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Fusion of ANNs as decoder of retinal spike trains for scene reconstruction
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Li, Wei, Raj, Alex Noel Joseph, Tjahjadi, Tardi and Zhuang, Zhemin (2022) Fusion of ANNs as decoder of retinal spike trains for scene reconstruction. Applied Intelligence, 52 . pp. 15164-15176. doi:10.1007/s10489-022-03402-w ISSN 0924-669X.
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Official URL: https://doi.org/10.1007/s10489-022-03402-w
Abstract
The retina is one of the most developed sensing organs in the hu- man body. However, the knowledge on the coding and decoding of the retinal neurons are still rather limited. Compared with coding (i.e., transforming vi- sual scenes to retinal spike trains), the decoding (i.e., reconstructing visual scenes from spike trains, especially those of complex stimuli) is more complex and receives less attention. In this paper, we focus on the accurate reconstruc- tion of visual scenes from their spike trains by designing a retinal spike train decoder based on the combination of the Fully Connected Network (FCN), Capsule Network (CapsNet) and Convolutional Neural Network (CNN), and a loss function incorporating the structural similarity index measure (SSIM) and L1 loss. CapsNet is used to extract the features from the spike trains, that are fused with the original spike trains and used as the inputs to FCN and CNN to facilitate the scene reconstruction. The feasibility and superiority of our model are evaluated on five datasets (i.e., MNIST, Fashion-MNIST, Cifar- 10, Celeba-HQ andCOCO). The model is evaluated quantitatively with four image evaluation indices, i.e., SSIM, MSE, PSNR and Intra-SSIM. The results show that the model provides a new means for decoding visual scene stim- uli from retinal spike trains, and promotes the development of brain-machine interfaces.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QP Physiology |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||
| Library of Congress Subject Headings (LCSH): | Retina -- Physiology, Retinal ganglion cells, Neural networks (Computer science), Artificial intelligence | |||||||||
| Journal or Publication Title: | Applied Intelligence | |||||||||
| Publisher: | Springer New York LLC | |||||||||
| ISSN: | 0924-669X | |||||||||
| Official Date: | October 2022 | |||||||||
| Dates: |
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| Volume: | 52 | |||||||||
| Page Range: | pp. 15164-15176 | |||||||||
| DOI: | 10.1007/s10489-022-03402-w | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Reuse Statement (publisher, data, author rights): | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10489-022-03402-w | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| Date of first compliant deposit: | 2 March 2022 | |||||||||
| Date of first compliant Open Access: | 11 March 2023 | |||||||||
| RIOXX Funder/Project Grant: |
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