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.