m8ta
You are not authenticated, login.
text: sort by
tags: modified
type: chronology
[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] Harris CM, Wolpert DM, Signal-dependent noise determines motor planning.Nature 394:6695, 780-4 (1998 Aug 20)

{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

{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____

{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

{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____