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

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aut.relation.endpage37
aut.relation.issue2
aut.relation.startpage23
aut.relation.volume1
aut.researcherKasabov, Nikola
dc.contributor.authorKasabov, N
dc.date.accessioned2014-03-21T01:04:35Z
dc.date.available2014-03-21T01:04:35Z
dc.date.copyright2012
dc.date.issued2012
dc.description.abstractSpatio- 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 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.
dc.identifier.citationNatural Intelligence: the INNS Magazine, vol.1(2), pp.23 - 37
dc.identifier.issn2164-8522
dc.identifier.urihttps://hdl.handle.net/10292/7028
dc.publisherThe International Neural Network Society (INNS)
dc.relation.urihttp://www.inns.org/assets/docs/ni_v1i2_w2012.pdf
dc.rightsAuthors retain the right to place his/her publication version of the work on a personal website or institutional repository for non commercial purposes. The definitive version was published in (see Citation). The original publication is available at (see Publisher’s Version).
dc.rights.accessrightsOpenAccess
dc.subjectSpatio-temporal data
dc.subjectSpectro-temporal data
dc.subjectPattern recognition
dc.subjectSpiking neural networks
dc.subjectGene regulatory networks
dc.subjectComputational neuro-genetic modeling
dc.subjectProbabilistic modeling
dc.subjectPersonalised modelling
dc.subjectEEG data
dc.titleEvolving, probabilistic spiking neural networks and neurogenetic systems for spatio- and spectro-temporal data modelling and pattern recognition
dc.typeJournal Article
pubs.elements-id117167
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
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