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Our model shows a high probability of motor neurons, sensory neurons, and various interneuron classes being of the same type.
CAP can be converted to a scaled version expressing the 5-year probability of motor diagnosis from study entry (5 years is roughly the average time to diagnosis for the converters in PREDICT-HD).
Sensory stimulation of the photo- or chemoreceptors transiently modulates the probability of motor switching, resulting in a movement of the cell towards more favorable regions in the environment [ 5, 6].
The perception process results in probabilistic motor resonances at each level, updated at each time step, with the prior probability of motor candidates set to its previous posterior probability (see "A Probabilistic Model of Motor Resonances").
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Furthermore, the probabilities of motor candidates at the three levels influence each other mutually.
The probability of each motor schema hypothesis thus depends on the probabilities of the associated motor program hypotheses, and indirectly on the related motor commands and evidence about each wrist.
In contrast, when CheY-P concentration is low, the probability of any motor rotating CW is essentially zero.
The second one also performs a Bayesian update but now on the probability of each motor unit conditioned on the higher-level hypotheses.
However, for 0.5 < ∆T/F low < 1 and stochastic stepping, there is a finite and increasing probability of inter-motor tension exceeding the stall force of the stronger motor ('Materials and methods'), resulting in the weaker motor winning the competition (60% < Φ out < 80%).
Finally, fully-adjusted probabilities of passing motor skill tests by obese children compared to normal-weight children were calculated (ratios of probabilities are reported here).
The first one acts as described above and calculates posterior probabilities of all motor structures given bottom-up evidence using a Bayesian update.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com