{"ID":6537553,"CreatedAt":"2026-07-14T02:54:43.516908796Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.11428","arxiv_id":"2607.11428","title":"From Wireless SNNs to SN P Systems: A Low-Energy Rule-Based Conversion","abstract":"Distributed wireless spiking neural networks (DWSNNs) are a promising paradigm for energy-efficient edge inference in resource-constrained environments such as wireless sensor networks (WSNs). Yet, two limitations persist: their internal decision process is opaque, and their residual energy footprint remains a limiting factor for ultra-low-power deployments. This paper proposes a systematic methodology to convert a trained DWSNN into an equivalent Spiking Neural P (SN P) system, a biologically-inspired, rule-based computational model drawn from membrane computing, by extracting symbolic firing rules from the hidden-layer spike activity. The resulting SN P system provides direct, human-readable decision explanations while consuming three orders of magnitude less energy than its parent SNN. Experiments on the Neuromorphic MNIST (N-MNIST) dataset with a two-layer fully connected SNN using phase encoding and Leaky Integrate-and-Fire (LIF) neurons show that the SN P system retains approximately 84% of the original classification accuracy (73.77% vs. 87.68%) while the output layer connectivity decreases from 1000 to 120 class-specific connections. This complexity reduction is governed by a parameter related to the number of relevant hidden neurons per class that can be chosen according to a trade-off between computational complexity reduction and output accuracy. These results position SN P systems as lightweight, interpretable surrogates for trained distributed wireless SNNs in neuromorphic edge deployments.","short_abstract":"Distributed wireless spiking neural networks (DWSNNs) are a promising paradigm for energy-efficient edge inference in resource-constrained environments such as wireless sensor networks (WSNs). Yet, two limitations persist: their internal decision process is opaque, and their residual energy footprint remains a limiting...","url_abs":"https://arxiv.org/abs/2607.11428","url_pdf":"https://arxiv.org/pdf/2607.11428v1","authors":"[\"Pietro Savazzi\",\"Mauro Marchese\",\"Anna Vizziello\",\"Fabio Dell'Acqua\"]","published":"2026-07-13T11:34:56Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}