Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture
Artificial intelligence (AI) is revolutionising neuroimaging by enabling automated analysis, predictive analytics, and the discovery of biomarkers for neurological disorders. However, traditional artificial neural networks (ANNs) face challenges in processing spatiotemporal neuroimaging data due to...
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MDPI AG
2025-06-01
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| author | Omar Garcia-Palencia Justin Fernandez Vickie Shim Nicola Kirilov Kasabov Alan Wang the Alzheimer’s Disease Neuroimaging Initiative |
| author_facet | Omar Garcia-Palencia Justin Fernandez Vickie Shim Nicola Kirilov Kasabov Alan Wang the Alzheimer’s Disease Neuroimaging Initiative |
| author_sort | Omar Garcia-Palencia |
| collection | DOAJ |
| description | Artificial intelligence (AI) is revolutionising neuroimaging by enabling automated analysis, predictive analytics, and the discovery of biomarkers for neurological disorders. However, traditional artificial neural networks (ANNs) face challenges in processing spatiotemporal neuroimaging data due to their limited temporal memory and high computational demands. Spiking neural networks (SNNs), inspired by the brain’s biological processes, offer a promising alternative. SNNs use discrete spikes for event-driven communication, making them energy-efficient and well suited for the real-time processing of dynamic brain data. Among SNN architectures, NeuCube stands out as a powerful framework for analysing spatiotemporal neuroimaging data. It employs a 3D brain-like structure to model neural activity, enabling personalised modelling, disease classification, and biomarker discovery. This paper explores the advantages of SNNs and NeuCube for multimodal neuroimaging analysis, including their ability to handle complex spatiotemporal patterns, adapt to evolving data, and provide interpretable insights. We discuss applications in disease diagnosis, brain–computer interfaces, and predictive modelling, as well as challenges such as training complexity, data encoding, and hardware limitations. Finally, we highlight future directions, including hybrid ANN-SNN models, neuromorphic hardware, and personalised medicine. Our contributions in this work are as follows: (i) we give a comprehensive review of an SNN applied to neuroimaging analysis; (ii) we present current software and hardware platforms, which have been studied in neuroscience; (iii) we provide a detailed comparison of performance and timing of SNN software simulators with a curated ADNI and other datasets; (iv) we provide a roadmap to select a hardware/software platform based on specific cases; and (v) finally, we highlight a project where NeuCube has been successfully used in neuroscience. The paper concludes with discussions of challenges and future perspectives. |
| format | Article |
| id | doaj-art-916e20d075504914b8c8f1b9537ea6e7 |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-916e20d075504914b8c8f1b9537ea6e72025-08-20T03:26:25ZengMDPI AGBioengineering2306-53542025-06-0112662810.3390/bioengineering12060628Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired ArchitectureOmar Garcia-Palencia0Justin Fernandez1Vickie Shim2Nicola Kirilov Kasabov3Alan Wang4the Alzheimer’s Disease Neuroimaging InitiativeAuckland Bioengineering Institute, The University of Auckland, Auckland 1010, New ZealandAuckland Bioengineering Institute, The University of Auckland, Auckland 1010, New ZealandAuckland Bioengineering Institute, The University of Auckland, Auckland 1010, New ZealandAuckland Bioengineering Institute, The University of Auckland, Auckland 1010, New ZealandAuckland Bioengineering Institute, The University of Auckland, Auckland 1010, New ZealandArtificial intelligence (AI) is revolutionising neuroimaging by enabling automated analysis, predictive analytics, and the discovery of biomarkers for neurological disorders. However, traditional artificial neural networks (ANNs) face challenges in processing spatiotemporal neuroimaging data due to their limited temporal memory and high computational demands. Spiking neural networks (SNNs), inspired by the brain’s biological processes, offer a promising alternative. SNNs use discrete spikes for event-driven communication, making them energy-efficient and well suited for the real-time processing of dynamic brain data. Among SNN architectures, NeuCube stands out as a powerful framework for analysing spatiotemporal neuroimaging data. It employs a 3D brain-like structure to model neural activity, enabling personalised modelling, disease classification, and biomarker discovery. This paper explores the advantages of SNNs and NeuCube for multimodal neuroimaging analysis, including their ability to handle complex spatiotemporal patterns, adapt to evolving data, and provide interpretable insights. We discuss applications in disease diagnosis, brain–computer interfaces, and predictive modelling, as well as challenges such as training complexity, data encoding, and hardware limitations. Finally, we highlight future directions, including hybrid ANN-SNN models, neuromorphic hardware, and personalised medicine. Our contributions in this work are as follows: (i) we give a comprehensive review of an SNN applied to neuroimaging analysis; (ii) we present current software and hardware platforms, which have been studied in neuroscience; (iii) we provide a detailed comparison of performance and timing of SNN software simulators with a curated ADNI and other datasets; (iv) we provide a roadmap to select a hardware/software platform based on specific cases; and (v) finally, we highlight a project where NeuCube has been successfully used in neuroscience. The paper concludes with discussions of challenges and future perspectives.https://www.mdpi.com/2306-5354/12/6/628spiking neural networksneuroimagingbrain-inspiredNeuCubeneuromorphic computing |
| spellingShingle | Omar Garcia-Palencia Justin Fernandez Vickie Shim Nicola Kirilov Kasabov Alan Wang the Alzheimer’s Disease Neuroimaging Initiative Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture Bioengineering spiking neural networks neuroimaging brain-inspired NeuCube neuromorphic computing |
| title | Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture |
| title_full | Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture |
| title_fullStr | Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture |
| title_full_unstemmed | Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture |
| title_short | Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture |
| title_sort | spiking neural networks for multimodal neuroimaging a comprehensive review of current trends and the neucube brain inspired architecture |
| topic | spiking neural networks neuroimaging brain-inspired NeuCube neuromorphic computing |
| url | https://www.mdpi.com/2306-5354/12/6/628 |
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