Modeling and identification of a realistic spiking neural network and musculoskeletal model of the human arm, and an application to the stretch reflex
This study develops a multi-level neuromuscular model consisting of topological pools of spiking motor, sensory and interneurons controlling a bi-muscular model of the human arm. The spiking output of motor neuron pools were used to drive muscle actions and skeletal movement via neuromuscular juncti...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
May 06, 2016
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| In: |
IEEE transactions on neural systems and rehabilitation engineering
Year: 2016, Volume: 24, Issue: 5, Pages: 591-602 |
| ISSN: | 1558-0210 |
| DOI: | 10.1109/TNSRE.2015.2478858 |
| Online Access: | Resolving-System, Volltext: https://doi.org/10.1109/TNSRE.2015.2478858
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| Author Notes: | Manish Sreenivasa, Member, IEEE, Ko Ayusawa, and Yoshihiko Nakamura, Fellow, IEEE |
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| 245 | 1 | 0 | |a Modeling and identification of a realistic spiking neural network and musculoskeletal model of the human arm, and an application to the stretch reflex |c Manish Sreenivasa, Member, IEEE, Ko Ayusawa, and Yoshihiko Nakamura, Fellow, IEEE |
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| 520 | |a This study develops a multi-level neuromuscular model consisting of topological pools of spiking motor, sensory and interneurons controlling a bi-muscular model of the human arm. The spiking output of motor neuron pools were used to drive muscle actions and skeletal movement via neuromuscular junctions. Feedback information from muscle spindles were relayed via monosynaptic excitatory and disynaptic inhibitory connections, to simulate spinal afferent pathways. Subject-specific model parameters were identified from human experiments by using inverse dynamics computations and optimization methods. The identified neuromuscular model was used to simulate the biceps stretch reflex and the results were compared to an independent dataset. The proposed model was able to track the recorded data and produce dynamically consistent neural spiking patterns, muscle forces and movement kinematics under varying conditions of external forces and co-contraction levels. This additional layer of detail in neuromuscular models has important relevance to the research communities of rehabilitation and clinical movement analysis by providing a mathematical approach to studying neuromuscular pathology. | ||
| 650 | 4 | |a Action Potentials | |
| 650 | 4 | |a Afferent Pathways | |
| 650 | 4 | |a Arm | |
| 650 | 4 | |a bicep stretch reflex | |
| 650 | 4 | |a bimuscular model | |
| 650 | 4 | |a bioelectric phenomena | |
| 650 | 4 | |a Biological systems modeling | |
| 650 | 4 | |a biomechanics | |
| 650 | 4 | |a clinical movement analysis | |
| 650 | 4 | |a cocontraction levels | |
| 650 | 4 | |a Computational modeling | |
| 650 | 4 | |a Computer Simulation | |
| 650 | 4 | |a disynaptic inhibitory connections | |
| 650 | 4 | |a dynamically consistent neural spiking patterns | |
| 650 | 4 | |a Efferent Pathways | |
| 650 | 4 | |a Force | |
| 650 | 4 | |a human arm | |
| 650 | 4 | |a Humans | |
| 650 | 4 | |a interneurons | |
| 650 | 4 | |a inverse dynamics computations | |
| 650 | 4 | |a kinematics | |
| 650 | 4 | |a Manganese | |
| 650 | 4 | |a mathematical approach | |
| 650 | 4 | |a Mathematical model | |
| 650 | 4 | |a medical rehabilitation | |
| 650 | 4 | |a Models, Neurological | |
| 650 | 4 | |a monosynaptic excitatory | |
| 650 | 4 | |a motor neuron pools | |
| 650 | 4 | |a Motor Neurons | |
| 650 | 4 | |a movement kinematics | |
| 650 | 4 | |a multilevel neuromuscular model | |
| 650 | 4 | |a muscle | |
| 650 | 4 | |a muscle actions | |
| 650 | 4 | |a Muscle Contraction | |
| 650 | 4 | |a muscle forces | |
| 650 | 4 | |a muscle spindles | |
| 650 | 4 | |a Muscle Strength | |
| 650 | 4 | |a Muscle, Skeletal | |
| 650 | 4 | |a musculoskeletal model | |
| 650 | 4 | |a Nerve Net | |
| 650 | 4 | |a neural engineering | |
| 650 | 4 | |a neural nets | |
| 650 | 4 | |a Neuromuscular | |
| 650 | 4 | |a Neuromuscular Junction | |
| 650 | 4 | |a neuromuscular junctions | |
| 650 | 4 | |a neuromuscular pathology | |
| 650 | 4 | |a Neurons | |
| 650 | 4 | |a neurophysiological parameter identification | |
| 650 | 4 | |a neurophysiology | |
| 650 | 4 | |a optimisation | |
| 650 | 4 | |a optimization methods | |
| 650 | 4 | |a realistic spiking neural network | |
| 650 | 4 | |a Reflex, Stretch | |
| 650 | 4 | |a Reproducibility of Results | |
| 650 | 4 | |a Sensitivity and Specificity | |
| 650 | 4 | |a sensory neuron | |
| 650 | 4 | |a skeletal movement | |
| 650 | 4 | |a spiking motor | |
| 650 | 4 | |a spinal afferent pathways | |
| 650 | 4 | |a Spinal Cord | |
| 650 | 4 | |a stretch reflex | |
| 650 | 4 | |a subject-specific model parameters | |
| 650 | 4 | |a Synaptic Transmission | |
| 650 | 4 | |a topological pools | |
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