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Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition

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Advances in Computational Intelligence (WCCI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7311))

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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.

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  1. Knowledge Engineering and Discovery Research Institute - KEDRI, Auckland University of Technology, New Zealand

    Nikola Kasabov

  2. Institute for Neuroinformatics, INI, ETH and University of Zurich, Switzerland

    Nikola Kasabov

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Editors and Affiliations

  1. Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, 710071, Xi’an, Shaanxi, China

    Jing Liu

  2. Dipartimento di Elettronica e Informazione, Politecnico di Milano, 20133, Milano, Italy

    Cesare Alippi

  3. Université Pierre et Marie Curie - Paris 6, LIP6, 4 place Jussieu, 75005, Paris, France

    Bernadette Bouchon-Meunier

  4. Department of Electrical & Computer Engineering, Portland State University, 97207, Portland, OR, USA

    Garrison W. Greenwood

  5. School of Engineering and Information Technology, University of New South Wales, 2600, Canberra, ACT, Australia

    Hussein A. Abbass

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© 2012 Springer-Verlag Berlin Heidelberg

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Kasabov, N. (2012). Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition. In: Liu, J., Alippi, C., Bouchon-Meunier, B., Greenwood, G.W., Abbass, H.A. (eds) Advances in Computational Intelligence. WCCI 2012. Lecture Notes in Computer Science, vol 7311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30687-7_12

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  • DOI: https://doi.org/10.1007/978-3-642-30687-7_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30686-0

  • Online ISBN: 978-3-642-30687-7

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