  In this paper we show the possibility of creating and identifying the
features of an artificial neural network (ANN) which consists of mathematical
models of biological neurons. The FitzHugh--Nagumo (FHN) system is used as an
example of model demonstrating simplified neuron activity. First, in order to
reveal how biological neurons can be embedded within an ANN, we train the ANN
with nonlinear neurons to solve a a basic image recognition problem with MNIST
database; and next, we describe how FHN systems can be introduced into this
trained ANN. After all, we show that an ANN with FHN systems inside can be
successfully trained and its accuracy becomes larger. What has been done above
opens up great opportunities in terms of the direction of analog neural
networks, in which artificial neurons can be replaced by biological ones.
\end{abstract}
