Here, c0 is the wave speed of the homogeneous neural field (β = 0), which is given by c0 = c λ0) with λ0 obtained by minimizing the function c defined by Equation 4.3 (see Equation 2.15).
By replacing in (94) the function c 3 ( k + 1, l ) with its expression given by (52), we obtained the constraint (7), i.e., z k + 1 l + 1 z k l − z k l + 1 z k + 1 l = 0.
It follows that λ0 is given by the minimum of the function c = - 1 + W λ (4.3).
Hence, it follows, by the argument principle, that the function c ~ ′ Open image in new window has zeros in D Open image in new window, which contradicts the conformality of c ~ Open image in new window.
By (3.3) and the continuity of the function c, we have η C ∈ L 1 ( [ − L C ∗, L C ∗ ] ).
The density function of the distribution function H can be derived by the density function C of the Copula function and the edge distribution function F and G as: hleft( x, yright)= cleft( F x), G y)right) f(x) g y) (23).
This dependence with high accuracy at cu < < 1 can be described by the function c u) = c(0)(1 - u2)/(1 + u2).
The orthogonal matrix Uopt(n ) can be obtained by minimizing the cost function C, which is shown as (8).
The first equality follows because the function inside the integral tends to zero at every point and is upper bounded by the following integrable function, c ′ ε 0 y + 1 1 ∂f ∂Θ Θ, Π ( i, y ) ≤ 1 + c ′′ ε 0 y + 1 2 1 ∂f ∂Θ Θ, Π ( i, y ) > 1.
a classification by biological class; b classification by molecular function; c classification by protein class.
