Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Extraction of Significant Features by Fixed-Weight Layer of Processing Elements for the Development of an Effective Spiking Neural Network Classifier

Alexander Sboev
* ORCID logo ,
Roman Rybka
,
Dmitry Kunitsyn
,
Alexey Serenko
ORCID logo ,
Vyacheslav Ilyin
,
Vadim Putrolaynen
Version 1 : Received: 24 November 2023 / Approved: 27 November 2023 / Online: 27 November 2023 (04:52:18 CET)

A peer-reviewed article of this Preprint also exists.

Sboev, A.; Rybka, R.; Kunitsyn, D.; Serenko, A.; Ilyin, V.; Putrolaynen, V. Extraction of Significant Features by Fixed-Weight Layer of Processing Elements for the Development of an Efficient Spiking Neural Network Classifier. Big Data Cogn. Comput. 2023, 7, 184. Sboev, A.; Rybka, R.; Kunitsyn, D.; Serenko, A.; Ilyin, V.; Putrolaynen, V. Extraction of Significant Features by Fixed-Weight Layer of Processing Elements for the Development of an Efficient Spiking Neural Network Classifier. Big Data Cogn. Comput. 2023, 7, 184.

Abstract

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 spikes number generated in the network, it is a practically useful for reducing energy consumption in spiking neural networks. Our findings reveal that the proposed method demonstrates a highest accuracy on Fisher’s iris and MNIST datasets with logistic regression. Furthermore, they surpass the accuracy of the conventional (non-spiking) approach using logistic regression in the case of Wisconsin Breast Cancer. We have also investigated the impact of non-stochastic spike generation on accuracy.

Keywords

spiking neural network; logistic functions, fixed weights; random weights; classification; feature extraction

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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