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NeuCube neuromorphic framework for spatio-temporal brain data and its python implementation


Scott, N; Kasabov, N; Indiveri, G (2013). NeuCube neuromorphic framework for spatio-temporal brain data and its python implementation. In: Lee, Minho; Hirose, Akira; Hou, Zeng-Guang; Kil, Rhee Man. Neural Information Processing. Berlin, Germany: Springer, 78-84.

Abstract

Classification and knowledge extraction from complex spatio-temporal brain data such as EEG or fMRI is a complex challenge. A novel architecture named the NeuCube has been established in prior literature to address this. A number of key points in the implementation of this framework, including modular design, extensibility, scalability, the source of the biologically inspired spatial structure, encoding, classification, and visualisation tools must be considered. A Python version of this framework that conforms to these guidelines has been implemented.

Abstract

Classification and knowledge extraction from complex spatio-temporal brain data such as EEG or fMRI is a complex challenge. A novel architecture named the NeuCube has been established in prior literature to address this. A number of key points in the implementation of this framework, including modular design, extensibility, scalability, the source of the biologically inspired spatial structure, encoding, classification, and visualisation tools must be considered. A Python version of this framework that conforms to these guidelines has been implemented.

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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:2013
Deposited On:13 Feb 2014 14:00
Last Modified:05 Apr 2016 17:33
Publisher:Springer
Series Name:Lecture Notes in Computer Science
Number:8228
ISSN:0302-9743
Publisher DOI:https://doi.org/10.1007/978-3-642-42051-1_11

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