title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ ÑÐ¿Ð°Ð¹ÐºÐ¾Ð²ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Research of training methods for spiking neural networks
Creators: name: ÐÐ°ÑÑÐ±Ð¸Ð½, ÐÐ¸ÑÐ¸Ð»Ð»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: ÑÐ¿Ð°Ð¹ÐºÐ¾Ð²Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÑÐ¸Ð½Ð°Ð¿Ñ
Ð¸Ð·Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð²ÐµÑÐ°
Ð¼ÐµÑÐ¾Ð´ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ðµ ÐºÐ¾Ð´Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
spiking neural network
synapse
weight changing
training method
time coding

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description: The objects of research are methods for training spiked neural networks.The purpose of the bachelor's work is the analysis and software implementation of the selected methods for training spiked neural networks, testing their performance in the study of the dynamics of changes in the weight of a neuron. Tasks that were solved during the study:Analysis of existing methods for training neural networks and the choice of methods for research.Compilation of mathematical models of the selected methods.Software implementation of each method.Research of the received realizations by means of experiments on a correctness and quality of the training made by them.In the course of the work, various methods for training spiked neural networks were considered; by analyzing the collected data, a method was chosen, the mathematical models of variations of which served as the basis for software implementation. The resulting software components were tested for correct operation and studied for the effect of specific methods on the neuron synapse weight.The obtained results can be used for software implementation of learning methods on third-party platforms, for using learning methods in the software implementation of a spiked neural network.The obtained implementations of the training methods make it possible to train spike neural networks to solve classical machine learning problems, and their variability provides the ability to select the desired conditions and work results.