通过赫布突触可塑性学习极限环
Learning limit cycles via Hebbian synaptic plasticity
浏览论文内容
中文总结 AI 辅助
研究高维非线性动力系统在特定输入和可塑性下的情况，发现周期性驱动可借可塑性让系统产生极限环，通过数值模拟表明极限环相在单样本轨迹易检测，平均曲线受有限尺寸效应影响。
中文摘要 AI 辅助
我们研究了高维非线性动力系统在受到非相干周期性输入和类赫布突触可塑性影响时的情况。研究结果揭示了一个惊人现象：取决于周期性驱动强度和突触可塑性之间的相互作用，系统的相图会产生一个区域，当两个输入都移除后，集体动力学能自发地进入极限环。这表明周期性驱动可通过可塑性在网络中留下持久的节律模式，使其能自行振荡。有限尺寸系统的数值模拟表明，极限环相在单样本轨迹上易于检测，而平均曲线受极限环周期样本间波动导致的强有限尺寸效应影响。
英文摘要
We investigate high-dimensional, non-linear dynamical systems when exposed to incoherent periodic inputs and Hebbian-like synaptic plasticity. Our findings reveal a striking phenomenon: depending on the interplay between the strength of the periodic drive and synaptic plasticity, the system's phase diagram can give rise to a region where, once both inputs are removed, the collective dynamics spontaneously settles into a limit cycle. This suggests that periodic drives can imprint lasting rhythmic patterns into the network through plasticity, effectively teaching it to oscillate on its own. Numerical simulations on finite size systems show that the limit cycle phase can be easily detected on single-sample trajectories, while averaged curves are affected by strong finite size effects due to sample-to-sample fluctuations of the period of the limit cycles.
