title: ÐÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð´Ð¸Ð½Ð°Ð¼Ð¸ÑÐµÑÐºÐ¸Ñ Ð¾Ð±ÑÐ°Ð·Ð¾Ð² Ð°ÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ ÑÐ»ÑÑ Ð¾Ð²Ð¾Ð³Ð¾ Ð½ÐµÑÐ²Ð° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ°Ð¼Ð¾Ð¾ÑÐ³Ð°Ð½Ð¸Ð·ÑÑÑÐ¸Ñ ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ Ð³ÑÐ°ÑÐ¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Classification of dynamic patterns of auditory nerve activity based on self-organizing neural graphs
Creators: name: ÐÐ½ÑÑÐ¾Ð¿Ð¾Ð², ÐÐ»ÐµÐºÑÐ°Ð½Ð´Ñ
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: 2020
Subjects: Ð¿Ð¾ÑÐ¾ÐºÐ¾Ð²ÑÐµ Ð´Ð°Ð½Ð½ÑÐµ
Ð´ÑÐµÐ¹Ñ ÐºÐ¾Ð½ÑÐµÐ¿ÑÐ¸Ð¸
ÑÐ°Ð¼Ð¾Ð¾ÑÐ³Ð°Ð½Ð¸Ð·ÑÑÑÐ¸ÐµÑÑ ÐºÐ°ÑÑÑ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð²
ÑÐ¿ÐµÐºÑÑÐ°Ð»ÑÐ½Ð°Ñ ÑÐµÐ¾ÑÐ¸Ñ Ð³ÑÐ°ÑÐ¾Ð²
ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð³ÑÐ°ÑÐ¾Ð²
stream data
concept drift
selforganising maps
signal classififcation
graph spectral theory
graph comprasion

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«ÐÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð´Ð¸Ð½Ð°Ð¼Ð¸ÑÐµÑÐºÐ¸Ñ Ð¾Ð±ÑÐ°Ð·Ð¾Ð² Ð°ÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ ÑÐ»ÑÑÐ¾Ð²Ð¾Ð³Ð¾ Ð½ÐµÑÐ²Ð° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ°Ð¼Ð¾Ð¾ÑÐ³Ð°Ð½Ð¸Ð·ÑÑÑÐ¸ÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ Ð³ÑÐ°ÑÐ¾Ð²Â». ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¿Ð¾ÑÐ¾ÐºÐ¾Ð²ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¼ÐµÑÐ¾Ð´Ð° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¿Ð¾ÑÐ¾ÐºÐ¾Ð²ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð² ÑÑÐ»Ð¾Ð²Ð¸ÑÑ Ð´ÑÐµÐ¹ÑÐ° ÐºÐ¾Ð½ÑÐµÐ¿ÑÐ¸Ð¸. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¿Ð¾ÑÐ¾ÐºÐ¾Ð²ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð±ÑÐ»Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ ÑÐ°Ð¼Ð¾Ð¾ÑÐ³Ð°Ð½Ð¸Ð·ÑÑÑÐ¸ÐµÑÑ ÐºÐ°ÑÑÑ ÐÐ¾ÑÐ¾Ð½ÐµÐ½Ð°, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÑÑÐ¾Ð¸Ð»Ð¸ÑÑ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° UbiSOM. Ð¢Ð°ÐºÐ¶Ðµ Ð´Ð»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ ÑÑÐ¾Ð´Ð¸Ð¼Ð¾ÑÑÐ¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð² ÐºÐ°ÑÑÑ Ð±ÑÐ» Ð²Ð²ÐµÐ´ÐµÐ½ Ð¼ÐµÑÐ°Ð½Ð¸Ð·Ð¼ ÑÑÐ¾Ð¼Ð»ÑÐµÐ¼Ð¾ÑÑÐ¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð¾Ð², Ð° ÑÐ°ÐºÐ¶Ðµ Ð±ÑÐ» Ð¼Ð¾Ð´Ð¸ÑÐ¸ÑÐ¸ÑÐ¾Ð²Ð°Ð½ Ð¼ÐµÑÐ°Ð½Ð¸Ð·Ð¼ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð´ÑÐµÐ¹ÑÐ° ÐºÐ¾Ð½ÑÐµÐ¿ÑÐ¸Ð¸ Ð´Ð»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÐµÐ³Ð¾ ÑÑÐ²ÑÑÐ²Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ Ðº Ð¸Ð·Ð¼ÐµÐ½ÐµÐ½Ð¸ÑÐ¼ Ð² Ð¿Ð¾ÑÐ¾ÐºÐµ. ÐÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ð²ÑÐ¿Ð¾Ð»Ð½ÑÐ»Ð°ÑÑ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÑÐ¾Ð¶ÐµÑÑÐ¸ Ð³ÑÐ°ÑÐ¾Ð², ÑÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐµ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÐºÐ°ÑÑ, Ð¾Ð±ÑÑÐµÐ½Ð½ÑÑ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¿Ð°ÐºÐµÑÐ½Ð¾Ð³Ð¾ ÑÐµÐ¶Ð¸Ð¼Ð°. ÐÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½ÑÐ¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð½Ð° ÑÐ·ÑÐºÐµ C++. ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð°Ð¿ÑÐ¾Ð±Ð¸ÑÐ¾Ð²Ð°Ð½ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÐµÑÑÐ¾Ð²ÑÑ Ð´Ð°Ð½Ð½ÑÑ, Ð¸Ð¼Ð¸ÑÐ¸ÑÑÑÑÐ¸Ñ Ð¿Ð¾ÑÐ¾Ðº Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð´ÑÐµÐ¹ÑÐ¾Ð² Ð² Ð¿Ð¾ÑÐ¾ÐºÐ°Ñ Ð¸ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ ÐºÐ°ÑÑ, ÑÐ°ÐºÐ¶Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð»ÑÑ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° ÑÐ»ÑÑÐ¾Ð²Ð¾Ð³Ð¾ Ð½ÐµÑÐ²Ð° Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÐºÐ°ÑÐµÑÑÐ²Ð° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸. ÐÐ¾ÑÐºÐ¾Ð»ÑÐºÑ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»ÑÑ Ð¼ÐµÑÐ¾Ð´ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÑÐ¾Ð¶ÐµÑÑÐ¸ Ð³ÑÐ°ÑÐ¾Ð², Ð´Ð»Ñ ÑÑÐºÐ¾ÑÐµÐ½Ð¸Ñ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¾ Ð¿ÑÐ¸Ð½ÑÑÐ¾ ÑÐµÑÐµÐ½Ð¸Ðµ Ð²ÑÑÐ¸ÑÐ»ÑÑÑ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾ ÐºÐ»Ð°ÑÑÐµÑÐ¾Ð² Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ DS2L-SOM, ÐºÐ¾ÑÐ¾ÑÑÐ¹ ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°Ð»ÑÑ Ñ SOINN Ð¸ ESOINN. Ð¡ÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¾, ÑÑÐ¾ Ð´Ð»Ñ ÑÑÐºÐ¾ÑÐµÐ½Ð¸Ñ ÑÐ°Ð±Ð¾ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÑÐµÐ»ÐµÑÐ¾Ð¾Ð±ÑÐ°Ð·Ð½Ð¾ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑ DS2L-SOM. ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¾ ÐµÐ³Ð¾ ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾ÑÑÑ Ð´Ð¸Ð½Ð°Ð¼Ð¸ÑÐµÑÐºÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÑÐ¸ÑÐ¾Ð²Ð°ÑÑ Ð¸Ð½ÑÐµÑÐ²Ð°Ð»Ñ, ÑÐ¾Ð´ÐµÑÐ¶Ð°ÑÐ¸Ðµ Ð³Ð¾Ð»Ð¾Ñ Ð¸ ÑÑÐ¼, ÑÑÐ¾ Ð³Ð¾Ð²Ð¾ÑÐ¸Ñ Ð¾ ÑÐ¾Ð¼, ÑÑÐ¾ Ð¾Ð½ Ð¼Ð¾Ð¶ÐµÑ Ð±ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½ Ð´Ð»Ñ Ð´ÐµÑÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐµÑÐ¸.
description: The topic of the graduate qualification work is: "Classification of dynamic images of auditory nerve activity on the basis of self-organizing neural graphs". This work is devoted to the research of the problem of stream data processing and development of the method of classifying stream data under the concept drift. As a result, self-organizing Kohonen maps were used to classify the stream data, which were constructed using the UbiSOM algorithm. Also to increase the convergence rate of the algorithm, the neuronal fatigue mechanism was introduced into the map, and the concept drift detection mechanism was modified to increase its sensitivity to changes in the flow. Classification of data was performed by estimating the similarity of graphs formed on the basis of maps trained with the help of batch mode. The proposed algorithm is implemented in C++ language. The algorithm was tested with the help of test data simulating the flow to estimate the quality of drift detection in the flows and the quality of mapping. The algorithm was also tested with the help of auditory nerve analysis to estimate the quality of classification. Since a similarity graph method was used for classification, it was decided to calculate the number of clusters using DS2L-SOM, which was compared to SOINN and ESOINN, to speed up the work. Comparison showed that it was reasonable to use DS2L-SOM to speed up the work of the algorithm. The algorithm has shown its ability to dynamically classify intervals containing voice and noise, suggesting that it can be used for speech detection.