Biological neural networks continuously adapt and modify themselves in response to experiences throughout their lifetime - a capability largely absent in artificial neural networks. Hebbian plasticity offers a promising path toward rapid adaptation in changing environments. Here, we introduce Hebbian Attractor Networks (HAN), a class of plastic neural networks in which local weight update normalization induces emergent attractor dynamics. Unlike prior approaches, HANs employ dual-timescale plasticity and temporal averaging of pre- and postsynaptic activations to induce either co-dynamic limit cycles or fixed-point weight attractors. Using simulated locomotion benchmarks, we gain insight into how Hebbian update frequency and activation averaging influence weight dynamics and control performance. Our results show that slower updates, combined with averaged pre- and postsynaptic activations, promote convergence to stable weight configurations, while faster updates yield oscillatory co-dynamic systems. We further demonstrate that these findings generalize to high-dimensional quadrupedal locomotion with a simulated Unitree Go1 robot. These results highlight how the timing of plasticity shapes neural dynamics in embodied systems, providing a principled characterization of the attractor regimes that emerge in self-modifying networks.

翻译：生物神经网络在其整个生命周期中不断根据经验进行适应和修改——这种能力在人工神经网络中基本缺失。赫布可塑性为在变化环境中的快速适应提供了一条有前景的路径。本文提出赫布型吸引子网络（HAN），这是一类可塑性神经网络，其中局部权重更新归一化会诱发涌现吸引子动力学。与先前方法不同，HAN采用双时间尺度可塑性及突触前、后激活的时间平均，以诱导共动态极限环或定点权重吸引子。通过使用模拟运动基准测试，我们深入揭示了赫布更新频率和激活平均如何影响权重动力学与控制性能。结果表明，较慢的更新结合平均化的突触前、后激活，可促进收敛到稳定权重构型，而较快的更新则产生振荡共动态系统。我们进一步证明了这些结论可推广至使用模拟Unitree Go1机器人的高维四足运动。这些结果强调了可塑性时序如何塑造具身系统中的神经动力学，为自修改网络中涌现的吸引子区域提供了原理性刻画。