AI 中文总结
研究受耦合超顺磁隧道结实验启发，聚焦具有非对称和时间延迟相互作用的随机二进制网络。通过分析与模拟，揭示时间延迟对网络动力学的影响，建立统一框架，表明非对称和延迟可作功能资源用于神经形态硬件与复杂网络动力学。
AI 中文摘要
随机二进制网络广泛用于描述复杂系统中的集体动力学和执行神经形态计算，但现实网络常包含超出传统理论的非对称相互作用和有限信号传播时间。本文受耦合超顺磁隧道结实验观测启发，研究具有非对称和时间延迟相互作用的随机二进制网络。发现时间延迟从根本上重塑了反对称耦合诱导的动力学，产生与实验一致的强振荡时间相关性。同时，足够长的延迟使稳态概率趋于相等状态占据。这些看似无特征的概率分布与明显的时间相关性共存，区别于平衡高温行为。还通过分析表明延迟诱导的均匀分布出现在广泛的随机网络中，而对称破缺偏置场恢复了具有定性修改行为的相互作用依赖稳态。五个耦合自旋网络的模拟表明这些效应在仅两个自旋的最小系统之外仍然存在。结果为对称瞬时相互作用与非对称或时间延迟相互作用之间的中间区域的随机二进制网络建立了统一框架，并表明非对称和延迟可作为神经形态硬件和复杂网络动力学中的功能资源。
英文摘要
Stochastic binary networks are widely used to describe collective dynamics in complex systems and to perform neuromorphic computation, yet realistic networks often contain both asymmetric interactions and finite signal propagation times that fall outside conventional theories. Here we study stochastic binary networks with asymmetric and time-delayed interactions motivated by experimental observations in coupled superparamagnetic tunnel junctions. We find that time delay fundamentally reshapes the dynamics induced by anti-symmetric couplings, producing strong oscillatory temporal correlations consistent with experiment. At the same time, sufficiently long delays drive the steady-state probabilities toward equal state occupations even in strongly coupled systems. These apparently featureless probability distributions coexist with pronounced temporal correlations, distinguishing them from equilibrium high-temperature behavior. We further show analytically that delay-induced uniform distributions emerge in a broad class of stochastic networks, while symmetry-breaking bias fields restore interaction-dependent steady states with qualitatively modified behavior. Simulations of networks with five coupled spins demonstrate that these effects persist beyond minimal systems with only two spins. Our results establish a unified framework for stochastic binary networks in the intermediate regime between symmetric instantaneous interactions and asymmetric or time-delayed interactions, and suggest that asymmetry and delay can be exploited as functional resources in neuromorphic hardware and complex network dynamics.
