title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÑÑÑÑÐºÑÑÑÐ½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ½Ð¾Ð¹ ÑÐ¿Ð°Ð¹ÐºÐ¾Ð²Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð° Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸
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: Development of the structural learning algorithm for a compartmental spiking neuron model for classification
Creators: name: ÐÑÐµÐ¼ÐµÐ½ÐºÐ¾, ÐÐ»Ð¸Ð·Ð°Ð²ÐµÑ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ÐµÐ³Ð¼ÐµÐ½ÑÐ½Ð°Ñ ÑÐ¿Ð°Ð¹ÐºÐ¾Ð²Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð°
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð¸Ð½ÐºÑÐµÐ¼ÐµÐ½ÑÐ½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐ¿Ð°Ð¹ÐºÐ¾Ð²ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
compartmental spiking neuron model
learning algorithm
classification
incremental learning
spiking neural networks

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description: The subject of the graduate qualification work is âDevelopment of the structural learning algorithm for a compartmental spiking neuron model for classificationâ.The given work is devoted to development of a structural learning algorithm for a compartmental spiking neuron model for application in incremental learning tasks. The research set the following goals:Research of scenarios and approaches to the implementation of incremental learning of a compartmental spiking neuron model.Development of the learning algorithm for a compartmental spiking neuron model.Implementation of the learning algorithm in a software platform for modeling neural networks based on a compartmental spiking neuron model.Experimental research of the learning algorithm for classification.The study resulted into options for implementing incremental learning of neural networks based on a compartmental spiking neuron model are proposed. The implemented structural learning algorithm for a compartmental spiking neuron model makes it possible to train the model with new training data from the state obtained after studying the previous training data. The algorithm makes it possible to train compartmental spiking neuron model with more than one training example. Further research can be directed to the application of the developed algorithm for solving problems of incremental learning.