|Title||Interfacing a biomimetic model of sensorimotor cortex with a musculoskeletal model and a robotic arm|
|Publication Type||Conference Paper|
|Year of Publication||2013|
|Authors||Dura-Bernal, S., Zhou X., Chadderdon G. L., Przekwas A., & Lytton WW.|
|Conference Name||Society for Neuroscience 2013 (SFN '13)|
|Keywords||SFN, Society for Neuroscience|
Computational models are an essential tool for making sense of neurophysiological data and for inferring the complex dynamics underlying brain function. We have developed new systems to interface biomimetic models of the brain with prosthetic/robotic limbs. These systems provide a test-bed for neuroprosthetic rehabilitative solutions and medical devices for the repair and enhancement of neuronal networks. Additionally, utilizing biomimetic neuronal modeling in brain-machine interfaces offers the possibility for finer control of prosthetics, and the ability to better understand the brain's own electrical signals. The biomimetic approach presents challenges in integrating technologies across multiple hardware and software platforms, so that the different components can communicate in real-time. 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 to drive mirroring motion of a Barrett Technology WAM robotic arm, which sent back information on its joint positions. In order to improve the biological realism of the motor control system, we interposed a realistic musculoskeletal arm model between the biomimetic model and the robot arm. The virtual musculoskeletal arm model received input from the BMM signaling neural excitation for each muscle. It then fed back realistic limb position information, including muscle fiber length, tendon length, and joint angles. This realistic proprioceptive information was used to control the robotic arm, leading to more realistic movements, and was used in the reinforcement learning process of the BMM, leading to more realistic neural dynamics. 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.