Hebbian learning is a biological principle that intuitively describes how neurons adapt their connections through repeated stimuli. However, when applied to machine learning, it suffers serious issues due to the unconstrained updates of the connections and the lack of accounting for feedback mediation. Such shortcomings limit its effective scaling to complex network architectures and tasks. To this end, here we introduce the Structural Projection Hebbian Representation (SPHeRe), a novel unsupervised learning method that integrates orthogonality and structural information preservation through a local auxiliary nonlinear block. The loss for structural information preservation backpropagates to the input through an auxiliary lightweight projection that conceptually serves as feedback mediation while the orthogonality constraints account for the boundedness of updating magnitude. Extensive experimental results show that SPHeRe achieves SOTA performance among unsupervised synaptic plasticity approaches on standard image classification benchmarks, including CIFAR-10, CIFAR-100, and Tiny-ImageNet. Furthermore, the method exhibits strong effectiveness in continual learning and transfer learning scenarios, and image reconstruction tasks show the robustness and generalizability of the extracted features. This work demonstrates the competitiveness and potential of Hebbian unsupervised learning rules within modern deep learning frameworks, demonstrating the possibility of efficient and biologically inspired learning algorithms without the strong dependence on strict backpropagation. Our code is available at https://github.com/brain-intelligence-lab/SPHeRe.

翻译：赫布学习是一种生物学原理，直观描述了神经元如何通过重复刺激调整其连接。然而，当应用于机器学习时，由于连接更新的无约束性以及缺乏对反馈调节的考量，该方法存在严重问题。这些缺陷限制了其有效扩展到复杂网络架构和任务的能力。为此，本文提出结构投影赫布表示（SPHeRe），这是一种新颖的无监督学习方法，通过局部辅助非线性模块整合了正交性和结构信息保持。结构信息保持的损失通过一个辅助的轻量级投影反向传播至输入层，该投影在概念上充当反馈调节机制，而正交性约束则确保了更新幅度的有界性。大量实验结果表明，在包括CIFAR-10、CIFAR-100和Tiny-ImageNet在内的标准图像分类基准测试中，SPHeRe在无监督突触可塑性方法中达到了最先进的性能。此外，该方法在持续学习和迁移学习场景中表现出强大效能，图像重建任务则验证了所提取特征的鲁棒性和泛化能力。本工作证明了赫布无监督学习规则在现代深度学习框架中的竞争潜力，展示了无需严格依赖反向传播的高效仿生学习算法的可能性。我们的代码公开于https://github.com/brain-intelligence-lab/SPHeRe。