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Sudden stretch of active muscle typically results in two characteristic electromyographic responses: the short latency M1 and the long latency M2. The M1 response originates from the monosynaptic Ia afferent reflex pathway. The M2 response is less well understood and is likely a compound response to different afferent inputs mediated by spinal and transcortical pathways. In this study the possible contribution of the Ia afferent pathway to the M2 response was investigated. A mechanism was hypothesized in which the M1 response synchronizes the motoneurons, and therewith their refractory periods. Stretch perturbation experiments were performed on the wrist and results were compared with a computational model of a pool of motoneurons receiving tonic and Ia afferent input. The simulations showed the same stretch amplitude, velocity, and duration-dependent characteristics on the M2 as found experimentally. It was concluded that the stretch duration effect of the M2 likely originates from the proposed Ia afferent mediated mechanism.

Abstract. Different control algorithms for the regulation of irrigation canals have been developed and applied throughout the world. Each of them can be characterized according to several criteria, among which are: the considered variables (controlled, measured, control action variables), the logic of control (type and direction), and the design technique. The following text presents definitions of these terms and a classification of the algorithms detailed in the literature. To summarize and compare algorithms, a structured table of the main published canal control algorithms is presented.

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In this study, a neuromusculoskeletal model was built to give insight into the mechanisms behind the modulation of reflexive feedback strength as experimentally identified in the human shoulder joint. The model is an integration of a biologically realistic neural network consisting of motoneurons and interneurons, modeling 12 populations of spinal neurons, and a one degree-of-freedom musculoskeletal model, including proprioceptors. The model could mimic the findings of human postural experiments, using presynaptic inhibition of the Ia afferents to modulate the feedback gains. In a pathological case, disabling one specific neural connection between the inhibitory interneurons and the motoneurons could mimic the experimental findings in complex regional pain syndrome patients. It is concluded that the model is a valuable tool to gain insight into the spinal contributions to human motor control. Applications lay in the fields of human motor control and neurological disorders, where hypotheses on motor dysfunction can be tested, like spasticity, clonus, and tremor.

In daily life humans integrate force and position feedback from mechanoreceptors, proprioception, and vision. With handling relatively soft, elastic objects, force and position are related and can be integrated to improve the accuracy of an estimate of either one. Sensory weighting between different sensory systems (e.g., vision and proprioception) has been extensively studied. This study investigated whether similar weighting can be found within the proprioceptive sensory system, more specifically between the modalities force and position. We hypothesized that sensory weighting is governed by object stiffness: position feedback is weighted heavier on soft objects (large deflections), while force feedback is weighted heavier on stiff objects (small deflections). Subjects were instructed to blindly reproduce either position or force while holding a one degree of freedom haptic manipulator that simulated a linear spring with one of four predetermined stiffnesses. In catch trials the spring was covertly replaced by a nonlinear spring. The difference in force (⌬F) and position (⌬X) between the regular and the catch trials revealed the sensory weighting between force and position feedback. A maximum likelihood estimation model predicted that: (1) task instruction did not affect the outcome measures, and (2) force feedback is weighted heavier with increasing object stiffness as was hypothesized. Both effects were found experimentally, and the subjects' sensory weighting closely resembled the optimal model predictions. To conclude, this study successfully demonstrated sensory weighting within the proprioceptive system.

Schuurmans et al. (1995a) presented a model for the design of water level controllers for irrigation and drainage canals that describes the essential characteristics of the processes relevant for canal control (such as water movements and control structures). This paper evaluates the accuracy of this model in two ways: (1) by comparing its frequency response with a model based on a finite difference approximation of the linearized St. Venant equations, and (2) by comparing simulation results with data from field experiments. Using these results, we characterize the accuracy of the model and discuss how these results can be taken into account in controller design.

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