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
- 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 biases analysis for direction, not magnitude; correct model is -- 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.
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