By Topic

Spiking neural network learning algorithms: Using learning rates adaptation of gradient and momentum steps

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Delshad, E. ; Comput. Eng. Dept., Islamic Azad Univ., Arak, Iran ; Moallem, P. ; Monadjemi, S.A.H.

In this paper we propose two learning algorithms for a spiking neural network which encodes information in the timing of spike trains. These algorithms are based on dynamic self adaptation for adapting the gradient learning rates (DS-η) and dynamic self adaptation for adapting the gradient learning rates and momentum (DS-ηα) algorithms. In our proposed algorithm, the optimum value for η was obtained from a parabolic function of error in both of these two algorithms and optimum value for α was obtained from our proposed adaptive algorithm. We performed a selection of benchmark problems to investigate the efficiency of our proposed algorithm. Compared to previously proposed algorithms such as SpikeProp and DS-ηα our algorithms, mod-DS-η and mod-DS-ηα, are faster than other methods in learning of the spiking neural networks.

Published in:

Telecommunications (IST), 2010 5th International Symposium on

Date of Conference:

4-6 Dec. 2010