A Neuronal Model of Central Pattern Generator to Account for Natural Motion Variation

Reza Sharif Razavian, Naser Mehrabi, John McPhee

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

We have developed a simple mathematical model of the human motor control system, which can generate periodic motions in a musculoskeletal arm. Our motor control model is based on the idea of a central pattern generator (CPG), in which a small population of neurons generates periodic limb motion. The CPG model produces the motion based on a simple descending command - the desired frequency of motion. Furthermore, the CPG model is implemented by a spiking neuron model; as a result of the stochasticity in the neuron activities, the motion exhibits a certain level of variation similar to real human motion. Finally, because of the simple structure of the CPG model, it can generate the sophisticated muscle excitation commands much faster than optimization-based methods.

Original languageEnglish (US)
Article number021007
JournalJournal of Computational and Nonlinear Dynamics
Volume11
Issue number2
DOIs
StatePublished - Mar 1 2016
Externally publishedYes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mechanical Engineering
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'A Neuronal Model of Central Pattern Generator to Account for Natural Motion Variation'. Together they form a unique fingerprint.

Cite this