Are non-ordered (Slabbed) Neural Networks in wide modern use and what does Fiesler (1994) mean by Clamping Function and Ontogenic function?
I have not heard of recent work on this kind of thing, but there's a huge amount of literature and I know only a tiny slice of it.
Today much of the work on neural networks is concerned with feedforward neural networks, which don't have a cyclical structure.  The most common form is
$$z(x) = L_n(\cdots (a(L_1(x))))$$
where $f:\mathbb{R}^n \to \mathbb{N}^k$, each $L_i$ is a linear function, and each $a$ represents an activation function: i.e., $a(x_1,\dots,x_d) = (a(x_1),\dots,a(x_d))$ and $a$ is a simple nonlinear function called the activation function.  The ReLU $a(x)=\max(0,x)$ is a common choice of activation function.  There is some cleverness in the structure of the linear layers.  Finally, the output of the neural network is $f(x)=\text{softmax}(z(x))$ where
$$\text{softmax}(z_1,\dots,x_k) = ({e^{z_1} \over e^{z_1} + \dots + e^{z_k}}, \dots, {e^{k_1} \over e^{z_1} + \dots + e^{z_k}}).$$
This is not the only architecture, but it is probably the most commonly seen, and as you can see, it has a relatively clean formalization.