Sentence examples for expected firing from inspiring English sources

While a close approximation of the (expected) firing rate of Rulkov neurons (Fig. 7) is needed to mimic their behavior quantitatively, the types of qualitative dynamics of the rate-reduced model do not depend on the exact choice of firing rate function.

As expected, firing rates increased with increasing contrast (two-factor anova, P < 0.001, contrast main effect).

While (S varsigma)) can readily be computed for any (varsigmainmathbb{R}) and we derived a concise expression for the expected firing rate, the infinite sum (30) cannot easily be evaluated.

If we add Gaussian noise to the threshold parameter θ in (SNM), it is natural to define an expected firing rate (langle Sranglecolonmathbb{R}mapstomathbb{R}), given by bigllangle S varsigma bigrrangle =frac{1}{sqrt{2pisigma^{2}}} int _{-infty}^{infty}e^{frac{-w^{2}}}S varsigma}}S(varsigma +w), mathrm{d}w, (29) where (sigma^{2}) is the variance of the noise.

Both firing rate functions (S_{i}colonmathbb{R}mapstomathbb{R}) are chosen to approximate the expected firing rate of Rulkov neurons (SNM) with a noise level of (sigma^{2}=frac{1}{4}) (Fig. 7), S_{1} varsigma)=S_{2}(varsigma)=frac{1}{6}+ frac{1}{12}operatorname{erf}biggl(frac{varsigma-0.0335}{0.6890} biggr)+frac{1}{12}operatorname{erf}biggl(frac{varsigma-0.7099}{0.8213} biggr).

However, recent studies have failed to detect the expected firing rate increase in the GPi (Wichmann et al. 1999; Raz et al. 2000; Tachibana et al. 2011).

They expected FIRE to last for 10 years.

Fuel characteristics and expected fire behavior and effects change as sagebrush steppe transitions into juniper woodlands.

The outputs of the system are indicative of maximum expected fire potential for the day, which occurs during the warmest part of the day, the mid- to late-afternoon.

The system operates as a standalone application and allows the end users to apply various fire environment (meteorology, topography, fuel) scenarios in order to estimate the characteristics of fuel complexes in Mediterranean pine stands, to predict the expected fire behavior, as well as to implement and evaluate fuel treatments for reducing the intensity and severity of fires in such ecosystems.

There were limited differences between the predictors selected in the FIRENPP and FIREnoNPP ensembles (Table 2) and between the spatial distributions of expected fire probabilities (Figures 1C and 1D).