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Fig. 9 Bump widths for a Gaussian threshold distribution.

The one-step optimal input problem is solved both for equispaced and generic sensor threshold distribution.

Fig. 15 Probability distributions of bump widths for a Gaussian threshold distribution.

Fig. 12 Bump solutions as a function of ϵ for a Gaussian threshold distribution.

Fig. 17 Probability distributions of bump widths for a non-Gaussian threshold distribution.

During load, the fibers break quasi-statically according to a stress threshold distribution.

In the first Wilson Cowan paper, it was assumed that these threshold distributions were either Poisson-like, or Gaussian.

Other parameter values are (kappa=5), (sigma^{2}=0.2), (L=100) Fig. 6 Mean speed versus ϵ for non-Gaussian threshold distributions.

To study the feasibility and test retest repeatability of a sensory threshold examination protocol (STEP) and report the quantitative sensory threshold distributions in healthy dogs.

Other parameter values are (sigma^{2}=1/kappa), (h_{0}=0.3), (epsilon =0.01), (L=100) Fig. 4 Instantaneous speed of a travelling front for non-Gaussian threshold distributions.

For all colony threshold distributions, the mean stimulus per site increases nonlinearly with the driving rate (Figure 10).