robot is capable of performing stable and rapid walking gaits. The simulation and experimental results show the effectiveness of the proposed method. High motion ability with the proposed network topology is provided considering walking frequency, forward speed, gait transition time, transient-state time, and steady-state comparisons with the literature.

Local CPG Self Growing Network Model with Multiple Physical Properties

Compared with traditional control methods, the advantage of CPG (Central Pattern Generator) network control is that it can significantly reduce the size of the control variable without losing the complexity of its motion mode output. Therefore, it has been widely used in the motion control of robots. To date, the research into CPG network has been polarized: one direction has focused on the function of CPG control rather than biological rationality, which leads to the poor functional adaptability of the control network and means that the control network can only be used under fixed conditions and cannot adapt to new control requirements. This is because, when there are new control requirements, it is difficult to develop a control network with poor biological rationality into a new, qualified network based on previous research; instead, it must be explored again from the basic link. The other direction has focused on the rationality of biology instead of the function of CPG control, which means that the form of the control network is only similar to a real neural network, without practical use. In this paper, we propose some physical characteristics (including axon resistance, capacitance, length and diameter, etc.) that can determine the corresponding parameters of the control model to combine the growth process and the function of the CPG control network. Universal gravitation is used to achieve the targeted guidance of axon growth, Brownian random motion is used to simulate the random turning of axon self-growth, and the signal of a single neuron is established by the Rall Cable Model that simplifies the axon membrane potential distribution. The transfer model, which makes the key parameters of the CPG control network—the delay time constant and the connection weight between the synapses—correspond to the axon length and axon diameter in the growth model and the growth and development of the neuron processes and control functions are combined. By coordinating the growth and development process and control function of neurons, we aim to realize the control function of the CPG network as much as possible under the conditions of biological reality. In this way, the complexity of the control model we develop will be close to that of a biological neural network, and the control network will have more control functions. Finally, the effectiveness of the established CPG self-growth control network is verified through the experiments of the simulation prototype and experimental prototype.

Bi-Directional Communication Between Neurons and Astrocytes Modulates Spinal Motor Circuits.

Evidence suggests that astrocytes are not merely supportive cells in the nervous system but may actively participate in the control of neural circuits underlying cognition and behavior. In this study, we examined the role of astrocytes within the motor circuitry of the mammalian spinal cord. Pharmacogenetic manipulation of astrocytic activity in isolated spinal cord preparations obtained from neonatal mice revealed astrocyte-derived, adenosinergic modulation of the frequency of rhythmic output generated by the locomotor central pattern generator (CPG) network. Live Ca2+ imaging demonstrated increased activity in astrocytes during locomotor-related output and in response to the direct stimulation of spinal neurons. Finally, astrocytes were found to respond to neuronally-derived glutamate in a metabotropic glutamate receptor 5 (mGluR5) dependent manner, which in turn drives astrocytic modulation of the locomotor network. Our work identifies bi-directional signaling mechanisms between neurons and astrocytes underlying modulatory feedback control of motor circuits, which may act to constrain network output within optimal ranges for movement.

Research on Motion Mode Switching Method Based on CPG Network Reconstruction

Traditional underwater robots have many shortcomings in terms of resistance to sea currents, stability and functionality. In order to improve the efficiency and quality of underwater operations, this paper proposes and designs a new underwater robot, the navigation and crawl underwater unmanned vehicle (NCUUV), it has two motion modes: swimming and crawling. In order to enable the robot to have the ability to switch motion modes, this paper designed a motion mode switching mechanism, and verified the effectiveness of the mechanism through experiments, which proved that the mechanism has good motion reliability and accuracy. In order to control NCUUV to smoothly switch the motion mode, a controller based on the central pattern generator (CPG) was developed. The upper controller reconstructs the CPG network in the middle controller according to the feedback information of the acoustic rangefinder. The CPG network in the middle controller has two configurations: a triangular fully symmetrical network and a hexagonal fully symmetrical network, and the CPG network can be freely changed between the two configurations according to the instructions of the upper controller. These two configurations control NCUUV’s swimming and crawling respectively, so as to realize the smooth switching between swimming and crawling. The experimental result shows that this control mechanism of motion mode switching can enable NCUUV to stably realize motion mode switching and the switching process is smooth and reliable.

Molecular mechanisms of homeostatic plasticity in central pattern generator networks.

Central pattern generator (CPG) networks rely on a balance of intrinsic and network properties to produce reliable, repeatable activity patterns. This balance is maintained by homeostatic plasticity where alterations in neuronal properties dynamically maintain appropriate neural output in the face of changing environmental conditions and perturbations. However, it remains unclear just how these neurons and networks can both monitor their ongoing activity and use this information to elicit homeostatic physiological responses to ensure robustness of output over time. Evidence exists that CPG networks use a mixed strategy of activity-dependent, activity-independent, modulator-dependent, and synaptically regulated homeostatic plasticity to achieve this critical stability. In this review, we focus on some of the current understanding of the molecular pathways and mechanisms responsible for this homeostatic plasticity in the context of central pattern generator function, with a special emphasis on some of the smaller invertebrate networks that have allowed for extensive cellular-level analyses that have brought recent insights to these questions.

Enhancing adaptability with local reactive behaviors for hexapod walking robot via sensory feedback integrated central pattern generator

Local reactive behaviors endow animals the ability to exhibit agile and dexterous performance when traversing challenging terrains. This paper presents a novel locomotion control method based on the central pattern generator (CPG) concept for hexapod walking robot with local reactive behavior to cope with terrain irregularities. Firstly, a two-layered CPG-based single-leg controller is developed to generate the rhythmical movement for each leg executing tripod walking. The Van der Pol oscillator is employed on the high-layer to construct a coupled CPG network which serves as a phase regulator (PR) to produce rhythmic signals with prescribed phase relations amongst neurons. On the low-layer, an auxiliary linear converter (LC) transforms these signals into the desired joint trajectories. Subsequently, by embodying the proprioceptive sensing and external tactile information as the sensory feedback, two typical local reactive mechanisms including the elevator reflex and searching reflex are achieved by virtue of on-line adjusting the coupling scheme of the PR and the coefficients of the LC. A locomotion control framework for hexapod walking robot is further established by combining the single-leg controller with a finite state machine to allocate swing/stance commands for individual joints in dealing with terrain perturbations. The effectiveness of the proposed method has been verified through both virtual model simulation and experiments on a physical hexapod platform.

Feedforward discharges couple the singing central pattern generator and ventilation central pattern generator in the cricket abdominal central nervous system

We investigated the central nervous coordination between singing motor activity and abdominal ventilatory pumping in crickets. Fictive singing, with sensory feedback removed, was elicited by eserine-microinjection into the brain, and the motor activity underlying singing and abdominal ventilation was recorded with extracellular electrodes. During singing, expiratory abdominal muscle activity is tightly phase coupled to the chirping pattern. Occasional temporary desynchronization of the two motor patterns indicate discrete central pattern generator (CPG) networks that can operate independently. Intracellular recordings revealed a sub-threshold depolarization in phase with the ventilatory cycle in a singing-CPG interneuron, and in a ventilation-CPG interneuron an excitatory input in phase with each syllable of the chirps. Inhibitory synaptic inputs coupled to the syllables of the singing motor pattern were present in another ventilatory interneuron, which is not part of the ventilation-CPG. Our recordings suggest that the two centrally generated motor patterns are coordinated by reciprocal feedforward discharges from the singing-CPG to the ventilation-CPG and vice versa. Consequently, expiratory contraction of the abdomen usually occurs in phase with the chirps and ventilation accelerates during singing due to entrainment by the faster chirp cycle.

Descending octopaminergic neurons modulate sensory-evoked activity of thoracic motor neurons in stick insects.

Neuromodulatory neurons located in the brain can influence activity in locomotor networks residing in the spinal cord or ventral nerve cords of invertebrates. How inputs to and outputs of neuromodulatory descending neurons affect walking activity is largely unknown. With the use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and immunohistochemistry, we show that a population of dorsal unpaired median (DUM) neurons descending from the gnathal ganglion to thoracic ganglia of the stick insect Carausius morosus contains the neuromodulatory amine octopamine. These neurons receive excitatory input coupled to the legs' stance phases during treadmill walking. Inputs did not result from connections with thoracic central pattern-generating networks, but, instead, most are derived from leg load sensors. In excitatory and inhibitory retractor coxae motor neurons, spike activity in the descending DUM (desDUM) neurons increased depolarizing reflexlike responses to stimulation of leg load sensors. In these motor neurons, descending octopaminergic neurons apparently functioned as components of a positive feedback network mainly driven by load-detecting sense organs. Reflexlike responses in excitatory extensor tibiae motor neurons evoked by stimulations of a femur-tibia movement sensor either are increased or decreased or were not affected by the activity of the descending neurons, indicating different functions of desDUM neurons. The increase in motor neuron activity is often accompanied by a reflex reversal, which is characteristic for actively moving animals. Our findings indicate that some descending octopaminergic neurons can facilitate motor activity during walking and support a sensory-motor state necessary for active leg movements.NEW & NOTEWORTHY We investigated the role of descending octopaminergic neurons in the gnathal ganglion of stick insects. The neurons become active during walking, mainly triggered by input from load sensors in the legs rather than pattern-generating networks. This report provides novel evidence that octopamine released by descending neurons on stimulation of leg sense organs contributes to the modulation of leg sensory-evoked activity in a leg motor control system.

A Fast Online Frequency Adaptation Mechanism for CPG-Based Robot Motion Control

In this letter, we present an online learning mechanism called the dual integral learner for fast frequency adaptation in neural central pattern generator (CPG) based locomotion control of a hexapod robot. The mechanism works by modulating the CPG frequency through synaptic plasticity of the neural CPG network. The modulation is based on tracking error feedback between the CPG output and joint angle sensory feedback of the hexapod robot. As a result, the mechanism will always try to match the CPG frequency to the walking performance of the robot, thereby ensuring that the entire generated trajectory can be followed with low tracking error. Real robot experiments show that our mechanism can automatically generate a proper walking frequency for energy-efficient locomotion with respect to the robot body as well as being able to quickly adapt the frequency online within a few seconds to deal with external perturbations such as leg blocking and a variation in electrical power. These important features will allow a hexapod robot to be more robust and also extend its operating time. Finally, the generality of the mechanism is shown by successfully applying it to a compliant robotic manipulator arm called GummiArm.

A Studyof Hopf and Amplitude-ControlledPhase Oscillators for Snake Robot Locomotion

In this paper, we propose a motion control which can produce rhythmic movements of a snake-like robot by using Center Pattern Generator. The snake robot is designed in SolidWorks environment and composed of a head, a propulsion mechanism and a tail connected the last link. The designed model is also adapted to Matlab/SimMechanics environment in order to observe the motion of the robot and analyze the constructed Central Pattern Generator networks. Central Pattern Generator based networks are carried out with Amplitude-Controlled Phase and Hopf oscillators to create rhythmic, stable, oscillatory and robust locomotion patterns. The both network structures are constructed with bidirectional chain models. In order to analyze the locomotion of the snake-like robot, S-type forward and C-type turning motions are performed. The simulation results show that the Amplitude-Controlled Phase oscillator with bidirectional chain network structure gives effective, smooth and robust control performance for both forward and turning motions of the snake robot. Thus, a base of more intelligent locomotion control approach with Amplitude-Controlled Phase oscillator is provided and some critical comparisons are performed.

Current Principles of Motor Control, with Special Reference to Vertebrate Locomotion.

The vertebrate control of locomotion involves all levels of the nervous system from cortex to the spinal cord. Here, we aim to cover all main aspects of this complex behavior, from the operation of the microcircuits in the spinal cord to the systems and behavioral levels and extend from mammalian locomotion to the basic undulatory movements of lamprey and fish. The cellular basis of propulsion represents the core of the control system, and it involves the spinal central pattern generator networks (CPGs) controlling the timing of different muscles, the sensory compensation for perturbations, and the brain stem command systems controlling the level of activity of the CPGs and the speed of locomotion. The forebrain and in particular the basal ganglia are involved in determining which motor programs should be recruited at a given point of time and can both initiate and stop locomotor activity. The propulsive control system needs to be integrated with the postural control system to maintain body orientation. Moreover, the locomotor movements need to be steered so that the subject approaches the goal of the locomotor episode, or avoids colliding with elements in the environment or simply escapes at high speed. These different aspects will all be covered in the review.

CPG-based autonomous swimming control for multi-tasks of a biomimetic robotic fish

This work emphasizes the artificial intelligent control of the developed biomimetic robotic fish (i-RoF) based on sensory feedback Central Pattern Generator (CPG) approach. In order to obtain rhythmic, robust and adaptable closed loop control performance, a novel control mechanism, composed of two subsystems; as a biological based CPG network and a Fuzzy Logic controller, is proposed. The CPG network behaves as an artificial spinal cord, inspired by Lampreys. It is constructed as unidirectional chained network with Sensory Neurons (SNs) which can perceive the external stimulus. The Fuzzy Logic control structure is also designed as a decision-making mechanism and a Finite State Machine (FSM) algorithm is proposed to perform the given missions autonomously. In the experimental studies, different scenarios are created for both situations as autonomous swimming and encountering obstacles while taking on a primary task, which is determined with yaw control. Autonomous swimming ability is performed in the real pool environment with proposed scenarios that are likely to be encountered in a real environment. It is observed that autonomous swimming performance is excellent and yaw control is quite good even in the presence of obstacles. It is also proved that the escape performance of the robot is very fast.

Programmable coupled oscillators for synchronized locomotion

The striking similarity between biological locomotion gaits and the evolution of phase patterns in coupled oscillatory network can be traced to the role of central pattern generator located in the spinal cord. Bio-inspired robotics aim at harnessing this control approach for generation of rhythmic patterns for synchronized limb movement. Here, we utilize the phenomenon of synchronization and emergent spatiotemporal pattern from the interaction among coupled oscillators to generate a range of locomotion gait patterns. We experimentally demonstrate a central pattern generator network using capacitively coupled Vanadium Dioxide nano-oscillators. The coupled oscillators exhibit stable limit-cycle oscillations and tunable natural frequencies for real-time programmability of phase-pattern. The ultra-compact 1 Transistor-1 Resistor implementation of oscillator and bidirectional capacitive coupling allow small footprint area and low operating power. Compared to biomimetic CMOS based neuron and synapse models, our design simplifies on-chip implementation and real-time tunability by reducing the number of control parameters.

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.

Snake Robot Controlled by Biomimetic CPGs

The design of biomimetic robot is one popular research. To achieve this goal, the reproduction of animal locomotion is mandatory. Animal locomotion is created by the activities of Central Pattern Generator (CPG). 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 field programmable gate array. We designed one unsupervised snake robot using this network of CPG. It is composed of one head wagon followed by seven slave wagons. Infrared sensors are also embedded in the head wagon. This robot can reproduce the locomotion of one snake.

Control and Optimization of a Bionic Robotic Fish Through a Combination of CPG model and PSO

Swimming speed and propulsive efficiency are critical indicators of a self-propelled bionic robotic fish in terms of swimming performance. This paper is devoted to achieving a maximum swimming speed and a higher propulsive efficiency for a four-joint robotic fish. A Newton–Euler method based dynamic model in conjunction with a central pattern generator (CPG) network serves to theoretically estimate fish-like swimming performance. In particular, each CPG unit based on limit cycles serves to drive one joint of the robotic fish. The overall CPG model is able to generate rhythmic signals for multimode swimming. To obtain the maximum average speed, a particle swarm optimization (PSO) algorithm is further utilized to optimize the feature parameters of the CPG model. Furthermore, the higher propulsive efficiency is sought within the same control framework. Simulations and experiments on the actual robotic fish demonstrate the improved propulsive performance and the effectiveness of the proposed control framework.

Adaptive Path Following and Locomotion Optimization of Snake‐Like Robot Controlled by the Central Pattern Generator

This work investigates locomotion efficiency optimization and adaptive path following of snake‐like robots in a complex environment. To optimize the locomotion efficiency, it takes energy consumption and forward velocity into account to investigate the optimal locomotion parameters of snake‐like robots controlled by a central pattern generator (CPG) controller. A cuckoo search (CS) algorithm is applied to optimize locomotion parameters of the robot for environments with variable fractions and obstacle distribution. An adaptive path following method is proposed to steer the snake‐like robot forward and along a desired path. The efficiency and accuracy of the proposed path following method is researched. In addition, a control framework that includes a CPG network, a locomotion efficiency optimization algorithm, and an adaptive path following method is designed to control snake‐like robots move in different environments. Simulation and experimental results are presented to illustrate the performance of the proposed locomotion optimization method and adaptive path following controller for snake‐like robots in complexity terrains.