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.

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.

Control of tumbling during the locust jump.

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.

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.