Coupling the neural and physical dynamics in rhythmic movements

A pair of coupled oscillators simulating a central pattern generator (CPG) interacting with a pendular limb were numerically integrated. The CPG was represented as a van der Pol oscillator and the pendular limb was modeled as a linearized, hybrid spring-pendulum system. The CPG oscillator drove the pendular limb while the pendular limb modulated the frequency of the CPG. Three results were observed. First, sensory feedback influenced the oscillation frequency of the coupled system. The oscillation frequency was lower in the absence of sensory feedback. Moreover, if the muscle gain was decreased, thereby decreasing the oscillation amplitude of the pendular limb and indirectly lowering the effect of sensory feedback, the oscillation frequency decreased monotonically. This is consistent with experimental data (Williamson and Roberts 1986). Second, the CPG output usually led the angular displacement of the pendular limb by a phase of 90° regardless of the length of the limb. Third, the frequency of the coupled system tuned itself to the resonant frequency of the pendular limb. Also, the frequency of the coupled system was highly resistant to changes in the endogenous frequency of the CPG. The results of these simulations support the view that motor behavior emerges from the interaction of the neural dynamics of the nervous system and the physical dynamics of the periphery

Neural Control of Interlimb Oscillations I: Human Bimanual Coordination

How do humans and other animals accomplish coordinated movements? How are novel combinations of limb joints rapidly assembled into new behavioral units that rnove together in in-phase or anti-phase movement patterns during complex movement tasks? A neural central pattern generator (CPG) model simulates data from human bimanual coordination tasks. As in the data, anti-phase oscillations at low frequencies switch to in-phase oscillations at high frequencies, in-phase oscillation occur both at low and high frequencies, phase fluctuations occur at the anti-phase in-phase transition, a "seagull effect" of larger errors occurs at intermediate phases, and oscillations slip toward in-phase and anti-phase when driven at intermediate phases. These oscillations and bifurcations are emergent properties of the CPG model in response to volitional inputs. The CPC model is a version of the Ellias-Grossberg oscillator. Its neurons obey Hodgkin-Huxley type equations whose excitatory signals operate on a faster time scale than their inhibitory signals in a recurrent on-center off-surround anatomy. When an equal cornmand or GO signal activates both model channels the model CPC: can generate both in-phase and anti-phase oscillations at different GO amplitudes. Phase transitions frorn either in-phase to anti-phase oscillations, or from anti-phase to in- phase oscillations, can occur in different pararncter ranges, as the GO signal increases.Air Force Office of Scientific Research (F49620-92-J-0499, 90-0083, F49620-92-J-0225, 90-0128); Office of Naval Research (N00014-92-J-1309); Army Research Office (DAAL03-0088); National Science Foundation (IRI-90-24877

Neural Control of Interlimb Oscillations I: Human Bimanual Coordination

How do humans and other animals accomplish coordinated movements? How are novel combinations of limb joints rapidly assembled into new behavioral units that rnove together in in-phase or anti-phase movement patterns during complex movement tasks? A neural central pattern generator (CPG) model simulates data from human bimanual coordination tasks. As in the data, anti-phase oscillations at low frequencies switch to in-phase oscillations at high frequencies, in-phase oscillation occur both at low and high frequencies, phase fluctuations occur at the anti-phase in-phase transition, a "seagull effect" of larger errors occurs at intermediate phases, and oscillations slip toward in-phase and anti-phase when driven at intermediate phases. These oscillations and bifurcations are emergent properties of the CPG model in response to volitional inputs. The CPC model is a version of the Ellias-Grossberg oscillator. Its neurons obey Hodgkin-Huxley type equations whose excitatory signals operate on a faster time scale than their inhibitory signals in a recurrent on-center off-surround anatomy. When an equal cornmand or GO signal activates both model channels the model CPC: can generate both in-phase and anti-phase oscillations at different GO amplitudes. Phase transitions frorn either in-phase to anti-phase oscillations, or from anti-phase to in- phase oscillations, can occur in different pararncter ranges, as the GO signal increases.Air Force Office of Scientific Research (F49620-92-J-0499, 90-0083, F49620-92-J-0225, 90-0128); Office of Naval Research (N00014-92-J-1309); Army Research Office (DAAL03-0088); National Science Foundation (IRI-90-24877

Proprioceptive Coupling within Motor Neurons Drives C. Elegans Forward Locomotion

Locomotion requires coordinated motor activity throughout an animal’s body. In both vertebrates and invertebrates, chains of coupled central pattern generators (CPGs) are commonly evoked to explain local rhythmic behaviors. In C. elegans, we report that proprioception within the motor circuit is responsible for propagating and coordinating rhythmic undulatory waves from head to tail during forward movement. Proprioceptive coupling between adjacent body regions transduces rhythmic movement initiated near the head into bending waves driven along the body by a chain of reflexes. Using optogenetics and calcium imaging to manipulate and monitor motor circuit activity of moving C. elegans held in microfluidic devices, we found that the B-type cholinergic motor neurons transduce the proprioceptive signal. In C. elegans, a sensorimotor feedback loop operating within a specific type of motor neuron both drives and organizes body movement.Chemistry and Chemical BiologyPhysic

Chaotic exploration and learning of locomotion behaviours

We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage

Bionic Control of Cheetah Bounding with a Segmented Spine

A cheetah model is built to mimic real cheetah and its mechanical and dimensional parameters are derived from the real cheetah. In particular, two joints in spine and four joints in a leg are used to realize the motion of segmented spine and segmented legs which are the key properties of the cheetah bounding. For actuating and stabilizing the bounding gait of cheetah, we present a bioinspired controller based on the state-machine. The controller mainly mimics the function of the cerebellum to plan the locomotion and keep the body balance. The haptic sensor and proprioception system are used to detect the trigger of the phase transition. Besides, the vestibular modulation could perceive the pitching angle of the trunk. At last, the cerebellum acts as the CPU to operate the information from the biological sensors. In addition, the calculated results are transmitted to the low-level controller to actuate and stabilize the cheetah bounding. Moreover, the delay feedback control method is employed to plan the motion of the leg joints to stabilize the pitching motion of trunk with the stability criterion. Finally, the cyclic cheetah bounding with biological properties is realized. Meanwhile, the stability and dynamic properties of the cheetah bounding gait are analyzed elaborately

Flight and Walking in Locusts–Cholinergic Co-Activation, Temporal Coupling and Its Modulation by Biogenic Amines

Walking and flying in locusts are exemplary rhythmical behaviors generated by central pattern generators (CPG) that are tuned in intact animals by phasic sensory inputs. Although these two behaviors are mutually exclusive and controlled by independent CPGs, leg movements during flight can be coupled to the flight rhythm. To investigate potential central coupling between the underlying CPGs, we used the muscarinic agonist pilocarpine and the amines octopamine and tyramine to initiate fictive flight and walking in deafferented locust preparations. Our data illustrate that fictive walking is readily evoked by comparatively lower concentrations of pilocarpine, whereas higher concentrations are required to elicit fictive flight. Interestingly, fictive flight did not suppress fictive walking so that the two patterns were produced simultaneously. Frequently, leg motor units were temporally coupled to the flight rhythm, so that each spike in a step cycle volley occurred synchronously with wing motor units firing at flight rhythm frequency. Similarly, tyramine also induced fictive walking and flight, but mostly without any coupling between the two rhythms. Octopamine in contrast readily evoked fictive flight but generally failed to elicit fictive walking. Despite this, numerous leg motor units were recruited, whereby each was temporarily coupled to the flight rhythm. Our results support the notion that the CPGs for walking and flight are largely independent, but that coupling can be entrained by aminergic modulation. We speculate that octopamine biases the whole motor machinery of a locust to flight whereas tyramine primarily promotes walking

Adaptive motor control in crayfish

International audienceThis article reviews the principles that rule the organization of motor commands that have been described over the past ®ve decades in cray®sh. The adaptation of motor behaviors requires the integration of sensory cues into the motor command. The respective roles of central neural networks and sensory feedback are presented in the order of increasing complexity. The simplest circuits described are those involved in the control of a single joint during posture (negative feedback±resistance re¯ex) and movement (modulation of sensory feedback and reversal of the re¯ex into an assistance re¯ex). More complex integration is required to solve problems of coordination of joint movements in a pluri-segmental appendage, and coordination of dierent limbs and dierent motor systems. In addition, beyond the question of mechanical ®tting, the motor command must be appropriate to the behavioral context. Therefore, sensory information is used also to select adequate motor programs. A last aspect of adaptability concerns the possibility of neural networks to change their properties either temporarily (such on-line modulation exerted, for example, by presynaptic mechanisms) or more permanently (such as plastic changes that modify the synaptic ecacy). Finally, the question of how``automatic'' local component networks are controlled by descending pathways, in order to achieve behaviors, is discussed.