Simulation and Genetic Evolution of Spiking Neural Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F10%3A00171589" target="_blank" >RIV/68407700:21240/10:00171589 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Simulation and Genetic Evolution of Spiking Neural Networks
Original language description
In this paper, an implementation of a simulator for spiking neural networks and learning algorithm using genetic evolution is described. We have implemented two neural models (simplified Spike Response Model and Integrate and Fire model) and two learningalgorithms - SpikeProp and a simple genetic learning algorithm (presented in this paper). This learning algorithm is easy to understand and does not require any special network topology like feed-forward networks and back-propagation algorithms or thorough investigation of network architecture. It is based on an assumption that small changes in network variables have a small impact on the output and big changes have a big impact, thus calculating a difference between a desired output and a real outputand mutating individuals according to a size of this difference we can expect a population to converge.
Czech name
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Czech description
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Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 7th EUROSIM Congress on Modelling and Simulation, Vol. 2: Full Papers
ISBN
978-80-01-04589-3
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
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Publisher name
Department of Computer Science and Engineering, FEE, CTU in Prague
Place of publication
Prague
Event location
Praha
Event date
Sep 6, 2010
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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