Understanding neuronal systems in movement control using Wiener/Volterra kernels: A dominant feature analysis

Xingjian Jing, David M. Simpson, Robert Allen, Philip L. Newland

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Although Volterra kernels have been extensively applied in modelling and analysis of biological systems, the relationship between the kernel characteristics and physiologically important features under study is still not revealed clearly. In this study, the link between Volterra kernels and dynamic response of neural systems which control animal movements was investigated and demonstrated using a dominant feature analysis. The new results show an effective but simplified method to use Volterra or Wiener kernels to understand and classify the neural systems which are responsible for the fundamental movements such as flexion and extension of animal limbs, and importantly demonstrate how the neuron pathways in locusts control joint activities of low and high frequency and perform fundamental joint movements such as position, velocity and acceleration. These results provide a useful insight into the nonlinear characteristics of neural systems in movement control and show a useful approach to the analysis of physiological systems using Volterra/Wiener kernels.
Original languageEnglish
Pages (from-to)220-232
Number of pages13
JournalJournal of Neuroscience Methods
Volume203
Issue number1
DOIs
Publication statusPublished - 15 Jan 2012

Keywords

  • Movement control
  • Neuronal systems
  • Volterra kernels

ASJC Scopus subject areas

  • Neuroscience(all)

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