Format

Send to

Choose Destination
Front Neurorobot. 2015 Nov 25;9:13. doi: 10.3389/fnbot.2015.00013. eCollection 2015.

Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm.

Author information

1
Department of Physiology and Pharmacology, State University of New York Downstate Medical Center Brooklyn, NY, USA.
2
CFD Research Corporation Huntsville, AL, USA.
3
Department of Physiology and Pharmacology, State University of New York Downstate Medical Center Brooklyn, NY, USA ; The Robert Furchgott Center for Neural and Behavioral Science, State University of New York Downstate Medical Center Brooklyn, NY, USA ; Joint Graduate Program in Biomedical Engineering, State University of New York Downstate and Polytechnic Institute of New York University Brooklyn, NY, USA.
4
Department of Physiology and Pharmacology, State University of New York Downstate Medical Center Brooklyn, NY, USA ; The Robert Furchgott Center for Neural and Behavioral Science, State University of New York Downstate Medical Center Brooklyn, NY, USA ; Joint Graduate Program in Biomedical Engineering, State University of New York Downstate and Polytechnic Institute of New York University Brooklyn, NY, USA ; Department of Neurology, State University of New York Downstate Medical Center Brooklyn, NY, USA ; Department of Neurology, Kings County Hospital Center Brooklyn, NY, USA.

Abstract

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.

KEYWORDS:

biomimetic; musculoskeletal arm; neuroprosthetics; reaching; robot arm; sensorimotor; spiking network; virtual arm

Supplemental Content

Full text links

Icon for Frontiers Media SA Icon for PubMed Central Icon for ModelDB
Loading ...
Support Center