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[0] Carmena JM, Lebedev MA, Crist RE, O'Doherty JE, Santucci DM, Dimitrov DF, Patil PG, Henriquez CS, Nicolelis MA, Learning to control a brain-machine interface for reaching and grasping by primates.PLoS Biol 1:2, E42 (2003 Nov)

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ref: Carmena-2003.11 tags: Carmena nicolelis BMI learning 2003 date: 01-08-2012 18:53 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-14624244[0] Learning to control a brain-machine interface for reaching and grasping by primates.

  • strong focus on learning & reorganization.
  • Jose's first main paper.
  • focuses on two engineering / scientific questions: what signal to use, and how much of it, and from where.
    • As for where, of course we suggest that the representation is distributed.
  • Quality of predictions: gripping force > hand velocity > hand position.
  • Showed silent EMGs during BMI control.
  • Put a robot in the feedback path; this ammounted for some nonlinearities + 60-90ms delay.
  • Predictions follow anatomical expectation:
    • M1 (33-56 cells) predicts 73% variance for hand pos, 66% velocity, 83% for gripping force .
    • SMA (16-19 cells) 51% position, 51% velocity, 19% gripping force.
    • They need a table for this shiz.
  • Relatively high-quality predictions. (When I initially looked at the data, I was frustrated with the noise!)
  • Learning was associated with increased contribution of single units.
    • appeared to be more 'learning' in SMA.
    • Training on a position model seemed to increase the ctx representation of hand position.
  • changes between pole control and brain control:
    • 68% of of sampled neurons showed reduced tuning in BCWOH
    • 14% no change
    • 18% enhanced tuning.
  • Directional tuning curves clustered in a band during brain control -- neurons clustering around the first PC?
    • All cortical areas tested showed increases in correlated firing -- arousal?
    • this puts some movements into the nullspace of the Wiener matrix. Or does it? should have had the monkey make stereotyped movements to dissociate movement directions.
  • Knocks {334} in that:
    • preferred directions were derived not from actual movements, but from firing rates during target appearance time windows.
    • tuning strength could have increased simple because the movements became straighter with practice.
  • From Fetz, {329}: Interestingly, the conversion parameters obtained for one set of trials provided increasingly poor predictions of future responses, indicating a source of drift over tens of minutes in the open-loop condition. This problem was alleviated when the monkeys observed the consequences of their neural activity in ‘real time’ and could optimize cell activity to achieve the desired goal under ‘closed-loop’ conditions.

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