Real-Time Neuromorphic Spectrum Intelligence Simulator

Abstract:We present the Real-Time Neuromorphic Spectrum Intelligence Simulator (RT-NuSIS), a modular framework to study spiking neural network (SNN) and memristor-inspired agents for dynamic spectrum access under constrained energy budgets and adversarial conditions. RT-NuSIS couples leaky integrate-and-fire neuronal dynamics, memristive synaptic models, physics-informed energy-harvesting models (triboelectric and RF), and adversary models including jamming and Byzantine behavior. We formalize the simulator mathematically, prove boundedness, present a mean-field adversary threshold, analyze per-step complexity, and provide a reproducible benchmark harness for energy-per-inference, latency, and robustness metrics. The codebase is modular, deterministic by seed, and designed for large-scale event-driven simulations.

7 pages, 4 figures, 2 tables. Accepted at the NeurIPS 2025 Workshop on Machine Learning and the Physical Sciences (ML4PS). Code and benchmark artifacts: this https URL

Signal Processing (eess.SP); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)

arXiv:2609.00585 [eess.SP]

(or arXiv:2609.00585v1 [eess.SP] for this version)

https://doi.org/10.48550/arXiv.2609.00585 arXiv-issued DOI via DataCite (pending registration)