Spatio-temporal fMRI data in the spiking neural network
Deep learning machine that employs Spiking Neural Network (SNN) is currently one of the main techniques in computational intelligence to discover knowledge from various fields. It has been applied in many application areas include health, engineering, finances, environment and others. This paper add...
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| Format: | Article |
| Language: | English |
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Indonesian Society for Knowledge and Human Development
2018
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| Online Access: | http://eprints.uthm.edu.my/5516/ http://eprints.uthm.edu.my/5516/1/AJ%202018%20%28874%29%20Spatio-temporal%20fMRI%20data%20in%20the%20spiking%20neural%20network.pdf |
| _version_ | 1848888572648620032 |
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| author | Saharuddin, Shaznoor Shakira Murli, Norhanifah |
| author_facet | Saharuddin, Shaznoor Shakira Murli, Norhanifah |
| author_sort | Saharuddin, Shaznoor Shakira |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Deep learning machine that employs Spiking Neural Network (SNN) is currently one of the main techniques in computational intelligence to discover knowledge from various fields. It has been applied in many application areas include health, engineering, finances, environment and others. This paper addresses a classification problem based on a functional Magnetic Resonance Image (fMRI) brain data experiment involving a subject who reads a sentence or looks at a picture. In the experiment, Signal to Noise Ratio (SNR) is used to select the most relevant features (voxels) before they were propagated in an SNN-based learning architecture. The spatio-temporal relationships between Spatio Temporal Brain Data (STBD) are learned and classified accordingly. All the brain regions are taken from data with label starplus-04847-v7.mat. The overall results of this experiment show that the SNR method helps to get the most relevant features from the data to produced higher accuracy for Reading a Sentence instead of Looking a Picture. |
| first_indexed | 2025-11-15T20:12:25Z |
| format | Article |
| id | uthm-5516 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:12:25Z |
| publishDate | 2018 |
| publisher | Indonesian Society for Knowledge and Human Development |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-55162022-01-13T06:29:03Z http://eprints.uthm.edu.my/5516/ Spatio-temporal fMRI data in the spiking neural network Saharuddin, Shaznoor Shakira Murli, Norhanifah TR624-835 Applied photography. Including artistic, commercial, medical photography, photocopying processes Deep learning machine that employs Spiking Neural Network (SNN) is currently one of the main techniques in computational intelligence to discover knowledge from various fields. It has been applied in many application areas include health, engineering, finances, environment and others. This paper addresses a classification problem based on a functional Magnetic Resonance Image (fMRI) brain data experiment involving a subject who reads a sentence or looks at a picture. In the experiment, Signal to Noise Ratio (SNR) is used to select the most relevant features (voxels) before they were propagated in an SNN-based learning architecture. The spatio-temporal relationships between Spatio Temporal Brain Data (STBD) are learned and classified accordingly. All the brain regions are taken from data with label starplus-04847-v7.mat. The overall results of this experiment show that the SNR method helps to get the most relevant features from the data to produced higher accuracy for Reading a Sentence instead of Looking a Picture. Indonesian Society for Knowledge and Human Development 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/5516/1/AJ%202018%20%28874%29%20Spatio-temporal%20fMRI%20data%20in%20the%20spiking%20neural%20network.pdf Saharuddin, Shaznoor Shakira and Murli, Norhanifah (2018) Spatio-temporal fMRI data in the spiking neural network. International Journal on Advanced Science, Engineering and Information Technology, 8 (6). pp. 2670-2676. ISSN 2088-5334 |
| spellingShingle | TR624-835 Applied photography. Including artistic, commercial, medical photography, photocopying processes Saharuddin, Shaznoor Shakira Murli, Norhanifah Spatio-temporal fMRI data in the spiking neural network |
| title | Spatio-temporal fMRI data in the spiking neural network |
| title_full | Spatio-temporal fMRI data in the spiking neural network |
| title_fullStr | Spatio-temporal fMRI data in the spiking neural network |
| title_full_unstemmed | Spatio-temporal fMRI data in the spiking neural network |
| title_short | Spatio-temporal fMRI data in the spiking neural network |
| title_sort | spatio-temporal fmri data in the spiking neural network |
| topic | TR624-835 Applied photography. Including artistic, commercial, medical photography, photocopying processes |
| url | http://eprints.uthm.edu.my/5516/ http://eprints.uthm.edu.my/5516/1/AJ%202018%20%28874%29%20Spatio-temporal%20fMRI%20data%20in%20the%20spiking%20neural%20network.pdf |