Brain-like intelligent systems need brain-like learning methods. Equilibrium Propagation (EP) is a biologically plausible learning framework with strong potential for brain-inspired computing hardware. However, existing im-plementations of EP suffer from instability and prohibi-tively high computational costs. Inspired by the structure and dynamics of the brain, we propose a biologically plau-sible Feedback-regulated REsidual recurrent neural network (FRE-RNN) and study its learning performance in EP framework. Feedback regulation enables rapid convergence by reducing the spectral radius. The improvement in con-vergence property reduces the computational cost and train-ing time of EP by orders of magnitude, delivering perfor-mance on par with backpropagation (BP) in benchmark tasks. Meanwhile, residual connections with brain-inspired topologies help alleviate the vanishing gradient problem that arises when feedback pathways are weak in deep RNNs. Our approach substantially enhances the applicabil-ity and practicality of EP in large-scale networks that un-derpin artificial intelligence. The techniques developed here also offer guidance to implementing in-situ learning in physical neural networks.

翻译：类脑智能系统需要类脑学习方法。均衡传播（EP）作为一种具有生物合理性的学习框架，在脑启发计算硬件领域展现出巨大潜力。然而现有EP实现存在不稳定性及过高的计算成本。受大脑结构与动力学启发，我们提出一种具有生物合理性的反馈调节残差循环神经网络（FRE-RNN），并研究其在EP框架下的学习性能。反馈调节通过降低谱半径实现快速收敛，这种收敛特性的改进使EP的计算成本与训练时间降低数个数量级，在基准任务中达到与反向传播（BP）相当的性能。同时，采用脑启发拓扑结构的残差连接有助于缓解深度RNN中反馈通路较弱时出现的梯度消失问题。我们的方法显著增强了EP在支撑人工智能的大规模网络中的适用性与实用性，所发展的技术也为物理神经网络中的原位学习实现提供了指导。