Closed-loop control of seizure activity via real-time seizure forecasting by reservoir neuromorphic computing

• URL: http://arxiv.org/abs/2505.02003v3
• Date: Thu, 25 Sep 2025 11:12:51 GMT
• Title: Closed-loop control of seizure activity via real-time seizure forecasting by reservoir neuromorphic computing
• Authors: Maryam Sadeghi, Darío Fernández Khatiboun, Yasser Rezaeiyan, Saima Rizwan, Alessandro Barcellona, Andrea Merello, Marco Crepaldi, Gabriella Panuccio, Farshad Moradi,
• Abstract summary: We present a neuromorphic reservoir computing hardware system capable of driving real-time personalized free-run stimulations.<n>The system achieves 83.33% accuracy in forecasting seizure occurrences during the training phase.<n>Our work demonstrates the potential of neuromorphic systems as a next-generation neuromodulation strategy for personalized DRE treatment.
• Score: 31.458406135473805
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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