Simulation system of spinal cord motor nuclei and associated nerves and muscles, in a Web-based architecture

Cisi, Rogério Rodrigues Lima; Kohn, André Fábio

doi:10.1007/s10827-008-0092-8

Cited by 97 publications

(202 citation statements)

References 110 publications

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“…The multi-scale structure of the simulator allows one to explore concepts ranging from single cell dynamics to whole muscle control, generating force and EMG. Due to its GUI, simulation configuration and data visualization are more straightforward than the previous version reported elsewhere (Cisi and Kohn, 2008). Its availability as a web application is a distinguishing characteristic in comparison to other packages (e.g., (Bower and Beeman, 1998;Brette et al, 2007;Carnevale and Hines, 2006;Delp et al, 2007)), mainly when viewed from the didactic perspective.…”

Section: Discussionmentioning

confidence: 99%

“…A complete description of the mathematical models implemented in the simulator may be found elsewhere (Cisi and Kohn, 2008;Elias et al, 2012a). Briefly, the simulator provides a detailed modeling of four spinal motor nuclei that command leg muscles responsible for ankle extension (Soleus -SOL; Gastrocnemius Medialis -GM; Gastrocnemius Lateralis -GL) and ankle flexion (Tibialis Anterior -TA).…”

Section: General Structurementioning

confidence: 99%

“…The GUI was greatly improved in the new version of the simulator in comparison with the previous one, which was mainly intended for computational neuroscience research (see Figure 2 in (Cisi and Kohn, 2008)). The new GUI is designed with interactive menus representing more general structures (e.g., number of neuronal elements as shown in Figure 2A) and internal menus permitting access to more specific elements.…”

Section: Graphical User Interface (Gui)mentioning

confidence: 99%

“…Within the absolute refractory period, which is primarily due to the Na + channel inactivation, no AP will be generated even for high intensity pulses (not shown). (Cisi and Kohn, 2008). (A) Selection of neuronal elements: In this screen, the user can select the elements that will be used in a specific simulation.…”

Section: Single Neuron Responses: Action Potential Synaptic Facilitamentioning

confidence: 99%

“…In addition, due to the inclusion of some aspects of muscle unit physiology, it may also be used to explore the intricacies of the generation of muscle force and muscle electrical activity. The first version of this neuromuscular system was intended for computational neuroscience research (Cisi and Kohn, 2008). The recently redesigned graphical user interface (GUI) and the inclusion of novel experimental protocols (called "Demonstrations" in the simulator) opened the way for its use for educational purposes.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Web-based neuromuscular simulator applied to the teaching of principles of neuroscience

Elias¹,

Kohn²

2013

RBEB

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Section: Discussionmentioning

confidence: 99%

Section: General Structurementioning

confidence: 99%

Section: Graphical User Interface (Gui)mentioning

confidence: 99%

Section: Single Neuron Responses: Action Potential Synaptic Facilitamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Web-based neuromuscular simulator applied to the teaching of principles of neuroscience

Elias¹,

Kohn²

2013

RBEB

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Coupled simulations and parameter inversion for neural system and electrophysiological muscle models

Homs‐Pons,

Lautenschlager,

Schmid

et al. 2024

GAMM-Mitteilungen

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The functioning of the neuromuscular system is an important factor for quality of life. With the aim of restoring neuromuscular function after limb amputation, novel clinical techniques such as the agonist‐antagonist myoneural interface (AMI) are being developed. In this technique, the residual muscles of an agonist‐antagonist pair are (re‐)connected via a tendon in order to restore their mechanical and neural interaction. Due to the complexity of the system, the AMI can substantially profit from in silico analysis, in particular to determine the prestretch of the residual muscles that is applied during the procedure and determines the range of motion of the residual muscle pair. We present our computational approach to facilitate this. We extend a detailed multi‐X model for single muscles to the AMI setup, that is, a two‐muscle‐one‐tendon system. The model considers subcellular processes as well as 3D muscle and tendon mechanics and is prepared for neural process simulation. It is solved on high performance computing systems. We present simulation results that show (i) the performance of our numerical coupling between muscles and tendon and (ii) a qualitatively correct dependence of the range of motion of muscles on their prestretch. Simultaneously, we pursue a Bayesian parameter inference approach to invert for parameters of interest. Our approach is independent of the underlying muscle model and represents a first step toward parameter optimization, for instance, finding the prestretch, to be applied during surgery, that maximizes the resulting range of motion. Since our multi‐X fine‐grained model is computationally expensive, we present inversion results for reduced Hill‐type models. Our numerical results for cases with known ground truth show the convergence and robustness of our approach.

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Multiscale modeling of the neuromuscular system: Coupling neurophysiology and skeletal muscle mechanics

Röhrle

Yavuz

Klotz

et al. 2019

WIREs Mechanisms of Disease

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Mathematical models and computer simulations have the great potential to substantially increase our understanding of the biophysical behavior of the neuromuscular system. This, however, requires detailed multiscale, and multiphysics models. Once validated, such models allow systematic in silico investigations that are not necessarily feasible within experiments and, therefore, have the ability to provide valuable insights into the complex interrelations within the healthy system and for pathological conditions. Most of the existing models focus on individual parts of the neuromuscular system and do not consider the neuromuscular system as an integrated physiological system. Hence, the aim of this advanced review is to facilitate the prospective development of detailed biophysical models of the entire neuromuscular system. For this purpose, this review is subdivided into three parts. The first part introduces the key anatomical and physiological aspects of the healthy neuromuscular system necessary for modeling the neuromuscular system. The second part provides an overview on state‐of‐the‐art modeling approaches representing all major components of the neuromuscular system on different time and length scales. Within the last part, a specific multiscale neuromuscular system model is introduced. The integrated system model combines existing models of the motor neuron pool, of the sensory system and of a multiscale model describing the mechanical behavior of skeletal muscles. Since many sub‐models are based on strictly biophysical modeling approaches, it closely represents the underlying physiological system and thus could be employed as starting point for further improvements and future developments. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models

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Simulation system of spinal cord motor nuclei and associated nerves and muscles, in a Web-based architecture

Abstract: A Web-based simulation system of the spinal cord circuitry responsible for muscle control is described.

Cited by 97 publications

References 110 publications

Web-based neuromuscular simulator applied to the teaching of principles of neuroscience

Web-based neuromuscular simulator applied to the teaching of principles of neuroscience

Coupled simulations and parameter inversion for neural system and electrophysiological muscle models

Multiscale modeling of the neuromuscular system: Coupling neurophysiology and skeletal muscle mechanics

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