m8ta
use https for features.
text: sort by
tags: modified
type: chronology
{847} is owned by tlh24.
[0] Gage GJ, Ludwig KA, Otto KJ, Ionides EL, Kipke DR, Naive coadaptive cortical control.J Neural Eng 2:2, 52-63 (2005 Jun)

[0] Jackson A, Mavoori J, Fetz EE, Correlations between the same motor cortex cells and arm muscles during a trained task, free behavior, and natural sleep in the macaque monkey.J Neurophysiol 97:1, 360-74 (2007 Jan)

[0] Isoda M, Hikosaka O, Role for subthalamic nucleus neurons in switching from automatic to controlled eye movement.J Neurosci 28:28, 7209-18 (2008 Jul 9)

[0] Evarts EV, Relation of pyramidal tract activity to force exerted during voluntary movement.J Neurophysiol 31:1, 14-27 (1968 Jan)

[0] Ashe J, Force and the motor cortex.Behav Brain Res 87:2, 255-69 (1997 Sep)[1] Cabel DW, Cisek P, Scott SH, Neural activity in primary motor cortex related to mechanical loads applied to the shoulder and elbow during a postural task.J Neurophysiol 86:4, 2102-8 (2001 Oct)[2] Cheney PD, Fetz EE, Functional classes of primate corticomotoneuronal cells and their relation to active force.J Neurophysiol 44:4, 773-91 (1980 Oct)[3] Evarts EV, Relation of pyramidal tract activity to force exerted during voluntary movement.J Neurophysiol 31:1, 14-27 (1968 Jan)[4] Evarts EV, Activity of pyramidal tract neurons during postural fixation.J Neurophysiol 32:3, 375-85 (1969 May)[5] Humphrey DR, Schmidt EM, Thompson WD, Predicting measures of motor performance from multiple cortical spike trains.Science 170:959, 758-62 (1970 Nov 13)[6] Thach WT, Correlation of neural discharge with pattern and force of muscular activity, joint position, and direction of intended next movement in motor cortex and cerebellum.J Neurophysiol 41:3, 654-76 (1978 May)[7] Wetts R, Kalaska JF, Smith AM, Cerebellar nuclear cell activity during antagonist cocontraction and reciprocal inhibition of forearm muscles.J Neurophysiol 54:2, 231-44 (1985 Aug)[8] Georgopoulos AP, Ashe J, Smyrnis N, Taira M, The motor cortex and the coding of force.Science 256:5064, 1692-5 (1992 Jun 19)[9] Kalaska JF, Cohen DA, Hyde ML, Prud'homme M, A comparison of movement direction-related versus load direction-related activity in primate motor cortex, using a two-dimensional reaching task.J Neurosci 9:6, 2080-102 (1989 Jun)[10] Li CS, Padoa-Schioppa C, Bizzi E, Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field.Neuron 30:2, 593-607 (2001 May)[11] Sergio LE, Kalaska JF, Systematic changes in directional tuning of motor cortex cell activity with hand location in the workspace during generation of static isometric forces in constant spatial directions.J Neurophysiol 78:2, 1170-4 (1997 Aug)[12] Sergio LE, Kalaska JF, Systematic changes in motor cortex cell activity with arm posture during directional isometric force generation.J Neurophysiol 89:1, 212-28 (2003 Jan)[13] Taira M, Boline J, Smyrnis N, Georgopoulos AP, Ashe J, On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional static isometric force.Exp Brain Res 109:3, 367-76 (1996 Jun)[14] Maier MA, Bennett KM, Hepp-Reymond MC, Lemon RN, Contribution of the monkey corticomotoneuronal system to the control of force in precision grip.J Neurophysiol 69:3, 772-85 (1993 Mar)[15] Hepp-Reymond M, Kirkpatrick-Tanner M, Gabernet L, Qi HX, Weber B, Context-dependent force coding in motor and premotor cortical areas.Exp Brain Res 128:1-2, 123-33 (1999 Sep)[16] Smith AM, Hepp-Reymond MC, Wyss UR, Relation of activity in precentral cortical neurons to force and rate of force change during isometric contractions of finger muscles.Exp Brain Res 23:3, 315-32 (1975 Sep 29)[17] Cooke JD, Brown SH, Movement-related phasic muscle activation. II. Generation and functional role of the triphasic pattern.J Neurophysiol 63:3, 465-72 (1990 Mar)[18] Almeida GL, Hong DA, Corcos D, Gottlieb GL, Organizing principles for voluntary movement: extending single-joint rules.J Neurophysiol 74:4, 1374-81 (1995 Oct)[19] Gottlieb GL, Latash ML, Corcos DM, Liubinskas TJ, Agarwal GC, Organizing principles for single joint movements: V. Agonist-antagonist interactions.J Neurophysiol 67:6, 1417-27 (1992 Jun)[20] Corcos DM, Agarwal GC, Flaherty BP, Gottlieb GL, Organizing principles for single-joint movements. IV. Implications for isometric contractions.J Neurophysiol 64:3, 1033-42 (1990 Sep)[21] Gottlieb GL, Corcos DM, Agarwal GC, Latash ML, Organizing principles for single joint movements. III. Speed-insensitive strategy as a default.J Neurophysiol 63:3, 625-36 (1990 Mar)[22] Corcos DM, Gottlieb GL, Agarwal GC, Organizing principles for single-joint movements. II. A speed-sensitive strategy.J Neurophysiol 62:2, 358-68 (1989 Aug)[23] Gottlieb GL, Corcos DM, Agarwal GC, Organizing principles for single-joint movements. I. A speed-insensitive strategy.J Neurophysiol 62:2, 342-57 (1989 Aug)[24] Ghez C, Gordon J, Trajectory control in targeted force impulses. I. Role of opposing muscles.Exp Brain Res 67:2, 225-40 (1987)[25] Sainburg RL, Ghez C, Kalakanis D, Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms.J Neurophysiol 81:3, 1045-56 (1999 Mar)[26] Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT, On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex.J Neurosci 2:11, 1527-37 (1982 Nov)[27] Ashe J, Georgopoulos AP, Movement parameters and neural activity in motor cortex and area 5.Cereb Cortex 4:6, 590-600 (1994 Nov-Dec)[28] Caminiti R, Johnson PB, Urbano A, Making arm movements within different parts of space: dynamic aspects in the primate motor cortex.J Neurosci 10:7, 2039-58 (1990 Jul)[29] Caminiti R, Johnson PB, Galli C, Ferraina S, Burnod Y, Making arm movements within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets.J Neurosci 11:5, 1182-97 (1991 May)[30] Matsuzaka Y, Picard N, Strick PL, Skill representation in the primary motor cortex after long-term practice.J Neurophysiol 97:2, 1819-32 (2007 Feb)[31] Fu QG, Suarez JI, Ebner TJ, Neuronal specification of direction and distance during reaching movements in the superior precentral premotor area and primary motor cortex of monkeys.J Neurophysiol 70:5, 2097-116 (1993 Nov)

[0] BASMAJIAN JV, Control and training of individual motor units.Science 141no Issue 440-1 (1963 Aug 2)

[0] Evarts EV, Activity of pyramidal tract neurons during postural fixation.J Neurophysiol 32:3, 375-85 (1969 May)[1] Evarts EV, Relation of pyramidal tract activity to force exerted during voluntary movement.J Neurophysiol 31:1, 14-27 (1968 Jan)

[0] Fetz EE, Perlmutter SI, Prut Y, Functions of mammalian spinal interneurons during movement.Curr Opin Neurobiol 10:6, 699-707 (2000 Dec)

[0] Matsuzaka Y, Picard N, Strick PL, Skill representation in the primary motor cortex after long-term practice.J Neurophysiol 97:2, 1819-32 (2007 Feb)

[0] Radhakrishnan SM, Baker SN, Jackson A, Learning a novel myoelectric-controlled interface task.J Neurophysiol no Volume no Issue no Pages (2008 Jul 30)

[0] Kawato M, Internal models for motor control and trajectory planning.Curr Opin Neurobiol 9:6, 718-27 (1999 Dec)

[0] Scott SH, Optimal feedback control and the neural basis of volitional motor control.Nat Rev Neurosci 5:7, 532-46 (2004 Jul)

[0] Chan SS, Moran DW, Computational model of a primate arm: from hand position to joint angles, joint torques and muscle forces.J Neural Eng 3:4, 327-37 (2006 Dec)

[0] Sergio LE, Hamel-Paquet C, Kalaska JF, Motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks.J Neurophysiol 94:4, 2353-78 (2005 Oct)[1] Hatsopoulos NG, Encoding in the motor cortex: was evarts right after all? Focus on "motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks".J Neurophysiol 94:4, 2261-2 (2005 Oct)[2] Cooke JD, Brown SH, Movement-related phasic muscle activation. II. Generation and functional role of the triphasic pattern.J Neurophysiol 63:3, 465-72 (1990 Mar)[3] Almeida GL, Hong DA, Corcos D, Gottlieb GL, Organizing principles for voluntary movement: extending single-joint rules.J Neurophysiol 74:4, 1374-81 (1995 Oct)[4] Gottlieb GL, Latash ML, Corcos DM, Liubinskas TJ, Agarwal GC, Organizing principles for single joint movements: V. Agonist-antagonist interactions.J Neurophysiol 67:6, 1417-27 (1992 Jun)[5] Corcos DM, Agarwal GC, Flaherty BP, Gottlieb GL, Organizing principles for single-joint movements. IV. Implications for isometric contractions.J Neurophysiol 64:3, 1033-42 (1990 Sep)[6] Gottlieb GL, Corcos DM, Agarwal GC, Latash ML, Organizing principles for single joint movements. III. Speed-insensitive strategy as a default.J Neurophysiol 63:3, 625-36 (1990 Mar)[7] Corcos DM, Gottlieb GL, Agarwal GC, Organizing principles for single-joint movements. II. A speed-sensitive strategy.J Neurophysiol 62:2, 358-68 (1989 Aug)[8] Gottlieb GL, Corcos DM, Agarwal GC, Organizing principles for single-joint movements. I. A speed-insensitive strategy.J Neurophysiol 62:2, 342-57 (1989 Aug)[9] Ghez C, Gordon J, Trajectory control in targeted force impulses. I. Role of opposing muscles.Exp Brain Res 67:2, 225-40 (1987)[10] Sainburg RL, Ghez C, Kalakanis D, Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms.J Neurophysiol 81:3, 1045-56 (1999 Mar)

[0] Hatsopoulos NG, Encoding in the motor cortex: was evarts right after all? Focus on "motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks".J Neurophysiol 94:4, 2261-2 (2005 Oct)

[0] Ashe J, Georgopoulos AP, Movement parameters and neural activity in motor cortex and area 5.Cereb Cortex 4:6, 590-600 (1994 Nov-Dec)

[0] Maier MA, Bennett KM, Hepp-Reymond MC, Lemon RN, Contribution of the monkey corticomotoneuronal system to the control of force in precision grip.J Neurophysiol 69:3, 772-85 (1993 Mar)[1] Smith AM, Hepp-Reymond MC, Wyss UR, Relation of activity in precentral cortical neurons to force and rate of force change during isometric contractions of finger muscles.Exp Brain Res 23:3, 315-32 (1975 Sep 29)

[0] Hepp-Reymond M, Kirkpatrick-Tanner M, Gabernet L, Qi HX, Weber B, Context-dependent force coding in motor and premotor cortical areas.Exp Brain Res 128:1-2, 123-33 (1999 Sep)

[0] Caminiti R, Johnson PB, Galli C, Ferraina S, Burnod Y, Making arm movements within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets.J Neurosci 11:5, 1182-97 (1991 May)

[0] Caminiti R, Johnson PB, Urbano A, Making arm movements within different parts of space: dynamic aspects in the primate motor cortex.J Neurosci 10:7, 2039-58 (1990 Jul)[1] Caminiti R, Johnson PB, Galli C, Ferraina S, Burnod Y, Making arm movements within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets.J Neurosci 11:5, 1182-97 (1991 May)

[0] Kalaska JF, Cohen DA, Hyde ML, Prud'homme M, A comparison of movement direction-related versus load direction-related activity in primate motor cortex, using a two-dimensional reaching task.J Neurosci 9:6, 2080-102 (1989 Jun)

[0] Georgopoulos AP, Ashe J, Smyrnis N, Taira M, The motor cortex and the coding of force.Science 256:5064, 1692-5 (1992 Jun 19)

[0] Wetts R, Kalaska JF, Smith AM, Cerebellar nuclear cell activity during antagonist cocontraction and reciprocal inhibition of forearm muscles.J Neurophysiol 54:2, 231-44 (1985 Aug)

[0] Thach WT, Correlation of neural discharge with pattern and force of muscular activity, joint position, and direction of intended next movement in motor cortex and cerebellum.J Neurophysiol 41:3, 654-76 (1978 May)

[0] Taira M, Boline J, Smyrnis N, Georgopoulos AP, Ashe J, On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional static isometric force.Exp Brain Res 109:3, 367-76 (1996 Jun)

[0] DeLong MR, Strick PL, Relation of basal ganglia, cerebellum, and motor cortex units to ramp and ballistic limb movements.Brain Res 71:2-3, 327-35 (1974 May 17)

[0] Ashe J, Force and the motor cortex.Behav Brain Res 87:2, 255-69 (1997 Sep)

[0] Kettner RE, Schwartz AB, Georgopoulos AP, Primate motor cortex and free arm movements to visual targets in three-dimensional space. III. Positional gradients and population coding of movement direction from various movement origins.J Neurosci 8:8, 2938-47 (1988 Aug)[1] Georgopoulos AP, Kettner RE, Schwartz AB, Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population.J Neurosci 8:8, 2928-37 (1988 Aug)[2] Schwartz AB, Kettner RE, Georgopoulos AP, Primate motor cortex and free arm movements to visual targets in three-dimensional space. I. Relations between single cell discharge and direction of movement.J Neurosci 8:8, 2913-27 (1988 Aug)[3] Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT, On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex.J Neurosci 2:11, 1527-37 (1982 Nov)

[0] Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT, On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex.J Neurosci 2:11, 1527-37 (1982 Nov)

[0] Ostry DJ, Feldman AG, A critical evaluation of the force control hypothesis in motor control.Exp Brain Res 153:3, 275-88 (2003 Dec)

[0] Cabel DW, Cisek P, Scott SH, Neural activity in primary motor cortex related to mechanical loads applied to the shoulder and elbow during a postural task.J Neurophysiol 86:4, 2102-8 (2001 Oct)

[0] Harris CM, Wolpert DM, Signal-dependent noise determines motor planning.Nature 394:6695, 780-4 (1998 Aug 20)

{99}
hide / / print
ref: Gage-2005.06 tags: naive coadaptive control Kalman filter Kipke audio BMI date: 09-13-2019 02:33 gmt revision:2 [1] [0] [head]

PMID-15928412[0] Naive coadaptive Control May 2005. see notes

____References____

{331}
hide / / print
ref: Jackson-2007.01 tags: Fetz neurochip sleep motor control BMI free behavior EMG date: 09-13-2019 02:21 gmt revision:4 [3] [2] [1] [0] [head]

PMID-17021028[0] Correlations Between the Same Motor Cortex Cells and Arm Muscles During a Trained Task, Free Behavior, and Natural Sleep in the Macaque Monkey

  • used their implanted "neurochip" recorder that recorded both EMG and neural activity. The neurochip buffers data and transmits via IR offline. It doesn't have all that much flash onboard - 16Mb.
    • used teflon-insulated 50um tungsten wires.
  • confirmed that there is a strong causal relationship, constant over the course of weeks, between motor cortex units and EMG activity.
    • some causal relationships between neural firing and EMG varied dependent on the task. Additive / multiplicative encoding?
  • this relationship was different at night, during REM sleep, though (?)
  • point out, as Todorov did, that Stereotyped motion imposes correlation between movement parameters, which could lead to spurrious relationships being mistaken for neural coding.
    • Experiments with naturalistic movement are essential for understanding innate, untrained neural control.
  • references {597} Suner et al 2005 as a previous study of long term cortical recordings. (utah probe)
  • during sleep, M1 cells exhibited a cyclical patter on quiescence followed by periods of elevated activity;
    • the cycle lasted 40-60 minutes;
    • EMG activity was seen at entrance and exit to the elevated activity period.
    • during periods of highest cortical activity, muscle activity was completely suppressed.
    • peak firing rates were above 100hz! (mean: 12-16hz).

____References____

{1211}
hide / / print
ref: Harris-1998.08 tags: noise wolpert harris motor planning Fitt velocity variance control theory date: 01-27-2013 22:33 gmt revision:1 [0] [head]

PMID-9723616[0] Signal-dependent noise determines motor planning.

  • We present a unifying theory of eye and arm movements based on the single physiological assumption that the neural control signals are corrupted by noise whose variance increases with the size of the control signal
    • Poisson noise? (I have not read the article -- storing here for future reference.)
  • This minimum-variance theory accurately predicts the trajectories of both saccades and arm movements and the speed-accuracy trade-off described by Fitt's law.

____References____

[0] Harris CM, Wolpert DM, Signal-dependent noise determines motor planning.Nature 394:6695, 780-4 (1998 Aug 20)

{232}
hide / / print
ref: Prescott-2006.01 tags: basal_ganglia action selection motor control robot date: 03-01-2012 17:56 gmt revision:4 [3] [2] [1] [0] [head]

PMID-16153803[0] The robot basal ganglia: action selection by an embedded model of the basal ganglia

  • they implemented a model of the basal ganglia in a robot. The model switches between competing (hypothetical) actions based on input salience. There are only a possible actions in their robot.
  • they reiterate the common conception that the basal ganglia are implicated in action selection: what to do next ( also mentioned are other functions - perception and cognition working memory and many other aspects of motor function. )
  • huh, interesting : cognitive psychologists have discovered that when an observable system has more than three interacting parts, it becomes very difficult for human minds to predict accurately how that system will change over time. (!!!) I dig disclaimers like this.
    • therefore, very limited understanding can be gleaned from informal, box and arrow style models.
      • I think the same is true of many biological analysis - including analysis of the immune and nervous systems - it needs to be at a much higher level of quantification
    • they also say that a model must be validated by placing it within the entire behavioral system.
  • the basal ganglia seem to be suitable for switching between competing channels & providing the required clean selection of a winner.
    • (1) striatal cells have up and down states, and can only switch between them with heavy coincident inputs.
    • (2) selective local inhibition between channels.
    • (3) dopamine innervation D1 = exitation; D2 = inhibition. I never really got how this enters their model; figure 1 seems like it would describe it, but it needs more math :)
    • (4) feedforward off-center, on surround network. they ref some other work..
      • I still don't feel like their explanation is the best (they use kinda wishy-washy terms) - though it is a step in the right direction.
  • people with schizophrenia sometimes switch cognitive focus rapidly; schizo is though to be due to a dopamine imbalance. Same problem with ADD.
    • treatment for ADD: amphetamine (blocks monoamine transporter, increases extracellular concentration of DA), ritalin. Both allow for heightened concentration: once you select a task, you stick with 'it' (the thought / prediction pathway) for longer. Dopamine is definintely involved in action selection, duhh.
    • their model supports this behavior: If the tonic dopamine level is very low, the robot has difficulty initiating actions; if the DA level is high, then it tends to select more than one action at the same time. (wait.. this implies that DA is too high in people with ADD? what? perhaps this is a consequence of the two different types of DA receptors? )
  • (...) basal ganglia - thalamo-cotrical loops my act to provide a positive feedback pathway that can maintain appropriate level of salience to selected behavior.
  • much of the input to the basal ganglia comprises collateral fibers from motor regions that project to the spinal cord and brainstem structures.
    • activity changes in the BG occur slightly after the beginning of EMG activity (good evidence!) Such signals may be important for controlling the maintenance and termination of selected behavior.

My thoughts:

  • what if the STN is involved in controlling the stability of neuronal activity - that is, preventing motor feedback instability by knocking down the gain. (whereas the cerebellum is involved in the balance and coordination interpretations of stability)
    • Normally, the human motor system is very stable, but when you lack dopamine innervation, you both cannot move (become very rigid) & have tremor (an inability to control cyclical oscillations).
      • That is, perhaps oscillation is due to a intrinsic inability to modulate gain.
      • more likely it is a manifestation/symptom of pathological activity in the control loop.

____References____

[0] Prescott TJ, Montes González FM, Gurney K, Humphries MD, Redgrave P, A robot model of the basal ganglia: behavior and intrinsic processing.Neural Netw 19:1, 31-61 (2006 Jan)

{1138}
hide / / print
ref: -0 tags: Romo basal ganglia movement control date: 02-24-2012 19:50 gmt revision:2 [1] [0] [head]

PMID-1483512 Role of the primate basal ganglia and frontal cortex in the internal generation of movements. I. Preparatory activity in the anterior striatum

  • Recorded from the head of the audate and rostral putamen.
  • Both spontaneous and cued / delayed-reward tasks.
  • Observed responses:
    • transient responses to cue, (2x as many to 'go' as 'nogo' cues)
    • sustained activity preceding the trigger stimulus or movement onset
      • Often this was ramp-like, indicating some sort of preparatory activity.
      • This could last 2-35 seconds, depending on the task, with a maximum of 80 s.
  • Premovement activity began 0.5-5.0s before movement onset (median 1 second).
    • Unrelated to saccadic eye movements.
    • 2/3 of these neurons were active only in spontaneous movements, and not in cued movements.
    • This is similar to activity in the frontal cortex; hence both are involved in preparing actions.

PMID-1483513 Role of primate basal ganglia and frontal cortex in the internal generation of movements. II. Movement-related activity in the anterior striatum.

  • Same experiments and recordings as above.
  • Time-locked responses to trigger, 60ms latency, independent of modality.
  • 44 neurons increased their activity before earlier EMG
  • 55 were activated with the movement,
  • 50 neurons were activated after movement onset.
  • I'm not entirely sure how this is different from above. (?)

{1038}
hide / / print
ref: Lempka-2010.12 tags: DBS current control PD date: 02-22-2012 18:25 gmt revision:2 [1] [0] [head]

PMID-20493764[0] Current-controlled deep brain stimulation reduces in vivo voltage fluctuations observed during voltage-controlled stimulation.

  • Obervation: DBS electrodes show impedance whcih varies with time and stimulation.
  • Current control may reduce the need for physicians to carefully adjust the stimulation parameters in the clinic.
  • Why did this take so long? It is a relatively obvious improvement. Perhaps efficiency -- voltage control allows longer battery life?

____References____

[0] Lempka SF, Johnson MD, Miocinovic S, Vitek JL, McIntyre CC, Current-controlled deep brain stimulation reduces in vivo voltage fluctuations observed during voltage-controlled stimulation.Clin Neurophysiol 121:12, 2128-33 (2010 Dec)

{1080}
hide / / print
ref: RodriguezOroz-2011.01 tags: DBS dopamine impulse control spain pamplona ventral beta date: 02-22-2012 17:02 gmt revision:9 [8] [7] [6] [5] [4] [3] [head]

PMID-21059746[0] Involvement of the subthalamic nucleus in impulse control disorders associated with Parkinson’s disease

  • recorded LFP in the STN of 28 patients.
    • of these 10 had impulse control disorders, 9 had dyskinesias, and 9 had no complications.
  • compared ON and OFF medication.
  • no difference between groups in off states.
  • large differences in ON states.
    • Impulse control problems: theta-alpha activity(4-10 Hz) 6 Hz mean.
      • Larger coherence with frontal regions 4-7.5 Hz.
    • Dyskinesias: higher frequency theta-alpha 8 Hz mean.
      • Higher coherence with motor areas, 7.5 - 10Hz.
    • No problems: no noticeable LFP oscillations (?).
  • PD patients often have side-effects of Punding and hobbyism.
    • Does meth treat PD? Selegiline does. Fascinating history there regarding combining MAOI + amphetamine --> effective PD drug.
    • Why does both meth and levodopa induce impulsivity?
    • Some of the other effects of L-DOPA treatment: hypersexuality, manic behavior or shopping.
    • Lesion of the subthalamic nucleus by infarction or tumor is associated with behavioral alterations including agitation, manic states and logorrhoea, with or without hemiballismus.
  • In some patients with ICD (impulse control disorders) induced by subthalamic nucleus deep brain stimulation, the abnormal behavior was provoked by stimulation with a ventral contact and suppressed by switching it off. (dorsal region is more motor).
    • In three patients with ICD, stimulation through the ventral contact induced a euphoric state -- PPN?
  • STN recordings from rats and monkeys modify their frequency in response to reward related tasks (Aron and Poldrack 2006); in humans the STN is active during an inhibition task (LI et al 2008).
  • LFP recordings from the treatment electrode were very low! 16uV.
  • Typical results show large differences between ON and OFF: ON show more activity > 60 Hz, OFF more < 60 Hz (Brown et al 2001; Brown 2003 Gatev et al 2006).
  • LFP recordings in PD patients from the STN showed that emotional stimulus led to a decrease in alpha power in the ventral contacts (Brucke et al 2007), whereas active movement led to a decrease in the beta power recorded in the dorsal subthalamic nucleus (Alegre et al 2005).
  • Original work on STN mediating impulsivity: Delong 1983 PMID-6422317 The neurophysiologic basis of abnormal movements in basal ganglia disorders.
    • Single cell studies in the basal ganglia of behaving animals have revealed specific relations of neuronal activity to movements of individual body parts and a relation to specific parameters of movement, particularly direction, amplitude, and velocity. (no fulltext available).

____References____

[0] Rodriguez-Oroz MC, López-Azcárate J, Garcia-Garcia D, Alegre M, Toledo J, Valencia M, Guridi J, Artieda J, Obeso JA, Involvement of the subthalamic nucleus in impulse control disorders associated with Parkinson's disease.Brain 134:Pt 1, 36-49 (2011 Jan)

{654}
hide / / print
ref: Isoda-2008.07 tags: STN switching motor control scaccades monkeys electrophysiology DBS date: 02-22-2012 15:02 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-18614691[0] Role for subthalamic nucleus neurons in switching from automatic to controlled eye movement.

  • we found neurons that showed a phasic change in activity specifically before volitionally controlled saccades which were switched from automatic saccades
  • A majority of switch-related neurons were considered to inhibit no-longer-valid automatic processes, and the inhibition started early enough to enable the animal to switch.
  • We suggest that the STN mediates the control signal originated from the medial frontal cortex and implements the behavioral switching function using its connections with other basal ganglia nuclei and the superior colliculus.
  • neurons have a really high rate of spiking - what we observe in DBS surgeries.
  • nice. There may be alternate explanations, but this one is plausible.

____References____

{12}
hide / / print
ref: Breit-2006.1 tags: parkinsons basal_ganglia palladium substantia_nigra motor_control striate date: 01-24-2012 22:10 gmt revision:1 [0] [head]

I wish i could remember where i got these notes from, so as to verify the somewhat controversial statements. I found them written on the back of a piece of scrap paper.

  • neurophysiological recordings in animals show that over half of basal ganglia neurons fire in response to motor activity but none are triggered by passive limb movement.
  • in parkinson's disease (PD), the substantia nigra actually becomes pale to the eye.
  • stimulation of the striatum does not result in low-threshold movements like stimulation of the cortex does.
  • palladium does not seem linked to motor planning. (just execution?)
  • stimulation of the caudate causes movement, i.e. head turning, while stimulation of the ventromedial caudate produces arrest and crouching movements. (Delgado etc)
  • large bilateral striatal leasions cause inattention.
  • striatal units appear to signal movement, not generate/compute it (really?)
  • in parkinson's disease, motor learning appears normal - it is the initial slowness that is abnormal :: PD relates to the quality of movement, not the quality of the motor commands. Thus, perhaps PD is a disease of gating/attention?
  • in PD, all reflexes except the Hoffman-reflex appear normal.
    • The primary difference between the H-reflex and the spinal stretch reflex is that the H-reflex bypasses the muscle spindle and, therefore, is a valuable tool in assessing modulation of monosynaptic reflex activity in the spinal cord. The H-reflex is an estimate of alpha motoneuron ( alphaalpha MN) excitability when presynaptic inhibition and intrinsic excitability of the alphaalpha MNs remain constant.
  • A lesion of the PPN (pedunculo pontine nucleus) was shown to restore decreased activity levels in the SNr and STN of a rat model of parkinson's (lesion of the SNc) PMID-17042796

{1067}
hide / / print
ref: Brown-2001.12 tags: EMG ECoG motor control human coherence dopamine oscillations date: 01-19-2012 21:41 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-11765129[0] Cortical network resonance and motor activity in humans.

  • good review.
  • No coherence between ECoG and eMG below 12 Hz; frequency coherence around 18 Hz.
    • This seen only in high-resolution ECoG; lower resolution signals blurs the sharp peak.
  • Striking narrowband frequency of coherence.
  • ECoG - ECoG coherence not at same frequency as EMG-ECoG.
  • Marked task-dependence of these coherences, e.g. for wrist extension and flexion they observed similar EMG/ECoG coherences; for different tasks using the same muscles, different patterns of coherence.
  • Pyramidal cell discharge tends to be phase-locked to oscillations in the local field potential (Murthy and Fetz 1996)
    • All synchronization must ultimately be through spikes, as LFPs are not transmitted down the spinal cord.
  • Broadband coherence is pathological // they note it occurred during cortical myclonus (box 2)
  • Superficial chattering pyramidal cells (!!) firing bursts of frequency at 20 to 80 Hz, interconnected to produce spike doublets (Jefferys 1996).
  • Dopamine restores coherence between EMG and ECoG in a PD patient.

____References____

[0] Brown P, Marsden JF, Cortical network resonance and motor activity in humans.Neuroscientist 7:6, 518-27 (2001 Dec)

{280}
hide / / print
ref: Evarts-1968.01 tags: Evarts motor control pyramidal tract M1 PTN tuning date: 01-16-2012 18:59 gmt revision:4 [3] [2] [1] [0] [head]

PMID-4966614[] Relation of pyramidal tract activity to force exerted during voluntary movement

  • PTNs with high conduction velocity tend to be silent during motor quiescence and show phasic activity with movement.
  • PTNs with lower axonal conduction velocities are active in the absence of movement; with movement they show both upward and downward modulations of the resting discharge.
  • many PTNs responded to a conditional stimulus before the movement.
  • in this study, they wanted to determine if phasic response was more correlated with displacement or with force.
    • did this with two different motions (flexion and extension) in two different force loads (opposing flexion and opposing extransion)
      • movements were slow (or at least nonballistic) and somewhat controlled - they had to last between 400 and 700ms.
      • monkeys usually carried out 3,000 cycles of the movement daily !!
  • "prior to the experiment, hte authour was biased to think that the displacement model (where the cortex commands a location/movement of the arm, which is then accomplished through feedback & feedforward mechanisms e.g. in the spinal cord) was correct; experimental results seem to indicate that force is very strongly represented in PTN population.
  • many PTN firing rates reflected dF/dt very strongly.
  • old, good paper. made with 'primitive' technology - but why do we need to redo this?

____References____

{911}
hide / / print
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.

{905}
hide / / print
ref: Wyler-1979.09 tags: operant control reinforcement schedule Wyler Robbins date: 01-07-2012 22:09 gmt revision:1 [0] [head]

PMID-114271[0] Operant control of precentral neurons: the role of reinforcement schedules.

  • Tried 3 different rewarding schedules:
    • Reward when the ISI was within a window 30-60ms
    • Differential reward, +2 or +3 when ISI was 45-60ms, +1 when 30-45ms
    • Nonspecific, constant applesauce reward.
  • No change in the mode of the ISI was observed, independent of reward schedules.

____References____

[0] Wyler AR, Robbins CA, Operant control of precentral neurons: the role of reinforcement schedules.Brain Res 173:2, 341-3 (1979 Sep 14)

{907}
hide / / print
ref: Wyler-1980.1 tags: Wyler Robbins operant control feedback date: 01-07-2012 22:09 gmt revision:1 [0] [head]

PMID-7418770[0] Operant control of precentral neurons: the role of audio and visual feedback.

  • Central point: though in previous studies of operant conditioning of precentral neurons visual and auditory feedback was employed, this proved unnecessary for the monkeys to gain control of their neurons.
  • All that is required is reinforcement / feedback when the ISI is in the target range.
  • Consistent with the idea that the monkey gets feedback from periphery, and not from audio / visual feedback.

____References____

[0] Wyler AR, Robbins CA, Operant control of precentral neurons: the role of audio and visual feedback.Exp Neurol 70:1, 200-3 (1980 Oct)

{904}
hide / / print
ref: Wyler-1980.05 tags: operant control motor learning interspike intervals ISI Wyler Lange Neafsey Robbins date: 01-07-2012 21:46 gmt revision:1 [0] [head]

PMID-6769536[0] Operant control of precentral neurons: Control of modal interspike intervals

  • Question: can monkeys control the ISI of operantly controlled neurons?
    • Answer: Seems they cannot. Operant and overt movement cells have about the same ISI, and this cannot be changed by conditioning.
  • Task requires a change from tonic to phasic firing, hence they call it "Differential reinforcement of Tonic Patterns".
    • That is, the monkey is trained to produce spikes within a certain ISI window.
    • PDP8 control, applesauce feedback.
    • modal ISI, in this case, means mode (vs. mean and median) of the ISI.
  • Interesting: "It was not uncommon for a neuron to display bi- or trimodal ISI distributions when the monkey was engaged in a movement unrelated to a unit's firing"
  • For 80% of the units, the more tightly a neuron's firing was related to a specific movement, the more gaussian its ISI became.
  • As the monkey gained control over reinforced units, the ISI became more gaussian.
  • Figure 2: monkey was not able to significantly change the modal ISI.
    • Monkeys instead seem to succeed at the task by decreasing the dispersion of the ISI distribution and increasing the occurrence of the modal ISI.
  • Monkeys mediate response through proprioceptive feedback:
    • Cervical spinal cord sectioning decreases the fidelity of control.
    • When contralateral C5-7 ventral roots were sectioned, PTN responsive to passive arm movements could not be statistically controlled.
    • Thus, monkeys operantly control precentral neurons through peripheral movements, perhaps even small and isometric contractions.
  • Excellent paper. Insightful conclusions.

____References____

[0] Wyler AR, Lange SC, Neafsey EJ, Robbins CA, Operant control of precentral neurons: control of modal interspike intervals.Brain Res 190:1, 29-38 (1980 May 19)

{773}
hide / / print
ref: work-2009 tags: bipolar opamp design current control microstimulation date: 01-06-2012 20:13 gmt revision:15 [14] [13] [12] [11] [10] [9] [head]

Recently I've been working on a current-controlled microstimulator for the lab, and have not been at all satisfied with the performance - hence, I decided to redesign it.

Since it is a digitally current-controlled stimulator, and the current is set with a DAC (MCP4822), we need a voltage controlled current source. Here is one design:

  • Because the output of the DAC is ground-referenced, and there is no negative supply in the design, the input buffers must be PNP transistors. These level-shift the input (0-2V, corresponding to 0-400uA) + 0.65V ( V beV_{be} ), and increase the current. Both are biased with 1uA here, though 10uA would also work (lazily, through 1M resistors - I've checked that these work well too). This sets the base current at about 10nA for Q2 and Q1.
  • Q3 and Q4 are a current-mirror pair. If Q1 Vb increases, Ie for Q3 will decrease, increasing Ib for Q4 and hence its Ic. This will decrease the base current in Q6 and Q5, as desired. On the other hand, increasing Q2 Vb will decrease Q4 Ic, increasing Ib in Q6 and Q5. The current mirror effects the needed negative feedback in the circuit. This mirror could also be implemented with PNP transistors, but it doesn't work as well as then the collector (which has voltage gain) is tied to the emitter of the input PNP transistors. Voltage gain is needed to drive Q5 / Q6.
  • Q5 & Q6 are Darlington cascaded NPN transistors for current gain. If Q6 is omitted, Ib in Q5 increases -> Ib in Q1 decreases -> Ic in Q3 decreases -> Ib in Q4 increases. This results in a set-point of Ib = 100nA in Q5 -> Ic ~10uA. (unacceptable for our task).

What I really need is a high-side regulated current source; after some fiddling, here is what I came up with:

  • V2 is from the DAC; for the testing, I just simulate with a votlage ramp. This circuit, due to the 5V biasing (I have 5V available for the DAC, hence might as well use it) works well up to about 4V input voltage - exactly what the DAC can produce.
  • Q1 and Q2 are biased through 1M resistors R6 and R8; their emitters are coupled to a common-emitter amplifier Q3 and Q4.
  • As the voltage across R1 increases, Ib in Q1 decreases. This puts more current through the base of Q4, increasing the emitter voltage on both Q3 and Q4. This reduces the current in Q3, hence reducing the current in Q5 -> the voltage across R1. feedback ;-)
  • I tried using a current mirror on the high-side, but according to spice, this actually works *worse*. Q5 & Q3 / Q4 have more than enough gain as it stands.
  • Yes, that's 100V - the electrodes we use have high impedance, so need a good bit of voltage to get the desired current.
  • Now, will need to build this circuit to verify that it actually works.

  • (click for the full image)
  • This simulates OK, but shows some bad transients related to switching - I'll have to inspect this more closely, and possibly tune the differential stage (e.g. remove the fast transient response - Q6 and Q12 seem to turn off before Q5 and Q11 do, which pulls the output to +50v briefly)

  • This is the biphasic, bipolar stimulator's response to a rising ramp command voltage, as measured by the current through R17. Note how clean the signal is :-) But, I'm sure that it won't look quite this nice in real life! Will try one half out on a breadboard to see how it looks.
  • Note I switched from NMOS switching transistors to NPN - Q15 and Q16 shunt the bias current from Q3/Q2 and Q8/Q9, keeping the output PNPs (Q5 and Q11). These transistors are in saturation, so they take 100-200ns to turn off, which should be fine for this application where pulse width is typically 100us.
  • I've fed the pull-down NPN base current from the positive supply here, so that as long as Q5 and Q11 are on, Q6 and Q12 are also on. The storage time here (not that it is much, the transistors are kept out of saturation via D1-4) helps to keep the mean difference in voltage between animal or stimulee's ground and isolated stimulator ground low. In previous stimulators the high-side was a near-saturation PNP, which pulled the voltage all the way to the positive supply when stimulation started. This meant that any stray capacitance had to be charged through the brain - bad!
    • Note this means that the emitter current through Q6 and Q12 is more than the current through R17 by that passed through Q14 and Q13. By design, this is 1/50th that through Q5 and Q11. This means that the actual stimulated current will be 95% of the commanded current, something which is easily corrected in software.

  • Larger view of the schematic. Still worried about stability - perhaps will need to add something to limit slew rate.
  • V2 on the right is the command voltage from the DAC.

  • The amplifier in figure 5 suffered from low bandwidth, primarily because the large resistors effected slow timeconstants, and because there was no short path to +50V from the high-side PNP transistors. This led to very slow turn-off times. To remedy this:
    • Bias current to Q3 & Q4 was increased (R6 & R8 decreased) -> more current to charge / discharge capacitance.
    • Common emitter resistor concomitantly decreased to 22k. This increases the collector current.
    • Pull-up resistors changed to a current mirror. This allows the current through Q4 to pull up the bases of Q5 and Q6, letting them turn off more quickly. If Q1 is off (e.g. voltage across R1 is high), Q4 will be on, and Q6 will source this current. etc.
  • With this done, I tested it on the breadboard & it oscillated. bad! Hence, I put a 1nf (10nf in the schematic) capacitor from the collector of Q3 to ground - hence limiting the slew rate. This abolished oscillations and led to a very pretty linear turn-on waveform.
  • However, the turn-off waveform was an ugly exponential. Why? With Q2 or Q10 fully on, Q3 will be off. Q4 will effectively recharge C1 through R7. As the voltage across R7 goes to zero, so does the charging current. Since I don't want to add in a negative supply, I simply shifted the base voltage of Q3 and Q4 using a diode, about as simple as you can get!
  • Eventually, I replaced R7 with a current source ... but this did not change the fall waveform that much; it is still (partially) exponential. Possibly this is from the emitter resistors on the high-side.

  • As of now, the final version - tested using surface mount devices; seems to work ok!
  • Note added transistor Q11 - this discharges / removes minority carriers from the base of Q8. Even though D1 and D2 guarantee a current-starved Q8 in previous designs, they leave no path to ground from the base, so this transistor was taking forever to turn off. This was especially the case when switching (recall this is one half of a H-bridge, and Q9 would actually be on the other side of the h-bridge), since the other sides' Q9 would push current, while Q8 would continue to conduct & sink current. This current through R1 would increase Q8 emitter voltage, reverse-biasing its' base-emitter junction, making the transistor take 100us of us to turn off. Bad, since the amplifier is intended to replicate 100us pulses! Anyway, Q11 neatly solves the problem (albeit with 100ns or so of saturated-switching storage time - something that Q10 has anyway).
  • D1 and D2 are no longer really necessary, but I've left them in this diagram for illustrative purposes. (and they improve storage time a bit).

  • Update as the result of testing. Changes:
    • Added emitter resistors on the two current mirrors (Q6, Q7; Q12, Q13). This eliminated stability problems
    • Changed the anti-saturation diodes to a resistor. This is needed as it takes some time for Q9 to turn off, and to avoid unbalanced currents through the electrode pairs, this charge should be pulled to ground through Q8. In the actual circuit, Q11 is driven with a 4-8us delayed version of the control signal V4 so that Q8 remains on longer than current source Q9.
    • Decreased C1 to 100pf; because the amplifier is more stable now, the slew rate can be increased.

{956}
hide / / print
ref: Fetz-1992 tags: Fetz 1992 Motor control M1 PV date: 01-06-2012 18:13 gmt revision:1 [0] [head]

bibtex: Fetz-1992 Are movement parameters recognizably coded in the activity of single neurons

  • Fetz seems to think that many of the reported correlations or specializations, whether in terms of latency or tuning, are largely a result of observer bias (e.g. to ignore non-obviously tuned cells), and that both simple and complex tuning to motor parameters can be found throughout the motor and premotor cortices.
    • Plus, evidence seems to point to the fact that most things happen simultaneously across multiple areas.
  • Shows that a neural-network model of M1 has complicated and interesting tuning within the hidden network, which he thinks is consistent with the observations.
  • Nice: "This suggests that the search for explicit coding may be diverting us from understanding distributed neural mechanisms that operate without literal representations. "

{285}
hide / / print
ref: notes-0 tags: motor control BMI M1 date: 01-06-2012 03:11 gmt revision:13 [12] [11] [10] [9] [8] [7] [head]

with: {277}

  • Correlations have been described between neuronal activity and the static and dynamic forces and torques generated across single joints
  • or by the whole arm
  • or by precision pinch [14,15,16]
  • or there are strong correlations to muscle activity [17,18,19,20,21,22,23,24,25]
  • or there is strong correlations to kinematic parameters
  • these kinematic parameters are dependent on location in the external workspace [10][28,29]
  • kinematic tuning can be subserved by training! [30]
  • distance to target representation [31]

____References____

{992}
hide / / print
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)

{929}
hide / / print
ref: Kim-2007.08 tags: Hyun Kim muscle activation method BMI model prediction kinarm impedance control date: 01-06-2012 00:19 gmt revision:1 [0] [head]

PMID-17694874[0] The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm.

  • First BMI that successfully predicted interactions between the arm and a force field.
  • Previous BMIs are used to decode position, velocity, and acceleration, as each of these has been shown to be encoded in the motor cortex
  • Hyun talks about stiff tasks, like writing on paper vs . pliant tasks, like handling an egg; both require a mixture of force and position control.
  • Georgopoulous = velocity; Evarts = Force; Kalaska movement and force in an isometric task; [17-19] = joint dependence;
  • Todorov "On the role of primary motor cortex in arm movement control" [20] = muscle activation, which reproduces Georgouplous and Schwartz ("Direct cortical representation of drawing".
  • Kakei [19] "Muscle movement representations in the primary motor cortex" and Li [23] [1] show neurons correlate with both muscle activations and direction.
  • Argues that MAM is the best way to extract impedance information -- direct readout of impedance requires a supervised BMI to be trained on data where impedance is explicitly measured.
  • linear filter does not generalize to different force fields.
  • algorithm activity highly correlated with recorded EMG.
  • another interesting ref: [26] "Are complex control signals required for human arm movements?"

____References____

[0] Kim HK, Carmena JM, Biggs SJ, Hanson TL, Nicolelis MA, Srinivasan MA, The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm.IEEE Trans Biomed Eng 54:8, 1520-9 (2007 Aug)
[1] Li CS, Padoa-Schioppa C, Bizzi E, Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field.Neuron 30:2, 593-607 (2001 May)

{330}
hide / / print
ref: BASMAJIAN-1963.08 tags: original BMI M1 human EMG tuning operant control Basmajian date: 01-05-2012 00:49 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-13969854[0] Control and Training of Individual Motor Units

  • humans have the ability to control the firing rate of peripheral motor units with a high resolution.
  • "The quality of control over anterior horn cells may determine the rates of learning" yup!
  • "Some learn such esquisite control that they soon can produce rhythms of contraction in one unit, imitating drum rolls etc"
  • the youngest persons were among both the best and worst learners.
  • after about 30 minutes the subject was required to learn how to repress the first unit and to recruit another one.
    • motor unit = anterior horn cell, its axon, and all the muscle fibers on which the terminal branches of the axon end. max rate ~= 50hz.
    • motor units can be discriminated, much like cortical neurons, by their shape.
    • some patients could recruit 3-5 units altogether - from one bipolar electrode!
      • in playback mode (task: trigger the queried unit), several subjects had particular difficulty in recruiting the asked-for units. "They groped around in their conscious efforts to find them sometimes, it seemed, only succeded by accident"
    • some patients could recruit motor units in the absence of feedback, but they were unable to explain how they do it.
  • 0.025 (25um) nylon-insulated Karma alloy EMG recording wire.
  • feedback: auditory & visual (oscilloscope).
  • motor units have a maximum rate, above which overflow takes place and other units are recruited (in accord with the size principle).
  • "The controls (are) learned so quickly, are so esquisite, are so well retained after the feedbacks are eliminated that one must not dismiss them as tricks"

____References____

{281}
hide / / print
ref: Evarts-1969.05 tags: Evarts pyramidal tract motor control M1 tuning date: 01-03-2012 23:08 gmt revision:2 [1] [0] [head]

PMID-4977837[0] Activity of Pyramidal Tract neurons during postural fixation

  • Force was thus dissociated from displacement, and it was possible to determine whether PTN discharges were related to position or force.
  • for the majority of PTNs discharge frequency was related to to the magnitude and rate of change of force rather than to the joint position or the speed of joint movement (same as the MUA in the Kinarm data!!)
  • task was simple: just try to avoid joint movement.
  • in comparison to [1] where PTN were related to force under joint displacement, this task shows they are still related to force even when the joint angle is fixed.
  • used sharpened tungsten electrodes to record 102 pyramidal tract neurons.
  • monkeys were trained to do the tasks in their home cages (obviously weren't recorded there - need to be headposted)
  • I'm not sure how he determined if it was or was not a pyramidal tract neuron.

____References____

{288}
hide / / print
ref: Fetz-2000.12 tags: motor control spinal neurons interneurons movement primitives Fetz review tuning date: 01-03-2012 23:08 gmt revision:4 [3] [2] [1] [0] [head]

PMID-11240278[0] Functions of mammalian spinal interneurons during movement

  • this issue of current opinion in neuro has many reviews of motor control
  • points out that the Bizzi results (they microstimulated & observed a force-field-primitive type organization)
    • others have found that this may be a consequence of decerebration + the structure of the biomechanical groupings of muscles. (see 'update').
  • intraspinal electrodes in the cat provide a secure and reliable method of eliciting forces and movements.
  • CM (corticomotor) cells more often represent synergistic groups of muscles, whereas premotor spinal interneurons are organized to target specific muscles.
    • CMs are therefore more strictly recruited for particular movements.
  • interneurons (IN) are, of course, arrayed in such a way so that antagonist and agonist muscles cross-inhibit eachother (for efficiency)
    • however, we are still able to control the endpoint impedance of the arm - how?
  • spinal interneurons modulate activity during wait period prior to movement!
    • there might be substantial interaction between the cortex and spinal cord.. subjects asked to imagine pressing a foot pedal showed enhanced reflexes in the involved soleus muscle.
      • cognitive priming?
  • spinal reflexes are strongly modulated in movement.

____References____

{902}
hide / / print
ref: Olson-2005 tags: Arizona rats BMI motor control training SVM single-unit left right closed-loop learning Olson Arizona date: 01-03-2012 23:06 gmt revision:1 [0] [head]

bibtex:Olson-2005 Evidence of a mechanism of neural adaptation in the closed loop control of directions

  • from abstract:
    • Trained rats to press left/right paddles to center a LED. e.g. paddles were arrow keys, LED was the cursor, which had to be centered. Smart rats.
      • Experiment & data from Olson 2005
    • Then trained a SVM to discriminate left/right from 2-10 motor units.
    • Once closed-loop BMI was established, monitored changes in the firing properties of the recorded neurons, specifically wrt the continually(?) re-adapted decoding SVM.
    • "but expect that the patients who use the devices will adapt to the devices using single neuron modulation changes. " --v. interesting!
  • First page of article has an excellent review back to Fetz and Schmidt. e.g. {303}
  • Excellent review of history altogether.
    • Notable is their interpretation of Sanchez 2004 {259}, who showed that most of the significant modulations are from a small group of neurons, not the large (up to 320 electrodes) populations that were actually recorded. Carmena 2003 showed that the population as a whole tended to group tuning, although this was imperfectly controlled.
  • Also reviewed: Zacksenhouse 2007 {901}
  • SVM is particularly interesting as a decoding algorithm as it weights the input vectors in projecting onto a decision boundary; these weights are experimentally informative.
  • Figure 7: The brain seems to modulate individual firing rate changes to move away from the decision boundary, or at least to minimize overlap.
  • For non-overt movements, the distance from decision function was greater than for overt movements.
  • Rho ( ρ\rho ) is the Mann-Whitney test statistic, which non-parametrically estimates the difference between two distributions.
  • δf(X t)\delta f(X_t) is the gradient wrt the p input dimensions o9f the NAV, as defined with their gaussian kernel SVM.
  • They show (i guess) that changes in ρ\rho are correlated with the gradient -- e.g. the brain focuses on neurons that increase fidelity of control?
    • But how does the brain figure this out??
  • Not sure if i fully understand their argument / support.
  • Conclusion comes early in the paper
    • figure 5 weakly supports the single-neuron modulation result.

{687}
hide / / print
ref: Fontani-2007.12 tags: mental training skilled motor control date: 01-03-2012 02:33 gmt revision:2 [1] [0] [head]

PMID-18229536[0] Effect of mental imagery on the development of skilled motor actions.

  • with trained subjects (performing something called Ura-Shuto-Uchi (Japanese? but the researchers are Italian)) showed a decrease in reaction time and EMG activity, as well as a increase in movement speed, muscle strength, power, and work. These results did not apply to untrained individuals. EEG also apparently changed vs. the untrained condition.

____References____

[0] Fontani G, Migliorini S, Benocci R, Facchini A, Casini M, Corradeschi F, Effect of mental imagery on the development of skilled motor actions.Percept Mot Skills 105:3 Pt 1, 803-26 (2007 Dec)

{957}
hide / / print
ref: -0 tags: Scott M1 motor control pathlets filter EMG date: 12-22-2011 22:52 gmt revision:1 [0] [head]

PMID-19923243 Complex Spatiotemporal Tuning in Human Upper-Limb Muscles

  • Original idea: M1 neurons encode 'pathlets', sophisticated high-level movement trajectories, possibly through the action of both the musculoskeletal system and spinal cord circuitry.
  • Showed that muscle pathlets can be extracted from EMG data, relkiably and between patients, implying that M1 reflects 'filter-like' properties of the body, and not high level representations.

{953}
hide / / print
ref: -0 tags: Moran Schwartz Todorov controversy PV M1 motor control date: 12-22-2011 22:04 gmt revision:0 [head]

PMID-11017157 One motor cortex, two different views.

  • Commentary on {950}
  • Refutes Todorov's stiff -muscle perturbation analysis, saying that it grossly misapproximates what the monkey is actually doing (drawing on a touchscreen vertical in front of it), as the model of the arm in this case would be held stiffly in front of the monkey, rather than realistically falling to the animal's side.
  • They also claim that any acceleration term would cause the PV tuning to lead with higher curvature, which is not what they saw (?)

{950}
hide / / print
ref: -0 tags: Todorov motor control models 2000 date: 12-22-2011 21:18 gmt revision:3 [2] [1] [0] [head]

PMID-10725930 Direct cortical control of muscle activation in voluntary arm movements: a model.

  • Argues that the observed high-level control of parameters (movement direction) is inconsistent with demonstrated low-level control (control of individual muscles / muscle groups, as revealed by STA [5] or force production [3]), but this inconsistency is false: the principle of low level control is correct, and high level control appears due to properties of the musculoskeletal system.
  • "Yet the same cells that encode hand velocity in movement tasks can also encode the forces exerted against external objects in both movement and isometric tasks [9,10].
  • The following other correlations have been observed:
    • arm position [11]
    • acceleration [12]
    • movement preparation [13]
    • target position [14]
    • distance to target [15]
    • overall trajectory [16]
    • muscle coactivation [17]
    • serial order [18]
    • visual target position [19]
    • joint configuration [20]
    • instantaneous movement curvature [7]
    • time from movement onset [15]
  • although these models can fit the data well, they leave a crucial question unanswered, namely, how such a mixed signal can be useful for generating motor behavior.
    • What? No! The diversity of voices gives rise to robust, dynamic computation. I think this is what Miguel has written about, will need to find a reference.
  • Anyway, all the motor parameters are related by the laws of physics -- the actual dimensionality of real reaches is relatively low.
  • His model: muscle activity simply reflects M1 PTN activity.
  • If you include real muscle parameters, a lot of the observed correlations make sense: muscle force depends not only on activation, but also on muscle length and rate of change of length.
  • In this scientific problem, the output (motor behavior) specified by the motor task is easily measured, and the input (M1 firing) must be explained.
    • Due to the many-to-one mapping, there is a large null-space of the inverse transform, so individual neurons cannot be predicted. Hence focus on population vector average.
  • Cosine tuning is the only activation pattern that minimizes neuromotor noise (derived in methods, Parseval's theorem)). Hence he uses force, velocity, and displacement tuning for his M1 cells.
  • Activity of M1 cells is constrained in endpoint space, hence depends only on behavioral parameters.
    • The muscles were "integrated out".
  • Using his equation, it is clear that for an isometric task, M1 activity is cosine tuned to force direction and magnitude -- x(t) is constant.
  • For hand kinematics in the physiological range with an experimentally measured inertia-to-damping ratio, the damping compensation signal dominates the acceleration signal.
    • Hence population x˙(t)\propto \dot x(t)
    • Muscle damping is asymmetric: predominant during shortening.
  • The population vector ... is equal not to the movement direction or velocity, but instead to the particular sum of position, velocity, acceleration, and force signals in eq. 1
  • PV reconstruction fails when movement and force direction are varied independently. [28]
  • Fig 4. Schwartz' drawing task -- {951} -- and shows how curvature, naturalistic velocity profiles, the resultant accelerations, and leading neuronal firing interact to distort the decoded PV.
    • Explains why, when assuming PV tuning, there seems to be variable M1-to-movement delay. At high curvature PV tuning can apprently lag movement. Impossible!
  • Fig 5 reproduces [21]
    • Mean firing rate (mfr, used to derive the poisson process spike times) and r^2 based classification remarkably different -- smoothing + square root biases toward finding direction-tuned cells.
    • Plus, as P, V, and A are all linearly related, a sum of the 3 is closer to D than any of the three.
    • "Such biases raise the important question of how one can determine what an individual neuron controls"
  • PV reversals occur when the force/acceleration term exceeds the velocity scaling term -- which is 'equivalent' to the triphasic burst pattern observed in EMG. Ergo monkeys should be trained to make faster movements.
  • The structure of your model -- for example firingrate=b 0+b xX+b yY+b mMfiringrate = b_0 + b_x X + b_y Y + b_m M biases analysis for direction, not magnitude; correct model is firingrate=b 0+b xmXM+b ymYM firingrate = b_0 + b_{xm}XM + b_{ym}YM -- multiplicative.
  • "Most of these puzzling phenomena arise from the feedforward control of muscle viscoelasticity."
  • Implicit assumption is that for the simple, overtrained, unperturbed movements typically studied, feedforward neural control is quite accurate. When you get spinal reflexes involved things may change. Likewise for projections from the red nucleus.

{945}
hide / / print
ref: -0 tags: Georgopoulos population vector arm motor control date: 12-20-2011 22:26 gmt revision:1 [0] [head]

PMID-3139485 Neural integration of movement: role of motor cortex in reaching.

  • Reviews his 2D and 3D population vector / cosine tuning results.
  • Isometric task in [13] varied as a sinusoidal function of load.
    • [1]3 Kalaska 1985 Area 4 and area 5: differences between the load direction-dependent discharge variability of cells during active postural fixation.
  • [14] suggests that separate motor cortical populations are concerned with the control of joint stiffness.
    • [14] Humphrey 1983 Seperate cortical systems for control of joint movement and joint stiffness: reciprocal activation and coactivation of antagonist muscles.
  • proximal muscles are controlled through C3-C4 propriospinal neurons, which receive input from corticospinal, rubrospinal, reticulospinal, and tectospinal tracts, and distribute axons to proximal motorneuron pools [25]
    • The propriospinal system seems to be selectively engaged during reaching movements [28].
    • There is corticspinal input on key inhibitory interneuron that mediates inhibition from afferent fibers to propriospinal neurons [29].
    • References in this from the cat.
  • This is similar to 'the sophisticated integration seen in the locomotor system' locomotive system.
  • From this, Georgopoulos supposes that the motor cortex is concerned with the specification of the direction of reaching in space.
  • He further supposes that this is enacted by individual motor cortical cells influence motoneuronal pools in a weighted fashion.
  • Looking back, I'm surprised at how clean his PV tuning plots are -- the neurons stop fiting when the monkey moves his arm in certain directions.

{529}
hide / / print
ref: delgado-0 tags: Delgado roborat ICMS stimulation control date: 12-16-2011 06:41 gmt revision:2 [1] [0] [head]

quote:

All the loose speculation provoked by roborats is ironic considering that the experiment is just a small-scale replay of a major media event that is 40 years old. In 1964, José Delgado, a neuroscientist from Yale University, stood in a Spanish bullring as a bull with a radio-equipped array of electrodes, or "stimoceiver," implanted in its brain charged toward him. When Delgado pushed a button on a radio transmitter he was holding, the bull stopped in its tracks. Delgado pushed another button, and the bull obediently turned to the right and trotted away. The New York Times hailed the event as "probably the most spectacular demonstration ever performed of the deliberate modification of animal behavior through external control of the brain."

from: http://discovermagazine.com/2004/oct/cover

from: http://www.angelfire.com/or/mctrl/chap16.htm

{912}
hide / / print
ref: Carlton-1981.1 tags: visual feedback 1981 error correction movement motor control reaction time date: 12-06-2011 06:35 gmt revision:1 [0] [head]

PMID-6457106 Processing visual feedback information for movement control.

  • Vusual feedback can correct movement within 135ms.
  • Measured this by simply timing the latency from presentation of visual error to initiation of corrective movement.

{914}
hide / / print
ref: Gandolfo-2000.02 tags: Gandolfo Bizzi dynamic environment force fields learning motor control MIT M1 date: 12-02-2011 00:10 gmt revision:1 [0] [head]

PMID-10681435 Cortical correlates of learning in monkey adapting to a new dynamical environment.

{903}
hide / / print
ref: Burnod-1982.11 tags: operant conditioning motor control learning Burnod Maton Calvet date: 11-26-2011 02:22 gmt revision:0 [head]

PMID-7140894 Short-term changes in cell activity of areas 4 and 5 during operant conditioning.

  • Seems that layers 4 and 5 act differently during operant conditioning of a simple task.
  • Layer 5 neurons become tuned to reward (?)
  • Can't get this article, have to go from the abstract.

{900}
hide / / print
ref: Helms-2003.01 tags: Schwartz BMI adaptive control Taylor Tillery 2003 date: 11-26-2011 00:58 gmt revision:1 [0] [head]

PMID-12929922 Training in cortical control of neuroprosthetic devices improves signal extraction from small neuronal ensembles.

  • Lays out the coadaprive algorithm.
  • with supervised / adaptive training, ML estimator is able to get 80% of the targets correct.
  • Reviews in the Neurosciences (conference) Workshop on Neural and Artificial Computation.

{772}
hide / / print
ref: -0 tags: xmos microcontroller microporcessor threading date: 08-11-2009 16:15 gmt revision:0 [head]

http://www.xmos.com/

  • Looks nice! They even publish their board designs and schematics - makes sense given they want their chips to be incorporated into products.
  • Their processors are 'event-driven', which seems to mean that they have 8 sets of registers, one set per thread, with presumably rapid switching between the threads. I did not investigate how excatly their processor works, whether this means they don't need DMA, etc.
  • -- an example with dual LAN8700 ethernet interfaces.

{734}
hide / / print
ref: -0 tags: Vanity Fair American dream control theory in politics and society date: 05-03-2009 17:11 gmt revision:3 [2] [1] [0] [head]

Rethinking the American Dream by David Kamp

  • check out the lights in the frame at the bottom, and the kid taking a picture center-right (image courtesy of Kodak, hence.)

  • (quote:) "Still, we need to challenge some of the middle-class orthodoxies that have brought us to this point—not least the notion, widely promulgated throughout popular culture, that the middle class itself is a soul-suffocating dead end."
    • Perhaps they should teach expectations management in school? Sure, middle class should never die - I hope it will grow.
  • And yet, this is still rather depressive - we all want things to continuously, exponentially get better. I actually think this is almost possible, we just need to reason carefully about how this could happen: what changes in manufacturing, consumption, energy generation, transportation, and social organization would gradually effect widespread improvement.
    • Some time in individual lives (my own included!) is squandered in pursuit of the small pleasures which would be better used for purposeful endeavor. Seems we need to resurrect the idea of sacrifice towards the future (and it seems this meme itself is increasingly popular).
  • Realistically: nothing is for free; we are probably only enjoying this more recent economic boom because energy (and i mean oil, gas, coal, hydro, nuclear etc), which drives almost everything in society, is really cheap. If we can keep it this cheap, or make it cheaper through judicious investment in new technologies (and perhaps serendipity), then our standard of living can increase. That is not to say that it will - we need to put the caloric input to the economy to good use.
    • Currently our best system for enacting a general goal of efficiency is market-based capitalism. Now, the problem is that this is an inherently unstable system: there will be cheaters e.g. people who repackage crap mortgages as safe securities, companies who put lead paint on children's toys, companies who make unsafe products - and the capitalistic system, in and of itself, is imperfect at regulating these cheaters (*). Bureaucracy may not be the most efficient use of money or people's lives, but again it seems to be the best system for regulating/auditing cheaters. Examined from a control feedback point-of-view, bureaucracy 'tries' to control axes which pure capitalism does not directly address.
    • (*) Or is it? The largest problem with using consumer (or, more generally, individual) choice as the path to audit & evaluate production is that there is a large information gradient or knowledge difference between producers and consumers. It is the great (white?) hope of the internet generation that we can reduce this gradient, democratize information, and have everyone making better choices.
      • In this way, I'm very optimistic that things will get continuously better. (But recall that optimality-seeking requires time/money/energy - it ain't going to be free, and it certainly is not going to be 'natural'. Alternately, unstable-equilibrium-maintaining (servoing! auditing!) requires energy; democracy's big trick is that it takes advantage of a normal human behavior, bitching, as the feedstock. )
  • Finally (quote:) "I’m no champion of downward mobility, but the time has come to consider the idea of simple continuity: the perpetuation of a contented, sustainable middle-class way of life, where the standard of living remains happily constant from one generation to the next. "
    • Uh, you've had this coming: stick it. You can enjoy 'simple continuity'. My life is going to get better (or at least my life is going to change and be interesting/fun), and I expect the same for everybody else that I know. See logic above, and homoiconic's optimism

{297}
hide / / print
ref: Matsuzaka-2007.02 tags: skill learning M1 motor control practice cortex date: 03-20-2009 18:31 gmt revision:1 [0] [head]

PMID-17182912[0] Skill Representation in the Primary Motor Cortex After Long-Term Practice

  • The acquisition of motor skills can lead to profound changes in the functional organization of the primary motor cortex (M1) yes
  • 2 task modes: random target acquisition, and one of 2 repeating sequences (predictable, repeating mode)
  • 2 years of training -> 40% of units were differentially active during the two task modes
  • variations in movement types in the two classes did not fully explain the difference in activity between the 2 tasks
    • M1 neurons are more influence by the task than the actual kinematics.

____References____

{287}
hide / / print
ref: Cooke-1990.03 tags: motor organization triphasic control EMG date: 03-11-2009 21:42 gmt revision:12 [11] [10] [9] [8] [7] [6] [head]

the organization of the human triphasic EMG control sequence:

  • PMID-2329356[0] Movement-related phasic muscle activation. II. Generation and functional role of the triphasic pattern.
  • PMID-8989378[1]
  • PMID-1629754[2]
  • PMID-2230915[3]
  • PMID-2329365[4]
  • PMID-2769335[5]
  • PMID-2769334[6]
  • PMID-3622686[7] Trajectory control in targeted force impulses. I. Role of opposing muscles.
    • Our findings emphasize that neuronal commands to opposing muscles acting at a joint must be adapted to constraints imposed by the properties of the neuromuscular plant.
  • PMID-10085332[8] Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms.
    • frictionless air-jet system, rapid movements, inertia perturbation via masses on the joints, surprise trials.
    • surprise trials were well predicted by an open-loop feedforward controller.
    • there was feedback compensation upon return-to-center: it is not all feedforward (of course!)

____References____

[0] Cooke JD, Brown SH, Movement-related phasic muscle activation. II. Generation and functional role of the triphasic pattern.J Neurophysiol 63:3, 465-72 (1990 Mar)[1] Almeida GL, Hong DA, Corcos D, Gottlieb GL, Organizing principles for voluntary movement: extending single-joint rules.J Neurophysiol 74:4, 1374-81 (1995 Oct)[2] Gottlieb GL, Latash ML, Corcos DM, Liubinskas TJ, Agarwal GC, Organizing principles for single joint movements: V. Agonist-antagonist interactions.J Neurophysiol 67:6, 1417-27 (1992 Jun)[3] Corcos DM, Agarwal GC, Flaherty BP, Gottlieb GL, Organizing principles for single-joint movements. IV. Implications for isometric contractions.J Neurophysiol 64:3, 1033-42 (1990 Sep)[4] Gottlieb GL, Corcos DM, Agarwal GC, Latash ML, Organizing principles for single joint movements. III. Speed-insensitive strategy as a default.J Neurophysiol 63:3, 625-36 (1990 Mar)[5] Corcos DM, Gottlieb GL, Agarwal GC, Organizing principles for single-joint movements. II. A speed-sensitive strategy.J Neurophysiol 62:2, 358-68 (1989 Aug)[6] Gottlieb GL, Corcos DM, Agarwal GC, Organizing principles for single-joint movements. I. A speed-insensitive strategy.J Neurophysiol 62:2, 342-57 (1989 Aug)[7] Ghez C, Gordon J, Trajectory control in targeted force impulses. I. Role of opposing muscles.Exp Brain Res 67:2, 225-40 (1987)[8] Sainburg RL, Ghez C, Kalakanis D, Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms.J Neurophysiol 81:3, 1045-56 (1999 Mar)[9] Seidler RD, Noll DC, Chintalapati P, Bilateral basal ganglia activation associated with sensorimotor adaptation.Exp Brain Res 175:3, 544-55 (2006 Nov)

{611}
hide / / print
ref: Radhakrishnan-2008.1 tags: EMG BMI Jackson motor control learning date: 10-03-2008 16:45 gmt revision:0 [head]

PMID-18667540[0] Learning a novel myoelectric-controlled interface task.

  • EMG-controlled 2D cursor control task with variable output mapping.
  • Subjects could learn non-intuitive output transforms to a high level of performance,
  • Subjects preferred, and learned better, if hand as opposed to arm muscles were used.

____References____

{532}
hide / / print
ref: bookmark-0 tags: Delgado Bulls microstimulation ICMS control implant date: 01-06-2008 18:05 gmt revision:2 [1] [0] [head]

http://www.biotele.com/Delgado.htm

  • stimulated the caudate to stop the charging bull.
  • interesting account of the later part of his life spent in Spain, when his popularity wained
  • Delgado still appears to have some quite radical tendencies, such as belief in the inexorable advance of technology, even if it is immoral/unethical.

{470}
hide / / print
ref: picture-0 tags: nordic state control diagram radio date: 10-22-2007 18:58 gmt revision:2 [1] [0] [head]

{129}
hide / / print
ref: Kawato-1999.12 tags: kawato inverse dynamics cerebellum motor control learning date: 04-09-2007 22:45 gmt revision:1 [0] [head]

PMID-10607637[0] Internal models for motor control and trajectory planning

  • in this review, I will discuss evidence supporting the existence of internal models.
  • fast coordinated arm movement canot be executed under feedback control, as biological feedback loops are slow and have low gains. hence, the brain mostly needs to control things in a pure feedforward manner.
    • visual feedback delay is about 150-200ms.
    • fast spinal reflexes still require 30-50ms; large compared to fast movements (150ms).
    • muscle intrinsic mechanical properties produce proportional (stiffness) and derivative (viscosity) gains without delay.
    • inverse models are required for fast robotics, too. http://www.erato.atr.co.jp/DB/
  • talk about switching external force field to gauge the nature of the internal model - these types of experiments verily prove that feedforward / model-based control is happening. has anyone shown what happens neuronally during the course of this learning? I guess it might be in my datar.

____References____

{106}
hide / / print
ref: Scott-2004.07 tags: Scott motor control optimal feedback cortex reaching dynamics review date: 04-09-2007 22:40 gmt revision:1 [0] [head]

PMID-15208695[0] PDF HTML summary Optimal feedback control and the neural basis of volitional motor control by Stephen S. Scott

____References____

{80}
hide / / print
ref: Chan-2006.12 tags: computational model primate arm musculoskeletal motor_control Moran date: 04-09-2007 22:35 gmt revision:1 [0] [head]

PMID-17124337[0] Computational Model of a Primate Arm: from hand position to joint angles, joint torques, and muscle forces ideas:

  • no study so far has been able to incorporate all of these variables (global hand position & velocity, joint angles, joint angular velocities, joint torques, muscle activations)
  • they have a 3D, 7DOF model that translate actual motion to optimized muscle activations.
  • knock the old center-out research (nice!)
  • 38 musculoskeletal-tendon units
  • past research: people have found correlations to both forces and higher-level parameters, like position and velocity. these must be transformed via inverse dynamics to generate a motor plan / actually move the arm.
  • used SIMM to optimize the joint locations to replicate actual movements...
  • assume that the torso is the inertial frame.
  • used infrared Optotrak 3020
  • their model is consistent - they can use the inverse model to calculate muscle activations, which when fed back into the forward model, results in realistic movements. still yet, they do not compare to actual EMG.
  • for working with the dynamic model of the arm, they used AUTOLEV
    • I wish i could figure out what the Kane method was, they seem to leverage it here.
  • their inverse model is pretty clever:
  1. take the present attitude/orientation & velocity of the arm, and using parts of the forward model, calculate the contributions from gravity & coriolis forces.
  2. subtract this from the torques estimated via M*A (moment of interia times angular acceleration) to yield the contributions of the muscles.
  3. perturb each of the joints / DOF & measure the resulting arm motion, integrated over the same period as measurement
  4. form a linear equation with the linearized torque-responses on the left, and the muscle torque contributions on the right. Invert this equation to get the actual joint torques. (presumably the matrix spans row space).
  5. to figure out the muscle contributions, do the same thing - apply activation, scaled by the PCSA, to each muscle & measure the resulting torque (this is effectively the moment arm).
  6. take the resulting 38x7 matrix & p-inverse, with the constraint that none of the muscle activations are negative, yielding a somewhat well-specified muscle activation. not all that complicated of a method

____References____

{277}
hide / / print
ref: Sergio-2005.1 tags: isometric motor control kinematics kinetics Kalaska date: 04-09-2007 22:33 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-15888522[0] Motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks.

  • see [1]
  • recorded 132 units from the caudal M1
  • two tasks: isometric and movement of a heavy mass, both to 8 peripheral targets.
    • target location was digitized using a 'sonic digitizer'. trajectories look really good - the monkey was well trained.
  • idea: part of M1 functions near the output (of course)
    • evidence supporting this: M1 rasters during movement of the heavy mass show a triphasic profile: one to accelerate the mass, one to decelerate it, and another to hold it steady on target. see [2,3,4,5,6,7,8,9,10]

____References____

{276}
hide / / print
ref: Hatsopoulos-2005.1 tags: motor control M1 Hatsopoulos date: 04-09-2007 22:26 gmt revision:1 [0] [head]

PMID-16160087[] Encoding in the Motor Cortex: Was Evarts Right After All? Focus on "Motor Cortex Neural Correlates of Output Kinematics and Kinetics During Isometric-Force and Arm-Reaching Tasks"

  • this is a discussion of the isometric vs. pendulum movement task. (and editorial focus)
    • in the isometric task, neurons are tuned to the direction of force, and fire anticipatorily.
    • in the movement task, they show a characteristic triphasic activity profile, very similar to what the muscles need.
  • neurons in the rostral bank of the CS seem to (almost) control muscle activation directly.
  • One possibility mentioned by the authors is that motor cortex may need to compensate for nonlinearities at the spinal motoneuron level as well as the low-pass filter characteristics of the muscles resulting in a nonlinear mapping between these neurons and the resulting forces at the hand.

____References____

{338}
hide / / print
ref: Ashe-1994.12 tags: Georgoplous motor control M1 S1 SUA electrophysiology 2D date: 04-09-2007 20:27 gmt revision:2 [1] [0] [head]

PMID-7703686[0] Movement parameters and neural activity in motor cortex and area 5

  • 290 cells in the motor cortex and 207 cells in area 5 (S1)
  • median R^2 = 0.581 & 0.530 in motor cortex
  • most prominent representation of target direction; least prominent representation of acceleration. (though statistically significant correlations were observed for all behavioral parameters)

Duke does not have online access to the article :(

____References____

{286}
hide / / print
ref: Maier-1993.03 tags: force motor control grip electrophysiology date: 04-09-2007 20:20 gmt revision:4 [3] [2] [1] [0] [head]

PMID-8463818[0] Contribution of the monkey corticomotoneuronal system to the control of force in precision grip

  • recorded 33 corticomotoneronal cells
  • used spike-triggered averaging to find putative pyramidal tract neurons.
  • considerable trail-by-trial variability in the cells activity-force relationship
  • and, in an earlier work: PMID-810360[1] Relation of activity in precentral cortical neurons to force and rate of force change during isometric contractions of finger muscles.

____References____

{345}
hide / / print
ref: HeppReymond-1999.09 tags: force motor control grip electrophysiology date: 04-09-2007 20:20 gmt revision:0 [head]

PMID-10473750[0] Context-dependent force coding in motor and premotor cortical areas.

  • here they found neurons related to dF/dt during another isometric precision grip task.

____References____

{344}
hide / / print
ref: Caminiti-1991.05 tags: transform motor control M1 3D population_vector premotor Caminiti date: 04-09-2007 20:10 gmt revision:2 [1] [0] [head]

PMID-2027042[0] Making arm movements within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets.

  • trained monkeys to make similar movements in different parts of external/extrinsic 3D space.
  • change of preferred direction was graded in an orderly manner across extrinsic space.
  • virtually no correlations found to endpoint static position: "virtually all cells were related to the direction and not to the end point of movement" - compare to Graziano!
  • yet the population vector remained an accurate predictor of movement: "Unlike the individual cell preferred directions upon which they are based, movement population vectors did not change their spatial orientation across the work space, suggesting that they remain good predictors of movement direction regardless of the region of space in which movements are made"

____References____

{294}
hide / / print
ref: Caminiti-1990.07 tags: transform motor control M1 3D population_vector premotor Caminiti date: 04-09-2007 20:07 gmt revision:4 [3] [2] [1] [0] [head]

PMID-2376768[0] Making arm movements within different parts of space: dynamic aspects in the primate motor cortex

  • monkeys made similar movements in different parts of external/extrinsic 3D space.
  • change of preferred direction was graded in an orderly manner across extrinsic space.
    • this change closely followed the changes in muscle activation required to effect the observed movements.
  • motor cortical cells can code direction of movement in a way which is dependent on the position of the arm in space
  • implies existence of mechanisms which facilitate the transformation between extrinsic (visual targets) and intrinsic coordinates
  • also see [1]

____References____

{337}
hide / / print
ref: Kalaska-1989.06 tags: motor control direction tuning force Kalaska date: 04-09-2007 19:59 gmt revision:2 [1] [0] [head]

PMID-2723767[0] A comparison of movement direction-related versus load direction-related activity in primate motor cortex, using a two-dimensional reaching task.

  • comparison to georoplous task:
    • "We demonstrate here that many of these cells show similar large continuously graded changes in discharge when the monkey compensates for inertial loads which pull the arm in 8 different directions"
  • the mean activity of the sample population under any condition of movement direction and load direction can be described reasonably well by a simple linear summation of the movement-related discharge without any loads, and the change in tonic activity of the population caused by the load, measured prior to movement
  • their data support the dual kinematics/dynamics encoding in the motor cortex.
    • but, to me, the data also supports direct control of the muscles.

____References____

{335}
hide / / print
ref: Georgopoulos-1992.06 tags: motor control force Georgopoulos date: 04-09-2007 19:56 gmt revision:1 [0] [head]

PMID-1609282[0] The motor cortex and the coding of force.

  • 2D isometric force, which dissociated force & changed in force.
  • cells are not tuned to the direction of the absolute force, but rather to the direction of both the visual cue and change in force (dF/dt) as measured using linear regressions in an isometric force task.

____References____

{343}
hide / / print
ref: Wetts-1985.08 tags: Kalaska isometric motor control dentate cerebellum purkinje M1 pyramidal tract direction tuning date: 04-09-2007 19:54 gmt revision:0 [head]

PMID-3928831[0] Cerebellar nuclear cell activity during antagonist cocontraction and reciprocal inhibition of forearm muscles. by kalaska concering the interpositus dentate & isometric task.

  • the dentate nucleus sends afferents to the premotor areas. GABAergic inhibition from purkinje cells.
  • not so much tuning in the dentate nucleus as M1, but positive correlation was found.
  • Purkinje cells had a general low-order negative tuning to muscle activations.

____References____

{284}
hide / / print
ref: Thach-1978.05 tags: monkeys motor control M1 cerebellum electropysiology date: 04-09-2007 19:53 gmt revision:3 [2] [1] [0] [head]

PMID-96223[0] Correlation of neural discharge with pattern and force of muscular activity, joint position, and direction of intended next movement in motor cortex and cerebellum.

  • recorded from M1 and interpositus/dentate nucleus of the cerebellum.
  • three classes of response in the interpositus/dentate and M1
    1. some in relation to the pattern of muscle activity
    2. some in relation to the position of the wrist
    3. some in relation to the next intended movement.

____References____

{339}
hide / / print
ref: Taira-1996.06 tags: 3D Georgopoulos SUA M1 force motor control direction tuning date: 04-09-2007 15:16 gmt revision:1 [0] [head]

PMID-8817266[0] On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional static isometric force.

  • 3D isometric joystick.
  • stepwise multiple linear regression.
  • direction of force is a signal especially prominent in the motor cortex.
    • the pure directional effect was 1.8 times more prevalent in the cells than in the muscles studied (!)

____References____

{328}
hide / / print
ref: DeLong-1974.05 tags: motor control basal ganglia cerebellum motor cortex DeLong putamen original date: 04-09-2007 01:51 gmt revision:1 [0] [head]

PMID-4219745[0] Relation of basal ganglia, cerebellum, and motor cortex units to ramp and ballistic limb movements.

  • monkey trained to make both ballistic movement and slow, pulling movements by pulling a manipulandum between three targets.
  • cells in the putamen discharged preferentially during slow movements.
    • consistent with a sequence / temporal scaling (?) role.
    • also consistent with the cerebellum creating rapid/feedforward trajectories.
  • cells in the motor cortex discharged for both types of movements, though a bit more for ballisic type movements (where the forces were higher).
  • paper is thankfully short and concise.
    • and also humble: "the mere correlation of unit discharge with some aspect of a movement without knowledge of the peripheral site influenced by the unit under study can only provide grounds for speculation".

____References____

{326}
hide / / print
ref: Ashe-1997.09 tags: motor control force direction magnitude M1 cortex date: 04-09-2007 01:10 gmt revision:0 [head]

PMID-9331494[0] Force and the motor cortex.

  • most M1 cells seem to be related to the direction of static force; fewer related to direction and magnitude; fewer yet to only magnitude.
  • dynamic forces: there is a stron correlation between the rate of change of force and the motor cortex firing
    • dynamic force seems to determine firing rate moreso than static force (e.g. resisting gravity)
    • I have definantly seen evidence of this with the kinarm experiments.

____References____

{296}
hide / / print
ref: Kettner-1988.08 tags: 3D motor control population_vector Schwartz Georgopoulos date: 04-05-2007 17:09 gmt revision:1 [0] [head]

A triptych of papers (good job increasing your publication count, guys!):

  • PMID-3411363[0] Primate motor cortex and free arm movements to visual targets in three-dimensional space. III. Positional gradients and population coding of movement direction from various movement origins.
    • propose multilinear model to predict firing rate of nneuron (a regression that is the same direction as the kalman filter)
    • i don't see how this is that much different from below (?)
  • PMID-3411362[1] Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population.
    • they show, basically, that they can predict movement direction (note this is different from actual movement!) using the poulation vector scheme.
  • PMID-3411361[2] Primate motor cortex and free arm movements to visual targets in three-dimensional space. I. Relations between single cell discharge and direction of movement.
    • 568 cells!!
    • 8 directional targets, again -- not sure how they were aranged; they say 'in approximately equal angular intervals'
    • these findings generalize the previous 2D results [3] (tuning to external space) to 3D

____References____

{295}
hide / / print
ref: Georgopoulos-1982.11 tags: georgopoulos kalaska caminiti M1 motor control tuning population_vector date: 04-05-2007 16:27 gmt revision:0 [head]

PMID-7143039[0] On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex

  • famous 8-target center out task
  • dot-product tuning
  • 75% of cells were found to be tuned.
  • posits the population code for directional movements - statistical summation & averaging, i presume.

____References____

{290}
hide / / print
ref: Ostry-2003.12 tags: force motor control review cortex M1 date: 04-05-2007 15:21 gmt revision:0 [head]

PMID-14610628[0] A critical evaluation of the force control hypothesis in motor control.

  • the target of this review is the inverse dynamics model of motor control, which is very successful in robots. however, it seems that the mammalian nervous system does things a bit more complicated than this.
  • they agree that motor learning is most likely the defining feature of the cortex (i think that the critical and essential element of the cortex is not what control solution it arrives at, but rather how it learns that solution given the anatomical connections development has endowed it with.
  • they also find issue with the failure to incorporate realistic spinal reflexes into inverse-dynamics models.
  • However, we find little empirical evidence that specifically supports the inverse dynamics or forward internal model proposals per se.
  • We further conclude that the central idea of the force control hypothesis--that control levels operate through the central specification of forces--is flawed.

____References____

{279}
hide / / print
ref: Cabel-2001.1 tags: Stephen Scott Kinarm motor control date: 04-04-2007 21:51 gmt revision:0 [head]

PMID-11600665[] Neural Activity in Primary Motor Cortex Related to Mechanical Loads Applied to the Shoulder and Elbow During a Postural Task

  • experiment w/ the kinarm. w/ Stephen Scott.
  • roughly equal numbers of neuons responsive to mechanical loads on shoulder, elbow, and both.
  • neural activity is also strongly influenced by the specific motor patterns used to perform a given task.

____References____

{29}
hide / / print
ref: bookmark-0 tags: machine_learning todorov motor_control date: 0-0-2007 0:0 revision:0 [head]

Iterative Linear Quadratic regulator design for nonlinear biological movement systems

  • paper for an international conference on informatics in control/automation/robotics

{37}
hide / / print
ref: bookmark-0 tags: Unscented sigma_pint kalman filter speech processing machine_learning SDRE control UKF date: 0-0-2007 0:0 revision:0 [head]

{109}
hide / / print
ref: bookmark-0 tags: motor learning control Wolpert Ghahramani date: 0-0-2007 0:0 revision:0 [head]

http://www.bcs.rochester.edu/people/alex/bcs547/readings/WolpertGhahr00.pdf

  • the curse of dimensionality: there are about 600 muscles in the human body; 2^600 >> than the # of atoms in the universe! we must structure this control problem.
  • there are about 200,000 alpha motor neurons.
  • damage to parietal cortex can lead to an inability to maintain state estimates of the limb (and other objects?)
  • damage to pareital cortex can lead to and inability to mentally simulate movement with the affected hand.
  • damage to the left pareital cortex can lead to a relative inability to determine wheither viewed movements are ones own or not.
  • state prediction can reduce the effect of delays in sensorimotor feedback loops.
    • example: soleus and gastrocinemus tightent before lifting a heavy load with the arms.
  • the primate CNS models both the expected sensory feedback and represents the likelihood of the sensory feedback given the context. e.g. if people think that they are moving, they will compensate for non-existent coriolis forces.
  • ''how are we able to learn a variety of contexts?
    • when subjects try to learn two different dynamics (e.g. forward and reverse on sideskates), interference occurs when they are presented in rapid sucession, but not when they are separated by several hours.)
  • has a good list of refs.

{110}
hide / / print
ref: bookmark-0 tags: ISO learning reflex inverse controller Porr date: 0-0-2007 0:0 revision:0 [head]

Iso learning approximates a solution to the inverse controller problem in an usupervised behavioral paradigm http://hardm.ath.cx/pdf/isolearning2002.pdf

  1. robot/actor whatever has a reflex after the presentation of a reward.
  2. the ISO learning mechanism learns to expect its own reflex -> anticipate actions, react at an appropriate time.
    1. a fixed reflex loop prevents arbitraryness by defining initial behavioral goal.
  3. iso means isotropic: all inputs are the same, and all can be used for learning.
  4. learning is proportional to the derivative of the output.
--
  • the central advantage of an (ideal) feed-forward controller is that it acts without the feedback-induced delay. The fatally damaging sluggishness of feedback systems makes this a highly desirable feature.
  • see figure 4 in the local paper. this basically looks like the cerebellum.. sorta. the controller takes predictive signal, and with this prior information, is able to learn the correct response to the disturbance.
  • they also include an interesting comparison to Sutton & Barto's reinforcement learning:
    • in ISO learning, the weights stabilize if a particular input condition is achieved;
    • in reinforcement learning, the weights are stabilized when a certain output condition is reached.

{141}
hide / / print
ref: learning-0 tags: motor control primitives nonlinear feedback systems optimization date: 0-0-2007 0:0 revision:0 [head]

http://hardm.ath.cx:88/pdf/Schaal2003_LearningMotor.pdf not in pubmed.

{140}
hide / / print
ref: Nakanishi-2005.01 tags: schaal adaptive control function approximation error learning date: 0-0-2007 0:0 revision:0 [head]

PMID-15649663 Composite adaptive control with locally weighted statistical learning.

  • idea: want error-tracking plus locally-weighted peicewise linear function approximation (though , I didn't read it all that much in depth.. it is complicated)

{151}
hide / / print
ref: Flash-2001.12 tags: Flash Sejnowski 2001 computational motor control learning PRR date: 0-0-2007 0:0 revision:0 [head]

PMID-11741014 Computational approaches to motor control. Tamar Flash and Terry Sejnowski.

  • PRR = parietal reach region
  • essential controviersies (to them):
    • the question of motor variables that are coded by neural populations.
    • equilibrium point control vs. inverse dynamics (the latter is obviously better/more correct)

{184}
hide / / print
ref: Harris-1998.08 tags: motor_control error variance optimal_control 1998 wolpert date: 0-0-2007 0:0 revision:0 [head]

PMID-9723616[0] Signal-dependent noise determines motor planning

  • key idea: neural control signals are corrupted by noise whose variance increases with the size of the control signal
  • this idea is sufficient to explain a number of features of human motor behavior.

____References____

{61}
hide / / print
ref: bookmark-0 tags: smith predictor motor control wolpert cerebellum machine_learning prediction date: 0-0-2006 0:0 revision:0 [head]

http://prism.bham.ac.uk/pdf_files/SmithPred_93.PDF

  • quote in reference to models in which the cerebellum works as a smith predictor, e.g. feedforward prediction of the behavior of the limbs, eyes, trunk: Motor performance based on the use of such internal models would be degraded if the model was inavailable or inaccurate. These theories could therefore account for dysmetria, tremor, and dyssynergia, and perhaps also for increased reaction times.
  • note the difference between inverse model (transforms end target to a motor plan) and inverse models 9is used on-line in a tight feedback loop).
  • The difficulty becomes one of detecting mismatches between a rapid prediction of the outcome of a movement and the real feedback that arrives later in time (duh! :)
  • good set of notes on simple simulated smith predictor performance.