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}

翻译：本文展示了构建并识别由生物神经元数学模型组成的人工神经网络（ANN）特征的可能性。以FitzHugh-Nagumo（FHN）系统为例，该模型展示了简化的神经元活动。首先，为揭示生物神经元如何嵌入人工神经网络，我们使用非线性神经元训练该网络，以解决基于MNIST数据库的基本图像识别问题；随后，我们描述了如何将FHN系统引入这一已训练的人工神经网络。最终，我们证明内部包含FHN系统的人工神经网络能够成功训练，且其准确率得以提升。上述工作为模拟神经网络方向开辟了广阔前景，其中人工神经元可被生物神经元替代。