How are ANN's, RNN's related to logistic regression and CRF's?
No, it is not possible.

There is a fundamental difference between these graphs and neural networks, despite both being represented by circles and lines/arrows.

These graphs (Probabilistic Graphical Models, PGMs) represent random variables $X=\{X_1,X_2,..,X_n\}$ (circles) and their statistical dependence (lines or arrows). They together define a structure for joint distribution $P_X(\boldsymbol{x})$; i.e., a PGM factorizes $P_X(\boldsymbol{x})$. As 
a reminder, each data point $\boldsymbol{x}=(x_1,..,x_n)$ is a sample from $P_X$. However, a neural network represents computational units (circles) and flow of data (arrows). For example, node $x_1$ connected to node $y$ with weight $w_1$ could mean $y=\sigma(w_1x_1+...)$. They together define a function $f(\boldsymbol{x};W)$.

To illustrate PGMs, suppose random variables $X_1$ and $X_2$ are features and $C$ is label. A data point $\boldsymbol{x}=(x_1, x_2, c)$ is a sample from distribution $P_X$. Naive Bayes assumes features $X_1$ and $X_2$ are statistically independent given label $C$, thus factorizes $P_X(x_1, x_2, c)$ as $P(x_1|c)P(x_2|c)P(c)$.

In some occasions, neural networks and PGMs can become related, although not through their circle-line representations. For example, neural networks can be used to approximate some factors of $P_X(\boldsymbol{x})$ like $P_X(x_1|c)$ with function $f(x_1,c;W)$. As another example, we can treat weights of a neural network as random variables and define a PGM over weights.