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  •   Open Research
  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
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Classification and Segmentation of fMRI Spatio-temporal Brain Data With a Neucube Evolving Spiking Neural Network Model

Doborjeh, MG; Capecci, E; Kasabov, N
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http://hdl.handle.net/10292/10382
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Abstract
The proposed feasibility analysis introduces a new methodology for modelling and understanding functional Magnetic Resonance Image (fMRI) data recorded during human cognitive activity. This constitutes a type of Spatio-Temporal Brain Data (STBD) measured according to neurons spatial location inside the brain and their signals oscillating over the mental activity period [1]; thus, it is challenging to analyse and model dynamically. This paper addresses the problem by means of a novel Spiking Neural Networks (SNN) architecture, called NeuCube [2]. After the NeuCube is trained with the fMRI samples, the 'hidden' spatio-temporal relationship between data is learnt. Different cognitive states of the brain are activated while a subject is reading different sentences in terms of their polarity (affirmative and negative sentences). These are visualised via the SNN cube (SNNc) and then recognized through its classifier. The excellent classification accuracy of 90% proves the NeuCube potential in capturing the fMRI data information and classifying it correctly. The significant improvement in accuracy is demonstrated as compared with some already published results [3] on the same data sets and traditional machine learning methods. Future works is based on the proposed NeuCube model are also discussed in this paper.
Keywords
Evolvings Spiking Neural Networks; fMRI; NeuCube; Spatio-Temporal Brain Data
Date
January 13, 2014
Source
2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), Orlando, FL, 2014, pp. 73-80. doi: 10.1109/EALS.2014.7009506
Item Type
Conference Contribution
Publisher
Institute of Electrical and Electronics Engineers Inc.
DOI
10.1109/EALS.2014.7009506
Publisher's Version
http://ieeexplore.ieee.org/document/7009506/
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Copyright © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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