**5. Conclusions**

In the current study, we present the new model of an LIF neuron based on one switching VO<sup>2</sup> element. The neuron circuit was modeled in the LTspice program, and, for the component emulating the switch, a voltage-controlled key was used, which the I – V characteristic corresponded to experimental data. During the simulation, the VO2-neuron model demonstrates biosimilar properties, such as spike latency, subthreshold oscillations, refractory period, threshold behavior, and spike frequency adaptation. A two-layer SNN was designed to allow pattern recognition. The coupling between the neurons of the input and output layers was implemented using excitatory connections, and, inside the output layer, the coupling used inhibitory connections. This architecture led to the activation of only one output neuron associated with the most similar pattern, according to the WTA principle. As an example, we studied the network that had nine input and threeoutput neurons, which was trained to recognize three patterns (3 × 3 pixels). A timing method of information coding was used, where the color intensity of the pixel was determined by the time delay between the spikes. The training was conducted using the supervised SPDT method, taking into account the time delay of pre-synaptic and post-synaptic spikes. To analyze the operation of the trained network, the images of distorted patterns from the training set were sent to the network input, and the images were correctly recognized in most cases. The network is capable of recognizing up to 10<sup>5</sup> images per second, and the classification process is highly dependent on the time parameters of the network and the effect of electrical switching. Network architecture has the potential for further scaling, which increases the speed of recognition and miniaturization of the components. In the future, we plan to continue the work toward optimization of both the neuron circuit and the network architecture for classifying images from standardized databases [64].

**Author Contributions:** Conceptualization, M.B. Methodology, M.B. and A.V. Software validation, M.B. Writing—original draft preparation, M.B. and A.V. Project administration, A.V.

**Funding:** The Russian Science Foundation (grant no. 16-19-00135) supported this research.

**Acknowledgments:** The authors express their gratitude to Andrei Rikkiev for the valuable comments in the course of the article translation and revision.

**Conflicts of Interest:** The authors declare no conflict of interest.
