# Analysis of the ReSuMe learning process for spiking neural networks

International Journal of Applied Mathematics and Computer Science (2008)

- Volume: 18, Issue: 2, page 117-127
- ISSN: 1641-876X

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topFilip Ponulak. "Analysis of the ReSuMe learning process for spiking neural networks." International Journal of Applied Mathematics and Computer Science 18.2 (2008): 117-127. <http://eudml.org/doc/207870>.

@article{FilipPonulak2008,

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.},

author = {Filip Ponulak},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {supervised learning; spiking neural networks; parametric analysis; learning window},

language = {eng},

number = {2},

pages = {117-127},

title = {Analysis of the ReSuMe learning process for spiking neural networks},

url = {http://eudml.org/doc/207870},

volume = {18},

year = {2008},

}

TY - JOUR

AU - Filip Ponulak

TI - Analysis of the ReSuMe learning process for spiking neural networks

JO - International Journal of Applied Mathematics and Computer Science

PY - 2008

VL - 18

IS - 2

SP - 117

EP - 127

AB - 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.

LA - eng

KW - supervised learning; spiking neural networks; parametric analysis; learning window

UR - http://eudml.org/doc/207870

ER -

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