<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>In this paper we perform an analysis of the learning process with the ReSuMe method and spiking neural networks (Ponulak, 2005; Ponulak, 2006b). We investigate how the particular parameters of the learning algorithm affect the process of learning. We consider the issue of speeding up the adaptation process, while maintaining the stability of the optimal solution. This is an important issue in many real-life tasks where the neural networks are applied and where the fast learning convergence is highly desirable.</p></abstract>

In this paper we perform an analysis of the learning process with the ReSuMe method and spiking neural networks (Ponulak, 2005; Ponulak, 2006b). We investigate how the particular parameters of the learning algorithm affect the process of learning. We consider the issue of speeding up the adaptation process, while maintaining the stability of the optimal solution. This is an important issue in many real-life tasks where the neural networks are applied and where the fast learning convergence is highly desirable.

Analysis of the ReSuMe Learning Process For Spiking Neural Networks

Institute of Control and Information Engineering Poznań, University of Technology, ul. Piotrowo 3a, 60-965 Poznań, Poland

International Journal of Applied Mathematics and Computer Science

{"article-title":"Analysis of the ReSuMe Learning Process For Spiking Neural Networks"}

In this paper we perform an analysis of the learning process with the ReSuMe method and spiking neural networks (Ponulak, 2005; Ponulak, 2006b). We investigate...

Analysis of the ReSuMe Learning Process For Spiking Neural Networks

Data publikacji: 16 cze 2008

Zakres stron: 117 - 127

DOI: https://doi.org/10.2478/v10006-008-0011-1

Słowa kluczowe
supervised learning, spiking neural networks, parametric analysis, learning window

This content is open access.

Analysis of the ReSuMe Learning Process For Spiking Neural Networks

Data publikacji: 16 cze 2008

Zakres stron: 117 - 127

DOI: https://doi.org/10.2478/v10006-008-0011-1

Słowa kluczowesupervised learning, spiking neural networks, parametric analysis, learning window

This content is open access.

Słowa kluczowe
supervised learning, spiking neural networks, parametric analysis, learning window