Neuron selection and visual training for population vector based cortical control

R. Wahnoun, Stephen Helms Tillery, Jiping He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

We have developed a method for training animals to control artificial devices from cortical signals [1-4]. In this report we describe a series of experiments designed to parameterize a cortical control algorithm without an animal having to move its arm. Instead, a highly motivated animal observes as the computer drives a cursor move towards a set of targets once each in a center-out task. From the neuronal activity recorded in this visual following task, we compute preferred directions for the neurons. We find that the quality of fit in this early set of trials is highly predictive of each neuron's contribution to the overall cortical control.

Original languageEnglish (US)
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Pages4607-4610
Number of pages4
Volume26 VI
StatePublished - 2004
EventConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
Duration: Sep 1 2004Sep 5 2004

Other

OtherConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004
CountryUnited States
CitySan Francisco, CA
Period9/1/049/5/04

Keywords

  • Algorithm
  • Cortical coding
  • Cortical control
  • Motor cortex
  • Neuroprosthetics

ASJC Scopus subject areas

  • Bioengineering

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  • Cite this

    Wahnoun, R., Helms Tillery, S., & He, J. (2004). Neuron selection and visual training for population vector based cortical control. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (Vol. 26 VI, pp. 4607-4610)