Header

UZH-Logo

Maintenance Infos

Evolving spiking neural networks and neurogenetic systems for spatio-and spectro-temporal data modelling and pattern recognition


Kasabov, N (2012). Evolving spiking neural networks and neurogenetic systems for spatio-and spectro-temporal data modelling and pattern recognition. In: Liu, Jing; Alippi, Cesare; Bouchon-Meunier, Bernadette; Greenwood, Garrison W; Abbass, Hussein A. Advances in Computational Intelligence. Berlin Heidelberg, Germany: Springer, 234-260.

Abstract

Spatio- and spectro-temporal data (SSTD) are the most common types of data collected in many domain areas, including engineering, bioinformatics, neuroinformatics, ecology, environment, medicine, economics, etc. However, there is lack of methods for the efficient analysis of such data and for spatio-temporal pattern recognition (STPR). The brain functions as a spatio-temporal information processing machine and deals extremely well with spatio-temporal data. Its organisation and functions have been the inspiration for the development of new methods for SSTD analysis and STPR. The brain-inspired spiking neural networks (SNN) are considered the third generation of neural networks and are a promising paradigm for the creation of new intelligent ICT for SSTD. This new generation of computational models and systems are potentially capable of modelling complex information processes due to their ability to represent and integrate different information dimensions, such as time, space, frequency, and phase, and to deal with large volumes of data in an adaptive and self-organising manner. The paper reviews methods and systems of SNN for SSTD analysis and STPR, including single neuronal models, evolving spiking neural networks (eSNN) and computational neuro-genetic models (CNGM). Software and hardware implementations and some pilot applications for audio-visual pattern recognition, EEG data analysis, cognitive robotic systems, BCI, neurodegenerative diseases, and others are discussed.

Abstract

Spatio- and spectro-temporal data (SSTD) are the most common types of data collected in many domain areas, including engineering, bioinformatics, neuroinformatics, ecology, environment, medicine, economics, etc. However, there is lack of methods for the efficient analysis of such data and for spatio-temporal pattern recognition (STPR). The brain functions as a spatio-temporal information processing machine and deals extremely well with spatio-temporal data. Its organisation and functions have been the inspiration for the development of new methods for SSTD analysis and STPR. The brain-inspired spiking neural networks (SNN) are considered the third generation of neural networks and are a promising paradigm for the creation of new intelligent ICT for SSTD. This new generation of computational models and systems are potentially capable of modelling complex information processes due to their ability to represent and integrate different information dimensions, such as time, space, frequency, and phase, and to deal with large volumes of data in an adaptive and self-organising manner. The paper reviews methods and systems of SNN for SSTD analysis and STPR, including single neuronal models, evolving spiking neural networks (eSNN) and computational neuro-genetic models (CNGM). Software and hardware implementations and some pilot applications for audio-visual pattern recognition, EEG data analysis, cognitive robotic systems, BCI, neurodegenerative diseases, and others are discussed.

Statistics

Citations

Altmetrics

Downloads

88 downloads since deposited on 07 Mar 2013
20 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2012
Deposited On:07 Mar 2013 09:51
Last Modified:05 Apr 2016 16:36
Publisher:Springer
Series Name:Lecture Notes in Computer Science
Number:7311
Number of Pages:27
ISSN:0302-9743
ISBN:978-3-642-30686-0
Additional Information:IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, Australia, June 10-15, 2012. The original publication is available at www.springerlink.com
Publisher DOI:https://doi.org/10.1007/978-3-642-30687-7_12

Download

Preview Icon on Download
Preview
Content: Accepted Version
Filetype: PDF
Size: 2MB
View at publisher