Radiation-Aware Meta-Plasticity (RAMP): A Bio-Inspired Learning Rule for Neuromorphic Computing in Radiation-Prone Environments

Radiation-Aware Meta-Plasticity (RAMP): A Bio-Inspired Learning Rule for Neuromorphic Computing in Radiation-Prone Environments | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 21 August 2025 V1 Latest version Share on Radiation-Aware Meta-Plasticity (RAMP): A Bio-Inspired Learning Rule for Neuromorphic Computing in Radiation-Prone Environments Authors : Solomon Mamo Banteywalu 0000-0001-9879-7342 [email protected] and Paul Leroux Authors Info & Affiliations https://doi.org/10.22541/au.175580538.80500974/v1 262 views 78 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Neuromorphic computing systems, particularly those based on spiking neural networks (SNNs), provide exceptional energy efficiency and real-time processing, making them attractive for space, nuclear, and high-energy physics (HEP) applications. Their deployment, however, is challenged by ionizing radiation, which induces stochastic degradation, weight drift, and functional failure in CMOS-based synaptic components. To address this, we propose Radiation-Aware Meta-Plasticity (RAMP), a bio-inspired learning rule that dynamically modulates spike-timing-dependent plasticity (STDP) in response to neuronal activity and radiation exposure. RAMP introduces a dose- and time-dependent learning rate, integrating cumulative radiation dose, a membrane-voltage-based environmental regularizer, and explicit modeling of radiation-induced effects including noise, single-event transients, and total ionizing dose (TID) drift. MATLAB simulations of Leaky Integrate-and-Fire neurons under Poisson spike trains and mixed radiation profiles demonstrate that RAMP reduces synaptic weight variance by 32.8% compared to standard STDP while preserving Hebbian learning and convergence. Statistical analysis confirms that RAMP selectively suppresses noise during bursts without halting long-term adaptation. Furthermore, the effective learning rate decays exponentially with dose, mimicking biological synaptic downscaling under chronic stress. These results establish RAMP as a foundation for radiation-resilient neuromorphic intelligence, with direct implications for aerospace autonomy, nuclear safety, and HEP experiments. Supplementary Material File (ramp_manuscript.docx) Download 665.05 KB Information & Authors Information Version history V1 Version 1 21 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords adaptive meta-plasticity neuromorphic computing radiation hardening spike-timing-dependent plasticity (stdp) Authors Affiliations Solomon Mamo Banteywalu 0000-0001-9879-7342 [email protected] Hawassa University Institute of Technology View all articles by this author Paul Leroux Katholieke Universiteit Leuven Faculteit Ingenieurswetenschappen View all articles by this author Metrics & Citations Metrics Article Usage 262 views 78 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Solomon Mamo Banteywalu, Paul Leroux. Radiation-Aware Meta-Plasticity (RAMP): A Bio-Inspired Learning Rule for Neuromorphic Computing in Radiation-Prone Environments. Authorea . 21 August 2025. DOI: https://doi.org/10.22541/au.175580538.80500974/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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