Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm.
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Your vote was cast
Thank you for your feedback
Thank you for your feedback
Neymotin, Samuel A
Francis, Joseph T
Lytton, William W
Journal titleFrontiers in neurorobotics
Publication Begin page13
MetadataShow full item record
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.
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.
The following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International
- Towards a real-time interface between a biomimetic model of sensorimotor cortex and a robotic arm.
- Authors: Dura-Bernal S, Chadderdon GL, Neymotin SA, Francis JT, Lytton WW
- Issue date: 2014 Jan 15
- Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis.
- Authors: Dura-Bernal S, Neymotin SA, Kerr CC, Sivagnanam S, Majumdar A, Francis JT, Lytton WW
- Issue date: 2017 Mar-May
- Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm.
- Authors: Dura-Bernal S, Li K, Neymotin SA, Francis JT, Principe JC, Lytton WW
- Issue date: 2016
- Reinforcement learning of two-joint virtual arm reaching in a computer model of sensorimotor cortex.
- Authors: Neymotin SA, Chadderdon GL, Kerr CC, Francis JT, Lytton WW
- Issue date: 2013 Dec
- Adaptive robotic control driven by a versatile spiking cerebellar network.
- Authors: Casellato C, Antonietti A, Garrido JA, Carrillo RR, Luque NR, Ros E, Pedrocchi A, D'Angelo E
- Issue date: 2014