ArticlePattern recognition letters2014
Towards a real-time interface between a biomimetic model of sensorimotor cortex and a robotic arm.
Article in Pattern recognition letters, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Large time step discrete-time modeling of sharp wave activity in hippocampal area CA3.Communications in nonlinear science & numerical simulation · 2019Article
- Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in Neuroscience.Frontiers in neuroinformatics · 2018Review
- Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm.Frontiers in neuroscience · 2016Article
- Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control.Frontiers in neurorobotics · 2015Article
- Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm.Frontiers in neurorobotics · 2015Article
- Towards real-time communication betweenJournal of computational surgery · 2014Article
- Multiscale modeling for clinical translation in neuropsychiatric disease.Journal of computational surgeryArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Brain-machine interfaces can greatly improve the performance of prosthetics. Utilizing biomimetic neuronal modeling in brain machine interfaces (BMI) offers the possibility of providing naturalistic motor-control algorithms for control of a robotic limb. This will allow finer control of a robot, while also giving us new tools to better understand the brain's use of electrical signals. However, the biomimetic approach presents challenges in integrating technologies across multiple hardware and software platforms, so that the different components can communicate in real-time. We present the first steps in an ongoing effort to integrate a biomimetic spiking neuronal model of motor learning with a robotic arm. The biomimetic model (BMM) was used to drive a simple kinematic two-joint virtual arm in a motor task requiring trial-and-error convergence on a single target. We utilized the output of this model in real time to drive mirroring motion of a Barrett Technology WAM robotic arm through a user datagram protocol (UDP) interface. The robotic arm sent back information on its joint positions, which was then used by a visualization tool on the remote computer to display a realistic 3D virtual model of the moving robotic arm in real time. This work paves the way towards a full closed-loop biomimetic brain-effector system that can be incorporated in a neural decoder for prosthetic control, to be used as a platform for developing biomimetic learning algorithms for controlling real-time devices.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.