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Evolving spiking neural networks for spatio and spectro-temporal pattern recognition


Kasabov, N (2012). Evolving spiking neural networks for spatio and spectro-temporal pattern recognition. In: 2012 6th IEEE International Conference Intelligent Systems (IS), Sofia, Bulgaria, 6 September 2012 - 8 September 2012. IEEE, 27-32.

Abstract

This paper provides a survey on the evolution of the evolving connectionist systems (ECOS) paradigm, from simple ECOS introduced in 1998 to evolving spiking neural networks (eSNN) and neurogenetic systems. It presents methods for their use for spatio-and spectro temporal pattern recognition. Future directions are highlighted.

Abstract

This paper provides a survey on the evolution of the evolving connectionist systems (ECOS) paradigm, from simple ECOS introduced in 1998 to evolving spiking neural networks (eSNN) and neurogenetic systems. It presents methods for their use for spatio-and spectro temporal pattern recognition. Future directions are highlighted.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Artificial Intelligence
Language:English
Event End Date:8 September 2012
Deposited On:28 Feb 2013 07:18
Last Modified:24 Jan 2022 00:22
Publisher:IEEE
Number of Pages:6
ISBN:978-1-4673-2276-8
Additional Information:© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
OA Status:Green
Publisher DOI:https://doi.org/10.1109/IS.2012.6335110
Related URLs:http://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=19762
  • Content: Accepted Version