Title
A limit-cycle self-organizing map architecture for stable arm control.
Abstract
Inspired by the oscillatory nature of cerebral cortex activity, we recently proposed and studied self-organizing maps (SOMs) based on limit cycle neural activity in an attempt to improve the information efficiency and robustness of conventional single-node, single-pattern representations. Here we explore for the first time the use of limit cycle SOMs to build a neural architecture that controls a robotic arm by solving inverse kinematics in reach-and-hold tasks. This multi-map architecture integrates open-loop and closed-loop controls that learn to self-organize oscillatory neural representations and to harness non-fixed-point neural activity even for fixed-point arm reaching tasks. We show through computer simulations that our architecture generalizes well, achieves accurate, fast, and smooth arm movements, and is robust in the face of arm perturbations, map damage, and variations of internal timing parameters controlling the flow of activity. A robotic implementation is evaluated successfully without further training, demonstrating for the first time that limit cycle maps can control a physical robot arm. We conclude that architectures based on limit cycle maps can be organized to function effectively as neural controllers.
Year
DOI
Venue
2017
10.1016/j.neunet.2016.10.005
Neural Networks
Keywords
Field
DocType
Limit cycle attractor,Neural architecture,Neural oscillation,Robotic arm control,Self-organizing map
Architecture,Robotic arm,Search engine,Inverse kinematics,Computer science,Self-organizing map,Limit cycle,Robustness (computer science),Artificial intelligence,Neural oscillation,Machine learning
Journal
Volume
Issue
ISSN
85
C
0893-6080
Citations 
PageRank 
References 
0
0.34
23
Authors
4
Name
Order
Citations
PageRank
Di-Wei Huang1978.38
Rodolphe J. Gentili2247.92
Garrett E Katz3124.32
James A. Reggia41000276.13