Spiking Neural Networks (SNNs), regarded as the third generation of neural networks, emulate the brain's information processing with unparalleled biological plausibility compared to traditional neural networks. However, their non-linear, event-driven dynamics pose significant challenges for training, and existing methods often deviate from neuroscientific principles of cortical learning. Drawing inspiration from predictive coding theory-a leading model of brain information processing-we propose PC-SNN, a novel learning framework that integrates predictive coding with SNNs to enable biologically plausible, local Hebbian plasticity without reliance on backpropagation. Unlike conventional SNN training approaches, PC-SNN leverages only local computations, aligning with the brain's distributed processing and overcoming the biological implausibility of global error propagation. Our classification model achieves competitive performance on the benchmark datasets, including Caltech Face/Motorbike, MNIST, and CIFAR10, surpassing state-of-the-art multi-layer SNNs. Furthermore, our predictive coding-based regression model outperforms backpropagation-based methods while adhering to local plasticity constraints, offering a scalable and biologically grounded alternative for SNN training. PC-SNN drives progress in neuromorphic computing through validating the adaptability of bio-inspired algorithms within spiking neural architectures, but also unveils novel understandings of neurocognitive learning processes, presenting a conceptual framework distinguished by its theoretical originality and functional efficacy.

翻译：脉冲神经网络（SNNs）被视为第三代神经网络，与传统神经网络相比，其模拟大脑信息处理的方式具有无与伦比的生物学合理性。然而，其非线性、事件驱动的动力学特性给训练带来了重大挑战，且现有方法常常偏离皮层学习的神经科学原理。受预测编码理论——一种主流的大脑信息处理模型——的启发，我们提出了PC-SNN，这是一种新颖的学习框架，它将预测编码与SNNs相结合，从而在不依赖反向传播的情况下实现生物学上合理的局部赫布可塑性。与传统的SNN训练方法不同，PC-SNN仅利用局部计算，符合大脑的分布式处理特性，并克服了全局误差传播在生物学上的不合理性。我们的分类模型在基准数据集（包括Caltech Face/Motorbike、MNIST和CIFAR10）上取得了具有竞争力的性能，超越了最先进的多层SNNs。此外，我们基于预测编码的回归模型在遵循局部可塑性约束的同时，其性能优于基于反向传播的方法，为SNN训练提供了一种可扩展且基于生物学原理的替代方案。PC-SNN不仅通过验证仿生算法在脉冲神经架构中的适应性来推动神经形态计算的进步，还揭示了关于神经认知学习过程的新理解，提出了一个以其理论原创性和功能有效性为特色的概念框架。