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Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm

Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories...

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Autores principales: Dura-Bernal, Salvador, Zhou, Xianlian, Neymotin, Samuel A., Przekwas, Andrzej, Francis, Joseph T., Lytton, William W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4658435/
https://www.ncbi.nlm.nih.gov/pubmed/26635598
http://dx.doi.org/10.3389/fnbot.2015.00013
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author Dura-Bernal, Salvador
Zhou, Xianlian
Neymotin, Samuel A.
Przekwas, Andrzej
Francis, Joseph T.
Lytton, William W.
author_facet Dura-Bernal, Salvador
Zhou, Xianlian
Neymotin, Samuel A.
Przekwas, Andrzej
Francis, Joseph T.
Lytton, William W.
author_sort Dura-Bernal, Salvador
collection PubMed
description Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of limb prosthetics.
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spelling pubmed-46584352015-12-03 Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm Dura-Bernal, Salvador Zhou, Xianlian Neymotin, Samuel A. Przekwas, Andrzej Francis, Joseph T. Lytton, William W. Front Neurorobot Neuroscience Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of limb prosthetics. Frontiers Media S.A. 2015-11-25 /pmc/articles/PMC4658435/ /pubmed/26635598 http://dx.doi.org/10.3389/fnbot.2015.00013 Text en Copyright © 2015 Dura-Bernal, Zhou, Neymotin, Przekwas, Francis and Lytton. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Dura-Bernal, Salvador
Zhou, Xianlian
Neymotin, Samuel A.
Przekwas, Andrzej
Francis, Joseph T.
Lytton, William W.
Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm
title Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm
title_full Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm
title_fullStr Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm
title_full_unstemmed Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm
title_short Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm
title_sort cortical spiking network interfaced with virtual musculoskeletal arm and robotic arm
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4658435/
https://www.ncbi.nlm.nih.gov/pubmed/26635598
http://dx.doi.org/10.3389/fnbot.2015.00013
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