## 1 Introduction  
The SPD NICA experiment aims to investigate the characteristics of dense matter in extreme conditions in the field of high-energy physics. To achieve this, the SPD collaboration proposes the installation of a universal detector in the second interaction point of the NICA collider, which enables the study of the spin structure of the proton and deuteron using polarized beams [1]. The results of the SPD experiment are expected to provide significant contributions to our understanding of the gluon content of nucleons and to complement similar studies at RHIC, EIC at BNL, and the fixed-target facilities at LHC at CERN. It is crucial to conduct simultaneous measurements using different processes within the same experimental setup to minimize possible systematic errors. Moreover, the experiment has the potential to investigate polarized and unpolarized physics during the first stage of NICA's operation, with reduced luminosity and collision energy of the proton and ion beams. As indicated in [1] and shown in Figure 1, the tracking part of the SPD setup includes the silicon vertex detector (SVD) and straw tube-based tracking (ST) system, which together consist of 53 measuring stations. Our focus is on the reconstruction of track measurements made in the SVD and ST systems. Unfortunately, both these detector systems have one significant disadvantage, caused by the data acquisition hardware itself. For instance, the straw tubes of every station are arranged in parallel in two layers, so that one of the coordinates of the passing track is read by the tubes of one layer and the other one by the other layer. When the number of passing tracks is more than one, this inevitably leads to the appearance of a large number of false readings--fake hits--in addition to the real ones. Recognition of the trajectories of particles involved in collisions, for all high-energy experiments, is the main stage in the reconstruction of events, necessary to assess the physical parameters of these particles and to interpret the processes under study. In physics experiments, tracking algorithms have evolved with the development of experimental facilities and technologies for particle detection. Therefore, many articles are devoted to the development of track recognition algorithms and programs. Traditionally, algorithms based on the Kalman filter (KF) [2] have been successfully used for tracking, being up to now the most effective tracking methods. A typical tracking scheme using the KF method consists of finding an initial track value, then extrapolating it to the next coordinate detector, and finding a hit belonging to the track near the extrapolated point. The procedure is then repeated, taking into account the newfound hit. This method naturally accounts for magnetic field inhomogeneity, multiple scattering and energy losses as the particle passes through matter. However, in recent years, due to the increase in both the luminosity of particle beams in high-energy physics (HEP) experiments and the multiplicity of events, many obvious shortcomings of the KF have become a serious obstacle in its tracking applications. These include the iterative nature of the KF and the considerable complexity of its parallel implementation, which leads to its poor scalability and limits the speed of data processing, especially with a large number of tracks. It has become clear that new approaches to  
Figure 1: General layout of the SPD setup [1]. tracking are required, taking advantage of modern hardware. The most promising was the application of tracking algorithms based on neural network applications. The first attempt to use artificial neural networks for track reconstruction was made by [PERSON] [3], using multilayer perceptrons and fully connected Hopfield neural networks [4; 5]. Later, imperfections in these neural network algorithms, a sharp drop in tracking efficiency with increasing noise levels, and multiplicity of events led to a modification of [PERSON]'s neural network algorithms, called elastic neural networks [6].