Modeling spinal sensorimotor control for reach task

Building a realistic neuronal model that simulates multi-joint arm and hand movements in 3D space

The question as to how the brain controls voluntary movements of the arm and hand still remains largely unsolved despite much research focused on behavioral studies, neurophysiological investigations, and neuronal modeling in computer science. This is because behavioral studies are usually performed without detailed knowledge of the underlying neuronal networks, neurophysiological studies often lack an understanding of the function, and neuronal models are frequently focused on a particular control problem with restricted knowledge of the underlying neuronal networks involved. Therefore, it seems appropriate to start by trying to integrate knowledge of neuronal networks with known function and computer based neuronal models to seek more realistic models that can better control robots or artificial limbs and hands. We propose to combine knowledge of a behavioral model for reaching with the hand toward an object, which is based on detailed knowledge of the underlying neuronal network, and a neuronal model that includes several functional levels, from the planning level via intermediate levels to the final level of control of motoneurons and muscles.

Mathematical models of proprioceptors. I. Control and transduction in the muscle spindle

We constructed a physiologically realistic model of a lower-limb, mammalian muscle spindle composed of mathematical elements closely related to the anatomical components found in the biological spindle. The spindle model incorporates three nonlinear intrafusal fiber models (bag(1), bag(2), and chain) that contribute variously to action potential generation of primary and secondary afferents. A single set of model parameters was optimized on a number of data sets collected from feline soleus muscle, accounting accurately for afferent activity during a variety of ramp, triangular, and sinusoidal stretches. We also incorporated the different temporal properties of fusimotor activation as observed in the twitchlike chain fibers versus the toniclike bag fibers. The model captures the spindle's behavior both in the absence of fusimotor stimulation and during activation of static or dynamic fusimotor efferents. In the case of simultaneous static and dynamic fusimotor efferent stimulation, we demonstrated the importance of including the experimentally observed effect of partial occlusion. The model was validated against data that originated from the cat's medial gastrocnemius muscle and were different from the data used for the parameter determination purposes. The validation record included recently published experiments in which fusimotor efferent and spindle afferent activities were recorded simultaneously during decerebrate locomotion in the cat. This model will be useful in understanding the role of the muscle spindle and its fusimotor control during both natural and pathological motor behavior.