Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface.

Published

Journal Article

Monkeys can learn to directly control the movements of an artificial actuator by using a brain-machine interface (BMI) driven by the activity of a sample of cortical neurons. Eventually, they can do so without moving their limbs. Neuronal adaptations underlying the transition from control of the limb to control of the actuator are poorly understood. Here, we show that rapid modifications in neuronal representation of velocity of the hand and actuator occur in multiple cortical areas during the operation of a BMI. Initially, monkeys controlled the actuator by moving a hand-held pole. During this period, the BMI was trained to predict the actuator velocity. As the monkeys started using their cortical activity to control the actuator, the activity of individual neurons and neuronal populations became less representative of the animal's hand movements while representing the movements of the actuator. As a result of this adaptation, the animals could eventually stop moving their hands yet continue to control the actuator. These results show that, during BMI control, cortical ensembles represent behaviorally significant motor parameters, even if these are not associated with movements of the animal's own limb.

Full Text

Duke Authors

Cited Authors

  • Lebedev, MA; Carmena, JM; O'Doherty, JE; Zacksenhouse, M; Henriquez, CS; Principe, JC; Nicolelis, MAL

Published Date

  • May 11, 2005

Published In

Volume / Issue

  • 25 / 19

Start / End Page

  • 4681 - 4693

PubMed ID

  • 15888644

Pubmed Central ID

  • 15888644

Electronic International Standard Serial Number (EISSN)

  • 1529-2401

Digital Object Identifier (DOI)

  • 10.1523/JNEUROSCI.4088-04.2005

Language

  • eng

Conference Location

  • United States