The input data x k to every neuron are summed up according to their weights and modified by the activation function K: The output f l (x k ) then serves as input to the l-th neuron of the next layer.
These simulations show that most of these variants work robustly, almost as well as backpropagation, and that multiplication by the derivatives of the activation functions is important.
This reduction, achieved by a convenient choice of the activation functions and the initial conditions of the synaptic weights, is developed in two steps.
In this paper, without assumptions of the boundedness and the monotonicity of the activation functions, by the Leray-Schauder alternative theorem in differential inclusion theory and some new analysis techniques, we study the existence of periodic solution for discontinuous neural networks with nonlinear growth activations.
Even if this reduction is achieved by a convenient choice of the activation functions and the initial conditions of the synaptic weights, the proposed architecture leads to a wide range of models among the most encountered in the literature assuring the interest of such a method.
We observed xenoestrogens binding to both ERs with affinities ranging from subnanomolar to micromolar values and acting in a subtype-dependent fashion as full agonists or partial agonists/antagonists by using different combinations of the activation functions 1 and 2 of ERα and ERβ.
The activation functions are given by (f(x t))=g(x t))=tanh(x t))), (tau=1).
The activation functions are described by the sector-like nonlinearities that are more general than the commonly used Lipschitz ones.
By approximating the activation function as a product of Heaviside-step functions, i.e. a blockpulse, we expect that it is possible to find implicit equations for the various phases of the traveling front and the speed using techniques as in [14].
Finally, we further explore the drug biphasic phenomena using the second-order mean excitatory and mean inhibitory synaptic drive model given by (124) and (125) with the activation function (f cdot)) given by (10).
