|M.Sc Thesis||Department of Electrical Engineering|
|Supervisors:||Prof. Emeritus Inbar Gideon|
|Prof. Meir Ron|
Low-level motor control circuits located in the spinal cord, also called “final path”, is activated by spike trains from higher level motor control levels and generate at output spike trains for individual muscle fibers, which are responsible for force generation.
In this thesis we investigated the role of inhibitory excitation in this specific case by building physiologically realistic simulation. Physiologically plausible models of required elements were constructed during simulation development, based on published experimental results. Functional behavior of every element in the circuit were extensively studied and carefully reproduced. Simulation included all required basic elements, such as α-motoneurons, Renshaw cells, muscle fibers and Golgi tendon organs. Specifically, connections between α-motoneurons and Renshaw cells were examined.
Changing relative strength of connection between the elements allowed observations of changes in characteristics of the circuit and force produced by the muscle. Simulation reproduced all experimental results, such as decorrelation of motoneuron firings, and suggested number of new criteria, which may be responsible for the presence and observed strength of recurrent inhibition. The effect of recurrent inhibition on decorrelation of motoneuron firings and their relative synchrony were shown, suggesting that decorrelation and synchronization of α-motoneuron's firings along with suppression of weaker muscle units after recruitment of stronger ones, indeed takes place in the presence of recurrent inhibition.
We argue that presence and strength of recurrent inhibition might be explained in terms of mean and variance of produced force. This criterion was never considered before as an influential factor.
Variance in produced force can change variance in movement. Significance of the effect depends on relative location and action of the muscle in the generated movement. For example, variance in force produced by the biceps muscle can influence more strongly the variance of hand movement than variance in muscles of fingers. Therefore, in order to keep the variance low in particular muscles, mechanisms of recurrent inhibition can be employed.