Monthly Archives: November 2019

Update 1

A model that computationally predicts data which is experimentally verified with microneurography [1] that exists is that of Vannucci, Falotico, and Laschi [2]. However, the Vannucci et al. model was only validated for experimental data (from Mileusnic et. al’s [3] paper’s Figure 3) at the 0.11 decimal of optimal fiber length / s muscle stretch speed. (Optimal fiber length is unitless, since it is (current muscle length (m))/(optimal fiber length of that muscle (m)).) Vannucci’s model wasn’t validated for the 0.66 decimal of optimal fiber length / s and 1.55 decimal of optimal fiber length / s speeds, which are speeds that were also included in Figure 3 of Mileusnic’s paper. If Vannucci’s model is only accurate for a certain speed, the model can’t be used for much (it can’t be used to simulate a particularly slow or fast muscle stretch), but if Vannucci’s model is accurate for the other speeds given (.66, 1.55) then it could be used for a lot more.
Therefore, I tried to see if Vannucci’s model worked for those other speeds by running the model while the input muscle lengths stretched at those other speeds, and Vannucci’s model didn’t appear to predict muscle spindle afferent firing rates at the other speeds.

[1] <>
[2] Vannucci, L., Falotico, E., & Laschi, C. (2017). Proprioceptive Feedback through a Neuromorphic Muscle Spindle Model. Frontiers in neuroscience, 11, 341. doi:10.3389/fnins.2017.00341
[3] Mileusnic, M. P., Brown, I. E., Lan, N., & Loeb, G. E. (2006). Mathematical models of proprioceptors. I. Control and transduction in the muscle spindle. Journal of neurophysiology, 96(4), 1772-1788. doi:10.1152/jn.00868.2005