Papers

Peer-reviewed
Aug, 2016

Three Gait Oscillations Switchable by a Single Parameter on Hard-Wired Central Pattern Generator Hardware Network

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES
  • Akihiro Maruyama
  • ,
  • Kentaro Tani
  • ,
  • Shigehito Tanahashi
  • ,
  • Atsuhiko Iijima
  • ,
  • Yoshinobu Maeda

Volume
E99A
Number
8
First page
1600
Last page
1608
Language
English
Publishing type
Research paper (scientific journal)
DOI
10.1587/transfun.E99.A.1600
Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG

We present a hard-wired central patter generator (CPG) hardware network that reproduces the periodic oscillations of the typical gaits, namely, walk, trot, and bound. Notably, the three gaits are generated by a single parameter, i.e., the battery voltage E-MLR, which acts like a signal from the midbrain's locomotor region. One CPG is composed of two types of hardware neuron models, reproducing neuronal bursting and beating (action potentials), and three types of hardware synapse models: a gap junction, excitatory and inhibitory synapses. When four hardware CPG models were coupled into a Z(4) symmetry network in a previous study [22], two neuronal oscillation patterns corresponding to four-legged animal gaits (walk and bound) were generated by manipulating a single control parameter. However, no more than two neuronal oscillation patterns have been stably observed on a hard-wired four-CPG hardware network. In the current study, we indicate that three neuronal oscillation patterns (walk, trot, and bound) can be generated by manipulating a single control parameter on a hard-wired eight-CPG (Z(4) x Z(2) symmetry) hardware network.

Link information
DOI
https://doi.org/10.1587/transfun.E99.A.1600
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000381564600014&DestApp=WOS_CPL
ID information
  • DOI : 10.1587/transfun.E99.A.1600
  • ISSN : 1745-1337
  • Web of Science ID : WOS:000381564600014

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