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dc.contributor.authorKasabov, N
dc.date.accessioned2014-03-21T01:03:19Z
dc.date.available2014-03-21T01:03:19Z
dc.date.copyright2012
dc.date.issued2014-03-21
dc.identifier.citation19th International Conference on Neural Information Processing held at Renaissance Doha City Center Hotel, Doha, Qatar, 2012-11-12 to 2012-11-15, published in: Lecture Notes in Computer Science
dc.identifier.urihttp://hdl.handle.net/10292/7025
dc.description.abstractSpatio- and spectro-temporal data are the most common data in many domain areas, including bioinformatics and neuroinformatics. Still there are no sufficient methods to model such data and to discover complex spatio-temporal patterns from it. The brain is functioning as a spatio-temporal information processing machine and brilliantly deals with spatio-temporal data, thus being a natural inspiration for the development of new methods for brain data modeling and pattern recognition. The presented research aims at the development of a 3D neurogenetic model of the human brain, called NeuCube, that can be efficiently utilized for spatio-temporal brain-gene data modeling and pattern recognition. The NeuCube is a 3D evolving probabilistic SNN (epSNN). epSNN are built on the principles of evolving connectionist systems [1] and eSNN in particular [2,3] and on probabilistic neuronal models (e.g. [4]). The latter extent the popular leaky integrate-and-fire spiking model with the introduction of some biologically plausible probabilistic parameters. The epSNN are evolving structures that learn and adapt to new incoming data in a fast incremental way. The overall architecture of the NeuCube is presented in [5]. It consists of a reservoir type brain structural map, an input module for converting input stimuli into spike trains, an eSNN classifier and a gene regulatory network module. The research explores different types of neuronal models and dynamic synapses, including a SPAN model [6,7] and a novel deSNN model that implements the time-to-first spike principle and Fusi’s algorithm implemented on the INI Zurich (www.ini.unizh.ch) SNN chip [8]. Examples of using the NeuCube architecture for brain data modeling are given on EEG-, fMRI-, MEG- and other types of brain spatio-temporal data with applications including BCI. Neurogenetic models are promising for modeling and prognosis of neurodegenerative diseases such as Alzheimer’s disease [9,10] and for personalized medicine in general [11]. Future research is expected to continue through tighter integration of knowledge and methods from information science, bioinformatics and neuroinformatics [12]. The research is relevant to the future development in the neuromorphic engineering area.
dc.publisherThe Neuromorphic Cognitive Systems group, at the Institute of Neuroinformatics, University of Zurich and ETH Zurich
dc.relation.urihttp://ncs.ethz.ch/projects/evospike/publications/mapping-learning-and-mining-of-spatiotemporal-brain-data-with-3d-evolving-spiking-neurogenetic-models/view
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in (see Citation). The original publication is available at (see Publisher's Version).
dc.titleMapping, learning and mining of spatiotemporal brain data with 3D evolving spiking neurogenetic models
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess
aut.conference.typeOral Presentation - Plenary
pubs.elements-id134926


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