发表机构
SIMATS Engineering; Saveetha University（SIMATS工程公司; 萨维塔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出RT-NuSIS模块化框架，用于研究受限能量与对抗条件下动态频谱接入的SNN及忆阻器启发智能体，完成数学形式化与相关分析，提供基准工具包支持大规模事件驱动模拟。
AI 中文摘要
我们提出了实时神经形态频谱智能模拟器（RT-NuSIS），这是一个模块化框架，用于研究受限能量预算和对抗条件下动态频谱接入的脉冲神经网络（SNN）和忆阻器启发智能体。RT-NuSIS 耦合了漏积发放神经元动力学、忆阻突触模型、物理信息能量收集模型（摩擦电和射频）以及包含干扰和拜占庭行为的对抗模型。我们对模拟器进行了数学形式化，证明了其有界性，给出了平均场对抗阈值，分析了每步复杂度，并提供了可复现的基准工具包，用于推理能耗、延迟和鲁棒性指标。该代码库模块化、按种子确定且设计用于大规模事件驱动模拟。
英文摘要
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.
Comments7 pages, 4 figures, 2 tables. Accepted at the NeurIPS 2025 Workshop on Machine Learning and the Physical Sciences (ML4PS). Code and benchmark artifacts: https://github.com/ka-cyber/Realtime-Neuromorphic-Simulator
