David W. Cofer

AnimatLab: A 3D graphics environment for neuromechanical simulations

The nervous systems of animals evolved to exert dynamic control of behavior in response to the needs of the animal and changing signals from the environment. To understand the mechanisms of dynamic control requires a means of predicting how individual neural and body elements will interact to produce the performance of the entire system. AnimatLab is a software tool that provides an approach to this problem through computer simulation. AnimatLab enables a computational model of an animals body to be constructed from simple building blocks, situated in a virtual 3D world subject to the laws of physics, and controlled by the activity of a multicellular, multicompartment neural circuit. Sensor receptors on the body surface and inside the body respond to external and internal signals and then excite central neurons, while motor neurons activate Hill muscle models that span the joints and generate movement. AnimatLab provides a common neuromechanical simulation environment in which to construct and test models of any skeletal animal, vertebrate or invertebrate. The use of AnimatLab is demonstrated in a neuromechanical simulation of human arm flexion and the myotactic and contact-withdrawal reflexes.

Neuromechanical simulation of the locust jump

SUMMARY The neural circuitry and biomechanics of kicking in locusts have been studied to understand their roles in the control of both kicking and jumping. It has been hypothesized that the same neural circuit and biomechanics governed both behaviors but this hypothesis was not testable with current technology. We built a neuromechanical model to test this and to gain a better understanding of the role of the semi-lunar process (SLP) in jump dynamics. The jumping and kicking behaviors of the model were tested by comparing them with a variety of published data, and were found to reproduce the results from live animals. This confirmed that the kick neural circuitry can produce the jump behavior. The SLP is a set of highly sclerotized bands of cuticle that can be bent to store energy for use during kicking and jumping. It has not been possible to directly test the effects of the SLP on jump performance because it is an integral part of the joint, and attempts to remove its influence prevent the locust from being able to jump. Simulations demonstrated that the SLP can significantly increase jump distance, power, total energy and duration of the jump impulse. In addition, the geometry of the joint enables the SLP force to assist leg flexion when the leg is flexed, and to assist extension once the leg has begun to extend.

Control of tumbling during the locust jump

SUMMARY Locust can jump precisely to a target, yet they can also tumble during the trajectory. We propose two mechanisms that would allow the locust to control tumbling during the jump. The first is that prior to the jump, locusts adjust the pitch of their body to move the center of mass closer to the intended thrust vector. The second is that contraction of the dorsolongitudinal muscles during the jump will produce torques that counter the torque produced by thrust. We found that locusts increased their take-off angle as the initial body pitch increased, and that little tumbling occurred for jumps that observed this relationship. Simulations of locust jumping demonstrated that a pitch versus take-off angle relationship that minimized tumbling in simulated jumps was similar to the relationship observed in live locusts. Locusts were strongly biased to pitch head-upward, and performed dorsiflexions far more often than ventral flexions. The direction and magnitude of tumbling could be controlled in simulations by adjusting the tension in the dorsolongitudinal muscles. These mechanisms allowed the simulations to match the data from the live animals. Control of tumbling was also found to influence the control of jump elevation. The bias to pitch head-upwards may have an evolutionary advantage when evading a predator and so make control of tumbling important for the locust.

The effect of sensory feedback on crayfish posture and locomotion: I. Experimental analysis of closing the loop.

The effect of proprioceptive feedback on the control of posture and locomotion was studied in the crayfish Procambarus clarkii (Girard). Sensory and motor nerves of an isolated crayfish thoracic nerve cord were connected to a computational neuromechanical model of the crayfish thorax and leg. Recorded levator (Lev) and depressor (Dep) nerve activity drove the model Lev and Dep muscles to move the leg up and down. These movements released and stretched a model stretch receptor, the coxobasal chordotonal organ (CBCO). Model CBCO length changes drove identical changes in the real CBCO; CBCO afferent responses completed the feedback loop. In a quiescent preparation, imposed model leg lifts evoked resistance reflexes in the Dep motor neurons that drove the leg back down. A muscarinic agonist, oxotremorine, induced an active state in which spontaneous Lev/Dep burst pairs occurred and an imposed leg lift excited a Lev assistance reflex followed by a Lev/Dep burst pair. When the feedback loop was intact, Lev/Dep burst pairs moved the leg up and down rhythmically at nearly three times the frequency of burst pairs when the feedback loop was open. The increased rate of rhythmic bursting appeared to result from the positive feedback produced by the assistance reflex.

The effect of sensory feedback on crayfish posture and locomotion: II. Neuromechanical simulation of closing the loop

Neuromechanical simulation was used to determine whether proposed thoracic circuit mechanisms for the control of leg elevation and depression in crayfish could account for the responses of an experimental hybrid neuromechanical preparation when the proprioceptive feedback loop was open and closed. The hybrid neuromechanical preparation consisted of a computational model of the fifth crayfish leg driven in real time by the experimentally recorded activity of the levator and depressor (Lev/Dep) nerves of an in vitro preparation of the crayfish thoracic nerve cord. Up and down movements of the model leg evoked by motor nerve activity released and stretched the model coxobasal chordotonal organ (CBCO); variations in the CBCO length were used to drive identical variations in the length of the live CBCO in the in vitro preparation. CBCO afferent responses provided proprioceptive feedback to affect the thoracic motor output. Experiments performed with this hybrid neuromechanical preparation were simulated with a neuromechanical model in which a computational circuit model represented the relevant thoracic circuitry. Model simulations were able to reproduce the hybrid neuromechanical experimental results to show that proposed circuit mechanisms with sensory feedback could account for resistance reflexes displayed in the quiescent state and for reflex reversal and spontaneous Lev/Dep bursting seen in the active state.

Paw-shake response and locomotion: can one CPG generate two different rhythmic behaviors?

Rhythmic limb movements like locomotion or paw-shake response are controlled by network of spinal circuits, known as central pattern generators (CPGs), as evidenced from locomotor-like and paw-shake like activity in limb peripheral nerves elicited in decerebrate or spinal animals with blocked neuromuscular transmission [4]. Unlike fictive locomotion and scratch, that are likely controlled by distinct CPGs [3], fictive paw-shake response has not been systematically investigated and it is not known whether it is controlled by a specialized CPG or by the CPG that also controls locomotion. In-vivo recordings of paw-shake motor patterns elicited by stimulation of paw skin afferents [7] have revealed high frequency hindlimb oscillations (~10 Hz) with atypical muscle synergies – reciprocal activation of anterior and posterior hindlimb muscles in each half of the paw-shake cycle; both anterior and posterior muscle groups include flexor and extensor muscles. We asked whether a paw-shake response with the atypical muscle synergies can be generated by a typical half-center locomotor CPG reciprocally activating flexor and extensor muscles. Using software AnimatLab [2] we developed a 5-segment cat hindlimb model with 12 Hill-type muscle actuators controlled by (1) a half-center CPG activating flexor and extensor muscles (two-joint muscles received both flexion- and extension-related signals [5,6]) and (2) proprioceptive input originated from the muscle spindle and Golgi tendon organ afferents. The CPG was modeled by two single-compartment spiking neurons in a half-center configuration. Other neurons (Ia-afferents, alpha-motor neurons, Ia-interneurons, and interneurons mediating autogenic and heterogenic reflex pathways) were modeled as non-spiking neurons (firing rate model based on work by [1]). Model parameters were adjusted such that computer simulations reproduced the recorded paw-shake mechanics and the anterior-posterior muscle activation patterns. The obtained results demonstrated that a half-center locomotor CPG can produce movement mechanics and muscle activity patterns typical for paw-shake responses if (1) the locomotor CPG is capable to operate at frequencies 3 to10 times higher than during locomotion and (2) synaptic weights in spinal circuits can be modified during paw-shake response. We speculate that the two conditions can be realized by sensory input from paw skin afferents.

Role of the semi-lunar process in locust jumping

itor> Meeting abstracts - A single PDF containing all abstracts in this Supplement is available here http://www. biomedcentral.co m/content/pdf/14 71-2202-8-S2-in fo.pdf

A 3D graphics toolkit for studying neural basis of adaptive behaviors

1. Background For decades neuroscientists have studied how the neural architecture produces adaptive behaviors. However, conducting experiments on real animals has its limitations. For example, attempting to take measurements from numerous neurons while the animal is moving is often difficult. Although a number of neural simulators, such as NEURON and GENESIS, have been developed to help study models of neurons and networks of neurons, existing neural simulators lack the capability to integrate animal behavior simulations with neuron or neural network simulations.

A 3D graphics environment for behavioral neurobiology research

∗ contact email: yzhu@cs.gsu.edu