NDLI: Virtual musculoskeletal arm and robotic arm driven by a biomimetic model of sensorimotor cortex with reinforcement learning

Content Provider	IEEE Xplore Digital Library
Author	Dura-Bernal, S. Chadderdon, G.L. Neymotin, S.A. Xianlian Zhou Przekwas, A. Francis, J.T. Lytton, W.W.
Copyright Year	2013
Description	Author affiliation: Dept. of Physiol. & Pharmacology, State Univ. of New York Downstate Med. Center, New York, NY, USA (Dura-Bernal, S.; Chadderdon, G.L.; Neymotin, S.A.; Francis, J.T.; Lytton, W.W.) \|\| CFD Res. Corp., Huntsville, AL, USA (Xianlian Zhou; Przekwas, A.)
Abstract	Neocortical mechanisms of learning sensorimotor control involve a complex series of interactions at multiple levels, from synaptic mechanisms to network connectomics. We developed a model of sensory and motor cortex consisting of several hundred spiking model-neurons. A biomimetic model (BMM) was trained using spike-timing dependent reinforcement learning to drive a simple kinematic two-joint virtual arm in a motor task requiring convergence on a single target. After learning, networks demonstrated retention of behaviorally-relevant memories by utilizing proprioceptive information to perform reach-to-target from multiple starting positions. We utilized the output of this model to drive mirroring motion of a robotic arm. In order to improve the biological realism of the motor control system, we replaced the simple virtual arm model with a realistic virtual musculoskeletal arm which was interposed between the BMM and the robot arm. The virtual musculoskeletal arm received input from the BMM signaling neural excitation for each muscle. It then fed back realistic proprioceptive information, including muscle fiber length and joint angles, which were employed in the reinforcement learning process. The limb position information was also used to control the robotic arm, leading to more realistic movements. This work explores the use of reinforcement learning in a spiking model of sensorimotor cortex and how this is affected by the bidirectional interaction with the kinematics and dynamic constraints of a realistic musculoskeletal arm model. It also paves the way towards a full closed-loop biomimetic brain-effector system that can be incorporated in a neural decoder for prosthetic control, and used for developing biomimetic learning algorithms for controlling real-time devices. 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.
Starting Page	1
Ending Page	1
File Size	1946049
Page Count	1
File Format	PDF
ISBN	9781479930074
DOI	10.1109/SPMB.2013.6736768
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Sociology Learning (artificial intelligence) Muscles Brain modeling Robot sensing systems Joints
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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