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Extraction of Significant Features by Fixed-Weight Layer of Processing Elements for the Development of an Efficient Spiking Neural Network Classifierстатья
Статья опубликована в высокорейтинговом журнале
Информация о цитировании статьи получена из
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 15 февраля 2024 г.
- Авторы: Sboev Alexander, Rybka Roman, Kunitsyn Dmitry, Serenko Alexey, Ilyin Vyacheslav, Putrolaynen Vadim
- Журнал: Big Data and Cognitive Computing
- Том: 7
- Номер: 4
- Год издания: 2023
- Издательство: MDPI
- Местоположение издательства: Basel, Switzerland
- Первая страница: 184
- Последняя страница: 184
- DOI: 10.3390/bdcc7040184
- Аннотация: In this paper, we demonstrate that fixed-weight layers generated from random distribution or logistic functions can effectively extract significant features from input data, resulting in high accuracy on a variety of tasks, including Fisher’s Iris, Wisconsin Breast Cancer, and MNIST datasets. We have observed that logistic functions yield high accuracy with less dispersion in results. We have also assessed the precision of our approach under conditions of minimizing the number of spikes generated in the network. It is practically useful for reducing energy consumption in spiking neural networks. Our findings reveal that the proposed method demonstrates the highest accuracy on Fisher’s iris and MNIST datasets with decoding using logistic regression. Furthermore, they surpass the accuracy of the conventional (non-spiking) approach using only logistic regression in the case of Wisconsin Breast Cancer. We have also investigated the impact of non-stochastic spike generation on accuracy.
- Добавил в систему: Ильин Вячеслав Анатольевич