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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)

[0] Carmena JM, Lebedev MA, Henriquez CS, Nicolelis MA, Stable ensemble performance with single-neuron variability during reaching movements in primates.J Neurosci 25:46, 10712-6 (2005 Nov 16)

[0] Patil PG, Carmena JM, Nicolelis MA, Turner DA, Ensemble recordings of human subcortical neurons as a source of motor control signals for a brain-machine interface.Neurosurgery 55:1, 27-35; discussion 35-8 (2004 Jul)

[0] Sanchez J, Principe J, Carmena J, Lebedev M, Nicolelis MA, Simultaneus prediction of four kinematic variables for a brain-machine interface using a single recurrent neural network.Conf Proc IEEE Eng Med Biol Soc 7no Issue 5321-4 (2004)

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ref: -2012 tags: cortex striatum learning carmena costa basal ganglia date: 09-13-2019 18:30 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-22388818 Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills.

  • Trained a mouse to control an auditory cursor, as in Kipke's task {99}. Did not cite that paper, claimed it was 'novel'. oops.
  • Summed neuronal firing rate of groups of 2 or 4 M1 neurons.
  • Auditory feedback was essential for the operant learning.
    • One group increased the frequency with increased firing rate; the other decreased tone with increasing FR.
  • Specific deletion of striatal NMDA receptors impairs the ability to learn neuroprosthetic skills.
    • Hence, they argue, cortico-striatal plastciity is required to learn abstract skills, such as this tone to firing rate target acquisition task.
  • Controlled by recording EMG of the vibrissae + injection of lidocane into the whisker pad.
  • One reward was sucrose solution; the other was a food pellet. When the rat was satiated on one modality, they showed increased preference for the opposite reward during BMI control -- thereby demonstrating intentionality. Clever!.
  • Noticed pronounced oscillatory spike coupling, the coherence of which was increased in low-frequency bands in late learning relative to early learning (figure 3).
  • Genetic manipulations: knockin line that expresses Cre recombinase in both striatonigral and striatopallidal medium spiny neurons, crossed with mice carrying a floxed allele of the NMDAR1 gene.
    • These animals are relatively normal, and can learn to perform rapid sequential movements, but are unable to learn precise motor sequences.
    • Acute pharmacological blockade of NMDAR did not affect performance of the neuroprosthetic skill.
    • Hence the deficits in the transgenic mice are due to an inability to perform the skill.

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ref: Ganguly-2011.05 tags: Carmena 2011 reversible cortical networks learning indirect BMI date: 01-23-2013 18:54 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-21499255[0] Reversible large-scale modification of cortical networks during neuroprosthetic control.

  • Split the group of recorded motor neurons into direct (decoded and controls the BMI) and indirect (passive) neurons.
  • Both groups showed changes in neuronal tuning / PD.
    • More PD. Is there no better metric?
  • Monkeys performed manual control before (MC1) and after (MC2) BMI training.
    • The majority of neurons reverted back to original tuning after BC; c.f. [1]
  • Monkeys were trained to rapidly switch between manual and brain control; still showed substantial changes in PD.
  • 'Near' (on same electrode as direct neurons) and 'far' neurons (different electrode) showed similar changes in PD.
    • Modulation Depth in indirect neurons was less in BC than manual control.
  • Prove (pretty well) that motor cortex neuronal spiking can be dissociated from movement.
  • Indirect neurons showed decreased modulation depth (MD) -> perhaps this is to decrease interference with direct neurons.
  • Quote "Studies of operant conditioning of single neurons found that conconditioned adjacent neurons were largely correlated with the conditioned neurons".
    • Well, also: Fetz and Baker showed that you can condition neurons recorded on the same electrode to covary or inversely vary.
  • Contrast with studies of motor learning in different force fields, where there is a dramatic memory trace.
    • Possibly this is from proprioception activating the cerebellum?

Other notes:

  • Scale bars on the waveforms are incorrect for figure 1.
  • Same monkeys as [2]

____References____

[0] Ganguly K, Dimitrov DF, Wallis JD, Carmena JM, Reversible large-scale modification of cortical networks during neuroprosthetic control.Nat Neurosci 14:5, 662-7 (2011 May)
[1] Gandolfo F, Li C, Benda BJ, Schioppa CP, Bizzi E, Cortical correlates of learning in monkeys adapting to a new dynamical environment.Proc Natl Acad Sci U S A 97:5, 2259-63 (2000 Feb 29)
[2] Ganguly K, Carmena JM, Emergence of a stable cortical map for neuroprosthetic control.PLoS Biol 7:7, e1000153 (2009 Jul)

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ref: Ganguly-2009.07 tags: Ganguly Carmena 2009 stable neuroprosthetic BMI control learning kinarm date: 01-14-2012 21:07 gmt revision:4 [3] [2] [1] [0] [head]

PMID-19621062 Emergence of a stable cortical map for neuroprosthetic control.

  • Question: Are the neuronal adaptations evident in BMI control stable and stored like with skilled motor learning?
    • There is mixed evidence for stationary neuron -> behavior maps in motor cortex.
      • It remains unclear if the tuning relationship for M1 neurons are stable across time; if they are not stable, rather advanced adaptive algorithms will be required.
  • A stable representation did occur.
    • Small perturbations to the size of the neuronal ensemble or to the decoder could disrupt function.
    • Compare with {291} -- opposite result?
    • A second map could be learned after primary map was consolidated.
  • Used a Kinarm + Plexon, as usual.
    • Regressed linear decoder (Wiener filter) to shoulder and elbow angle.
  • Assessed waveform stability with PCA (+ amplitude) and ISI distribution (KS test).
  • Learning occurred over the course of 19 days; after about 8 days performance reached an asymptote.
    • Brain control trajectory to target became stereotyped over the course of training.
      • Stereotyped and curved -- they propose a balance of time to reach target and effort to enforce certain firing rate profiles.
    • Performance was good even at the beginning of a day -- hence motor maps could be recalled.
  • By analyzing neuron firing wrt idealized movement to target, the relationship between neuron & movement proved to be stable.
  • Tested to see if all neurons were required for accurate control by generating an online neuron dropping curve, in which a random # of units were omitted from the decoder.
    • Removal of 3 neurons (of 10 - 15) resulted in > 50% drop in accuracy.
  • Tried a shuffled decoder as well: this too could be learned in 3-8 days.
    • Shuffling was applied by permuting the neurons-to-lags mapping. Eg. the timecourse of the lags was not changed.
  • Also tried retraining the decoder (using manual control on a new day) -- performance dropped, then rapidly recovered when the original fixed decoder was reinstated.
    • This suggests that small but significant changes in the model weights (they do not analyze what) are sufficient for preventing an established cortical map from being transformed to a reliable control signal.
  • A fair bit of effort was put into making & correcting tuning curves, which is problematic as these are mostly determined by the decoder
    • Better idea would be to analyze the variance / noise properties wrt cursor trajectory?
  • Performance was about the same for smaller (10-15) and larger (41) unit ensembles.

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ref: Bossetti-2004.06 tags: Bossetti wolf Carmena Nicolelis latency wireless BMI recording date: 01-08-2012 21:16 gmt revision:2 [1] [0] [head]

IEEE-1300783 (pdf) Transmission latencies in a telemetry-linked brain-machine interface

  • quote: "examines the relationships between the ratio of output to average input bandwidth of an implanted device and transmission latency and required queue depth".
  • can use to explain why I decided on the fixed-bandwidth method. must measure the latency on my system .. how?
  • firing bursts results in high latencies in a variable-bandwidth queued system.
  • Tested in 32-neuron ensemble.
  • require output bandwidth / input bandwidth to be at least 4 to get sub-10ms max latency.

____References____

Bossetti, C.A. and Carmena, J.M. and Nicolelis, M.A.L. and Wolf, P.D. Transmission latencies in a telemetry-linked brain-machine interface Biomedical Engineering, IEEE Transactions on 51 6 919 -924 (2004.06)

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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.

____References____

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ref: Kim-2006.06 tags: Hyun Kim Carmena Nicolelis continuous shared control gripper BMI date: 01-06-2012 00:20 gmt revision:2 [1] [0] [head]

IEEE-1634510 (pdf) Continuous shared control for stabilizing reaching and grasping with brain-machine interfaces.

  • The pneumatic gripper for picking up objects.
  • 70% brain control, 30% sensor control optimal.
  • Talk about 20Hz nyquist frequency for fast human motor movements, versus the need to smooth and remove noise.
  • Method: proximity sensors
    • collision avoidance 'pain withdrawal'
    • 'infant palmar grasp reflex'
    • Potential field associated with these sensors to implement continuous shared control.
  • Not! online -- used Aurora's data.

____References____

Kim, H.K. and Biggs, J. and Schloerb, W. and Carmena, M. and Lebedev, M.A. and Nicolelis, M.A.L. and Srinivasan, M.A. Continuous shared control for stabilizing reaching and grasping with brain-machine interfaces Biomedical Engineering, IEEE Transactions on 53 6 1164 -1173 (2006)

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ref: Carmena-2005.11 tags: carmena BMI nicolelis single-unit variability 2005 date: 01-01-2012 17:31 gmt revision:2 [1] [0] [head]

PMID-16291944[0] Stable ensemble performance with single-neuron variability during reaching movements in primates.

  • correlation between the firing of single neurons and movement parameters was nonstationary over 30-60 minute recording sessions.
  • yet! you could get stable prediction of arm movements, suggesting that movement parameters are redundantly encoded.
  • this, in turn, implies that you do not need a stable recorded population for good predictions.
  • suggest that the variance itself could be a means of neuronal 'computation' or exploration based on perturbations.
    • later Carmena papers do not mention this.

____References____

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ref: Patil-2004.07 tags: BMI DBS Parag Patil Carmena Turner date: 12-14-2011 23:45 gmt revision:4 [3] [2] [1] [0] [head]

PMID-15214971[0] Ensemble recordings of human subcortical neurons as a source of motor control signals for a brain-machine interface

  • they do not show ISI or autocorrelations functions for any of the neurons. however, some of the continuous recordings look really *excellent*.
  • there are a lot of comments at the end of the paper, may which are quite intelligent & informative.
    • motor control becomes defocused in Parkinsons, e.g. too many motor units respond to a movement. This is reduced with the use of dopamine agonists like atrophine.
  • Parag Patil's Homepage

____References____

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ref: Sanchez-2004.01 tags: BMI nicolelis florida Carmena Principe date: 04-06-2007 21:02 gmt revision:3 [2] [1] [0] [head]

PMID-17271543[] http://hardm.ath.cx:88/pdf/sanchez2004.pdf

____References____