"... We introduce a space-state model in which the discharge activity
of motor neurons is modeled as inhomogeneous Poisson processes
and propose a method to quantify an abstract latent trajectory
that represents the common input received by motor neurons. The
approach also approximates the variation in synaptic noise in the
common input signal. The model is validated with four data sets:
a simulation of 120 motor units, a pair of integrate-and-fire
neurons with a Renshaw cell providing inhibitory feedback, the
discharge activity of 10 integrate-and-fire neurons, and the
discharge times of concurrently active motor units during an
isometric voluntary contraction. The simulations revealed that a
latent state-space model is able to quantify the trajectory and
variability of the common input signal across all four
conditions. When compared with the cumulative spike train method
of characterizing common input, the state-space approach was more
sensitive to the details of the common input current and was less
influenced by the duration of the signal. The state-space
approach appears to be capable of detecting rather modest changes
in common input signals across conditions."
Reference:
1 .
Feeney DF, Meyer FG, Noone N, Enoka RM (2017) A latent low-dimensional common input drives a pool of motor neurons: a probabilistic latent state-space model. J Neurophysiol 118:2238-2250 [PubMed]
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