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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.02003 |
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| _version_ | 1866911174957727744 |
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| author | Sadeghi, Maryam Khatiboun, Darío Fernández Rezaeiyan, Yasser Rizwan, Saima Barcellona, Alessandro Merello, Andrea Crepaldi, Marco Panuccio, Gabriella Moradi, Farshad |
| author_facet | Sadeghi, Maryam Khatiboun, Darío Fernández Rezaeiyan, Yasser Rizwan, Saima Barcellona, Alessandro Merello, Andrea Crepaldi, Marco Panuccio, Gabriella Moradi, Farshad |
| contents | Closed-loop brain stimulation holds potential as personalized treatment for drug-resistant epilepsy (DRE) but still suffers from limitations that result in highly variable efficacy. First, stimulation is typically delivered upon detection of the seizure to abort rather than prevent it; second, the stimulation parameters are established by trial and error, requiring lengthy rounds of fine-tuning, which delay steady-state therapeutic efficacy. Here, we address these limitations by leveraging the potential of neuromorphic computing. We present a neuromorphic reservoir computing hardware system capable of driving real-time personalized free-run stimulations based on seizure forecasting, wherein each forecast triggers an electrical pulse rather than an arbitrarily predefined fixed-frequency stimulus train. The system achieves 83.33% accuracy in forecasting seizure occurrences during the training phase. We validate the system using hippocampal spheroids coupled to 3D microelectrode array as a simplified testbed, achieving seizure reduction >97% during the real-time processing while primarily using instantaneous stimulation frequencies within 20 Hz, well below what typically used in clinical practice. Our work demonstrates the potential of neuromorphic systems as a next-generation neuromodulation strategy for personalized DRE treatment, leveraging their sparse and event-driven processing for real-time applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02003 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Closed-loop control of seizure activity via real-time seizure forecasting by reservoir neuromorphic computing Sadeghi, Maryam Khatiboun, Darío Fernández Rezaeiyan, Yasser Rizwan, Saima Barcellona, Alessandro Merello, Andrea Crepaldi, Marco Panuccio, Gabriella Moradi, Farshad Artificial Intelligence Emerging Technologies Human-Computer Interaction Closed-loop brain stimulation holds potential as personalized treatment for drug-resistant epilepsy (DRE) but still suffers from limitations that result in highly variable efficacy. First, stimulation is typically delivered upon detection of the seizure to abort rather than prevent it; second, the stimulation parameters are established by trial and error, requiring lengthy rounds of fine-tuning, which delay steady-state therapeutic efficacy. Here, we address these limitations by leveraging the potential of neuromorphic computing. We present a neuromorphic reservoir computing hardware system capable of driving real-time personalized free-run stimulations based on seizure forecasting, wherein each forecast triggers an electrical pulse rather than an arbitrarily predefined fixed-frequency stimulus train. The system achieves 83.33% accuracy in forecasting seizure occurrences during the training phase. We validate the system using hippocampal spheroids coupled to 3D microelectrode array as a simplified testbed, achieving seizure reduction >97% during the real-time processing while primarily using instantaneous stimulation frequencies within 20 Hz, well below what typically used in clinical practice. Our work demonstrates the potential of neuromorphic systems as a next-generation neuromodulation strategy for personalized DRE treatment, leveraging their sparse and event-driven processing for real-time applications. |
| title | Closed-loop control of seizure activity via real-time seizure forecasting by reservoir neuromorphic computing |
| topic | Artificial Intelligence Emerging Technologies Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.02003 |