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The problem of classification using a compartmental spiking neuron model is considered. The state of the art of spiking neural networks analysis is carried out. It is concluded that there are very few works on the study of compartmental neuron models. The choice of a compartmental spiking model is justified as a neuron model for this work. A brief description of such a model is given, and its main features are noted in terms of the possibility of its structural reconfiguration. The method of structural adaptation of the model to the input spike pattern is described. The general scheme of the compartmental spiking neurons’ organization into a network for solving the classification problem is given. The time-to-first-spike method is chosen for encoding numerical information into spike patterns, and a formula is given for calculating the delays of individual signals in the spike pattern when encoding information. Brief results of experiments on solving the classification problem on publicly available data sets (Iris, MNIST) are presented. The conclusion is made about the comparability of the obtained results with the existing classical methods. In addition, a detailed step-by-step description of experiments to determine the state of an autonomous uninhabited underwater vehicle is provided. Estimates of computational costs for solving the classification problem using a compartmental spiking neuron model are given. The conclusion is made about the prospects of using spiking compartmental models of a neuron to increase the bio-plausibility of the implementation of behavioral functions in neuromorphic control systems. Further promising directions for the development of neuromorphic systems based on the compartmental spiking neuron model are considered.

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The purpose of this work is to develop a compartment spiking neuron model as an element of growing neural networks. Methods. As part of the work, the CSNM is compared with the Leaky Integrate-and-Fire model by comparing the reactions of point models to a single spike. The influence of hyperparameters of the proposed model on neuron excitation is also investigated. All the described experiments were carried out in the Simulink environment using the tools of the proposed library. Results. It was concluded that the proposed model is able to qualitatively reproduce the reaction of the point classical model, and the tuning of hyperparameters allows reproducing the following patterns of signal propagation in a biological neuron: a decrease in the maximum potential and an increase in the delay between input and output spikes with an increase in the size of the neuron or the length of the dendrite, as well as an increase in the potential with an increase in the number of active synapses. Conclusion. The proposed compartment spiking neuron model allows to describe the behavior of biological neurons at the level of pulse signal conversion. The hyperparameters of the model allow tuning the neuron responses at fixed other neuron parameters. The model can be used as a part of spiking neural networks with details at the level of compartments of neurons dendritic trees.

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