Biology subjects

Sobinov, A.

Publications and source records attributed to Sobinov, A..

2 recordsLinked to original sources

bioRxiv2019

Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials

Computational models of the musculoskeletal system are scientific tools used to study human movement, quantify the effects of injury and disease, and plan surgical interventions. Additionally, these models could also be used to intuitively link biological control signals and realistic high-dimensional articulated prosthetic limbs. However, implementing fast and accurate musculoskeletal computations that can be used to control a prosthetic limb in real-time is a challenging problem. As muscles typically span multiple joints, the wrapping over complex geometrical constraints changes their moment arms and length as a function of joint angle and, thus, their ability to generate joint torques. As a result of these biomechanical complexities, calculating these muscle state variables in real-time is a difficult simulation problem. Here, we report a method to accurately and efficiently calculate these variables for the forearm muscles that actuate the hand and wrist across multiple postures. The posture dependent muscle geometry, moment arms and lengths of modeled muscles, were captured using autogenerating polynomials that expanded their optimal selection of terms using information measurements. The iterative process approximated 33 musculotendon actuators, each spanning up to 6 DOFs in an 18 DOF model of the human arm and hand, defined over the full physiological range of motion. Using these polynomials, the entire forearm anatomy could be computed in <10 {micro}s, which is far better than what is required for real-time performance, and with low errors in moment arms (below 5%) and lengths (below 0.4%). Moreover, we demonstrate that the number of elements in these autogenerating polynomials does not increase exponentially with the increase in complexity of muscles, increasing linearly instead. The similar structure and function of muscles are represented with specific invariant polynomial terms. Dimensionality reduction using the polynomial terms alone resulted in clusters comprised of muscles with similar functions, suggesting that the polynomials themselves captured biologically relevant features of muscle structure and function. We propose that this novel method of describing musculoskeletal biomechanics might further improve the applications of detailed and scalable models for the description of human movement.

physiology↗

bioRxiv2017

Analytical solution of a bilateral Brown-type central pattern generator for symmetric and asymmetric locomotion

The coordinated activity of muscles is produced in part by spinal rhythmogenic neural circuits, termed central pattern generators (CPGs). A classical CPG model, proposed conceptually by T.G. Brown in 1911, is a system of coupled oscillators that transform locomotor drive into coordinated and gait-specific patterns of muscle recruitment. A system of ordinary differential equations with a physiologically-inspired coupling locus of interactions captures the timing relationship for bilateral coordination of limbs in locomotion and is typically solved numerically. Consequently, it is intriguing to have a full analytical description of this plausible CPG architecture to illuminate the functionality within this structure. Here, we provided a closed-form analytical solution contrasted against the previous numerical method. The computational load of the analytical solution was decreased by an order of magnitude when compared to the numerical approach (relative errors, <0.01%). The analytical solution tested and supported the previous finding that the input to the model can be expressed in units of the desired limb locomotor speed. Furthermore, we performed parametric sensitivity analysis in the context of controlling asymmetric locomotion and documented two possible mechanisms associated with either an external drive or intrinsic CPG parameters. The results support the idea that many different combinations of network states, even within the same anatomical CPG structure, may generate the same behavioral outcomes.\n\nAuthor SummaryUsing a simple process of leaky integration, we developed an analytical solution to a robust model of spinal pattern generation. We analyzed the ability of this neural element to exert locomotor control of the signal associated with limb speeds, which represent high-level modality within the motor system. Furthermore, we tested the ability of this simple structure to embed steering control using the velocity signal in the models inputs or within the internal connectivity of its elements. Both mechanisms can produce the same behavioral outcome, pointing to the methodological challenges of modeling central pattern generators and demonstrating the possibility of spinal circuit adaptations to asymmetric short- or long-term conditions in health and disease.

neuroscience↗