Neuromusculoskeletal modeling with neural and finite element models (Volk et al, 2021)

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Accession:267184
"In this study, we present a predictive NMS model that uses an embedded neural architecture within a finite element (FE) framework to simulate muscle activation. A previously developed neuromuscular model of a motor neuron was embedded into a simple FE musculoskeletal model. Input stimulation profiles from literature were simulated in the FE NMS model to verify effective integration of the software platforms. Motor unit recruitment and rate coding capabilities of the model were evaluated. The integrated model reproduced previously published output muscle forces with an average error of 0.0435 N. The integrated model effectively demonstrated motor unit recruitment and rate coding in the physiological range based upon motor unit discharge rates and muscle force output."
Reference:
1 . Volk VL, Hamilton LD, Hue DR, Shelburne KB, Fitzpatrick CK (2021) Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling Scientific Reports 11:22983
Model Information (Click on a link to find other models with that property)
Model Type: Neuromuscular Junction;
Brain Region(s)/Organism: Spinal motoneuron;
Cell Type(s): Spinal cord motor neuron slow twitch;
Channel(s): I CAN; I K; I K,Ca; I Calcium; I Na,p;
Gap Junctions:
Receptor(s):
Gene(s):
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Simulation Environment: NEURON; NetPyNE; FORTRAN;
Model Concept(s): Multiscale; Rate-coding model neurons; Motor control;
Implementer(s):
Search NeuronDB for information about:  I Na,p; I K; I K,Ca; I CAN; I Calcium;
 
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