If the mean firing rate is above the targets, then g and x both decrease to lower the mean firing rate to near (r_{{{x}} }). 2.
Each model is evaluated using two metrics: mean firing rate and modulation gain.
Moreover, the divergence increases with increasing pairwise correlation and decreasing mean firing rate.
which we model by a homogeneous Poisson process with mean firing rate and spike times.
If the mean firing rate is below (r_{{{x}} }), then g and x increase, both acting to increase the mean firing rate until it is in the neighborhood of (r_{{{x}} }).
As it does so, x changes in the opposite direction to keep the mean firing rate near (r_{{{x}} }). .
(50), (53), and (56) into Eq. (49), we obtain the mean firing rate for the AD model.
Main Outcome Measures: Surface electromyogram, surface-detected motor unit action potential amplitude (S-MUAP), mean firing rate, force (MVC), motor unit index.
More precisely, the mean firing rate N ( t ) is implicitly given by N ( t ) : = − a ( N ( t ) ) ∂ p ∂ v ( V F, t ) ≥ 0. (1.6).
which in the limit leads to the same mean firing rate as for the AD model Eq. (14) in the limit of.
(G) Mean firing rate plotted divided by current intensity for DRG neurons in the absence and presence of BmK I (BmK I: n = 5; control: n = 7).
