Show simple item record

dc.contributor.authorDura-Bernal, Salvador
dc.contributor.authorZhou, Xianlian
dc.contributor.authorNeymotin, Samuel A
dc.contributor.authorPrzekwas, Andrzej
dc.contributor.authorFrancis, Joseph T
dc.contributor.authorLytton, William W
dc.date.accessioned2023-04-10T17:16:59Z
dc.date.available2023-04-10T17:16:59Z
dc.date.issued2015-11-25
dc.identifier.citationDura-Bernal S, Zhou X, Neymotin SA, Przekwas A, Francis JT, Lytton WW. Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm. Front Neurorobot. 2015 Nov 25;9:13. doi: 10.3389/fnbot.2015.00013. PMID: 26635598; PMCID: PMC4658435.en_US
dc.identifier.issn1662-5218
dc.identifier.doi10.3389/fnbot.2015.00013
dc.identifier.pmid26635598
dc.identifier.urihttp://hdl.handle.net/20.500.12648/8577
dc.description.abstractEmbedding 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.
dc.language.isoenen_US
dc.relation.urlhttps://www.frontiersin.org/articles/10.3389/fnbot.2015.00013/fullen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectbiomimeticen_US
dc.subjectmusculoskeletal armen_US
dc.subjectneuroprostheticsen_US
dc.subjectreachingen_US
dc.subjectrobot armen_US
dc.subjectsensorimotoren_US
dc.subjectspiking networken_US
dc.subjectvirtual armen_US
dc.titleCortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm.en_US
dc.typeArticle/Reviewen_US
dc.source.journaltitleFrontiers in neuroroboticsen_US
dc.source.volume9
dc.source.beginpage13
dc.source.endpage
dc.source.countryUnited States
dc.source.countrySwitzerland
dc.description.versionVoRen_US
refterms.dateFOA2023-04-10T17:17:00Z
html.description.abstractEmbedding 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.
dc.description.institutionSUNY Downstateen_US
dc.description.departmentPhysiology and Pharmacologyen_US
dc.description.departmentNathan Kline Institute for Psychiatric Researchen_US
dc.description.degreelevelN/Aen_US
dc.identifier.journalFrontiers in neurorobotics


Files in this item

Thumbnail
Name:
fnbot-09-00013.pdf
Size:
4.434Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International