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

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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.


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

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ref: work-0 tags: no free lunch wolpert coevolution date: 07-19-2010 12:54 gmt revision:2 [1] [0] [head]


  • Just discovered this. It makes perfect sense - bias free learning is 'futile'. Learning need be characterized by its biases, which enable faster or better results in particular problem domains.
  • Equivalently: any two algorithms are equivalent when their performance is averaged across all possible problems. (This is not as strong as it sounds, as most problems will never be encountered).
  • Wolper 1996 provides an excellent geometric interpretation of this: the quality of the search/optimization algorithm within a particular domain iis proporational to the inner product of its expected search stream with the actual (expected?) probability distribution of the data.
  • However! with coevolutionary algorithms, there can be a free lunch - "in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems." Wolpert 2005
    • claims that this does not (??) hold in biological evolution, where there is no champion. Yet biology seems all about co-evolution.
    • coevolution of a backgammon player details how it may be coevolution + the structure of the backgammon game, not reinforcement learning, which led Tesauro to his championship-level player. Specifically, coevolutionary algorithms tend to get stuck in local minima - where both contestants play mediocre and draw - but this is not possible in backgammon; there is only one winner, and the games must terminate eventually.
      • These authors introduce a very interesting twist to improve coevolutionary bootstrapping: Firstly, the games are played in pairs, with the order of play reversed and the same random seed used to generate the dice rolls for both games. This washes out some of the unfairness due to the dice rolls when the two networks are very close - in particular, if they were identical, the result would always be one win each.

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ref: bookmark-0 tags: motor learning control Wolpert Ghahramani date: 0-0-2007 0:0 revision:0 [head]


  • 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.

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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.


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ref: bookmark-0 tags: smith predictor motor control wolpert cerebellum machine_learning prediction date: 0-0-2006 0:0 revision:0 [head]


  • 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.